Smart Grid 3.0: Computational and Communication Technologies (Power Systems) 3031385055, 9783031385056

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Power Systems

Bhargav Appasani Nicu Bizon   Editors

Smart Grid 3.0 Computational and Communication Technologies

Power Systems

Electrical power has been the technological foundation of industrial societies for many years. Although the systems designed to provide and apply electrical energy have reached a high degree of maturity, unforeseen problems are constantly encountered, necessitating the design of more efficient and reliable systems based on novel technologies. The book series Power Systems is aimed at providing detailed, accurate and sound technical information about these new developments in electrical power engineering. It includes topics on power generation, storage and transmission as well as electrical machines. The monographs and advanced textbooks in this series address researchers, lecturers, industrial engineers and senior students in electrical engineering. **Power Systems is indexed in Scopus**

Bhargav Appasani · Nicu Bizon Editors

Smart Grid 3.0 Computational and Communication Technologies

Editors Bhargav Appasani School of Electronics Engineering Kalinga Institute of Industrial Technology Bhubaneswar, India

Nicu Bizon Faculty of Electronics Communications and Computers The National University of Science and Technology POLITEHNICA Bucharest, Pites, ti University Centre Pites, ti, Romania

ISSN 1612-1287 ISSN 1860-4676 (electronic) Power Systems ISBN 978-3-031-38505-6 ISBN 978-3-031-38506-3 (eBook) https://doi.org/10.1007/978-3-031-38506-3 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Foreword

This book discusses the recent developments in computational techniques and communication infrastructure that has led to the modernization of the smart grid. It is obvious that now the smart grid represents an evolved version of the electrical grid, with improved monitoring and control capabilities, which continues to be improved in future implementations. So, this book discusses emerging computational technologies, such as cloud computing, blockchain, deep learning, machine learning, big-data analytics, etc. along with the emerging communication technologies, such as the 5G, internet of things (IoT), etc., encompassing several applications, such as electric vehicles, widearea monitoring systems, home automation, advanced metering infrastructure, etc., from the perspective of modernization of smart grid. This book is the first of its kind on smart grid’s upgrade to version 3.0, discussing the current status of emerging technologies and the utility of big data analytics, blockchain, cloud computing, deep learning IoT, etc., on the existing smart grid applications to infuse them with intelligence and make them proactive. Thus, the content of this book is interdisciplinary, involving knowledge of electrical and electronics engineering, communication, signal processing, data analysis and artificial intelligence (machine learning, deep learning, etc.) and optimization. Therefore, the content of this book is addressed to students and specialists (researchers and engineers) in electrical engineering, power systems, communication, data scientists, and for industry personnel to develop grid with proactive intelligence and the techniques used in its design. In conclusion, it is noteworthy that the content of the book chapters is presented gradually and theoretically in detail as necessary to understand the problems and

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effective techniques for implementing the smart grid with proactive intelligence, being highly recommended for study in education and research. May 2023

Prof. Dr. habil. Phatiphat Thounthong King Mongkut’s University of Technology North Bangkok Bangkok, Thailand

Preface

The power grid has come a long way since its inception, evolving from a simple system for delivering electricity to a complex and intelligent network. Today, we stand at the cusp of a new era in power grid systems—Smart Grid 3.0. This edited book, titled Smart Grid 3.0: Computational and Communication Technologies, comprising of 15 chapters, explores the advancements in computational and communication technologies that have paved the way for this next phase of evolution in the power grid. The first chapter “Smart Grid 3.0: Grid with Proactive Intelligence” sets the stage by introducing the concept of Smart Grid 3.0, highlighting its key aspect of proactive intelligence. This chapter emphasizes the importance of emerging technologies like artificial intelligence, the Internet of Things, blockchain, big data, 5G, edge computing, and cloud computing in equipping the power grid with proactive intelligence. By gathering and analyzing real-time data, the grid can make informed decisions and take pre-emptive actions to optimize its performance. The second chapter “Blockchain for Energy Management: Smart Meters, Home Automation, and Electric Vehicles” delves into the application of blockchain technology in energy management, particularly in the context of smart meters, home automation, and electric vehicles. The chapter explores the decentralized architecture of blockchain, its potential benefits in the energy sector, and how it can enable the creation of neighborhood microgrids and eliminate intermediaries between producers and consumers. In the third chapter “Engineering Applications of Blockchain Based Crowdsourcing Concept in Active Distribution Grids”, the focus shifts to the concept of crowdsourcing in active distribution grids. The chapter explores how crowdsourcing can help mitigate energy scarcity and promote the selling of energy produced by small-scale distributed energy sources. It discusses the potential benefits for prosumers, distribution network operators, and consumers, highlighting the need for a comprehensive methodology to ensure efficient and sustainable energy provisioning. The fourth chapter “Machine Learning-Based Approaches for Transmission Line Fault Detection Using Synchrophasor Measurements in a Smart Grid” addresses the

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crucial issue of fault detection in transmission lines using synchrophasor measurements. The chapter highlights the role of machine learning-based approaches, such as K-Nearest Neighbour, Support Vector Machine, and Logistic Regression, in effectively detecting and classifying faults. Real-time implementation of these algorithms on a physical laboratory transmission line is presented, showcasing their effectiveness in ensuring the reliability and stability of transmission lines. Power quality analysis takes the center stage in the fifth chapter “Data Mining-Based Approaches in the Power Quality Analysis”. The chapter discusses the challenges associated with maintaining high power quality in distribution networks and proposes the use of data mining techniques for power quality monitoring and analysis. By extracting relevant features from current and voltage measurements, these techniques can identify areas with power quality issues and assist decision-makers in improving the performance of electric distribution networks. The sixth chapter “Machine Learning and Deep Learning Approaches for Energy Management in Smart Grid 3.0” focuses on the application of machine learning and deep learning approaches in energy management systems within smart grids. With the increasing complexity of energy systems, these techniques offer valuable tools for analyzing large amounts of data and optimizing energy usage. The chapter reviews various machine learning and deep learning algorithms, their critical applications, advantages, and disadvantages in the context of smart grids. The seventh chapter “Evolutionary Algorithms for Load Frequency Control of Renewable Microgrid” explores load frequency control (LFC) in renewable microgrids. The chapter discusses the challenges posed by heavy fluctuations in voltage and frequency in islanded multi-microgrid systems. It introduces evolutionary algorithms such as Gravitational Search Algorithm, Particle Swarm Optimization, Teaching Learning-based Optimization, and Grey Wolf Optimization for optimizing the parameters of PID controllers, enhancing the dynamic performance of microgrids. In the eighth chapter “Agents-Based Energy Scheduling of EVs and Smart Homes in Smart Grid”, the focus shifts to energy scheduling in smart grids, specifically for electric vehicles and smart homes. The chapter presents optimal energy scheduling techniques using agents-based approaches, enabling energy flow control and minimizing electricity costs. It highlights the integration of renewable energy sources, energy storage systems, and EV charging points in smart homes, emphasizing the benefits of energy management and optimization techniques. In the ninth chapter “Advanced Control Functionalities of Smart Grids from Communication and Computational Perspectives”, the authors shed light on the sophisticated control schemes necessary to manage the complexity of Smart Grid 3.0. This chapter emphasizes the increasing requirements for communication and computational capabilities as control strategies become more advanced. It provides an overview of the advanced control functionalities of key components such as transmission and distribution systems, microgrids, distributed energy resources, and smart homes. The tenth chapter “Multistage PD-(1+PI) Controller Design for Frequency Control of a Microgrid Considering Demand Response Program”, addresses the crucial issue

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of load-frequency control in fully-renewable microgrids. The chapter presents a multistage controller design that combines Proportional Derivative (PD) and One + Proportional Integral (1+PI) controllers to ensure system stability and responsiveness. Furthermore, it explores the integration of demand response programs to compensate for the uncertainties and nonlinear variables inherent in microgrid operations. The eleventh chapter is titled “Solid State Transformer: Topologies, Design and Its Applications in a Smart Grid”. The chapter highlights the significance of Solid State Transformers (SST) in enhancing the grid’s efficiency, monitoring, and control capabilities. It discusses the design process of SSTs, including power electronic converters and medium or high-frequency transformer design. The chapter also explores the role of SSTs in the Energy Internet as Energy Routers, showcasing their potential in improving reliability and power density. The twelfth chapter “Emerging Communication Technologies for V2X: Standards and Protocols”, explores the integration of Electric Vehicles (EVs) into Intelligent Transportation Systems (ITS) and the Smart Grid. It examines the communication standards and protocols that enable EVs to interact wirelessly with the grid infrastructure and Road Side Units (RSUs). The chapter provides insights into existing communication protocols and emerging technologies, such as IEEE 802.11bd and New Radio (NR) V2X, designed to enhance reliability, low latency, and high throughput communications for autonomous vehicles and driving use cases. The growing importance of the Internet of Things (IoT) in smart homes and smart cities is the focus of the thirteenth chapter “Internet of Things for Smart Homes and Smart Cities”. Titled “Internet of Things for Smart Homes and Smart Cities,” the chapter explains the architecture and enabling technologies of IoT. It explores the application of IoT in smart home environments, covering protocols, communication mediums, and important IoT-based services. Additionally, it delves into the concept of smart cities, discussing architecture and popular services enabled by IoT technology. The fourteenth chapter “Advancements in DC Microgrids: Integrating Machine Learning and Communication Technologies for a Decentralized Future”, delves into the concept and components of DC microgrids. It explores different control strategies used in microgrids and highlights the advancements in machine learning and communication technologies that further enhance their efficiency and reliability. The chapter provides valuable insights into the decentralized future of DC microgrids and their contribution to a sustainable power system. Lastly, the fifteenth chapter “Advanced Communication and Computational Technologies in a Sustainable Urban Context: Smart Grids, Smart Cities and Smart Health”, discusses the potential of modern communication and computational technologies in overcoming environmental and socio-economic challenges in cities and their potential applications in smart grids, smart cities, and smart health. The chapter emphasizes the need for a holistic approach to building sustainable and equitable cities, taking into account the environmental impacts beyond city boundaries. The book as a whole addresses the critical role of computational and communication technologies in shaping the future of the electricity grid. Each chapter

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provides unique insights into specific aspects of Smart Grid 3.0 and highlights the advancements, challenges, and opportunities presented by these technologies. The editors and authors of this book have brought together their expertise and research to create a comprehensive resource for researchers, professionals, and students interested in the field of smart grids and energy systems. The interdisciplinary nature of the topics covered ensures that readers will gain a well-rounded understanding of the subject matter. It is our hope that this book will serve as a valuable reference and guide for those seeking to navigate the complexities of Smart Grid 3.0. By exploring the advanced control functionalities, controller designs, communication protocols, and emerging technologies, readers will gain insights into the transformative potential of computational and communication technologies in creating a more sustainable and efficient energy future. We would like to express our sincere gratitude to all the contributors who have shared their knowledge and expertise in this book. Their dedication and efforts have made this publication possible. Lastly, we extend our appreciation to the readers for their interest in Smart Grid 3.0 and their commitment to advancing the field. We hope that the insights and knowledge shared in this book will inspire further research and innovation in the pursuit of a smarter and more sustainable energy ecosystem. Bhubaneswar, India Pites, ti, Romania

Bhargav Appasani Nicu Bizon

Contents

Smart Grid 3.0: Grid with Proactive Intelligence . . . . . . . . . . . . . . . . . . . . . Bhargav Appasani

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Blockchain for Energy Management: Smart Meters, Home Automation, and Electric Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Florentina Magda Enescu and Nicu Bizon

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Engineering Applications of Blockchain Based Crowdsourcing Concept in Active Distribution Grids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bogdan-Constantin Neagu, Gheorghe Grigoras, and Florina Scarlatache

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Machine Learning-Based Approaches for Transmission Line Fault Detection Using Synchrophasor Measurements in a Smart Grid . . . . . . . Kunjabihari Swain, Ankit Anand, Indu Sekhar Samanta, and Murthy Cherukuri Data Mining-Based Approaches in the Power Quality Analysis . . . . . . . . Gheorghe Grigoras, Bogdan-Constantin Neagu, and Florina Scarlatache

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Machine Learning and Deep Learning Approaches for Energy Management in Smart Grid 3.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 Amitkumar V. Jha, Bhargav Appasani, Deepak Kumar Gupta, Srinivas Ramavath, and Mohammad S. Khan Evolutionary Algorithms for Load Frequency Control of Renewable Microgrid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 Nilesh Kumar Rajalwal and Deep Shekhar Acharya Agents-Based Energy Scheduling of EVs and Smart Homes in Smart Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185 Muhammad Waseem Khan, Guojie Li, Keyou Wang, Muhammad Numan, Linyun Xiong, Sunhua Huang, and Muhammad Azam Khan

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Advanced Control Functionalities of Smart Grids from Communication and Computational Perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221 A. Paspatis, E. Pompodakis, I. Katsigiannis, and E. Karapidakis Multistage PD-(1+PI) Controller Design for Frequency Control of a Microgrid Considering Demand Response Program . . . . . . . . . . . . . . 241 Hossein Shayeghi and Alireza Rahnama Solid State Transformer: Topologies, Design and Its Applications in a Smart Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275 Selami Balci, Saban Ozdemir, Necmi Altin, and Ibrahim Sefa Emerging Communication Technologies for V2X: Standards and Protocols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301 Yasin Kabalci and Ural Mutlu Internet of Things for Smart Homes and Smart Cities . . . . . . . . . . . . . . . . . 331 Nuri Kapucu and Mehmet Bilim Advancements in DC Microgrids: Integrating Machine Learning and Communication Technologies for a Decentralized Future . . . . . . . . . . 357 Necmi Altin and Süleyman Emre Eyimaya Advanced Communication and Computational Technologies in a Sustainable Urban Context: Smart Grids, Smart Cities and Smart Health . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 389 Patrick Moriarty

Smart Grid 3.0: Grid with Proactive Intelligence Bhargav Appasani

Abstract The power grid has undergone significant transformations over the past century, expanding its role beyond providing reliable electricity to consumers. Today, it has evolved into a sophisticated and intelligent network, encompassing various applications that rely on advanced technologies. This article explores the concept of Smart Grid 3.0, the next phase of evolution in power grid systems, which has been made possible by recent advancements in computational power, storage capabilities, and high-speed communication. One key aspect of Smart Grid 3.0 is proactive intelligence, which enhances the grid’s efficiency and reliability. This chapter highlights the importance of proactive intelligence and discusses how emerging technologies such as artificial intelligence (AI), the Internet of Things (IoT), Blockchain, Big Data, 5G, edge computing, cloud computing, etc., can equip the power grid with proactive intelligence. These technologies enable the grid to gather and analyze real-time data, make informed decisions, and take preemptive actions to optimize performance. It will delve into the various technologies, their applications in a smart grid context, and the relevant protocols employed. By presenting a thorough analysis, this chapter aims to serve as valuable reference material for future research on smart grid systems and their integration with cutting-edge technologies, leading to a more efficient, resilient, and sustainable energy infrastructure. Keywords Smart Grid 3.0 · Artificial intelligence · Blockchain · 5G · Internet of Things (IoT) · Proactive intelligence

Acronyms AI AMI AMR

Artificial intelligence Advanced metering infrastructure Automatic meter reading

B. Appasani (B) School of Electronics Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar 751024, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 B. Appasani and N. Bizon (eds.), Smart Grid 3.0, Power Systems, https://doi.org/10.1007/978-3-031-38506-3_1

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ANN CC CNN DER DR EC eMBB EV FC IDS IoT LoRaWAN LSTM mMTC NB-IoT PMU QoS SCADA SVM URLLC V2G

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Artificial neural network Cloud computing Convolutional neural network Distributed energy resources Demand response Edge computing Enhanced mobile broadband Electric vehicles Fog Computing Intrusion detection systems Internet of things Long Range Wide Area Network Long short term memory Massive machine-type communications Narrow-band internet of things Phasor measurement unit Quality-of-service Supervisory control and data acquisition Support vector machines Ultra-reliable and low-latency communications Vehicle to grid

1 Introduction Electricity is the lifeblood of modern society, powering our homes, businesses, and industries. Traditionally, the power grid has played a crucial role in reliably delivering electricity from generation sources to consumers [1]. However, with the rapid advancements in technology, the role of the power grid has expanded beyond its traditional function [2]. The power grid has evolved into a complex and intelligent system known as the smart grid. Unlike its predecessor, the smart grid is not limited to the simple task of power distribution. It incorporates a range of technologies that enable it to monitor, analyze, and control the bidirectional flow of electricity more efficiently and sustainably. The transformation of the power grid into a smart grid was necessitated by several factors, including the need for enhanced efficiency, reliability, sustainability, and the integration of renewable energy sources [3]. Some key drivers that led to this transformation: • Growing Energy Demand: As global energy demand rises, traditional power grids face challenges in meeting the increasing electricity needs. The smart grid addresses this by optimizing energy generation, transmission, and distribution processes, ensuring efficient utilization of resources.

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• Ageing Infrastructure: Many power grids were built decades ago and now face infrastructure challenges [4]. Upgrading and modernizing the grid infrastructure becomes crucial for maintaining reliability, reducing power outages, and improving overall performance. • Integration of Renewable Energy: The shift towards a cleaner and more sustainable energy mix necessitates the integration of renewable energy sources, such as solar and wind, into the grid [5]. The smart grid enables the seamless integration and management of these intermittent energy sources, balancing supply and demand effectively. • Energy Efficiency and Conservation: The smart grid enables better monitoring and management of energy consumption [6]. It provides consumers with real-time data on their energy usage, encouraging energy conservation and empowering them to make informed choices about their consumption patterns. • Grid Resiliency and Reliability: Natural disasters, cyber threats, and other disruptions can impact grid reliability. The smart grid incorporates advanced monitoring and control systems that enhance grid resiliency. It allows for the early detection of faults, automatic rerouting of power, and quicker restoration during outages. • Decentralization and Distributed Energy Resources: The smart grid facilitates the integration of distributed energy resources (DERs) like rooftop solar panels and small-scale wind turbines [7]. It enables bi-directional energy flow, allowing users to generate and sell surplus electricity back to the grid, promoting a more decentralized energy system. • Advanced Monitoring and Control: The smart grid leverages advanced technologies such as sensors, automation, and real-time data analytics to monitor grid conditions, detect faults, and optimize operations. This level of monitoring and control improves grid efficiency, reduces losses, and enables proactive maintenance. • Consumer Empowerment: The smart grid provides real-time energy usage information, personalized energy management tools, and pricing incentives. This encourages energy conservation, cost savings, and active consumer participation in grid operations. The evolution of the power grid can be classified into three phases: Smart Grid 1.0, Smart Grid 2.0, and Smart Grid 3.0 [8]. Many factors have motivated this evolution, including technological, economic, and policy factors. Among them, technology has played a pivotal role in shaping the shift from the traditional power grid to the latest version of the smart grid. Smart Grid 1.0 marked the initial foray into digitalization, introducing technologies like Supervisory Control and Data Acquisition (SCADA) systems to monitor grid operations. Smart Grid 2.0 took this further by incorporating advanced metering infrastructure (AMI) and demand response programs to optimize energy consumption. We stand on the cusp of Smart Grid 3.0, the next phase of this transformative journey. Recent advancements in computational power, storage capabilities, and high-speed communication drive Smart Grid 3.0. It leverages emerging technologies such as artificial intelligence (AI), the Internet of Things (IoT), blockchain, Big Data analytics, 5G, etc., to equip the power grid with proactive

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Number of Publications

400 350 300 250 200

IoT AI 5G Blockchain Big Data

150 100 50 0 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023

Year Fig. 1 Use of advanced communication and computational technologies in smart grid

intelligence. The proliferation of advanced communication and computational technologies into the smart grid can be further corroborated by the publication statistics obtained from the Scopus database, illustrated in Fig. 1. The statistics shown in Fig. 1., clearly demonstrate a noticeable increase in the adoption of these technologies across various smart grid applications over the past five to six years. AI and Big Data algorithms enable the grid to analyze vast amounts of data in real time, enabling predictive maintenance, fault detection, and load forecasting [9]. IoT devices and sensors provide granular visibility into grid operations, enabling better asset management and optimization [10]. Blockchain technology ensures secure and transparent transactions within the grid, while 5G networks enable fast and reliable communication between grid components [11]. These technologies offer numerous benefits to the smart grid. They enhance grid resiliency, enable efficient integration of renewable energy sources, facilitate demand-side management, and support grid decentralization. Real-life examples demonstrate the potential impact of these technologies. For instance, AI-powered algorithms have helped utilities identify and prevent power outages, while IoT-enabled smart meters have empowered consumers with real-time energy consumption data, promoting energy conservation. This chapter aims to provide a comprehensive discussion on Smart Grid 3.0 and the role of emerging technologies in equipping the grid with proactive intelligence. The chapter will have five sections. The next section evolution of power grid to Smart Grid 3.0. It discusses the relevant technologies used at every phase of evolution. The third section discusses the various technologies used in Smart Grid 3.0, the relevant protocols, and their mappings to the various smart grid application. Section 4 discusses the challenges that these technologies still present for achieving

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the full potential and complete grid automation, and finally, the conclusions are presented in the final section of the chapter.

2 Evolution of Power Grid to Smart Grid 3.0 In the early twentieth century, the development of power grids marked a significant milestone in the widespread adoption of electricity [11]. The earliest power grids, also called the “Legacy Grid” were primarily designed to generate and distribute electricity for lighting and power industrial machinery and appliances. These power grids provided a reliable and centralized source of electrical energy to communities, replacing individual and localized power generation methods. The functions of the earliest power grids can be broadly categorized into power generation, transmission, and distribution: • Power Generation: Power plants, often utilizing steam or hydroelectric power, were established to generate electricity. Steam power plants typically employ coal or oil as fuel to heat water and produce steam that drives turbines connected to electrical generators. Hydroelectric power plants harness falling or flowing water energy to turn turbines. These power plants produced alternating current (AC) electricity, which was preferred for its ability to be transmitted over longer distances more efficiently. • Transmission: The generated electricity needed to be transmitted over long distances from power plants to cities and towns. High-voltage transmission lines, typically operating at tens of thousands of volts, were employed to minimize energy losses during transmission. These lines carried the electricity from power plants to substations closer to the population centers. • Distribution: Substations received the high-voltage electricity from transmission lines and transformed it to lower voltages suitable for local distribution. Operating at lower voltages, distribution lines carried the electricity from substations to homes, businesses, and other consumers. These lines were often supported by utility poles or placed underground. Several key technologies were employed in early 20th-century power grids, to perform these functions [12]: • Generators: Large electrical generators, driven by steam turbines or hydro turbines, produced the electricity in power plants. These generators utilized electromagnetic principles to convert mechanical energy into electrical energy. • Transformers: Transformers played a crucial role in power transmission and distribution. They stepped up the voltage for efficient transmission over long distances and stepped it down for safe distribution to consumers. Alternating current was well-suited for voltage transformation using transformers. • Transmission Lines: Overhead transmission lines, supported by towers or poles, were constructed to transmit electricity over long distances. These lines were

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typically made of conductive materials like copper or aluminium, with insulation to prevent power loss due to leakage.

2.1 Smart Grid 1.0 Some sources consider that Smart Grid 1.0 began in 2010 [13], with the popularity of renewable energy resources. However, much prior to this, in the late twentieth century and early twenty-first century, the power grid underwent significant advancements and modernization to meet the growing demands of electricity and improve its efficiency, reliability, and control. This period witnessed the emergence of new technologies that revolutionized grid operations. Even though the main function of the grid was still to meet the electricity requirements, the scale of generation increased in magnitude and area. Also, renewable energy resources began to proliferate the grid. Some of the key technologies used during this phase are: • SCADA: SCADA systems introduced in 1960’s, revolutionized power grid management by providing real-time monitoring and control capabilities [14]. SCADA allowed operators to remotely monitor equipment, collect data, and make informed decisions regarding grid operation and maintenance. It improved the overall reliability and efficiency of the grid. • Phasor Measurement Units (PMUs): PMUs are devices that measure the precise electrical conditions (phasors) of the power system, including voltage, current, and frequency, in real time [15]. They were invented in 1990’s for improved monitoring and control. By synchronizing their measurements, PMUs enable the creation of a synchronized phasor measurement network, which helps to identify grid disturbances and assess grid stability with greater accuracy and speed. These devices have higher resolution compared to SCADA, but are more expensive. • Automatic Meter Reading (AMR): AMR is a technology used in the power grid to automate the collecting of energy consumption data from utility meters [16]. It involves using specialized meters equipped with communication capabilities to transmit metering data remotely, eliminating the need for manual meter readings. AMR systems enable utilities to gather accurate and timely information about energy consumption without physically accessing each metering point. AMR evolved into AMI in the subsequent years.

2.2 Smart Grid 2.0 The early 2010s witnessed a significant increase in the deployment of renewable energy sources within the power grid, resulting in the next phase of evolution, Smart Grid 2.0. This proliferation was driven by several factors, including favourable government policies, declining costs of renewable technologies, increased environmental awareness, and advancements in renewable energy generation and grid

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integration technologies. Integration of renewable energy resources increased the complexity of the grid, necessitating the need for advanced monitoring, flexible energy storage, distributed energy management, bi-directional energy flow control, etc. [17]. Some key technologies in this regard are: • AMI: AMI involves the deployment of smart meters that enable two-way communication between utilities and consumers [18]. Smart meters provide detailed energy consumption data, facilitate remote meter reading, enable time-of-use pricing, and support demand response programs. • Distribution Automation: Distribution automation technologies monitor and control the distribution network more effectively. These include fault detection and isolation systems, self-healing capabilities, and automated switches that help to minimize power outages and optimize distribution operations. • Grid-Connected Energy Storage: Energy storage systems, such as batteries and flywheels, are integrated into the grid to store excess energy during periods of low demand and release it during high-demand periods. Grid-scale energy storage enhances grid stability, supports renewable energy integration, and enables load management. • Demand Response (DR) Systems: DR systems allow consumers to adjust their electricity usage in response to price signals or grid conditions. These systems encourage load shifting, load shedding, and participation in demandside management programs, leading to better grid balancing and reduced peak demand. • Advanced Analytics and Grid Optimization: Advanced analytics, machine learning, and artificial intelligence techniques are applied to grid data to optimize grid operations, improve energy forecasting, identify anomalies, and enhance grid planning and asset management. • Microgrids and Localized Generation: Microgrids are small-scale power systems operating independently or in coordination with the main grid. They integrate localized generation sources (such as solar panels or wind turbines) with energy storage and advanced control systems, enabling localized energy management, resilience, and grid support.

2.3 Smart Grid 3.0 The advancements in data generation, data storage, and computing hardware resulted in the rise of technologies such as AI, big data, blockchain, etc. This was not possible prior to 2010’s when data processing hardware was not readily available. Combined with high-speed communication technologies, such as, LoRaWAN, NB-IoT, 5G, etc., the smart grid entered the next phase of evolution. Smart Grid 3.0 represents an evolution of the power grid, where advanced intelligence and data-driven technologies are harnessed to enhance grid operations, optimize energy management, and improve overall grid performance. Some key features of this grid are:

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• The consumer is greatly empowered to monitor their energy usage, receive personalized insights, and make informed decisions to optimize their energy consumption, reduce costs, and contribute to grid stability through demand response programs. Smart home technologies and IoT devices can be integrated into the grid, enabling intelligent household energy management. • Using AI and predictive analytics in Smart Grid 3.0 enables utilities to move from reactive to proactive asset management. By analyzing historical data and sensor inputs, the grid can forecast the health and performance of various assets, such as transformers, power lines, and substations. This enables utilities to schedule maintenance activities, reduce downtime, and extend the lifespan of critical grid infrastructure. • It provides utilities with enhanced situational awareness through advanced visualization and monitoring tools. Real-time data from smart meters, sensors, and other grid devices are aggregated and analyzed to give utilities a holistic view of the grid’s health, performance, and potential risks. This enables utilities to effectively detect and respond to grid disturbances, outages, and cybersecurity threats. • Big data analytics, AI, and high-speed communication can quickly identify and isolate faults, automatically reconfigure the grid, and restore power to affected areas. This enables faster fault detection, isolation, and recovery, minimizing the impact of outages and improving the overall reliability of the grid. • It supports the integration of electric vehicles (EVs) into the grid infrastructure. This function includes smart charging infrastructure, vehicle-to-grid (V2G) capabilities and demand management systems.

Fig. 2 Progress in communication and computational Technologies at different phases of grid evolution

Communication and Computational Technologies

The various technologies used in different phases of grid evolution are summarized in Fig. 2. It is important to exercise caution when interpreting the illustration depicted in Fig. 2. The technologies utilized in different phases of the grid’s evolution are not exclusive to those specific phases. There is an overlap in the technologies employed

5G, LoRaWAN, AI, Blockchain, Big Data, IoT, etc. Power Line Communication, 2G, 3G, Digital Computers 4G, Optimization algorithms, Fiber Optic Communication Telephone, Manual Computation

1900's

1980's

2010's Year

2020's

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across different phases. The figure highlighted the distinct technologies primarily associated with each phase and were not extensively utilized in the earlier phases. For instance, machine learning, a subset of AI, was utilized even in Smart Grid 2.0. However, it was not the predominant technology of that particular phase. The figure intends to emphasize the key technologies that emerged as significant advancements during each phase of grid evolution, showcasing their transformative impact on the grid’s functionality and capabilities. The evolution of the smart grid has been a dynamic process characterized by the continuous integration and evolution of various technologies to enhance grid functionality, efficiency, and sustainability.

3 Smart Grid 3.0: Communication and Computational Technologies Communication and computational technologies that led to the materialization of Smart Grid 3.0 are shown in Fig. 3. Also shown are the smart grid functions that use the respective technologies. Figure 3. depicts the most important functions of the grid, which can be further elaborated or subdivided. Also, some of the functions can overlap. For example, smart cities may include EVs and AMI. The subsequent sections will discuss these technologies in the parlance of the smart grid.

3.1 AI for Smart Grid AI plays a crucial role in improving the efficiency and reliability of smart grid systems. Its utilization enables the achievement of self-healing capabilities, optimization and control of specific grid operations, and the generation of more precise, reliable, and comprehensive results compared to traditional methods. By emulating the cognitive functions of grid operators, AI enhances the resilience and dependability of smart grid systems. Diverse AI techniques can be applied to smart grid, including machine learning, deep learning, fuzzy logic, expert systems, and artificial neural networks (ANNs). Such techniques serve various functions: load forecasting, power grid stability assessment, fault detection, and addressing security concerns. Some key areas where AI is driving transformative changes in Smart Grid 3.0 are [19]: • Load forecasting: It is an essential function in Smart Grid that involves predicting the future electricity demand to ensure that the power supply meets the demand [20]. AI techniques such as linear regression, fuzzy-neuro model, ANN and long short term memory (LSTM) can be used to analyze historical data and predict future electricity demand accurately [21]. This can help utilities optimize their

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B. Appasani IoT, AI, Machine Learning, Blockchain, Edge Computing, Cloud Computing

IoT, AI, Machine Learning, Cloud Computing, Edge Computing, LoRaWAN, 5G, Blockchain

5G, NB-IoT, LoRaWAN, Cloud Computing, Big Data Analytics, Blockchain

DERs AMI AMI

EVs IoT, AI, Machine Learning, Blockchain, Edge Computing, Cloud Computing,LoRaWAN, 5G, NB-IoT Smart cities and Smart homes

Smart Grid 3.0

5G, IoT, AI, Machine Learning, Big Data Analytics, Cloud Computing, Edge Computing, Monitoring and Blockchain Control

Energy Management IoT, AI, Machine Learning, Blockchain, Edge Computing, Cloud Computing

Fig. 3 Functions of Smart Grid 3.0 with the respective communication and computational technologies

power generation and distribution systems, reduce energy waste, and improve energy efficiency. • Stability assessment: It involves monitoring the power grid’s stability and identifying potential issues that could lead to blackouts or other disruptions. AI techniques such as support vector machines (SVMs), ANNs, random forests, CNNs, etc., can be used to analyze real-time data from sensors installed throughout the power grid [22, 23]. This can help utilities identify potential problems before they occur and take corrective action to prevent outages. • Fault detection: It is another critical area where AI is transforming Smart Grid. Faults in the power grid can cause significant disruptions, so detecting them quickly and accurately is essential. AI techniques such as ANNs, extreme learning machines (ELMs), and ensemble techniques can be used to analyze data from sensors installed throughout the power grid and identify potential faults before they cause significant damage [24]. • Security: It is also a crucial area where AI is transforming the industry. As Smart Grid becomes more complex and interconnected, they become more vulnerable to cyber-attacks. AI techniques such as machine learning can be used to detect

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anomalies in network traffic patterns that could indicate a cyber-attack. Additionally, AI-based intrusion detection systems (IDS), deep learning-based IDS, and reinforcement learning-based IDS can monitor network traffic for suspicious activity and alert security personnel when an attack is detected [25].

3.2 Blockchain for Smart Grid Blockchain technology is a decentralized and transparent digital ledger that records transactions across multiple computers. It allows for the secure and immutable data storage, creating a tamper-resistant and auditable system. Transactions are bundled into blocks, cryptographically linked to form a chain, and distributed across a network of computers, known as nodes. This decentralized nature eliminates the need for intermediaries, reduces the risk of data manipulation, and ensures trust among participants. Blockchain can be used in various areas of smart grids, including [26]: • Decentralized energy trading: Blockchain technology enables the implementation of peer-to-peer energy trading, facilitating efficient and cost-effective energy distribution. By reducing reliance on centralized power grids, blockchain promotes the utilization of renewable energy sources, leading to a more sustainable energy ecosystem. • AMI: Blockchain provides a secure and efficient framework for storing and managing smart-meter data. This enhances billing accuracy and enables effective energy usage monitoring, contributing to improved operational efficiency. • EV charging: Blockchain enables secure transactions between EV owners and charging stations, ensuring reliable and transparent payment processes. Additionally, blockchain allows for peer-to-peer sharing of charging resources, promoting efficient utilization of EV infrastructure. • Demand response management: Blockchain technology can establish a decentralized system for managing demand response programs. These programs encourage consumers to reduce energy consumption during peak periods in exchange for financial incentives, and blockchain ensures fair and reliable execution of such programs. • Green certificates issuance: Blockchain facilitates the issuance of digital certificates that validate the origin and sustainability of renewable energy sources. This transparency and traceability promote the wider adoption of renewable energy by providing verifiable proof of its environmental impact. • Microgrids: Blockchain enables the creation of microgrids, which are selfcontained power grids that operate independently of centralized grids. By leveraging blockchain’s capabilities, microgrids enhance the resilience and reliability of local power systems, fostering energy autonomy and adaptability. • Information security: Leveraging the decentralized architecture of blockchain, Smart Grid systems can securely store sensitive data, such as customer information and transaction records. This safeguards the integrity of the grid and mitigates the

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risks of cyber-attacks and data breaches, ensuring the confidentiality and reliability of critical grid operations.

3.3 Bigdata Analytics for Smart Grid The evolution of smart grid relies significantly on utilising big data analytics. To succeed in the smart grid energy paradigm, it is essential to effectively acquire, transmit, process, visualize, interpret, and utilize big data. The substantial volume of raw data obtained from smart meters and other sources within the smart grid is not inherently comprehensible or immediately valuable. It requires a reliable and consistent ability to process, analyze, and comprehend the information contained within this vast amount of data. Bigdata analytics finds it use in the following areas [27]: • Demand-side response: Big data analytics enables utilities to effectively manage demand-side response by analyzing customer usage patterns and forecasting future demand. This analysis facilitates utilities in adjusting their supply accordingly, mitigating the risk of grid overload during peak hours. • User service: Utilizing big data analytics, utilities can enhance user service by providing real-time information on energy consumption, billing, and outage management. This empowers utilities to offer improved customer service while minimizing downtime. • Energy efficiency: Big data analytics is pivotal in identifying areas where energy efficiency enhancements can be implemented. It identifies inefficient appliances or buildings that consume excessive energy, facilitating targeted measures to optimize energy consumption. • Renewable energy integration: Big data analytics supports the seamless integration of renewable energy sources into the grid. Through analysing weather patterns and predicting renewable energy output, utilities can effectively balance supply and demand, promoting the efficient utilization of renewable resources. • Grid operation and control: Real-time monitoring of power system operation and control is enabled through big data analytics. By promptly detecting anomalies or faults, utilities can proactively address potential issues before they escalate into outages or other operational problems. This proactive approach enhances the reliability of the grid and reduces downtime.

3.4 Cloud, Fog, and Edge Computing for Smart Grid Edge computing, cloud computing, and fog computing are all useful for the smart grid in different ways [28]. The differences can be explained as follows: • Edge Computing: Edge computing involves performing data processing tasks at the edge devices, such as sensors and smart meters, rather than relying solely

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on centralized cloud systems. This approach alleviates the burden on cloud computing infrastructure by reducing network traffic and enabling real-time processing at the edge devices. Additionally, edge computing enhances data security by keeping sensitive information localized within the edge devices. • Cloud Computing: Cloud computing offers centralized control for data storage and processing in smart grid environments. It efficiently manages large volumes of data from diverse sources and provides scalable resources for processing and storage. Leveraging cloud computing enables the deployment of advanced analytics and machine learning algorithms, facilitating the optimization of energy generation, transmission, and consumption processes within the smart grid. • Fog Computing: Fog computing is a hybrid approach combining edge and cloud computing to enable low-latency processing close to edge devices. By conducting data processing near edge devices, fog computing reduces network congestion and improves response times for real-time applications in the smart grid. Furthermore, fog computing facilitates distributed decision-making by enabling localized processing at various levels of the smart grid hierarchy. These technologies find applications across various smart grid supply chain segments, encompassing energy generation, transmission, and consumption processes. • Energy Generation: Within the domain of energy generation, edge devices, including sensors and smart meters, play a pivotal role in collecting pertinent data concerning renewable energy sources like solar panels and wind turbines [29]. Leveraging edge computing or fog computing, this data can be locally processed to optimize energy production and curtail inefficiencies. Cloud computing further enables advanced analytics, facilitating accurate energy demand prediction and optimal energy generation. • Energy Transmission: Transmission and distribution networks rely on edge devices to monitor power quality, identify faults, and manage load balancing. By harnessing fog nodes, these networks can execute localized processing for realtime control and fault management, minimizing network congestion [30]. Cloud computing is a centralized control platform for the entire network, supporting comprehensive analytics to optimize the transmission and distribution processes. • Energy Consumption: In energy consumption, edge devices like smart thermostats and appliances collect data on energy usage patterns in residential and commercial settings. This data can be locally processed using edge computing or fog computing techniques to optimize energy consumption and mitigate wastage. Furthermore, cloud computing empowers advanced analytics capabilities, enabling accurate energy demand prediction and optimized energy consumption.

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3.5 5G for Smart Grid The implementation of 5G networks holds immense potential for transforming the capabilities and efficiency of smart grids. These networks offer a range of advantages that can significantly enhance the performance and effectiveness of smart grid applications [31]. Firstly, 5G networks provide high-quality service, ensuring reliable and high-speed connectivity for various smart grid functionalities. This includes advanced metering, demand response programs, and distributed energy resource management. The robust connectivity offered by 5G networks enables seamless communication and data exchange, facilitating real-time monitoring and control of the grid. Moreover, the scalability of 5G networks is a crucial advantage for smart grids. With the increasing number of connected devices and users within the grid ecosystem, 5G networks can accommodate the growing demand and ensure uninterrupted communication and data transmission. This scalability is vital for supporting the expanding range of smart grid applications and services. Another significant benefit of 5G networks in smart grids is the improvement in reliability and efficiency. By enabling real-time monitoring, control, and optimization of energy flows across the grid, 5G networks enhance grid reliability and minimize downtime. The high-speed and low-latency capabilities of 5G networks enable swift responses to anomalies and faults, ensuring a more stable and resilient grid infrastructure. Furthermore, integrating 5G networks opens up new avenues for business models within the energy sector. Energy providers can leverage the capabilities of 5G to offer innovative services and solutions to their customers. For instance, dynamic pricing based on real-time energy consumption data becomes more feasible, enabling consumers to make informed decisions about their energy usage. Additionally, 5G networks facilitate the deployment of energy storage systems and support efficient electric vehicle charging infrastructure, contributing to the growth of sustainable and eco-friendly energy practices. The 5G architecture for Smart Grid involves a virtualized infrastructure layer, as shown in Fig. 4 [31]. This means that the network resources are abstracted from the underlying physical infrastructure and can be dynamically allocated and managed based on the needs of different smart grid applications and services. Slicing refers to dividing a physical network into multiple virtual networks, each with its own resources and characteristics. In the context of 5G for Smart Grid, slicing enables the creation of dedicated network slices for different smart grid applications and services, such as advanced metering, demand response, and distributed energy resources management. Each slice can have its quality-of-service (QoS) requirements, security policies, and traffic management rules, which allows for more efficient and flexible use of network resources. The International Telecommunication Union (ITU) has endorsed the standards and specifications developed by the 3rd Generation Partnership Project (3GPP) for 5G, specifically addressing the domains of Enhanced Mobile Broadband (eMBB), UltraReliable and Low-Latency Communications (URLLC), and Massive Machine-Type Communications (mMTC) [32]. These three categories of 5G services have emerged

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EVs eMBB Access Node

Edge Cloud Transport Node

URLLC URLLC

Generation

Core Cloud

Core Node

Smart Homes

mMTC

Radio Access Network

Transport Network

Core Network

AMI

Fig. 4. 5G architecture for smart grid

as crucial facilitators for various smart grid applications and services, including advanced metering, demand response, distributed energy resources management, and electric vehicle charging. The eMBB classification offers high-speed connectivity and ample bandwidth to cater to data-intensive applications that demand substantial throughput and minimal latency. URLLC, on the other hand, focuses on providing ultra-reliable and low-latency communication capabilities to support mission-critical applications necessitating real-time control and monitoring of energy flows within the grid. Lastly, mMTC addresses massive connectivity requirements, enabling seamless integration of numerous low-power devices intermittently communicating with the grid.

3.6 IoT for Smart Grid IoT-Enabled Smart Grids can significantly impact energy efficiency and sustainability [33]. Using sensors and smart metering, Smart Grids can monitor power flow throughout the electrical grid in real-time, allowing for more efficient operation at all power generation, transmission, and distribution levels. This increased efficiency can lead to reduced energy waste and lower consumer costs. Moreover, IoT-Enabled Smart Grids can seamlessly integrate with other smart entities, such as smart appliances, homes, buildings, and cities, to access and control more devices over the Internet. This integration allows for better management of energy consumption by optimizing energy usage based on real-time data analytics. A four-layered architecture is considered for IoT-enabled Smart Grids. The layers are [34]: • Physical Layer: This layer is the foundation of the architecture and includes the physical facilities and executors of the Smart Grid. All distributed and decision-making instructions are carried out at this layer to provide the system’s desired functionality. Additionally, the bidirectional energy flow between power generation, transmission, distribution, and customers happens inside this layer.

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• Communication Layer: This layer provides communication between different components of the Smart Grid, including sensors, smart meters, and other devices. It enables real-time data exchange between different layers of the architecture. • Data Processing Layer: This layer processes data collected from various sensors and devices in real-time to provide insights into energy consumption patterns and other relevant information. • Application Layer: This layer provides various applications that can be used to manage and control different aspects of the Smart Grid, such as demand response programs or microgrid management systems. Table 1 summarizes some of the commonly used communication technologies for IoT-enabled Smart Grids, along with their characteristics, protocols and potential applications:

4 Challenges and Possible Solutions The advent of Smart Grid 3.0 brings forth a vision of an intelligent energy ecosystem that optimizes energy generation, transmission, and consumption through advanced communication and computational technologies. However, despite significant progress in these domains, several challenges persist in achieving complete proactive intelligence within the Smart Grid 3.0 paradigm with these technologies.

4.1 Challenges for Implementing AI in Smart Grid One of the main challenges is the lack of standardization in data formats and communication protocols across different smart grid components [35]. This can make integrating AI algorithms into existing systems difficult and ensure interoperability between different devices. To address this challenge, researchers have proposed developing common data models and communication standards that can be used across different smart grid components. Another challenge is the need for highquality data to train AI models effectively. Smart grid systems generate vast amounts of data, but this data may be incomplete or inaccurate, which can affect the performance of AI algorithms. To overcome this challenge, researchers have proposed using techniques such as data cleaning and augmentation to improve the quality of training data. A further challenge is the need for explainable AI models that can provide insights into how decisions are made [36]. In some cases, black-box AI models may be difficult to interpret or explain, limiting their usefulness in critical applications such as fault detection and diagnosis. To address this challenge, researchers have proposed developing transparent AI models that clearly explain their decisions. Finally, there are also concerns around cybersecurity and privacy when implementing AI in smart grid systems [37]. As these systems become more interconnected

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Table 1 Communication technologies for IoT-enabled smart grid [33] Communication Characteristics technology

Data rate

Range

Protocols

Potential Applications

Up to 100 m

IEEE 802.11a/b/g/ n/ac/ax

Smart homes, smart buildings

Wi-Fi

High Up to 1 bandwidth, Gbps short range, low latency

Zigbee

Low power consumption, low data rate, mesh network topology

Up to 250 Up to 100 m Kbps

IEEE 802.15.4/ Zigbee Alliance standards

Smart meters, demand response systems

5G

Wide coverage Up to Up to 500 m area, high several 20 bandwidth, high Gbps latency

3GPP 5G standard

Electric vehicle integration, remote monitoring and control

LoRaWAN

Long-range Up to 50 communication, Kbps low power consumption, low data rate

Three LoRaWAN kilometers standard (urban), up to tens of kilometers (rural)

Bluetooth

Short-range Up to 2 communication, Mbps low power consumption, high data rate

Up to 10 m

NB-IoT

Low power consumption, extended coverage, low data rate

Up to 250 Several Kbps kilometers

Smart agriculture, smart cities

Bluetooth low Home energy (BLE) automation protocol systems based on IEEE 802.15.1 standard 3GPP NB-IoT Smart standard metering, asset tracking

and reliant on digital technologies, they may become more vulnerable to cyber-attacks or unauthorized access. To mitigate these risks, researchers have proposed developing secure communication protocols and implementing robust cybersecurity measures such as encryption and access control.

4.2 Challenges for Blockchain in Smart Grid Implementing blockchain for smart grid applications faces several challenges. These include the inefficiencies of consensus mechanisms, integration with legacy systems,

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regulatory challenges, data privacy concerns, and lack of standardization [26, 38]. Consensus mechanisms can be slow and resource-intensive, limiting scalability [39]. Smart grids often rely on legacy systems that may not be compatible with blockchain technology. The regulatory landscape for energy markets is complex and varies by region, creating legal barriers to adoption. Blockchain’s transparent nature can raise concerns about data privacy and security. Finally, there is a lack of standardization in developing and implementing blockchain-based solutions for smart grids, making it difficult to ensure interoperability between different systems and platforms [40]. Possible solutions to the challenges of implementing blockchain for smart grid applications include using alternative consensus mechanisms that are more efficient, such as proof-of-stake or delegated proof-of-stake, developing middleware or APIs that can bridge the gap between blockchain-based systems and legacy systems, working with regulators to develop clear guidelines and standards for blockchainbased solutions in energy markets, using privacy-enhancing technologies such as zero-knowledge proofs or homomorphic encryption to protect sensitive data, and developing industry-wide standards and protocols for blockchain-based smart grid applications, as well as promoting interoperability between different systems and platforms.

4.3 Challenges for Bigdata Analytics in Smart Grid Using big data analytics in smart grids presents several challenges [27, 41]. These include making decisions on mapping the data collection infrastructure to the desired applications, applying new architecture and tools to manage grid data as streams in real-time, transforming processes throughout utilities to support the big data infrastructure, and managing large volumes of raw data generated by smart grids. To address these challenges, utilities can invest in developing new architectures and tools that can handle the high volume, velocity, and variety of data generated by smart grids in real-time. They can also train staff on new tools and technologies and change existing utility processes and workflows to support big data analytics. Additionally, utilities can implement security measures to protect sensitive customer information and address ethical concerns related to handling such information. Finally, they can collaborate with IT companies to tap into their expertise in big data analytics and develop solutions that meet their specific needs.

4.4 Challenges for Edge, Fog, and Cloud Computing in Smart Grid The use of Edge computing, Fog computing, and Cloud computing in the Smart Grid presents several challenges that must be addressed to ensure their successful

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deployment and operation [28]. Edge computing devices such as sensors or smart meters have limited processing power and storage capacity, which can limit their ability to perform complex computations or analytics. Network connectivity issues can also affect communication with other devices or cloud services. Security and privacy concerns are also challenging, especially if they are not properly secured or protected. Scalability issues can arise when deploying many edge devices in different geographical locations managed by different organizations. Interoperability issues can also arise when integrating different edge devices from different vendors. Fog computing nodes face similar challenges as edge devices, including scalability issues, network connectivity issues, security and privacy concerns, interoperability issues, latency and bandwidth issues, and cost-effectiveness. Cloud computing faces latency and bandwidth issues due to network connectivity requirements between edge devices and cloud services. Security and privacy concerns are also challenging when sensitive data is stored or processed in the cloud. Dependence on network connectivity is another challenge that can affect real-time applications. Possible solutions to these challenges include further research and development in these technologies to overcome technical limitations such as processing power, storage capacity, network connectivity, security measures, interoperability standards, and latency reduction techniques. Using standard communication protocols can help address interoperability challenges, while using encryption techniques can help address security concerns. Deploying hybrid architectures that combine Edge computing with Fog Computing or Cloud Computing can help address scalability challenges while reducing latency requirements for real-time applications. Finally, developing cost-effective hardware platforms for Edge Computing and Fog Computing nodes can help reduce deployment costs while improving performance.

4.5 Challenges for 5G and IoT in Smart Grid Implementing 5G in Smart Grid faces many challenges. These include interference and coverage issues, security and privacy concerns, standardization and interoperability challenges, and cost-effectiveness. Deploying more base stations, advanced antenna technologies, and network optimization can be considered for interference and coverage issues. End-to-end encryption, authentication mechanisms, access control policies, and intrusion detection systems can be implemented for security and privacy concerns. Common standards, protocols, interfaces, and testing procedures can be developed to address standardisation and interoperability challenges. Finally, for cost-effectiveness issues related to infrastructure deployment and operation costs of 5G networks for Smart Grids new business models such as public–private partnerships or revenue-sharing schemes can be explored. IoT devices in Smart Grids offer many benefits, such as increased efficiency and reliability. However, it also presents several challenges that need to be addressed.

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One of the main challenges is security and privacy concerns, which can be mitigated through encryption, authentication, and other security measures. Interoperability issues can be addressed through standardization efforts and the development of common communication protocols. Scalability can be improved by using cloud computing and other technologies that enable flexible resource allocation. Reliability can be enhanced through redundancy and fault-tolerant design. Big data analytics and machine learning algorithms can improve data management. Regulatory compliance can be ensured by developing regulatory frameworks that address data privacy, cybersecurity, and other aspects of Smart Grid operations. Finally, costs can be reduced through economies of scale and public–private partnerships that leverage government funding to support Smart Grid deployment in underserved areas. Overall, addressing these challenges will require a combination of technical solutions, policy interventions, and collaboration between stakeholders across different sectors. Overall successful implementation of advanced technologies in Smart Grid and achieving the desired proactive intelligence requires a comprehensive approach to addressing these challenges through collaboration among stakeholders from the energy sector, computing industry, telecommunication industry, academia, government agencies and standardization bodies.

5 Conclusions With the aid of computational and communication technologies, the smart grid has entered the next phase of its evolution called Smart Grid 3.0. The earlier versions of the smart grid did have access to power hardware, and high-speed communication to equip itself with proactive intelligence. AI, bigdata analytics, blockchain, 5G, NB-IoT, etc., have transformed how data is analyzed in the grid, resulting in better decision-making and enhanced services. This chap-ter discussed the evolution of the grid to the present state, along with the discussion on the role of various technologies in this metamorphosis. It also discussed the various challenges in achieving the desired proactive intelligence. This chapter is a foundation for future research and development in Smart Grid 3.0. By understanding the evolution of the smart grid, the role of various technologies, and the existing challenges, researchers can delve deeper into addressing these challenges and further advancing the smart grid ecosystem’s intelligence, reliability, and sustainability.

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Blockchain for Energy Management: Smart Meters, Home Automation, and Electric Vehicles Florentina Magda Enescu and Nicu Bizon

Abstract Today, more than ever, reducing energy consumption, air pollution, and other causes and effects associated with global warming are topics of interest to researchers. Traditionally, when making transactions with trust between parties, the help of banks or through an intermediary institutional framework is used. However, blockchain technology can change this system by imposing a decentralized architecture without an intermediary between parties when making a transaction. The applicability of blockchain technology in the present case is applied in the energy sector. The architectural principles, events and benefits that can be obtained in this direction are analyzed, such as the adoption of renewable resources, the influence produced due to climate changes for the technical balancing of the network, the creation of neighborhood microgrids (tenants’ associations) in order to lower energy prices by using photovoltaic panels (PV) and micro-wind turbines networks at the community level, and the elimination of intermediaries between producers and consumers. It can be said that this sector can be addressed in applications aimed at smart meters, the smart home and electric vehicle charging. In the energy system’s architecture, a prosumers hybrid appears between the producer and the consumer. In this way, through the additional quantity achieved through renewable systems, an increase in the generated energy and the corresponding decrease in the purchase price are obtained. This chapter proposes to create an association of green energy producers F. M. Enescu (B) Faculty of Electronics, Communication and Computers, University of Pitesti, 110040 Pitesti, Romania e-mail: [email protected] N. Bizon Faculty of Electronics Communications and Computers, The National University of Science and Technology POLITEHNICA Bucharest, Pites, ti University Centre, Pites, ti, Romania University Politehnica of Bucharest, Splaiul Independentei Street No. 313, 060042 Bucharest, Romania ICSI Energy, National Research and Development Institute for Cryogenic and Isotopic Technologies, 240050 Ramnicu Valcea, Romania N. Bizon e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 B. Appasani and N. Bizon (eds.), Smart Grid 3.0, Power Systems, https://doi.org/10.1007/978-3-031-38506-3_2

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to manage the entire production and respective energy consumption and surplus. If the energy produced is not consumed or sold, the association will find storage solutions to make consumption available within the imposed limits, and the purchase price is low. Through a mobile application, the entire process will be followed by each association member and globally by its board of directors. Keywords Blockchain · Distributed ledger · Smart meter · Smart house · Electric vehicle · Peer-to-peer energy trading · Prosumer · Renewable energy

Abbreviations PV DAO IoT IT TOS DC AC UML ID CNP ABI GUI TSO DSO

Photovoltaic panels Distributed autonomous organization Internet of things Information technology Transmission operating standard Direct current Alternating current Unified modeling language ID card Personal numerical code Application binary interface Graphical user interface Transmission system operators Distribution system operators

1 Introduction In the present context, there is a growing concern about optimizing electricity production and reducing the associated costs. Various factors, including environmental sustainability, resource scarcity, and economic considerations, drive this growing concern. One key objective is to shift towards a greater reliance on renewable energy sources, such as solar, wind, and hydroelectric power, as they offer cleaner and more sustainable alternatives to traditional fossil fuel-based generation. However, integrating renewable energy into existing power systems presents unique challenges. Renewable sources are inherently intermittent and decentralized, which introduces complexities in terms of grid stability, load balancing, and efficient energy management. Additionally, the decentralized nature of renewable energy generation calls for new mechanisms that enable seamless coordination, data exchange, and transactions between various stakeholders, including energy producers, consumers, and prosumers (individuals or entities that consume and produce energy).

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In this context, emerging technologies like blockchain are gaining attention as potential solutions to address these challenges. Blockchain is a decentralized and distributed ledger technology that allows for the secure, transparent, and tamperresistant recording of transactions and data across a network of computers. By leveraging blockchain, various aspects of the energy sector can be optimized, ranging from energy trading and billing to grid management and asset tracking. Blockchain technology offers several advantages in the realm of renewable energy. It enables peer-to-peer energy trading, allowing prosumers to directly sell their excess energy to consumers, eliminating the need for intermediaries and reducing transaction costs. Moreover, blockchain-based smart contracts can automate and enforce energy agreements, ensuring trust and transparency in energy transactions. The immutability and transparency of blockchain also enhance the traceability and certification of renewable energy sources, enabling consumers to make informed choices about their energy consumption. The state-of-the-art in this parlance is discussed. In [1], the authors address the evolving landscape of electrical energy systems, focusing on integrating renewable energy sources. The study highlights the environmental impact of these developments, particularly on global warming and greenhouse gas emissions. The authors emphasize the need for decentralized, small-scale electrical networks and propose utilizing blockchain technology to enable efficient data storage and trading of green energy. A blockchain-based smart microgrid framework with network constraints is proposed to enhance interoperability and communication across different platforms. In [2], the authors underscore the increasing adoption of blockchain technology in renewable energy management processes and its potential to facilitate the transition from fossil fuels to sustainable energy sources by offering viable solutions. The efficiency of blockchain technology in energy trading is analyzed by Dorfleitner et al. [3]. While the study finds limited success in utilizing blockcain for energy trade, the authors suggest exploring its application in establishing a consensus mechanism, indicating prospects for operational implementation. Blockchain technology holds promise for applications within the electricity system, with several use cases [4]. In [5], a comparative study is conducted between Internet of Things (IoT) and blockchain applications in intelligent energy networks, focusing on payment options and capitalization. The impact of blockchain technology on the energy system in the Netherlands is examined in research [6]. A comparison is made between the existing and new systems incorporating blockchain. However, the study reveals that the electrical system does not observe significant disruptions and system remodeling. In [7], a blockchain-based platform with significantly increased bandwidth is proposed, enabling enhanced scalability and performance. The benefits and novelty of blockchain technology in the energy industry are explored in [8] through a systematic review of published literature. The authors conclude that blockchain technology can support the energy system, providing opportunities for small-scale green energy producers/consumers to participate in the energy market and capitalize on their energy generation. The viability of this technology in the market will be determined over time, as stated in [8].

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After reviewing the key advancements in blockchain applications, this chapter concisely overviews essential concepts such as blockchain, token, transaction, and smart contracts. Furthermore, three specific studies on smart homes, electric vehicles, and smart metering are presented and analyzed. Under the traditional energy system, consumers were issued invoices to pay the producer and distributor for their energy consumption. Additionally, consumers were assigned specific distributors for meter reading purposes. However, the energy landscape has undergone significant transformations with the introduction of smart meters, the integration of renewable energy sources, and the application of blockchain technology in various operational scenarios. Nowadays, consumers have the opportunity to not only produce their own energy but also to engage in advantageous energy transactions. They can procure energy when needed at favorable prices and, conversely, sell or inject surplus energy back into the national energy system. The integration of blockchain technology with smart homes, electric vehicles, and smart meters contributes to an increase in energy efficiency, the adoption of renewable energy sources, and a subsequent reduction in greenhouse gas emissions. This synergistic approach enables improved energy management, enhanced sustainability, and a transition towards a greener and more environmentally friendly energy ecosystem. All these issues are discussed from a technical point of view in Sects. 3–5 of this chapter. The chapter ends with conclusions. The main purpose of the analysis is to debate different situations generated by smart metering, smart homes in communities and electro-mobility.

2 Blockchain Technology—Definition, Evolution, and Operation Any transaction is generated by the connection between products and information that are often not fully accessible to the population. Presently, the energy sector encounters similar challenges, as transactions between energy producers and consumers rely on intermediaries within the energy distribution sector. Trust between parties, and eliminating intermediaries, are the directions that need to be changed by the emergence of new technologies such as the blockchain. Considering these things, this technology can be defined as a way to establish trust between unknown parties to make secure exchanges between members of a network (peer-to-peer trading), without anyone from the outside intervening between them.

2.1 Definition and Structure Blockchain can be described as a sequential collection of records organized into blocks, wherein cryptographic techniques are employed to establish interconnections. The process involves the preservation of the cryptographic hash of the

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preceding block when constructing a new block, thereby forming a continuous series commonly referred to as a chain. So, a blockchain is a growing list of records, called blocks, linked together using cryptography. Each block contains a cryptographic hash of the previous block, a timestamp, and transaction data (generally represented as a Merkle tree). The timestamp proves that the transaction data existed when the block was published to enter its hash. Because each block contains information about the previous block, it forms a chain, with each additional block reinforcing the ones before it. Blockchains are, therefore, resistant to altering their data because once recorded, the data in a particular block cannot be retroactively altered without altering all subsequent blocks. A peer-to-peer network typically manages blockchains as a publicly distributed ledger where nodes collectively adhere to a protocol to communicate and validate new blocks. Although blockchain records are not immutable because forks are possible, blockchains can be considered secure by design and exemplify a distributed computing system with high tolerance to Byzantine Fault attacks.

2.1.1

Building a Blockchain

The blockchain consists of transaction blocks with data distributed across multiple computers. For this reason, if someone wants to modify the content of a block, all the blocks that were created later must be modified. The peer-to-peer network will be used for data management without a special server. Blocks store transactions that are validated and can be found in the Merkle tree. The link between the blocks is similar to that of a chain. Single blocks can be produced simultaneously. Blocks that are not allowed in the chain are called orphan blocks (see Fig. 1). In Fig. 1, the main chain is represented in black, the longest blockchain in the structure, drawn from the genesis block (represented by the green block). Purple blocks are considered orphan blocks; they exist outside the main chain. The time required for the network to complete the additional block is called the block time. For Ethereum, this is between 14 and 15 s. Figure 2 shows the general scheme of operation of blockchain technology.

2.1.2

Data Security

Data security is high in the case of a peer-to-peer network that uses blockchain technology. Regarding the attacks that may occur, it can be said that the risk is low because data storage is decentralized. Public key cryptography methods are the basis of this system. Asymmetric cryptography is used to create the digital signature. The signature is used in the signing and verification stage. In this system, two keys are used—the private one (confidential—for signing transactions) and the public one. The process is as follows:

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Fig. 1 Formation of blockchains

Fig. 2 The general scheme of operation of the blockchain technology

Stage 1: The sender—sends a message to the receiver. • When signing—the issuer will encrypt the message with the help of the private key; • Sends the original message to the receiver accompanied by encryption; Step 2: Receiver—check the message. • Decrypt the message with the issuer’s public key; • Check whether the content has been modified (see Fig. 3).

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Fig. 3 Asymmetric encryption

Blockchain is highlighted by: decentralization (transactions made by consensus), persistence (a transaction cannot be deleted, their validation is done quickly) and anonymity (the real identity of a user is known). There are three types of blockchains: public (visibility of records and participation in the consensus process is for everyone), private (nodes from a specific organization participate) and consortium (multiple organizations participate, and a small part of nodes will be selected to determine the consensus). Decentralized security is ensured by mining. Miners validate a transaction. For the allotted time, the mineral is rewarded with cryptocurrencies. Miners validate and add a new transaction to the blockchain’s public ledger. On average, a block is mined once every 10 min. The goal of mining is to find a value for the nonce (a random 32-bit number) that will result in a hash value of the block smaller than the nBits target field. So, billions of nonce values will be tried before the block gets a valid hash value. The data is verified through mathematical algorithms, generating a hash for each block. This label includes characters that correspond with the transactions in the block. When a fraud/error event occurs, the hash will not be appropriate in value, resulting in an error. The previous block will permanently have a link with the hash value from each block, which is permanently verified by mining on computers owned by network members (see Fig. 4).

2.2 Blockchain Technology Evolution 1. Stage 1.0: in 2008, the notion of Bitcoin appeared in an article whose author was given the pseudonym Satoshi Nakamoto. 2. Stage 2.0: Decentralized databases and transactions between parties through the smart contract appear. If certain contractual conditions that have been defined are met, digital protocols will be executed automatically without intermediaries.

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Fig. 4 Mining Process

These things spawned a public blockchain. Access will be granted after payment of a fee for system clients. 3. Stage 3.0: This concept is currently undergoing experimentation. Organizations and associations are utilizing smart contracts to facilitate transactions. One notable example is the Distributed Autonomous Organization (DAO), which is structured as a hierarchical association governed by social consensus and mutually agreed-upon rules to leverage expertise.

2.3 Smart Contracts Smart contracts are computer codes designed to create, enforce, and execute contracts. They facilitate exchanges between parties with unknown real identities, eliminating the need for intermediaries. Smart contracts introduce a new dimension to individuals’ access to the Internet, where the user plays a central role. Notably, smart contracts enable control over personal data, eliminating the necessity for third parties to possess specific data for transaction verification. This facilitates compatible, permissible, and efficient negotiations. Within a distributed public ledger, a smart contract undergoes the following stages: coding (the logic is written according to the parties’ requirements), public ledger (the encrypted code is distributed across the ledger network), execution (the code is received by a computer in the network for execution and verification by other network computers, and the network updates the distributed ledgers). a. Smart Contracts in digital identity.

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2.4 The Token Concept The monetization value quantifies the transaction between the parties through the virtual currency, which is a particularity of the token. Generating tokens: • To stimulate the transaction verification process—a process called mining; • Capital absorption—rewarding with tokens to those who participate in the blockchain project; • Both the users who create/deliver values for other users/associations—i.e. development and operation [9].

3 Architecture of an Association of Producers/Energy Distributors Electricity is transported from the producer to the central consumer through the distribution system operator (DSO). While production and transportation were initially considered integrated entity, efforts are underway to decentralize these processes. The ongoing advancements in Internet of Things (IoT) systems are expected to bring significant transformations within this domain [10]. The continuous development of IoT systems will lead to major changes in this field [10]. In conjunction with the advancement of Information Technology (IT), the energy sector is witnessing the emergence of green energy and the concept of prosumers. Prosumers, who possess the dual role of energy producers and consumers, can store energy. In light of these developments, various architectural models can be delineated; some may incorporate blockchain technology, while others may not. Therefore, the following scenarios can be formulated and compared: 1. 2. 3. 4.

Prosumer within a conventional energy system. Prosumer integrated with the grid. Prosumer association with peer-to-peer energy exchange within the grid. Utilization of battery storage for surplus energy.

3.1 Peer-to-Peer—DSO Networks In the conventional system, electrical energy is generated in a centralized manner. Transmission lines (TOS) are utilized to distribute the energy to consumers. At present, energy is being generated at the household level. The Distribution System Operator (DSO) is responsible for injecting all the green energy produced by prosumers’ photovoltaic (PV) panels into the national grid [11]. The DSO plays a crucial role in distributing the required energy to private energy producers using

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PV and other consumers within the system (refer to the architecture depicted in Fig. 5). This scenario involves: 1. Prosumer: Refers to the household that possesses photovoltaic (PV) panels and has the capability to generate green energy. 2. Smart meters: Installed devices that consistently record the production and consumption of electricity. 3. Electric Vehicle (EV) charging socket: Infrastructure enabling the charging of electric vehicles. 4. Distribution System Operator (DSO): Distributes the required energy to all consumers within the system. 5. Green energy: Produced by the prosumer and injected into the conventional energy distribution system. 6. DSO: The organization responsible for collecting all the energy generated by private producers, including prosumers, and distributing it to meet the energy requirements of all producers and consumers within the system. 7. Consumers: Refers to individuals or entities, whether prosumers or not, who receive energy from the DSO distribution system. This scenario includes monitoring and recording the electric energy generated by the prosumer’s PV panels using smart meters while directing the generated energy towards the conventional energy carrier and distributor. Based on this premise, prosumers have several options for managing the energy generated by their photovoltaic (PV) systems, which include: 1. Self-consumption: Utilizing the generated energy for their own household consumption. 2. Energy exchange with the Distribution System Operator (DSO): Engaging in energy transactions with the DSO. 3. Supplying energy to other consumers within the community: Distributing surplus energy to fellow consumers.

Fig. 5 Architecture of peer—DSO networks

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The formation of associations among multiple prosumers offers enhanced costeffectiveness. These associations enable prosumers to actively participate in the management processes related to electricity production and consumption.

3.2 Peer-to-Peer—Microgrid Networks In the case of prosumer-grid systems (peer-to-peer microgrids), electric energy is generated collectively at the association level. The formation of these associations offers significant advantages: 1. Collective energy purchasing: Associations can collectively purchase energy from suppliers when needed. 2. Development opportunities: Revenues obtained from negotiations and sales enable the association to foster development and growth. 3. Enhanced negotiation power: Many partners within the association enhance credibility during energy purchase negotiations, leading to favorable pricing. 4. Open energy systems: These systems operate based on decentralized energy distribution networks utilizing photovoltaic systems (PV), ensuring efficient energy distribution through peer-to-peer mechanisms [12–15]. These systems are accessible to association members. The establishment of associations is proposed to optimize negotiations with the wholesale energy market and foster community-driven green energy production. Each household within the association possesses PV panels and manages the generation and consumption of energy through smart meters. This setup facilitates the creation of microgrids, enabling independent energy production and storage. The association benefits from having green energy for internal consumption while effectively managing surplus energy for development and economic gains (refer to Fig. 6). Forming associations of prosumers is a cost-effective approach that allows them to actively participate in the management of electricity production and consumption processes. Individual households and the association through smart metering technologies can continuously monitor both energy production and consumption. This scenario involves the same components mentioned above, but the architecture is different and so the components communicate differently: 1. Prosumers: These are electricity producers using PV systems, organized into associations. Each prosumer node includes power equipment and a control subsystem for data processing. 2. Consumers: Individuals or entities that receive energy through the distribution system operated by the Distribution System Operator (DSO). 3. DSO: Responsible for distributing the required energy to all consumers within the association’s external system. It includes an external energy balancer and consumption meter.

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F. M. Enescu and N. Bizon

Fig. 6 Architecture of peer to peer—microgrid

4. Smart meters: Devices that measure the production and consumption of electricity. 5. Electric Vehicle (EV) charging stations/parking. 6. TSO: The Transport System Operator facilitates the connection between consumers within the association and the external supply network. It does not participate in the internal processes of the association. The system responsible for monitoring and controlling the association nodes collects data from the smart meters installed in the association members’ households. The injection or withdrawal of electricity occurs at the main supply. The data from association nodes and individual smart meters, which indicate the power flow direction, are aggregated. Energy transfers take place among association members or to the conventional system, which then supplies consumers outside the association. The management of household consumption aims to reduce energy waste and associated costs for each association member.

3.2.1

Case Study

Smart Home is a residence or modern house where appliances and devices can be controlled by a remote owner from anywhere in the world with an internet connection, any smart device such as a mobile or laptop. A house is also considered intelligent through effective control of energy management. We constantly seek solutions to improve/reduce energy consumption [16, 17]. An important mechanism in this regard is consumer awareness regarding reducing consumption and improving the production/consumption ratio. An important contribution to the application of these directions is the involvement of the Internet of Things (IoT). In [18], an application is presented through which the consumer is constantly warned about the energy consumption in the home. A comparison is

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made between the predicted and energy consumption values of the devices found in a smart home. Warning messages are sent so that the consumer can intervene and take measures to save energy depending on costs [19, 20]. The frequency control of the energy system, through balancing services, is carried out by the distribution system operators (DSOs) by ensuring high flexibility and finding new energy resources. Such resources can be provided by smart homes located in distribution networks. Different operations and economic results are discussed for the smart home considering real-world data [21–24]. The proposed system consists of photovoltaic solar panels, battery charge controller, an accumulator battery, and a junction box with a sinusoidal direct current (DC)/AC inverter. Supply from the AC network is provided if the chosen energy sources do not meet the consumption; it gives power to the national electricity network when it has an excess from the voltaic sources. A special meter (bi-directional meter) registers the difference in power to understand the consumption. The voltage requirement in a power system is directly proportional to the power demand. To minimize losses in the direct current (DC) side, solar panels are chosen with a voltage of either 24 or 48 V, which reduces the current flow. Higher input voltages also enhance the efficiency of inverters. The solar panels are arranged in three rows, consisting of a total of six panels. The energy consumption of appliances (Aconsp ) is calculated as follows: Aconsp = I A P × N

(1)

where IAP is the instantaneous absorbed power, and N is the number of hours of operation. For example, Aconsp of the Laptop is 30 Wh × 6 h = 0.18 kWh; Aconsp for a ironing machine is 1800 Wh × 1 h = 1.8 kWh. In this study, the year 2023 was considered, encompassing the following time parameters: Calendar Days: 365. Working Days: 243. Days Off: 117. These calculations and time parameters provide essential data for assessing the studied context’s energy requirements and consumption patterns. Table 1 shows the annual consumption of electronic devices/household appliances on the weekend, around 9.337 kWh. This can be corroborated from Fig. 7. This represents a day when the family members are at home and have various household activities (cooking, washing clothes, cleaning, and watching television shows). The energy consumption of various household appliances used during the morning period on a typical working day, including light bulbs, coffee machine, microwave oven, refrigerator, and others, is outlined in Table 2 (refer to Fig. 8). Based on the data presented, the total energy consumption during this period amounts to 0.762 kWh. This information provides valuable insights into the energy usage patterns of these appliances during the morning hours on working days.

24

6

12.5

400

2000

28

Washing machine

Vacuum cleaner

Refrigerator

120

Coffee machine

Total housing consumption

800

1800

Microwave

Ironing machine

700

Stove-oven

1

1

1

2

4

2

40

75

Bedroom

8

60

Kitchen

Bathroom

2

70

40

TV

Hall

1

2

4

5

Mobile phone 2

6

30

Laptop

LED bulb

Time/ hours

Instant consumption (Wh)

Electrical equipment/ household appliances

1

1

1

1

1

1

1

2

1

1

1

1

3

1

1

Pieces

9337.5

120

1800

800

1400

150

160

480

160

420

672

2000

800

187.5

8

180

Consumption—weekend day (Wh)

9.3375

0.12

1.8

0.8

1.4

0.15

0.16

0.48

0.16

0.42

0.672

2

0.8

0.1875

0.008

0.18

Consumption—weekend day (kWh)

Table 1 Consumption of electronic devices/household appliances on a weekend day/year

0.18

0.18

0.18

0.18

0.18

0.18

0.18

0.18

0.18

0.18

0.18

0.18

0.18

0.18

0.18

0.18

Euro price per kWh

1.68075

0.0216

0.324

0.144

0.252

0.027

0.0288

0.0864

0.0288

0.0756

0.12096

0.36

0.144

0.03375

0.00144

0.0324

Total price—weekend day Euro/kWh

1092.4875

14.04

210.6

93.6

163.8

17.55

18.72

56.16

18.72

49.14

78.624

234

93.6

21.9375

0.936

21.06

Annual consumption—days off (weekend) (kWh)

36 F. M. Enescu and N. Bizon

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2500 2000

1800

Wh

2000 1400

1500 800

1000 500

800

672

480

420 180

8

187.5

160

160 150

120

0

Household Appliance Fig. 7 Graphic representation of the related consumption on a weekend day

Table 3 presents the energy consumption of various household appliances utilized during the evening when homeowners return from work. The appliances include light bulbs, a vacuum cleaner, a washing machine, an oven, refrigerator, TV, etc. (refer to Fig. 9). According to the data, the total energy consumption during this time frame amounts to 5.148 kWh. This information offers valuable insights into the energy usage patterns of these appliances during the evening hours, allowing for a better understanding of the overall energy consumption dynamics in the studied context. The analysis considers a four-story block staircase as the subject of study. The staircase comprises a total of five landings, with each landing equipped with two fluorescent lights, as indicated in Table 4. To determine the energy consumption of the fluorescent lights, the following calculation method is employed: Neon Consumption = Instantaneous Absorbed Power × Number of Hours of Operation. By applying this calculation method, the energy usage of each fluorescent light can be estimated based on its power rating and the duration of the operation. This information provides insights into the energy consumption patterns associated with the lighting system in the block staircase. Moreover, it is important to note that the energy consumption of the fluorescent lights should be considered in the context of the overall energy usage within the block’s staircase, considering other energyconsuming components or systems present in the facility. The analysis reveals that the total energy consumption, as shown in Table 5, amounts to 102.2 kWh, encompassing two main components: 1. Stair Lighting Consumption: This includes the energy consumed for the illumination of the staircase, which accounts for a certain portion of the total consumption. 2. Tenant’s Apartment Consumption: This refers to the electricity consumed by the tenant’s apartment, representing another significant portion of the overall energy usage.

2

12.5

400

2000

28

Mobile phone

LED bulb

Washing machine

Vacuum cleaner

Refrigerator

Total housing consumption

120

Coffee machine

0.1

800

1800

Microwave

Ironing machine

0

700

Stove—oven

0.5

0.2

0.2

0.5

40

75

Bedroom

0.5

60

Kitchen

Bathroom

1

0.5

70

40

TV

Hall

0

0

1

1

1

30

2

Laptop

Instant consumption Time/hours per day (Wh)

Electrical equipment/ household appliances

1

1

1

1

1

1

1

2

1

1

1

1

1

1

1

Pieces

24

360

80

0

37.5

20

30

40

70

56

0

0

12.5

2

30

Consumption day—morning (Wh)

1.524

0.048

0.72

0.16

0

0.075

0.04

0.06

0.08

0.14

0.112

0

0

0.025

0.004

0.06

Consumption in the morning—time interval 06-08 h (kWh)

Table 2 Consumption of electronic devices/household appliances in the morning for a working day/year

0.762

0.024

0.36

0.08

0

0.0375

0.02

0.03

0.04

0.07

0.056

0

0

0.0125

0.002

0.03

Consumption—day morning (kWh)

188.976

5.952

89.28

19.84

0

9.3

4.96

7.44

9.92

17.36

13.888

0

0

3.1

0.496

7.44

Annual consumption working days—morning (kWh)

38 F. M. Enescu and N. Bizon

Wh

Blockchain for Energy Management: Smart Meters, Home Automation … 400 350 300 250 200 150 100 50 0

39 360

30

2 12.5 0

0

56 70 40 30 20 37.5

80 0

24

Household Appliance Fig. 8 Graphical representation of consumption on a working day morning

Table 5 shows the consumption breakdown for the three scenarios observed within a day. These calculations provide valuable insights into the energy consumption patterns of the stair lighting, tenants’ apartments, and overall energy usage within the context of the examined cases. The objective is to ensure that the energy produced by a specific number of photovoltaic panels can adequately cover the energy consumption within the time interval of 08:00 to 20:00. This requirement necessitates determining the number of panels needed, considering both the available surface area and certain conditions. To calculate the number of panels required, the following method is employed: Total consumption/number of hours = 102.2 kWh/12 h = 8.5 kWh. Given that the surface area of each panel is 1.84 m2 , and the power developed by a single panel is 0.5 kW, the subsequent calculation yields the result of 18 panels needed. These panels, with a combined power output of 9.9 kW, will be arranged in three rows, as detailed in Table 6. The daily consumption for the specified time interval of 08:00 to 20:00 is 8.5 kWh. On weekdays from Monday to Friday, this energy consumption can be distributed among 2–3 apartments based on the usage patterns of the occupants. Among the 15 apartment owners, eight are presumed to be at home during this interval, while seven are assumed to be at work. Accordingly, a consumption allocation per apartment has been proposed, taking into account the activity of the owners, as outlined in Table 7. This allocation considers the varying energy needs and usage patterns of the apartment occupants, reflecting the distribution of consumption across the available apartments during the specified time period. A proposed photovoltaic system consisting of 18 panels with a total capacity of 10 kW costs 250.000 EURO, including commissioning. The expected operational lifespan of the system is at least 25 years. The cost per apartment for the purchase of this system amounts to 16.500 EURO. In summary, the energy generated by the photovoltaic panels, estimated at approximately 10 kWh, can be fully utilized by

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Table 3 Consumption of electronic devices/household appliances in the evening Electrical equipment/ household appliance

Total Total Pieces Consumption Consumption housing housing day—evening day—evening consumption consumption (Wh) (kWh) (Wh)

Annual consumption working days—evening (kWh)

Laptop

30

3

1

90

0.09

22.32

Mobile phone

2

1

3

6

0.006

1.488

LED bulb

12.5

3

3

112.5

0.1125

27.9

Washing machine

400

1

1

400

0.4

99.2

Vacuum cleaner

2000

0.5

1

1000

1

248

Refrigerator

28

4

1

112

0.112

27.776

TV

70

3

1

210

0.21

52.08

Hall

40

0.5

2

40

0.04

9.92

Kitchen

60

2

1

120

0.12

29.76

Bedroom

40

0.5

1

20

0.02

4.96

Bathroom

75

0.5

1

37.5

0.0375

9.3

Stove—oven 700

3

1

2100

2.1

520.8

Microwave

800

0

1

0

0

0

Ironing machine

1800

0.5

1

900

0.9

223.2

Coffee machine

120

0

1

0

0

0

5148

5.148

1276.704

Total housing consumption

2500

2100

Wh

2000 1500

1000

900

1000 500

90

400 6 112.5

112 210 40 120 20 37.5

0

Household Appliance Fig. 9 Graphical representation of consumption on a working day evening

0

0

Instant consumption Wh

18

Electrical equipment

Scale consumption

12

Time/hours 10

Pieces 2160

Consumption Wh

Table 4 Consumption of lighting devices on the scale of the block

2.16

Consumption kWh 0.9

Price per kWh EURO

1.94

23.32

Consumption price Consumption kwh EURO price 12 h/kwh

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Table 5 Presentation of consumption for the three cases presented in one day Total consumption: 15 houses in the morning 06–08 h

1.52 × 15 = 22.8

Total consumption: 15 houses in the afternoon 18–20 h

5.15 × 15 = 77.25

Light bulb consumption 12 h

2.16

Total energy consumption time interval 06–20 h

102.2

Table 6 The need for photovoltaic panels Number of panels

Panel surface (m2 )

Developed power/day (W)

Developed power/day (kW)

Panel size (mm)

Total of rows

1

1.84

550

0.55

1765 × 1048 × 35

18

33.12

9900

9.9

1766 × 1048 × 3 35

Table 7 Consumption proposal Apartment 1

Apartment 2

Apartment 3

Child—schoolboy

Housewife

Retired

Equipment

Consumption

Equipment

Consumption

Equipment

Consumption kWh

Electric

kWh

Electric

kWh

Electric

Laptop

0.18

Gas electric oven

1.4

Mobile phone 0.01

Mobile phone 0.01

Washing machine

0.8

Led bulb

0.19

TV

0.42

Vacuum cleaner

2

Refrigerator

0.67

Microwave oven

0.8

TV

0.42

TV

0.42

Refrigerator

0.67

Total

hall

0.16

Hall

0.16

Refrigerator

0.67

Coffee machine

0.12

2.08

5.45

1.57

Total consumption for the 3 apartments: 9.1 kWh

the residents of a residential block when categorized into two groups based on their schedules. The application of blockchain technology in the energy sector is highly advantageous for the following reasons: 1. The advancement of renewable energy technologies, accompanied by declining costs of PV panels and wind turbines, has prompted the emergence of hybrid consumer/producer entities in the electrical network architecture. The decentralized nature of individual PV-based electricity generation enables the adoption of

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blockchain-based architectures, leveraging distributed databases to achieve high operational efficiency. 2. The implementation of microgrids, storage solutions utilizing innovative technologies, bidirectional energy metering, and smart contract-based trading can be effectively managed through blockchain platforms. 3. Adopting a blockchain-based network of prosumers enables the direct interaction between energy producers and consumers, eliminating the need for intermediaries. This transaction model, depicted in Fig. 10, simplifies business processes and streamlines the energy supply chain. Overall, the application of blockchain technology offers significant advantages in the energy sector, facilitating the integration of renewable energy sources, enhancing operational control, and fostering direct interactions among energy stakeholders.

Fig. 10 Blockchain transaction model

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3.3 Association of Renewable Energy Producers with Surplus Energy Injected into the National System that Distributes Energy to Consumers In the case of the association of prosumers and consumers (energy-consuming neighborhoods) using grid peer to grid and DSO networks, the green energy produced by the association can be used by the members of the association, sold outside it, respectively injected into the conventional energy system (see Fig. 11—the route marked in red—from DOS; the route marked in green—green energy from PV). The association manages the production/consumption and evaluates the energy left unconsumed by the association. In this case, there would be the following possibilities: Surplus injection into the conventional system; • Sales to other neighborhoods, household consumers or companies. The association presents several advantages such as: • Low costs for the energy consumed by the members of the association; • For energy purchased on demand, can negotiate a better price with TSO/DSO suppliers;

Fig. 11 Architecture grid peer to grid and DSO networks

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• The revenues obtained from the sale of surplus energy can be invested for the purchase of PV and offered for use by households that wish to join the association; • Charging electric vehicles (EV) at power sockets placed in the association’s parking lot, respectively, using the parking lot as a site for new PV systems. In this situation, the canopies in the parking lot with PVs will be sources of protection (shading) for EVs. • The presence of EV charging stations in the parking lot brings new income to the association from the use of EVs that have owners from outside the association [25]. This Scenario Involves: 1. 2. 3. 4. 5.

Prosumer from associations; Consumers from associations; Non-association consumers; DOS—Organization of the conventional distribution system; Energy generated by PV panels that is injected into the national.

Actions: The information indicating the amount of green energy generated by the association is permanently tracked by the smart meter that stores the data collected from the smart meters belonging to all the prosumers in the association that make up that node. The electrical energy supply of the physical subsystem is connected to the common bus. A power transfer is made from each community node to the central control node. At the end of a transfer, the transaction in the current block is made by retaining the node identification, the total energy and exchange directives. As the number of readings from a node is greater, it will be the one that will contribute to validating the data regarding the energy exchange. The transaction can be validated for execution or not. The sale to consumers external to the association, respectively to the centralized system, is carried out through the smart contract.

3.4 Renewable Energy Producer/consumer of an Association Using a Batteries Stack In situations where surplus energy needs to be stored, information architectures are designed to enhance energy independence by leveraging available storage solutions. These architectures typically involve open energy systems that utilize batteries to store unused energy. The stored energy can then be distributed to local users within the association or to external users, as required. Efficient distribution of energy is achieved by implementing a peer-to-peer system. These networks operate independently from the conventional energy grid and are characterized as open systems (refer to Fig. 12).

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Fig. 12 Architecture of shared balance storage

In this system, the energy storage batteries are buffer elements achieving an energy balance. The energy not consumed at a given moment is directed to these accumulators, from where it will be extracted when needed, so it is discharged. The monitoring of the system is carried out with the help of smart meters that are equipped with the houses of the association members. The production/consumption/charge–discharge status of the batteries is permanently monitored. This Scenario Involves: 1. 2. 3. 4.

Prosumer from associations that produces/consumes renewable energy; Consumers from associations; Non-association consumers; Green energy storage accumulators, which can be shared when needed in collaboration with DSO and TSO; 5. Smart meters, which manage all the electricity produced/consumed. Actions: Physically, the power nodes are connected to the common power bus for electricity injection/consumption. The remaining energy is collected and directed permanently to the central stack of batteries. System use—defining the main actors Unified Modeling Language (UML) diagram of the use cases together with the participating actors: (see Fig. 13).

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Fig. 13 UML diagram

Case 1. Use Case Name: Create Identity. Main actor: Prosumer/Consumer. Prerequisites 1. The Prosumer/Consumer is on the appropriate page in the Web application 2. The Prosumer/Consumer must have enough ether in his account to carry out the transaction. Main (Successful) Scenario 1. The Prosumer/Consumer enters the data related to his own identity 2. The system saves the identity in the blockchain and redirects the prosumer/ consumer to the main screen. Case 2. Use case name: Balance inquiry. Main Actor: The Prosumer/Consumer. Prerequisites 1. The Prosumer/Consumer is on the appropriate Web page 2. The Prosumer/Consumer must have enough coins in their account to display them. Main scenario: 1. The Prosumer/Consumer logs in to the page successfully 2. The Prosumer/Consumer accesses the card with his identity where he can check the balance. Case 3. Use Case Name: Site Login. Main Actor: The Prosumer/Consumer. Prerequisites: The Prosumer/Consumer is on the appropriate page. Main Scenario 1. The Prosumer/Consumer entering login data (username and password)

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Fig. 14 Functional diagram of the application

The login page will close and the main page will load with the ID card. A functional diagram for the proposed application is described in Fig. 14.

4 Application of Blockchain Technology for Proposed Energy Architectures 4.1 Smart Contract for the Login Application The solidity language is used for going through the stages: 1. Create smart contract Step 1—create a document with the extension—.sol—extension specific to the solidity language. Step 2—create code in the file created in Step 1. Step 3—creating the actual contract—with the variables and their associated type for recognizing the user whose name differs from the file in which it is written. In the present case, the variables are: • name, surname, personal numerical code (CNP)—string type; • Variable of type int—called balance will have the purpose of storing the amount of coins that a user has available. Step 4—create a constructor (public type)—mini database. Here too, a value of coins assigned to any user is specified to test some functions (eg, 200 coins assigned to any user).

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• The “login” function with parameters: ex. name, first name, numeric code— used to enter the data and returns—an “error” string variable, if the conditions are not met, and it seems that the variables in the constructor are empty. • The “You are logged in as” function—its purpose being to display the information from the constructor, therefore it needs 4 output variables, 3 of them of string type for Name, Surname and CNP, but also an int type variable to display the balance. • The “Withdrawal” function—has an input variable of type int called “amount” in which a number can be entered to be withdrawn from the user’s “balance”. It is a public function, for transfer between two users • “Add” function—the user will be able to add an amount of coins to his “balance”. • The “Balance Display” function—displays the current “balance” that changes by using the other functions. 2. Checking the source code: using the debugger in the platform card. 3. Compilation of the contract—choose the solidity variant appropriate to the source code from the list of compilers: • Then choose the Ethereum virtual machine on which to compile, from the list provided by the platform; • Select the contract to be compiled. • Get the contract’s Application Binary Interface (ABI)—it’s the binary interface between the Ethereum-based contract and any other module that interacts with it. This guarantees that the binary code deposited in the Ethereum blockchain can be called without confusing the functions within the called contract and that the requested or added data is within the generated contract. 4. Deploying the smart contract: the purpose of observing the contract’s functionality (it is tested), one of the Ethereum accounts is chosen for, which must hold some coins to pay the “gas” fee. This will result in a graphical user interface (GUI) for users to access.

4.2 Results—Testing and Validating the Behavior of the Smart Contract and the Web Application Access the mini-interface produced by the platform following the deployment made in the account with the corresponding hash: 1. Login to the page 2. Activate the main page—of the Marketplace application (see Fig. 15). The consumer’s name must not be the same as the prosumer’s to buy electricity. From the MetaMask menu, a pop-up page will be opened where the consent to

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Fig. 15 Login stages and marketplace application

Fig. 16 Metamask confirmation

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Fig. 17 List of products after electricity was purchased

continue the payment will be requested and the Gas fee will be charged, as shown in Fig. 16. After the transaction has been made, the main page will change the list of products, and instead of the seller’s hash, the buyer’s will appear (see Fig. 17).

5 Discussions and Future Perspectives Many applications in the energy field can be completed and adapted to the new blockchain technologies. The important factors that contribute to this are: 1. The use of renewable energy resources and the problems of climate change in recent years. The acceptable price of photovoltaic systems and micro-wind turbines is added to all this. New partners appear in the energy system— prosumers. A combination of energy producers and household consumers first forms prosumers. But these things can generate problems in balancing the network from a technical point of view. Transactions and energy production by domestic consumers allow the emergence of new architectures that use blockchain technology to manage distributed energy production. 2. Microgrids—networks organized at the local level. The electrical energy produced here can automatically be controlled, stored, and traded through smart contracts. 3. Intermediaries involved in the development of the process—the energy system contains producers and consumers. All who come out of this sphere are considered intermediaries. All these intermediaries can be eliminated by applying blockchain technology in the system. In the future, the creation of zonal prosumer associations will contribute to developing the current energy system. Worldwide, there are concerns regarding the use of blockchain technology, such as: Bankymoon from South Africa—smart energy metering in schools using blockchain to pay bills:

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• Fortum from Finland—I propose to reduce energy consumption by involving the Internet in the metering of household appliances; • InviroHub from South Africa—offers solutions in the intelligent consumption of water, gas and electricity, through intelligent online metering; • Lo3 Energy from the USA—in real time verifies the production/consumption of energy and analyzes the data that generates it; • o3 Energy from the USA—proposes to reduce the energy costs of household consumers by applying hardware and software solution technologies. The applications that will use blockchain technology allow: automatic payment of bills, encryption of renewable energy data and respective charging/sharing of electric vehicles [26–29]. For the energy area, the involvement of communities in the green energy generation process will increase their capacity to govern in the energy field. Strong echo from the financial and decision-making point of view at the social level. Smart contracts will contribute to the elimination of corruption and bureaucratic inefficiency and will massively reduce administration costs. In this way, the way of thinking of the members of the associations will also change. All these things will lead to the dynamism of the energy sector. Technological innovation will be felt at the industrial level. This technology will have to be implemented very carefully, for its mass adoption. Blockchain technology is what will contribute to the development and improvement of activity in this sector. The consumers can get benefits such as: • The adoption of renewable resources, the influence produced due to climate changes for the technical balancing of the network. • The creation of neighborhood microgrids (tenants’ associations) to lower energy prices by using photovoltaic panels (PV) and micro-wind turbine networks at community level. • Elimination of intermediaries between producers and consumers. In the energy system’s architecture, a prosumers hybrid appears between the producer and the consumer. In this way, through the amount of additional electricity produced through renewable systems, an increase in the generated energy is obtained and, respectively, a decrease in the purchase price. Energy communities will evolve in the next years into a Distributed Autonomous Organizations (DAOs) that include actors from their own sector of activity. In order to create such systems, it is proposed to go through the following stages: • Consumer cooperative aggregation: This stage involves documenting and analyzing energy consumption within a residential setting, specifically focusing on smart homes and the energy management required to optimize consumption. • Upstream integration as a trader: Building upon the consumption data analysis from the previous stage, the association engages in energy procurement from suppliers to minimize energy wastage. Real-time solutions for surplus energy storage or purchase are implemented to ensure efficient utilization.

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• Microgrid integration: The association establishes a microgrid by acquiring renewable energy sources and developing the necessary infrastructure within the community. This enables energy transactions between the association’s shareholders outside the blockchain platform. A dedicated mobile application is created to manage the energy performance of individual consumers. • Financing system implementation and energy poverty alleviation: To combat energy poverty and facilitate equipment purchases, the association acquires green energy-producing equipment and offers it to members in the form of a shared resource. Surplus energy is distributed to members who require additional energy beyond their own production capacity or directed to the national energy system. Members contributing surplus energy for trading purposes are incentivized and encouraged. • Integration of Decentralized Autonomous Organization (DAO): The final step involves integrating DAO into a network of networks. A virtual platform is developed to connect these communities worldwide, featuring its currency and highlighting the financial benefits of energy trading within the system. This strategic growth of the token-based system attracts new members to these DAOs.

6 Conclusions Because for quite a long time, the energy system did not have an optimal option in terms of efficiency, cost and sustainability, now this blockchain technology and renewable resource, storage and grid technologies are emerging, which enable the creation of microgrids. Blockchain technology eliminates intermediary companies from the system, which only increases energy costs. Thus, prosumers can share energy resources in a self-sustaining energy community. So, as time goes on, it is predicted that energy independence will increase and air pollution will decrease. It is not yet known when the current system will be outdated and switched to a hybrid one, how long the hybrid system will work and when a distributed system of microgrids will be implemented globally. The road will be long, and we will have to overcome many obstacles using new microgrid architectures and technologies waiting to be proposed. Obviously, the greatest advantages are recorded by consumers who can turn into prosumers and will have an important role in the distributed energy system. In conclusion, it can be said that applications targeting smart meters, smart home, electric vehicle charging and so on can benefit both prosumers and consumers. The benefits of this type of approach could be the following: • Involvement and responsibility of the end user; with the prosumer’s appearance and the private producer’s stimulation, the desire to optimize the energy consumption and develop local associations will appear. • Transparency and simplification of the cost structure; investment in renewable sources will become a source of income, developing the spirit of competition within the community and lowering energy costs.

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• Increasing the quality of energy and the degree of resilience of the network; if a node in the network fails, it will not affect the continued operation of the network, so the energy and system quality will be much better controlled. Data encryption and validation by the blockchain presents a factor of resistance to cyber-attacks in energy trading. • Reducing the carbon footprint and combating the causes of global warming; here a reference must be made to encouraging the use of electric cars by using renewable energy in the system of charging stations. • Increasing the system’s efficiency; reducing energy losses on transmission and distribution lines. The decentralized blockchain network correlates the hardware for automation and smart contracts in energy transactions. Tracking consumption online allows the distribution of energy consumption in the home by hourly intervals (see presentation of the smart home, where production/costs can be permanently checked). • Increasing the degree of energy security of the system; the use of local energy systems based on green energy will no longer allow the appearance of vulnerabilities and will therefore increase energy security. • Stimulation of competition between producers of renewable energy sources and related technologies in microgrids; the goal is to reduce costs between producers/ technologies of renewable energy sources. • Redistribution of intermediate agents’ profits at the level of the energy community; the advantage of an association applying blockchain technology will allow decisions to be made by consensus by its members. • Increased resistance to fluctuations in the energy market; association members will be less affected by possible crises/prices in the energy sector or other speculations that may appear on the centralized electricity market. • A solution to combat energy poverty; people disadvantaged in terms of access to green energy sources can now more easily join these producers’ associations, contributing to the increase in the number of association members and ultimately to the reduction of global warming. • The innovative financing solution; the association can issue tokens to increase the capital through community members’ participation and through the contribution of people outside the association.

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20. Kim H, Choi H et al. (2021) A systematic review of the smart energy conservation system: from smart homes to sustainable smart cities. Renew Sustain Energy Rev 140:110755. https:// doi.org/10.1016/j.rser.2021.110755 21. Andreadou N, Papaioannou I, Masera M (2019) Interoperability testing methodology for smart grids and its application on a DSM use case—a tutorial. Energies 12(1):8. https://doi.org/10. 3390/en12010008 22. Kumari A, Gupta R, Tanwar S (2021) Amalgamation of blockchain and IoT for smart cities underlying 6G communication: a comprehensive review. Comput Commun 172:102–118. https://doi.org/10.1016/j.comcom.2021.03.005 23. Moniruzzaman M, Khezr S, Yassine A, Benlamri R (2020) Blockchain for smart homes: review of current trends and research challenges. Comput Electr Eng 83:106585. https://doi.org/10. 1016/j.compeleceng.2020.106585 24. Umar A, Kumar D, Ghose T (2022) Blockchain-based decentralized energy intra-trading with battery storage flexibility in a community microgrid system. Appl Energy 322:119544. https:// doi.org/10.1016/j.apenergy.2022.119544 25. Ahl A, Yarime M et al (2020) Exploring blockchain for the energy transition: opportunities and challenges based on a case study in Japan. Renew Sustain Energy Rev 117:109488. https:// doi.org/10.1016/j.rser.2019.109488 26. Hampton H, Foley A (2022) A review of current analytical methods, modelling tools and development frameworks applicable for future retail electricity market design. Energy 260(1):124861. https://doi.org/10.1016/j.energy.2022.124861 27. Samuel O, Javaid N, Turki Ali A, Kuma N (2022) Towards sustainable smart cities: a secure and scal able trading system for residential homes using blockchain and artificial intelligence. Sustain Cities Soc 76:103371. https://doi.org/10.1016/j.scs.2021.103371 28. Nai Fovino I, Andreadou N, Geneiatakis D, Giuliani R, Kounelis I, Lucas A, Marinopoulos A, Martin T, Poursanidis I, Soupionis I, Steri G (2021) Blockchain in the energy sector, WP3—use cases identification and analysis. EUR 30782 EN, Publications Office of the European Union, Luxembourg, 2021. ISBN 978-92-76-40552-8. JRC125521. https://doi.org/10.2760/061600 29. BEUC (2018) Electricity aggregators: starting off on the right foot with consumers. BEUCX2018-010. https://www.beuc.eu/publications/beuc-x-2018-010_electricity_aggregators_sta rting_off_on_the_right_foot_with_consumers.pdf. Accessed 20 April 2020

Engineering Applications of Blockchain Based Crowdsourcing Concept in Active Distribution Grids Bogdan-Constantin Neagu, Gheorghe Grigoras, and Florina Scarlatache

Abstract The future active distribution networks (ADNs) must ensure “smart” features like flexibility, accessibility, reliability, and high power quality for all consumers. Increased adoption of small-scale distributed energy sources (SSDES) helps decarbonise ADNs. In the present context, society must ensure the comprehensibility of the benefits stemming from smart electricity across the entirety of the population while concurrently ensuring that the provisioning process is achieved in an environmentally sustainable and efficient manner. Energy poverty is a lack of access to clean and affordable energy, resulting in soaring energy costs. The crowdsourcing concept, introduced by Surowiecki (The wisdom of crowds, Anchor, San Diego, CA, 2005), can be used to mitigate energy scarcity. It can be a useful tool for allowing the crowd to do community service within a specific geographic region. According to Romania’s Energy Regulation National Agency’s Order No. 228, launched on December 28, 2018, the prosumers can sell the energy-produced SSDES on the free market. More automated trading strategies aim to improve the benefits of peers who trade electricity in local community markets. The main aim is to quantify the distortion effects and introduce a stringent and comprehensive methodology integrating the distribution network operator (DNO), prosumers, and consumers. This chapter compare the ADNs cost saved by the households when prosumers move to increase their revenue, and the DNOs act to improve the benefits derived from an optimal network operation. Keywords Renewable sources · Prosumers · Crowdsourcing concept · Transactive energy · Active distribution networks · Blockchain

B.-C. Neagu (B) · G. Grigoras · F. Scarlatache Electrical Engineering Faculty, Power Engineering Department, “Gheorghe Asachi” Technical University of Iasi, Bd. Dimitrie Mangeron, No. 21-23, 700050 Iasi, Romania e-mail: [email protected] G. Grigoras e-mail: [email protected] F. Scarlatache e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 B. Appasani and N. Bizon (eds.), Smart Grid 3.0, Power Systems, https://doi.org/10.1007/978-3-031-38506-3_3

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Abbreviations AND SSDES DNO PV LV SG SM P2P LMM CES FCFS MU

Active distribution networks Small-scale distributed energy sources Distribution network operator Photovoltaic Low voltage Smart grids Smart meter Peer-to-peer Local microgrid market Crowdsourcing energy systems First come first serve Monetary unit

1 Introduction The increasing demand for electricity, coupled with the constraints imposed by global warming and climate change, necessitates exploring and utilising novel renewable energy resources with environmentally-friendly attributes. A result of this aim is the increasing number of SSDES. These green sources may instantly operate as ADN users (prosumers) with energy self-production. Significantly, there has been a remarkable surge in the adoption of solar photovoltaic panels (PVs) in recent years, driven by incentives provided by the European Union (EU) communities, including Romania (refer to [1]). This development substantiates the aforementioned concept and transforms it into an undeniable business reality. The base of bidirectional energy flows rising from the transactive energy in the local communities, as well as the need to decrease the power loss, led to changes in the ADN in Low Voltage (LV) grids, leading in the direction of a higher active and cost-effective Smart Grids (SGs). Energy efficiency saves money and resources, representing a necessity for flexible adaptation to users’ demands. Because electricity plays a fundamental role in modern lifestyles, users’ load characteristics must reflect the people’s lives at work and leisure. The volume of available information necessary for the operation, management, planning, and security of the ADNs has gradually increased with technological development, requiring the introduction of the calculation technique and intelligent solutions [2]. Even if the prosumers have high benefits, their behavior is intermittent, so the DNOs must consider a comprehensive ADN planning strategy. Furthermore, they should be able to bind to the grid and operate independently (autonomously). The ADNs, which were created for cities, incorporate local energy supply to meet the specific demand of the customers. Active consumers and prosumers are identified in the framework of recent paradigms of energy independence, energy policy, and

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distributive management. The ADN is viewed as a tool for constructing a coordinate system for SSDES fair integration, which will be an edifying challenge for DNOs that will require another operation plan. The prosumers make up the active cells of the ADNs, and each cell will supply installations to the DNO to maximize grid hosting power and execute SSRES in a successful and effective operating manner [3]. In our country, energy saving is improving as local suppliers (prosumers) with a rated capacity of up to 27 kilowatts create more electricity [4]. These funds are transferred to the supplier with whom a bilateral agreement has previously been negotiated [5]. Prosumer action must still be evaluated to store excess power during periods of low demand and transmit it when demand rises [6]. A recent report from the European Union’s Smart Grids Task Force [7] offers a specific mandate to develop a smart meter (SM) strategy to meet the needs of the emerging electricity market by adopting a flexible and adaptable measuring technology. As a result, the SM must ensure that useful information on the pattern of the prosumers’ generation is available [8]. The proliferation of SSDES changes the operating conditions and management requirements of the ADNs, which must now integrate new technologies and procedures. The SG principle has been applied in Romania on various scales and measures in recent years, with most of the hardware being tested and accepted but in isolation. The presumption of a structure or unifying architecture of interfaces and protocols based on norms and standards is fundamental. As a result, a reference framework for data sharing between devices and ADNs must be specified, enabling the interaction of services, utilities, protocols, devices, and interfaces [9]. Figure 1 illustrates the transition from classical electricity grids to ADNs.

Fig. 1 The transition from classical grids to ADNs

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The increasing number of prosumers with various distributed energy recourses promotes the P2P (peer-to-peer) electricity transaction in the SGs for less cost, more flexibility, lower carbon footprints, and higher reliability. Recent research on the prosumers’ behavior in electric distribution systems has increased in recent years. A distributed privacy-preserving P2P energy transaction approach has been proposed to minimize the overall objective of renewable generation curtailment penalty, adjustment cost, and operation cost while satisfying linearized distribution network power flow, power line thermal, and voltage limit constraints. The integration of prosumers into ADNs has become a serious concern in Romania in the last 2 years, with the national government pushing this practice using bonuses [1]. Figure 2 shows the overall quantity of over 3500 prosumers connected to Romanian DNOs’ LV-ADN. [10]. The behavior of prosumers becomes a critical issue for aggregators, suppliers, and DNOs. This legal system provides prosumers with several benefits [1], including the following Fig. 2. Law 184/2018 establishes a mechanism to encourage the share of renewables as a significant move forward in Romania’s legislation of prosumer status as the following: • prosumers with self-generation electricity units with a rated capacity of a maximum of 27 kW per individual home, residential blocks, residential, commercial, or industrial areas are covered by the scheme; • electricity delivery operators must contact prosumers according to the regulatory authority’s relevant regulations; • prosumers can sell electricity surplus to suppliers with whom they have agreements at a price equivalent to the weighted medium price reported on the dayahead market the previous year; suppliers with whom prosumers have a deal must take over the energy at the latter’s order;

Fig. 2 The number of prosumers from each DSOs in Romania

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• exemption from excise duties for prosumers for the volume of electricity produced from renewable sources for personal use and the surplus supply sold to suppliers; • exemption of prosumers as individual citizens from the duty to purchase renewable certificates for electricity production and used for self-final use, beyond power plants technical losses, on an annual and quarterly basis; • going to benefit from the service of regularization between the value of electricity delivered and the value of electricity used in the grid by electricity suppliers for which they have electricity supply deals. According to the legislation, energy suppliers having agreements with prosumers ask to buy power at the weighted mean day-ahead spot price from the preceding year. As a result, the retailer gains from not paying the distribution system tariff because the prosumer will sell their extra energy on the market. The trading mechanism provides a basic approach, limiting both sides’ options (consumers want to buy power at lower prices, and prosumers want to sell it). By not encouraging prosumers to set custom sale rates, it ignores disparities in generation costs and installed power. There is no opportunity to stimulate local generation. Consumers are unable to purchase power directly from prosumers, limiting their ability to trade with specific prosumers [11]. More automated trading strategies aim to improve the benefits of peers who trade electricity in the local market of microgrids (LMM). A comprehensive methodology considering the DNOs and stakeholders, which to quantify the highlighted distortion effects, is proposed in the chapter. The authors will compare the ADNs cost saved by the households when prosumers move to increase their revenue, and the DNOs act to improve the benefits derived from an optimal ADNs operation.

2 Crowdsourcing Energy System Crowdsourcing, a term first proposed in 2005 by James Surowiecki [12], can be used to alleviate energy poverty [13]. Crowdsourcing [14] is a new development in which users’ ideas are combined with the mutual intelligence of the crowd [15]. To make these crowdsourced sensor cloud data accessible, developing a servicebased solution is critical. It can also be an important way for the crowd to have a service-sharing community within a metropolitan region by allowing them to use their smartphones [16]. In this crowdsourced service community, consumers can benefit from the services of their neighbours. Since the crowd (i.e., service providers) is mobile, providing crowdsourced applications to customers is constrained by their spatial and temporal proximity, i.e., all service providers and users must be in the same geographic area simultaneously. Selecting and composing services from such a vast number of constantly evolving crowdsourced sensor cloud services to meet users’ needs in real-time and dependent on spatiotemporal features is a major challenge. As a result, new spatial–temporal service collection and composition innovations

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are important approaches for using spatial–temporal crowdsourcing as a forum for service provisioning. A crowdsourced energy system (see Fig. 3) includes a plurality of distributed energy resources managed by crowdsources of the system, a power network to which the distributed energy resources are connected, and a system operator that manages energy trading transactions and energy delivery within the system. The system operator operating at least one computing device configured to obtain day-ahead peer-topeer energy trading transaction requests from crowdsources for energy to be delivered from the distributed energy resources, estimate day-ahead energy load and solar forecasts, determine optimal power flow for the delivery of energy, and schedule delivery of energy from the distributed energy resources across the power network based upon the energy trading transaction requests, the estimated forecasts, and the determined optimal power flow [17]. It may be useful for allowing the general public to do community service within a certain geographic region. Several billions of dollars have been spent on blockchain analysis in recent years to maximize its capacity and determine its appropriateness in various economic domains [1]. However, not all sectors can completely embrace blockchain technologies. The current technological opportunities must be analyzed in each particular case, along with the challenges that end-users are confronted with and how a modern open architecture could provide value to them. Fortunately, the electricity sector is an excellent nominee for blockchain-based advancement. It integrates a complex supply chain with need to increase transparency and improve data management. It

Fig. 3 A particular model for crowdsourcing energy system

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also has a high-volume trade segment that will benefit from the immediate settlement. Blockchain’s clarity and immutability will inspire business and customer end-users.

3 Blockchain Technology A blockchain represents a decentralized (distributed) register of the number of transactions that occur in an ADN. This network is made up of nodes that are operated by different entities and use a cryptographic protocol to verify transactions. The protocol has an accuracy through which the data introduced in the register can’t be reversed or altered. It is unchangeable, stable, and clear. More than that, the difference of blockchain is represented by the distributed software ledger of trusted and verified transactions which is structured in blocks and maintained by network nodes, see Fig. 4. Blockchain platforms are fully decentralized and distributed to the level of ADN nodes, making them difficult to hack and exploit by malicious actors [18]. A defining feature of blockchain technology is the immutability of the data. In simple terms, once the data are recorded and validated by the entire network and can no longer be modified. It is possible because the data related to any transaction is dispersed through the AND and verified to prevent fraud. Many projects have been undertaken to consider the potential synergies between the blockchain and the power system. Blockchain technology has the potential to solve various problems in the electricity sector and lead to the achievement of energy consumption goals, including compensating for financing gaps in different initiatives [19]. The transition to smart grids involves accepting challenges that need to be known and finding the most effective means to overcome them. Blockchain-based network technologies will result in structural improvements that will necessitate the participation of logistics and service providers, equipment suppliers, policymakers, and, last but not least,

Fig. 4 The comparation between centralised and distributed ledger of transaction for blockchain technology

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final users. The power market has a lot of promise for blockchain implementation due to more challenges [11, 20, 21]: Climate change and the need to incorporate clean energy sources have led to the advancement of technologies like PV panels and wind turbines, which are becoming more affordable. Their technical characteristics present opportunities to develop new actors represented by prosumers. Prosumers pose a threat to the existing configuration of electricity networks, posing technological problems for DNOs in maintaining energy balance. Nevertheless, electricity generation at the household level (typically with PV panels on the roof) represents a major potential for advancing blockchain technology-based architectures because it takes advantage of the distributed nature of electricity production and has a performance that no other paradigm can equal. The creation of technologies that make the transition to AND was depicted in Fig. 1. Communications and networking components, inverters, bidirectional smart meters, and energy storage are all examples of technologies that have been implemented. As a result of this evolution, energy has become a more controllable, storable, and easily quantifiable substance. It is appropriate for trading across “smart” agreements. Local energy generation cooperatives founded by community residents oversee the development of energy communities. The ADN, which incorporates blockchain technology, maybe a workaround for providing the cleanest and cheapest electricity forms to the poorest customers and conserving resources by more responsible use. They achieve these goals by creating unity programs to lower vulnerable members’ electricity bills and supplying them with resources and training on reducing consumption. They still use the profits from electricity generation to help poor and low-income families improve their living conditions. Introducing a blockchain-based prosumer network simplifies the design of exchange models, replacing intermediaries for electricity trading. There are many attempts to unlock the high potential that blockchain technology has in accelerating the transition to green energy. The immutability of data from the blockchain occurs due to the synergy of the three technologies: cryptographic keys, a distributed register, and a validation protocol. Records kept on a blockchain can be considered reliable, and the operator or aggregator cannot access them, see Fig. 5. The transaction variables, such as sender, receiver, transaction value, and so on, are calculated in an ADN when a prosumer or an energy supplier and a prospective consumer agree to make an electricity transaction. Each transaction is secured and replicated through the entire network for data verification and storage on the local level. Each network member verifies, confirms, and saves the transaction data’s validity automatically. Thus, confidence is given by the network members who become witnesses and guarantors of each transaction. All information related to electricity transactions is mixed with identical information from all transactions in the network that takes place simultaneously to form a block. Subsequently, that block is added to a blockchain that brings together all the transactions ever made in the network publicly and transparently. Once added to the chain, the blocks and, implicitly all the transactions are no longer editable and become a detailed report of all the necessary elements to keep track of.

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Fig. 5 The transactive energy based on blockchain technology

Mathematical algorithms that assign a hash to each block verify its data. The hash is a collection of letters and numbers created from the related data from the transactions in a given block. The network periodically checks the hash value for each block in relation to the value of the previous block. In this way, it is impossible to any attempt to defraud data related to a transaction, as well as any attempt to cancel or reverse the transaction.

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4 Enhanced Prosumers Trading Approach 4.1 Problem Formulation This section explains the mathematical model for selling surplus energy generated by SSDES (residential solar panels) between peers in the local ADN markets— prosumers (Pros) and consumers (Cons). When “prosumers” are thought of as energy producers that sell their excess energy, they will sell the surplus of generated energy (Wgp) if their consumptions (Woc) are less than their own generation. Blockchain technology begins to function after this limit is met. If the energy demand (W d ) volumes are greater than W gp , the total prosumers offered energy (W of ) is set by taking into account total produced power (W g ) by prosumers as seen in the flowchart: n ∑

Wok,f h =

n n ∑ ( k, h k, h ) ∑ k, h Ug · I g − Woc

k=1

k=1

(1)

k=1

U g and I g for prosumer k, at hour h, is the operation voltage and current value. For the n lines, the total energy losses (ΔW T ) are calculated as: n ∑

ΔWTi,h =

i=1

n ∑

n )2 ∑ ( ( i, h i, h ) Um · I m Rli, h · Ili, h +

i=1

(2)

i=1

where Rl is the resistance, I l are the current value for the i line, and U m ·I m is the miscellaneous energy loss. After that, by subtracting the Eq. (2) from (1), the provided energy (W of ) can be calculated as: n ∑ i=1

Woi,h f =

n ∑ i=1

i,h ΔWgp −

n ∑ i=1

i,h ΔWoc −

n ∑

ΔWTi,h

(3)

i=1

Energy Smart Contracts (Peer-to-Peer—P2P), representing the “translation” or “transposition” in the code of a contract to automatically verify the fulfilment of certain conditions and to automatically execute actions when the conditions are determined between the parties, are reached and verified [12].

4.2 The Blockchain-Based Crowdsourcing Algorithm Design for P2P Energy Transactions A mathematical model is proposed for computing the hourly excess offered by prosumers to local consumers through Crowdsourcing Energy Systems. Table 1

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presents the pseudocode of the proposed approach. Also, Fig. 6 shows the flowchart of a blockchain-based prosumers energy trading system for peer-to-peer contracts. The total price for the W of is determined through multiplication with the energy per unit price (PP2P), assured by negotiating process between Pros and Cons (from a local ADN) using P2P “smart” contracts. Following that, the Wof status for each available Pros is updated, and each Prs is alerted by the blockchain method depicted in Fig. 6. Consumers who have already signed a P2P deal tend to purchase electricity from the nearby Prs when W d is higher than W gp . When a vendor and a client plan to do Table 1 The pseudocode of the proposed algorithm Step 1. Input data details: consumer load profile (C), prosumer generation (G), and presumer price (PR) Step 2. Initialize the acquisition quatities (A) and financial settlement (F) Step 3. Initialize the unsold surplus (us): us = 0 Step 4. Start the P2P energy trading method based on blockchain texhnology using a AND crowdsourcing energy system, Fig. III.9.6 4.1 for each hour: h = 1…24 for each presumer: k = 1…np compute surplus: S (h, k): S (h, k) = G (h, k) − C (h, k) if S (h, k) > 0 surplus = S (h, k); 4.2 build a temporary consumer priority (proce, length): Distribute the surplus (srp): set initial consumer index: w = 0; while srp > 0 or (w < nc) k = k + 1; if the consumer has a P2P contract: substract the available surplus from its trading offer; if the surplus exceeds the consumer contract quantity: update the remaining surpkus; the contract from consumer w is fulfilled; else the contract from consumer k is partially fulfilled and the surplus is depleted; update matrix by subtracting from the served consumer demand the fulfilled contract; update acquisition matrix A for hour h according to the served consumer k, serving prosumer ix an traded quantity 4.3 Update line the consumer load profile 4.4 Update the unsold surplus: us = us + srp; Step 5. Compute the hourly and total electricity sold by prosumers to each consumer and the electricity traded hourly and daily by all prosumers, using matrices A and F

68 Fig. 6 The energy trading approach based on P2P contracts in the blockchain environment

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business together, they decide the factors, such as the receiver, sender, and transaction size, among other aspects. When Cons update their demand ledger status, the blockchain module generates a W d list based on the necessary amount of energy, W r , in order to compare both the W d and W of lists in the system. In addition, all inferred Pros and Cons receive a note informing them of the transaction status. The Wof would be moved from the μG (Pros) to the Cons if they comply with the transaction status. P2P crypto can be used as a virtual payment method. Another function of blockchain in our algorithm is to determine if the remaining electricity surplus in the AND satisfies the W d list or if there are no prosumers willing to sell electricity. In this case, the sales can’t be stored, so Cons would have to wait before sale deals become open. The Cons would then see advertisements for sale deals, and the blockchain system will sell power to customers based on the first come, first served (FCFS) concept. If W of is greater than zero, or W d from Cons persists, the algorithm begins. Finally, after the trade process is over, the Pros and Cons will be notified. Blockchain technology enables stable anonymous purchases based on the FCFS theorem, which states that prosumers or market administrators have no control over trading partners and that purchasing deals are met regardless of quantity or price, with only the trading system’s positioning moment taken into account. The algorithm reproduces this approach at each trading interval by randomly allocating each customer and prosumer preferences. In addition, since each offer’s time index is special in the blockchain scheme, an embedded rule in the algorithm states that no two customers should have the same trading preferences. As a result, there is no need for a secondary requirement in this situation. Distributed algorithms have the potential to realize privacy-preserving P2P energy transactions since no party has direct access to all participants’ private information. Figure 7 indicates the transaction mechanism. A short comment must add regarding the proposed distributed algorithm-based information-exchanging, even if Step 4 from Table 1 has been treated extensively

Fig. 7 The translation of the energy trading method on blockchain technology

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in [18]. It refers to the fact that access to private information is not allowed by eavesdroppers. Thus, only six transaction steps (A,…, F) are synthetically proposed, see Fig. 7. First, if someone requests a transaction (step A), this transaction is broadcasted to a P2P crowdsourcing energy system (CES), characterized by ADN peers. In the second step (B), the CES peers validate the transaction and the user’s status using the proposed FCFS algorithm. Step C applies blockchain technology when a verified transaction can involve cryptocurrency, smart contracts, records, or other transaction information. Once verified, the current transaction is combined with others to create a new information block for the ledger (Step D). The new blocks are added to the blockchain system (grey colour has been used in Fig. 6 to highlight it) in a permanent and unalterable way. The last step (F) is the complete confirmation of the transaction.

5 Case Study The performance of the proposed distributed P2P energy transaction in the CES environment approach has been investigated through case studies in a 28-bus active distribution system belonging to a Romanian DNO (see Fig. 8), which provides 27 single-phase residential users with 4-wire three-phase power line (NFA2X 50 OLAL + 3 × 70 mm2). The distance between the LV poles is between 36 and 42 m. The prosumers, seen on 6, 7, 15, 21, and 27 buses, want to sell their excess electricity to other ADN customers. The case study considers all consumers integrated into the local market, receiving electricity through smart P2P contracts from the prosumers. The consumption and generation profiles associated with the consumers and prosumers have been uploaded from the Smart Metering system [11]. The sampling step is by 1 h during a day. For considered prosumers, Table 2 shows the energy surplus available for trade in the considered interval. The transaction process would spread this surplus among customers or prosumers, as presented in the previous section. Over the trading cycle, each prosumer’s energy price is assumed to be stable. The average fixed price for customers to purchase energy (monetary unit—MU/kWh) from a traditional market operator is 0.72 MU/kWh, including taxes. In other words, the fixed price at which prosumers will sell electricity back to the grid has been set at 0.196 MU/kWh for 2021 (0.251 for 2020) [22], whereas local prosumer sale rates have been set at [0.40, 0.55] MU/kWh. The study of Fig. 9 revealed that local generation accounts for 22.8% of usage between 06:00 and 18.00, with an hourly surplus that never exceeded demand in every selling cycle. Via P2P contracts, prosumers sell all energy sums in the local ADN. Bus 1 is empty, and each prosumer cannot sell their excess energy, so it is sold on the market. The energy transaction considers the priority order as incentivising specific prosumers, based on the “close” tiers, technology, agreement, or social welfare enhancing in the crowdsourcing energy environment. The following study, applied in the case of an

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Fig. 8 The diagram of a real active distribution network Table 2 Local generation (in kWh) and selling prices (in MU/kWh) Hour

Prosumer Nodes 6

7

15

21

27

06

0.00

0.00

1.95

1.59

0.00

07

0.00

0.26

1.59

1.81

0.00

08

0.00

0.70

1.59

1.73

0.67

09

0.74

1.06

2.23

1.75

1.44

10

1.12

1.09

1.30

2.29

1.61

11

1.89

1.40

2.78

2.04

1.66

12

2.33

1.23

1.88

1.82

1.60

13

2.29

1.41

2.83

0.69

1.51

14

1.35

1.39

2.95

1.18

1.37

15

1.18

1.05

1.55

2.03

1.11

16

0.00

0.41

1.32

0.82

0.56

17

0.00

0.00

1.06

0.00

0.00

18

0.00

0.00

1.16

1.17

0.00

10.90

9.99

24.17

18.90

11.51

0.43

0.40

0.48

0.55

0.43

Total Selling price

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Fig. 9 Local generation and consumption, in kWh

emerging country, highlights the advantages of the proposed energy trading algorithm. The consumers’ money savings and increasing the market flexibility through “smart” contracts represent the main objectives of the algorithm. Because the P2P contracts are already signed between peers based on the FCFS blockchain principle, it must be mention that only participant from bus 28 do not receive the electricity surplus, with insignificant daily electricity consumption. Figure 10 shows the traded energy quantities from applying the proposed mathematical model. Moreover, Table 3 presents the daily electricity amounts from prosumers purchased by consumers.

Fig. 10 The electricity achieved by the consumers in kWh

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Table 3 The traded prosumers excess and prices (in MU/kWh) Bus

The electricity excess, in kWh P6

P7

P15

P21

P27

Total kWh

P2P price

Total cost/revenue for Cons

for Pros

2

0.860

0.000

0.000

0.176

0.641

1.678

0.743

1.208

0.329

3

0.000

1.154

2.962

1.394

1.599

7.109

3.338

5.118

1.393

4

0.378

0.000

0.000

0.000

0.000

0.378

0.163

0.272

0.074

5

0.000

0.000

0.181

0.749

0.559

1.489

0.739

1.072

0.292

8

0.244

1.048

0.603

2.761

2.773

7.430

3.525

5.350

1.456

9

0.000

0.002

2.046

0.773

1.106

3.927

1.884

2.827

0.770

10

2.295

1.356

0.122

1.361

0.000

5.133

2.336

3.695

1.006

11

1.845

0.745

1.130

0.620

0.000

4.340

1.975

3.125

0.851

12

0.000

0.645

2.572

0.668

0.000

3.885

1.860

2.797

0.761

13

0.150

0.056

0.000

0.000

0.000

0.206

0.087

0.148

0.040

14

1.116

0.691

2.141

2.140

1.372

7.460

3.551

5.371

1.462

16

1.917

1.632

1.634

3.631

0.000

8.814

4.259

6.346

1.728

17

0.000

1.331

0.294

0.000

0.000

1.625

0.674

1.170

0.319

18

0.000

0.263

1.144

0.000

0.000

1.407

0.654

1.013

0.276

19

0.000

0.298

0.017

0.000

0.000

0.315

0.127

0.227

0.062

20

0.000

0.000

1.100

1.722

0.000

2.822

1.475

2.032

0.553

22

0.412

0.000

1.136

0.000

0.353

1.901

0.874

1.369

0.373

23

0.000

0.410

3.090

0.000

0.000

3.500

1.647

2.520

0.686

24

0.000

0.000

2.430

1.649

3.108

7.187

3.410

5.174

1.409

25

0.742

0.368

1.242

1.260

0.000

3.612

1.755

2.601

0.708

26

0.940

0.000

0.324

0.000

0.000

1.264

0.560

0.910

0.248

The last columns correspond to the following indicators: • the overall electricity bought by a customer; • the tariff charged by consumers to prosumers for the surplus electricity achieved through the signed “smart” contracts; • the imposed tariff that consumers could have paid to the ADN provider at 0.72 MU per one kilowatt; • finally, the regulated price paid by prosumers to the grid aggregator at 0.196 MU per one kilowatt. Figure 11 presents the prosumers’ financial benefits associated with the price paid for the consumers through the “smart” contracts and regulated price if the excess was deposited straight into the ADN. Consumers can also feel the effects of purchasing at a LMM. The variations between the standardized price, which customers must pay, and the P2P price used in the proposed algorithm, which is often smaller, are seen in Fig. 12.

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Fig. 11 The P2P and imposed tariff achieved by prosumers from LMM

Fig. 12 The P2P and imposed tariff gained by the consumers

6 Conclusions The use of a blockchain in the energy sector allows obtaining different advantages: provision of the local clearing process to run to reconcile planned and actual consumptions, as recorded by consumers’ meters; reduction of transaction costs; local provision of ancillary services; local frequency and tension regulation; support of local production of electricity by renewable energy sources; provide a register of all energy transactions; tracing of green electricity. In the crowdsourcing energy systems, prosumers can sell the surplus to peers (both consumers and/or prosumers)

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at a lower tariff than the imposed price, which is higher than the sale price of their electricity surplus to the network. In crowdsourcing energy systems, prosumers can sell electricity to prosumers at a lower price than the regulated tariff, which is higher than the sale price of their electricity surplus to the network. Testing the proposed trading mechanism has been done for the case with prosumers SSDES. If there is a surplus, the blockchain-based P2P contract solutions have proven to be the most convenient considering both energy quantity and price. Prosumers would pump excess electricity into the local DNO in the physical ADN, and customers would draw power similarly based on the power flow rule. The findings show that the DNOs benefit from optimal energy flows between prosumers and high-power-demand users. The proposed research, on the other hand, only included 24 daily trading hours, although the process may be used longer. A successful transaction depends on the proposed steady-state, with the following factors: prosumers’ surplus and distance between users.

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Machine Learning-Based Approaches for Transmission Line Fault Detection Using Synchrophasor Measurements in a Smart Grid Kunjabihari Swain, Ankit Anand, Indu Sekhar Samanta, and Murthy Cherukuri

Abstract Modern power technology represents the epitome of engineering achievement, comprising a remarkable interconnected network of elements spanning vast regions. Transmission lines are vital in facilitating power transfer across extensive distances worldwide. The continuous operation and reliability of these transmission lines heavily rely on their effective monitoring and fault mitigation capabilities. Various factors, including natural disasters and other causes, can give rise to faults in transmission lines, which impede the seamless delivery of power. Timely identification and resolution of such faults are paramount to avoid service disruptions and mitigate the risk of cascading blackouts. Phasor Measurement Units (PMUs) have emerged as indispensable devices for monitoring and analyzing transmission lines, offering a dynamic perspective of their behavior due to their high reporting rate. PMUs enable operators to monitor power flow at different locations within the grid, thereby aiding in maintaining system stability and optimizing grid efficiency. These capabilities are essential in realizing the objectives of the smart grid 3.0 paradigm. Swift restoration of transmission line functionality necessitates the rapid detection, classification, and clearance of faults. Digital signal processing algorithms and machine learning techniques have emerged as critical tools in achieving these objectives efficiently. The advent of numerous machine learning algorithms, coupled with their real-time implementation capabilities, has empowered their robust deployment for fault detection and classification in physical transmission lines. This chapter presents the real-time implementation of the machine learning algorithms on a physical laboratory 200 km transmission line. Additionally, it compares the effectiveness of machine learning methods like K-Nearest Neighbour, Support Vector Machine, and Logistic Regression.

K. Swain · A. Anand · M. Cherukuri (B) Department of Electrical and Electronics Engineering, NIST Institute of Science and Technology, Berhampur, India e-mail: [email protected] I. S. Samanta Department of Computer Science and Engineering, ITER, SOA University, Bhubaneswar, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 B. Appasani and N. Bizon (eds.), Smart Grid 3.0, Power Systems, https://doi.org/10.1007/978-3-031-38506-3_4

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Keywords Transmission line · Phasor measurement unit · Machine learning · Wide area measurement system · Innovation

Abbreviations PMU SCADA GPS SPM WAMS PDC LabVIEW Single Line to Ground Double Line to Line Double Line to Ground Line to Line to Line EVPA ECPA KNN SVM LR

Phasor Measurement Units Supervisory Control and Data Acquisition Global Positioning System Synchronised Phasor Measurements Wide Area Measurement System Phasor Data Concentrator Laboratory Virtual Instrument Engineering Workbench LG LL LLG LLL Equivalent Voltage Phasor Angle Equivalent Current Phasor Angle K-Nearest Neighbour Algorithm Support Vector Machine Logistic Regression

1 Introduction The electrical utility system is a complicated technical infrastructure that incorporates large numbers of elements that travel thousands of kilometers from the point of generation to the end user. Each module operates together to ensure that every user has a constant power supply. Among the most crucial elements of power system is transmission line, which is in charge of distribution of electricity. The transmission line fault will disrupt the uninterrupted supply of electricity. These lines are prone to numerous faults due to their exposure to harsh environmental weather conditions. Insufficient fault detection and classification of these transmission lines faults can lead to cascading faults and consequent blackout of large topographical areas, resulting in significant revenue damages. Previously, analogue assessments based on supervisory control and data acquisition (SCADA) were utilized to detect faults at scan rates of one scan every few seconds [1]. These scan rates were insufficient for attaining fault detection in the transmission line. The development of phasor measurement units (PMUs) with scan rates of up to 60 samples per second allowed for direct measurement of the voltage bus angle. PMU can help measure the dynamic behavior of a transmission line over a large geographical area by using time-stamped

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data from a global positioning system (GPS) [2]. This enables operators to make the right decisions promptly, preventing power system network blackouts. Thus PMUs are instrumental in fulfilling the goals of smart grid 3.0. Smart grid 3.0 is an evolved, enlightened intelligent smart grid [3]. In such a grid, data is transformed into proactive intelligence. PMUs provide real-time assessments of power system health to provide system operators with better information for deterring catastrophic outages. It is envisaged that once all the initiatives related to PMUs are accomplished, there will be a huge number of PMUs providing substantially greater coverage of the bulk power system [4]. The situational awareness approach has gained acceptance in various fields, including military applications, space vehicles, and network security [5, 6]. Transmission lines transport large amounts of power across great distances from generating units to consumers. They are, therefore, the main elements of power system. It is essential to detect transmission line fault, and classify and clear it as quickly as possible. To detect and classify transmission line faults, various techniques are used in the literature [7–9]. Despite hundreds of kilometers between sending side and receiving side of transmission lines, PMU-based synchronised phasor measurements (SPM) have shown to be a useful tool for real-time monitoring of both sides of a transmission line for widearea situational awareness. The digital revolution in information and communication technology is ongoing. As a result, SPM has come to represent the power system rather than SCADA. While SCADA acquires data at a pace of around 1–2 s, SPM uses a complete cycle discrete Fourier transform to report data at a rate of 50 phasors per second. Numerous study teams have employed synchrophasor data to evaluate the transmission system’s sufficiency for fault finding [10, 11]. These studies utilized a reporting rate of 50–60 phasors per second for the full cycle discrete Fourier transform system. People’s opinions have changed as a result of real-time monitoring, although phasor reporting rates have been steadily rising.

2 LabVIEW Based Synchrophasor Measurements 2.1 Phasor Measurement Unit (PMU) Phasor measurement units (PMUs) have significantly improved power system situational awareness (SA) because of its capability to monitor synchronised phasors of voltage and current [12]. This research aims to provide a real-time view of the phase angles of bus voltages in the power system network. Phasor measurement unit and SCADA are two existing power grid systems that can only provide a steady state view of the energy system with a high data flow latency [13]. The development of phasor measurement units made it simpler to synchronise voltage and current waveforms at geographically distant locations with respect to the global positioning system. PMU outperforms SCADA in terms of speed, effectiveness, and dependability. It

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gives operators access to the power system’s dynamic behaviour as well as limited situational awareness. PMUs are time-synchronized, high-speed measuring devices that keep track of the voltage and current values in the grid, transform them to phasor representations using sophisticated computing, and then safely send those representations to a centralised server [14]. PMUs measure the time-stamped voltages and currents of the monitored feeders and buses and are located in power system substations. A suitable location is chosen to collect data from several substations, and a comprehensible image of the state power system is produced by lining up the time stamps of measurements. PMU technology is being utilised to enhance utilities’ energy supply planning and failure prevention. It can be utilised to improve state estimates, protection, control, and wide-area monitoring. Using PMU technology, utilities can monitor grid dynamics in real time and make better judgments on safeguarding and sustaining the power supply. PMU, a backbone of a wide-area monitoring system network, were placed strategically across the electrical grid to cover the entire grid. Information from PMUs is gathered by a Phasor data concentrator at a central location and sent to a supervisory control and data acquisition system. GPS deployed at the PMU location offers precise time and time synchronisation between many PMUs [15]. The data voltage and current acquisition module, communication module, and GPS signal receiver module are the primary parts of a PMU. Rapid data transfer is required for a fully functional Wide Area Measurement System (WAMS) network within the phasor data sampling frequency. Currents and voltages acquired from the secondary windings of the substation-located current and voltage transformers are used as analogue inputs to devices [16]. The frequency response of the anti-aliasing filters is determined by the sampling rate selected for the sampling operation. All analogue signals have the same phase shift and attenuation thanks to anti-aliasing filters. Additionally, they guarantee that the differences in phase angle and the relative magnitudes of the various signals are unaltered. The GPS is used to determine the receiver’s coordinates, but for PMUs, the signal that matters most is the one pulse per second. The applications of PMU are analysis after a disturbance, stability assessment, monitor thermal overload, restoration of the power system, estimating a state, real-time command, adaptive defense. The challenges in implementing PMU are choosing an appropriate location for PMU placement, SCADA and synchrophasor technology integration, communications delays, and low-frequency oscillations monitoring, power system waveform distortion making prediction challenging, hefty computational demand, creating instruments for thorough post facto analysis. Phasor information from PMUs installed in various places is utilised. WAMSs are a cutting-edge measurement tool for data collection. They are responsible for gathering data and deriving value from it. PMUs are substation-based devices that measure the voltage and current of all the feeders and buses under observation. Figure 1 shows the integration architecture of PMU data. For diagnostic purposes, the measurements are kept on data storage devices that may be retrieved from a distance. As soon as the measurements are taken, the phasor data is also made available for real-time applications in a constant stream [17]. Since the local storage capacity

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Fig. 1 Integration of PMU data

is constrained, the data associated with an interesting power system event must be marked for permanent storage in order to prevent overwriting when the local storage is full.

2.2 Phasor Estimation Consider a pure sinusoid quantity x(t) = X m cos(wt + θ ). The phasor representation Xm j θ Xm e = √ of this pure sinusoid quantity is X = √ (cos θ + j sin θ ) and is shown in 2 2 Fig. 2. The phasor is approximated by the well-known discrete Fourier transform using samples taken during one period of the nominal power system frequency as given in Eq. (1).

Fig. 2 Sinusoid and its phasor representation

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√ X = X r e + j X im =

    N  2nπ 2nπ 2 xn cos + j xn sin N n=1 N N

(1)

where xn is the data sample, N is the number of samples in a period, and X r e and X im are the real and imaginary components of the actual synchrophasor. The phase angle is the angle between the time the signal is measured and the input signal peak, and the phasor’s magnitude is equal to the input signal’s root mean square value. On the basis of the chosen sample rate, it is assumed that the input signal has been filtered in accordance with the Nyquist criterion [18]. It is with the help of the current phasor and voltage phasor, the detection and location of faults can be done faster compared to the conventional data [7, 11].

2.3 LabVIEW Based SPM As a tool to aid automated measurements, LabVIEW was first introduced in 1986 to make scientists and engineers as proficient with it as financial analysts were with spreadsheets. Laboratory Virtual Instrument Engineering Workbench is what the name LabVIEW refers to. The visual programming language LabVIEW was developed as a system-design platform and development environment to enable the creation of any kind of system. National Instruments developed LabVIEW as a platform for controlling test apparatus. However, its applications now go far beyond test instrumentation and cover system design and operation. It is a graphical dataflow language that is sometimes abbreviated as “G” language. The SPM can be obtained by developing a virtual PMU in LabVIEW. The front and back panel of the system developed in LabVIEW are shown in Fig. 3. Figure 3a shows the LabVIEW front panel, indicating the phasor diagram of transmission line three phase voltage and currents. Figure 3b shows the Transmission line faults are of two types. The first is a series fault (also known as an open circuit fault), which arises when two conductors (phases) in the system become open as a consequence of a damaged line. Shunt faults, also known as short circuit faults, originate when two or more phases make contact with the ground or with one another [18, 19].

2.4 Fault Detection from SPMs Line-to-ground short circuit faults or transient faults account for 75–80% of power network and power circuit failures, respectively. In power networks, single line-toground faults are the most frequent. Additionally, the shunt fault can be categorized as either an unbalanced fault (asymmetrical fault) or a balanced fault (symmetrical fault) [20, 21]. The different transmission line faults are summarized as follows.

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Fig. 3 LabVIEW implementation transmission line faults and their detection. a Front panel. b Block diagram

1. Single Line to Ground Fault (LG): This fault occurs when one of the conductors (phases) in a transmission line unintentionally comes into contact with the ground. It leads to an abnormal current flow and can result in power disruptions and potential damage to the equipment. 2. Line to Line Fault (LL): Line to Line faults happen when two adjacent conductors (phases) within a transmission line make direct contact with each other. This fault can cause a short circuit, resulting in high current flows and potentially damaging the system components. 3. Double Line to Ground Fault (LLG): This fault involves two conductors (phases) of a transmission line simultaneously making unintended contact with the ground. It can lead to abnormal current flow, potential equipment damage, and disruptions in the power supply. 4. Three Phase Faults (LLL): Three Phase faults occur when all three conductors (phases) in a transmission line come into direct contact with each other. This fault

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causes a severe short circuit, resulting in a significant and potentially destructive current flow. It poses a high risk to the system’s stability and can lead to widespread power outages and equipment damage. These different types of faults can occur due to various factors, such as equipment failures, environmental conditions, or human errors. Timely detection and mitigation of these faults are crucial to maintaining the integrity and reliability of the transmission line system. The frequency of occurrence for different types of faults, ranked from most frequent to least frequent, follows a specific order. The most frequently occurring fault is the Single Line to Ground Fault, followed by the Line to Line Fault, the Double Line to Ground Fault, and finally, the Three Phase Faults. On the other hand, the severity of faults, from most severe to least severe, presents a different sequence. The Three Phase Faults are deemed the most severe, followed by the Double Line to Ground Fault, the Line to Line Fault, and the Line to Ground Fault, considered the least severe among the listed faults. The features from the voltage and current components are obtained using the phasors measured by the PMU. From these values Equivalent voltage phasor angle (EVPA) and equivalent current phasor angle (ECPA) are computed using Eqs. (2) and (3). E V P A = tan

−1



EC P A = tan−1



Vds Vqs Ids Iqs

 (2)  (3)

where, V ds , V qs , I ds , and I qs are direct and quadrature components of voltage and current, respectively and are calculated using Eq. (4) and (5).   ⎡V ⎤ A 1 1 2 1− − √2 √2 ⎣ VB ⎦ × × × 3 0 23 − 23 VC ⎤ ⎡       IA 1 1 2 1− −√2 Ids cosθ sinθ √2 = × ⎣ IB ⎦ × × Iqs −sinθ cosθ 3 0 23 − 23 IC



Vds Vqs





cosθ sinθ = −sinθ cosθ



(4)

(5)

where, V A , VB , VC and I A , I B , IC are the three phase voltage and current respectively. Finally, machine learning algorithms are used to classify the fault based on the EVPA and the ECPA. This is shown in Fig. 4

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Fig. 4 Implementation of machine learning algorithms

Fig. 5 KNN algorithm

3 Machine Learning Algorithms 3.1 KNN Algorithm The k-nearest neighbour algorithm (KNN) is a non-parametric, supervised learning classifier that utilizes nearness to create classifications or predictions about the cluster analysis of data points. Although it can be utilized to solve classification or regression problems, it is most commonly used as a classification algorithm [22]. A majority vote is required to choose a class label for classification concerns. Even though this is technically “plurality voting,” literature more frequently refers to votes as “majority votes.“ Technically, “majority voting” necessitates more than 50%, and it is most effective if only two choices exist. The key distinction between classification and regression problems is that classification is used for discrete values while regression is utilized for continuous ones. The distance between two points must be determined before a classification. One of most popular measure is Euclidean distance, which is discussed further below. The KNN technique belongs to a “lazy learning” model class, which only store training datasets rather than going through a training phase [23, 24]. It is also known as an instance-based or memory-based learning method because it relies heavily on memory to retain data for training. Figure 5 shows the classification using the KNN algorithm.

3.2 Support Vector Machine One prominent supervised learning method for classification and regression issues is Support Vector Machine (SVM) [25]. Nonetheless, classification issues are the

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main use for it in Machine Learning. The main goal of the SVM algorithm is to determine the best decision boundary or line for classifying the n-dimensional space to simplify classifying fresh data points. A hyperplane is the ideal choice boundary [26]. SVM selects the extreme vectors and points that will aid in forming the hyperplane. The algorithm is known as the Support Vector Machine, and these extreme circumstances are referred to as support vectors. Consider the diagram below, which shows two discrete categories divided by a decision boundary or hyperplane. SVM is demonstrated in the following example. If we encounter a strange cow with goatlike characteristics, we may use the SVM classifier to create a model to determine whether it is a cow or a goat. In order to teach our model about the many traits of cows and goats and to prepare it for testing with this weird species, we would first train it with a variety of photos of cows and goats. The support vector then builds a selection line between these two data sets (cow and goat), selecting extreme cases (support vectors). The two types of SVM are linear SVM and nonlinear SVM. When a set of data can be divided into two classes with just one solid line, it is said to be linearly separable, and Linear SVM is the classifier that is utilized. Linear SVM is used for linearly differentiated information. Non-Linear SVM is used for non-linearly divided data, therefore if a dataset cannot be classified using a clean line, it is considered non-linear data, and Non-Linear SVM is the classifier that was employed [27]. Figure 6 shows the classification using SVM algorithm.

Fig. 6 SVM algorithm

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3.3 Logistic Regression Logistic regression (LR) is a statistical method used to model a discrete outcome’s probability based on an input variable [28]. Originally employed in the biological sciences during the early twentieth century, logistic regression also found applications in various social science domains. It is specifically employed when the dependent variable (target) is categorical [29]. To illustrate its significance, consider the identification of spam emails as an example. If we attempt to address this problem using linear regression, we would need to establish a threshold for classification. However, this approach can lead to significant consequences in real-time scenarios. This highlights the limitations of linear regression for classification tasks. Logistic regression, on the other hand, is preferred as it enables outcomes that precisely fall between 0 and 1, rendering it suitable for probabilistic classification.

4 Experimental Setup and Results Discussion Figure 7 shows the connection diagram of the components used to implement the proposed algorithms. The setup begins with connecting a 3-phase line-line voltage source, which is subsequently linked to an isolation transformer to provide protective measures. Subsequently, a 3-phase autotransformer is employed to conduct tests at varying voltage and current conditions. To ensure fault protection, a 4-pole contactor is integrated, designed to disconnect the line in the event of a fault occurrence. The setup then proceeds to incorporate a 200 km laboratory transmission line, with a 3-phase load applied at a distance of 200 km. Figure 8 depicts the experimental arrangement employed for real-time laboratory transmission line analysis. The acquisition of 3-phase voltage and current measurements is facilitated using an FPGA-based

Fig. 7 Connection diagram

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Fig. 8 Experimental set up

controller equipped with NI-9242 and NI-9246 voltage and current modules, respectively [30, 31]. The FPGA controller is programmed through a Dell Workstation PC connected via a gigabit Ethernet connection. LabVIEW software is employed on the workstation PC to implement various algorithms on the acquired current signal. The voltage and current data captured from the FPGA controller are acquired using LabVIEW software. Subsequently, different machine learning algorithms are applied to the voltage and current data for fault detection and classification within the transmission line. Total 1000 samples for each fault are considered for the classification. 600 samples are considered for training, 200 for testing and the remaining 200 samples are taken for validation. Table.1 shows the fault classification accuracy using K-NN, LR and SVM. The confusion matrix for classification validation using K-NN, LR and SVM, respectively, are plotted in Figs. 9, 10, and 11, respectively. Classification accuracy, evaluated using a confusion matrix, provides valuable insights into the performance of different classification methods such as Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Logistic Regression (LR). The confusion matrix allows us to assess the accuracy of predictions by breaking them down into four categories: true positives, true negatives, false positives, and false negatives. For SVM, the accuracy can be determined by examining the number of correctly classified instances using a hyperplane-based approach. KNN assesses accuracy by measuring the proportion of correctly classified instances based on the majority vote of their nearest neighbors. On the other hand, LR calculates Table 1 Comparison of fault classification accuracy of K-NN, LR and SVM

Fault type

KNN (%)

LR (%)

SVM (%)

LG

89.0

85.00

96.00

LL

88.0

84.00

91.00

LLG

89.00

85.00

93.00

LLL

90.00

88.00

95.00

LG

89.0

85.00

96.00

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Fig. 9 Confusion matrix in K-NN classification

accuracy by estimating the probabilities of class membership. By comparing the confusion matrices of these methods, we can gain a comprehensive understanding of their classification accuracy and make informed decisions when selecting the most suitable method for a given problem. The accuracy in classifying the fault in SVM method is more comparing to KNN and LR.

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Fig. 10 Confusion matrix in LR classification

Fig. 11 Confusion matrix in SVM classification

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5 Conclusion PMUs, with their real-time phasor measurements, improve the power system’s situational awareness. This chapter suggests using machine learning methods for fault detection and classification to improve transmission line situational awareness. The detection and classification technique was realized in LabVIEW because it is excellent for real-time data acquisition and has visual displays that power system operators can use to initiate enhanced situational awareness-based control and protection decisions. The result was validated using a 200 km real-time physical transmission line. It compared three techniques: the KNN, SVM, and LR. SVM outperforms KNN and LR in real-time fault detection and classification, with the highest accuracy of 96.00%.

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Data Mining-Based Approaches in the Power Quality Analysis Gheorghe Grigoras, Bogdan-Constantin Neagu, and Florina Scarlatache

Abstract The future distribution networks must ensure smart features like flexibility, accessibility, reliability, and high-power quality for all consumers. If the first three features can be satisfied by the Distribution Network Operators (DNOs) until a certain level depending on the investments, the power quality in each node and consumption point is not always easy, and there is no way for non-compliant electricity to be withdrawn from the supply chain or rejected by the consumer. Monitoring the power quality indicators and the analysis of their compliance within the established limits in each electric distribution substation (EDS) and consumption point (currently, all consumers, especially those which use the modern technologies, can be considered as disruptors) is of particular interest to both in terms of limiting damage to consumers, but, also, to ensure the appropriate economic indicators for the DNOs. The authors analyzed the disturbances from files containing the current and voltage measurements (CVMs), downloaded from the power analyzers installed in the electric distribution substations, to find the “hot” areas where there are problems with power quality. The analysis has been performed with Data Mining techniques, particularly clustering, including the extraction of the features associated with the performance indicators corresponding to the voltage quality and continuity of electricity supply. The obtained results highlighted the effectiveness of the new approaches to identify the areas with power quality issues and can help the Decision-Maker in the planning and operation process of electric distribution networks.

G. Grigoras (B) · B.-C. Neagu · F. Scarlatache Power Engineering Department, Faculty of Electrical Engineering, “Gheorghe Asachi” Technical University of Iasi, Bd. Dimitrie Mangeron, No. 21-23, 700050 Iasi, Romania e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 B. Appasani and N. Bizon (eds.), Smart Grid 3.0, Power Systems, https://doi.org/10.1007/978-3-031-38506-3_5

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Keywords Power quality analysis · Voltage quality · Continuity of electricity supply · Data mining · Clustering

Abbreviations CVM DM DNO EDS LV MA MV NA PA SAIDI SAIFI VIG

Current and voltage measurements Decision-maker Distribution network operator Electric distribution substation Low voltage Middle area Medium voltage Nearby area Peripheral area System average interruption duration index System average interruption frequency index Voltage irregularity degree

1 Introduction The power quality influences the daily life of all consumers connected to the distribution networks. The most significant effects are felt on the industrial production processes affecting their performance indicators. Regarding the performance indicators associated with the quality of distribution service, they represent an essential benchmark in the technical–economic analysis related to the planning/modernization of low voltage (LV) and medium voltage (MV) distribution networks [1, 2]. The power quality is quantified by its two main components: the power supply and the voltage quality. From a technical point of view, it is closer to the voltage quality so that any deviation of the amplitude, frequency, or the sinusoidal waveform from the rated values representing a potential quality problem [1]. The power quality does not depend only on the efforts of the Distribution Network Operator (DNO). The consumers connected to its electric distribution network represent the other component that can influence the power quality. Some consumers can cause disruptive influences in the connection point, affecting the proper supply of other consumers connected to the same network. Thus, the proper operating of electrical equipment/devices/appliances occurs at a supply voltage within a range by ± 10% around the rated value [3, 4]. A significant part of the equipment currently used, especially electronic devices and computers, needs a high power quality. However,

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the same equipment often distorts the supply voltage due to their nonlinear characteristics, leading in many cases to the appearance of a non-sinusoidal current when a sinusoidal voltage is applied [5]. Ensuring the power quality in each consumption point is not an easy task, and there is no alternative for the energy with an unsatisfactory quality to be with-drawn from the supply chain or rejected by the consumer. Monitoring the power quality indicators should be done in each node or connection point of each disruptive consumer from the electric networks, and their analysis should indicate the location within the established limits, presenting a particular interest both in terms of limiting damage to consumers, but also in ensuring appropriate performance indicators of the DNOs [6, 7]. The consumers’ request to have a high-power supply quality can be seen as a normal one. From the point of view of the DNOs, the power quality monitoring can include several aspects and directions: the premise of customer loyalty, the fact that the prompt detection of anomalies which helps to maintain in service of the production/transmission equipment and installations, and the long run it is use-fully from the perspective of investments or other strategic decisions. Also, ensuring the power quality becomes more complicated due to an increase in the number of sources with discontinuities in the electricity generation which appear in the energy hybrid systems, wind turbine farms, or photovoltaic installations connected to the network [7, 8]. Each of these issues can have different causes. Some of them happen because of the technical infrastructure, so that an incident in a node from the network can lead to voltage drops that affect the sensitive consumers. Other issues are due to the harmonics which arise in the consumer’s installations and can spread in the network, affecting other consumers [9, 10]. The quality of power supply to the industrial enterprises can influence the technical and economic performance of the enterprise, especially if the modern technologies are part of the production processes. To these consumers, the most common disturbances are long interruptions (from a few minutes to a few hours) and voltage drops in which the voltage reaches a low value for a short time [5, 11]. An inadequate power quality leads to the decrease of production and the life cycle of the equipment or significant amounts of the waste in the technological process. The following categories can cover the disturbances that occur in the energy supply [4, 12, 13]: • • • • •

harmonic distortion; short and long interruptions; overvoltage, under voltage; voltage drops and voltage fluctuations (flicker); transient phenomena.

All disturbance categories have the permissible deviation ranges of the parameters at the customer’s point depending to the voltage level (MV or LV), see Fig. 1 [3]. Also, Fig. 2 presents how to perceive the distribution represented in the voltage–time plan of the voltage drop, undervoltage, and overvoltage, respectively the short and long interruptions [3].

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Fig. 1 The specifications according to EN 50160 on the power quality [4]

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Fig. 2 Comparison between the voltage drop, undervoltage, and overvoltage, respectively the short and long interruptions, adapted after [3]

The importance of power quality issue has increased with the decentralization of power systems, considering, at the same time, the principles of the energy market (competition between producers/suppliers enforcing, theoretically, highquality standards), as well as the technical–economic indicators by integrating energy resources with different generation profiles. From a more extensive perspective, power quality assurance represents the base to increase energy efficiency in the generation, transmission, and distribution processes [2, 14]. The data from various locations from the electrical network leads to largesize databases, seen by a Decision-Maker (DM) as multi-dimensional records with combined possibilities to be analyzed. New features can be identified with the help of Data Mining techniques, which can influence the decisional processes. The rules of these discovery knowledge techniques can correlate the data or generate new information with deep meanings in terms of power quality. Thus, the understanding of the processes using the statistical processing can lead to identifying the consumers with high potential to affect the power quality and, respectively, at the simulation of the effects associated with the application of specific measures [15]. The obtained data are useful for consumers to evaluate the power quality indicators in the common connection point and the level of possible disturbances on the supply network. This information is also useful for electricity suppliers and the DNOs to take the necessary measures to maintain the power quality within the accepted limits.

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Some questions may arise once the importance of monitoring the power quality assumes by the DNOs: 1. Are sensors or power analysers in the network that can provide on-line the values of the significant parameters (voltages and currents)? 2. Are consoles/monitors to ensure the Human Machine Interface through which the Decision-Maker can follow these parameters and obtain the power quality indicators? 3. Is it possible to develop software applications based on the capable algorithms “to understand” these parameters, indicating the points/areas where there are problems? The authors tried and managed to find the answer to the three questions. They analysed all records provided by the specialized devices (power analysers) from the supply points (mainly EDSs) containing the phase currents and voltages to identify the areas with “inadequate” power quality. A Clustering-based Data Mining methodology has been proposed to discover the “hidden” information associated with voltage quality and continuity of electricity supply. The obtained results highlighted the advantages of the new methodology compared to those used by the DNOs to identify the areas with power quality issues, helping the Decision-Maker to apply the best strategies to improve the quality of distribution service. The structure of the chapter is the following: Sect. 2 presents the characteristic indicators associated with the voltage quality and continuity of electricity supply provided in the Power Quality Normative, Sect. 3 explains the steps of the clusteringbased Data Mining methodology, Sect. 4 details the obtained results, and Sect. 5 highlights the conclusions.

2 Performance Indicators for the Electricity Distribution Service The analysis starts from the performance indicators defined in the power quality standards for the electricity distribution service approved in the European countries. Following the terms of these standards, the DNOs must provide to the energy regulators the values of the performance indicators. The performance indicators refer to the low voltage level and rural and urban areas. They allow an assessment of the performance of distribution service and refer to the continuity of electricity supply and the technical power quality, see Fig. 3 [1, 4, 16]. Regarding the effects on the consumers, a classification of the indicators in two categories has been done: • general indicators, which highlight very well the activity carried out by the DNOs. They cannot guarantee the imposed values for each consumer.

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Fig. 3 The components of the distribution service quality [4]

• certified indicators, with minimum levels, respected in each case individually, see Fig. 1. The conditions regarding the means for announcing and recording the interruptions in the power supply and the proceedings for the planning of the interruptions needed for the development and maintenance operations, respectively repair activities of installations following accidental events, are stipulated in the standards. The DNOs must record and calculate the following indicators at the MV and LV levels regarding the continuity of electricity supply in their operating areas [4, 16–18]. System Average Interruption Frequency Index (SAIFI) represents the average number of interruptions supported by the consumers. The calculation relation considers a division of the total number of discontinued consumers, having an interruption duration more than 3 min, by the total number of consumers: ∑NL I S AI F I =

i=1

NT

Ni

[interruptions/year]

(1)

System Average Interruption Duration Index (SAIDI) represents the average interruption duration at the DNO level, calculated as a weighted average. The calculation relation considers a division of the aggregated periods of the long interruptions (more than 3 min) by the total number of consumers: ∑NL I S AI D I =

i=1

(Ni · Ti ) [minutes/year] NT

(2)

where N LI —the number of the long interruptions; N i —the total number of discontinued consumers having an interruption duration more than 3 min; N T —the total number of consumers. The voltage quality refers to the disturbances having as an effect increasing/ decreasing the supply voltage due to the dynamic variation of the loads requested by the consumers. It is quantified through the voltage deviation calculated with the formula [19, 20]:

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ΔUt(i ) =

Ut(i) − Ur · 100 [%] t = 1, . . . , T Ur

(3)

where U t represents the measured value of the voltage at the moment t of the analysed duration T in a supply point i (electric distribution substation or connection point of the consumer) and U r is the rated voltage. The small values of the voltage deviations compared to the rated voltage can be interpreted by the DNOs through states associated with the normal operating conditions of the networks. The average value of the voltage deviation in the analysed duration T can represent another performance indicator to quantify the voltage quality in a supply point [20]: (i) ΔUav =

T T 1 ∑ Ut(i) − Ur 100 ∑ Ut(i ) − Ur = [%] T t=1 Ur T t=1 Ur

(4)

From a statistical point of view, two parameters associated with voltage deviations could be defined. One of them refers to the standard voltage deviation calculated with the following formula: (i) σΔU

┌ | T ( )2 |1 ∑ (i) √ ΔUt(i ) − ΔUav [%] = T t=1

(5)

The other parameter corresponds the voltage irregularity degree, VIG, whose calculation can be performed based on the following formula [19]: V IG

(i)

( ) T T )2 1002 ∑ Ut(i) − Ur 1 ∑( (i) ΔUt = = [%2 ] T t=1 T t=1 Ur

(6)

Depending on the value of VIG, the DNOs can classify the voltage quality in the following categories [21, 22]: • • • •

Very good (VIG ≤ 10 [%2 ]), Good (VIG ∈ 10–20 [%2 ]), Moderate (VIG ∈ 20–50 [%2 ]), Unsatisfactory (VIG ≥ 50 [%2 ]).

3 Data Mining-Based Analysis of the Power Quality The innovations in information technology have made it possible to acquire and store large data amounts. Many activity fields, including the electricity, are becoming increasingly dependent on data collection, storage, and processing. However, the excess of data encounters difficulties in finding the features that correspond to a

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Fig. 4 The components of the distribution service quality

specific objective. Thus, a new research direction emerged that supported the analysis of information extracted from existing data in databases, called data mining. Generally, Data Mining represents a data analysis process to extract useful information used to optimize performance indicators. There are situations when a DNO has a large-size database, and a human operator could not process it efficiently. Through applying a Data Mining algorithm, the “hidden” features are discovered easily. These techniques can understand patterns and apply them in feature selection using proper algorithms. The features extracted from the databases are used in predictive models, understanding the relationships between records or contents associated with the database [23]. There are three categories where the Data mining techniques can be grouped, depending on the type of problem: • Classification and regression. It represents the most common category of applications, consisting of building the forecasting models belonging to a set of classes (classification) or regression values. From this category, the following approaches can be applied with success: decision trees, the Bayes technique, neural networks, and K-Nearest Neighbors. • The analysis of associations and successions. This category generates descriptive models that highlight correlation rules between the attributes of a data set. • Cluster analysis. It obtains the groups with similar entities or highlights the entities that differ substantially from a group. Figure 4 presents the main steps of Data Mining-based analysis. The following operations take place inside each step [16, 24]: • Data cleaning supposes a removal of irrelevant or “atypical” data; • Data integration considers the data sources integrated into the Data Mining process. A common trend in information applications is to perform data cleaning and integration as a pre-processing step, in which the storage of resulting data is done in a data warehouse; • Data selection. The significant features are identified to be used in the next step; • Data normalization. Data are transformed or consolidated into forms suitable for the data mining process. The simplest normalization of the data is between 0 and 1 with the help of the relation [23, 24]: xi,∗ j =

xi, j − xi,min , i = 1, . . . , n; j = 1, . . . , m xi,max − xi,max

(7)

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where x i,j —one of the features associated with a record X ( j) = [x 1,j x 2,j … x i,j … x n,j ], j = 1, …, m, from the database; x i,min —the minimum value of the feature x i ; x i,max —the maximum value of the feature x i,j *—the normalized value. • Data Mining. It represents the essential process in which intelligent methods are applied to extract patterns from data. K-means clustering method was chosen in the power quality analysis being most used in the scientific and industrial applications. • Pattern Evaluation. This step is necessary to identify the knowledge based on particular measures of their degree of interest; • Knowledge presentation. Visualization and presentation techniques are used to present to the Decision-Maker the discovered knowledge. The name of the K-means algorithm comes from the partition of the database in K clusters C k , k = 1, …, K, chosen a priori, whose mean value represents the centroids of all elements inside each cluster. The Decision Maker (DM) indicates parameter K at the starting point of the clustering process. The clustering algorithm considers that the centroids are positioned in the representation space of the training data, and they will try to find the optimal positions in the learning process that will best characterize all the training data. This algorithm is part of the unsupervised learning category because only training elements are indicated as input data without specifying the centroid to which they belong. The purpose is to obtain the highest similarity between the elements inside of each cluster [25]. The main steps are indicated in Fig. 5 and details in the following [2, 20]. Step 1. The maximum number of the clusters are determined using the relation [23, 26]: K max =

√ m

(8)

where m represents the total number of the records from the database. Step 2. A number K of the centroids (K = 2, …, K max ) is chosen, such that each centroid will be represented in a space having the same size as the data-base. Step 3. For each centroid, all its features are randomly generated. The number of features is similar to the size of the database. The algorithm can also start with an otherwise initialized centroid, but also other methods of initializing centroid could be proposed. Step 4. For each element from the database, the similarity between it and all centroids generated in Step 2 is calculated. That element will fit the centroid to which it is most similar (grouping based on similarity). Step 5. After grouping, it is considered that all the elements grouped at a centroid form a cluster. For a centroid to best characterize that data, it should be in the middle of it. For this, the average value of all features associated with the elements grouped in a cluster is calculated. Thus, each cluster will be characterized by a new centroid.

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Fig. 5 The steps of the K-means clustering algorithm

Step 6. The algorithm from Step 5 is resumed as long as the convergence function changes. The convergence function, denoted E, is calculated as the sum of all distances from each element to the centroid to which it belongs. If the convergence function does not change, it can say that the algorithm is over.

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E=

K ∑ ∑

) ( D X ( j ), mk

(9)

k=1 X ( j ) ∈Ck

where D(X ( j) , mk ) represents the Euclidean distance calculated in n-dimensional space: (

D X ( j ), mk

)

┌ | nk |∑ ( )2 =√ xi, j − m k

(10)

i=1

x i,j is a feature from the record X ( j) , mk is the centroid of cluster C k , k = 1, …, K. Step 7. Evaluating the quality of partitions is done using some measures: Rsquared, Root-mean-square standard deviation, Calinski-Harabasz index, Dunn’s indices, Davies-Bouldin index, or Silhouette coefficient. The measure based on the Silhouette coefficient is most used due to their robustness. The formula underlying this index is [23, 26–28]: SC =

1 ∑ 1 ∑ r (X ) − s(X ) K i n k X ∈C max[r (X ), s(X )]

(11)

k

s(X ) =

∑ 1 D(X, Y ) n k − 1 Y ∈C ,Y /= X k

⎞ ∑ 1 r (X ) = min⎝ D(X, Y )⎠ c/=k n c Y ∈C ⎛

(12)

(13)

c

where C k , C c are the clusters from the clusters’ set; nk —the number of elements from cluster C k ; nc —the number of elements from cluster C c ; D(X, Y )—the Euclidean distance between records X and Y from the data-base. Step 8. The partition K = 2, …, K max with the maximum SC will represent the optimal solution of the clustering process.

4 Testing the Methodology The Data Mining-based proposed methodology was tested in the power quality analysis based on the performance indicators associated with the voltage quality and continuity of the electricity supply. The analysis has been performed using the databases containing the CVMs performed in the EDSs belonging to a Romanian DNO.

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4.1 Voltage Quality Analysis The database contains the 752 files with CVMs performed in the rural EDSs on the LV side, including the phase currents and voltages recorded for a week. Because the supply feeders have lengths from a few to tens of kilometers, the distribution of the EDSA is on a wide surface, and, for this reason, three areas were established considering the electric distance from the supply point: Nearby, Middle, and Peripheral. The share of the EDSs in the three areas is the following: 65% (487 EDSs) in the Middle area (MA), 20% (149 EDSs) in the Nearby area (NA), and 15% (116 EDSs) in the Peripheral area (PA). The methodology has been applied for each area so that three databases have been prepared for the clustering process. The performance indicators (the mean of voltage deviation, standard deviation, and irregularity degree) were calculated from the CVMs, for each EDS. There were no atypical data in the records, so all data have been stored in the warehouse. Each indicator was normalized using relation (7) such that the values are located in the range [0, 1]. The K-means clustering algorithm has been applied for K = 2, …, K max , where K max has different values depending on the number of the EDSs assigned to each area. Figure 6 presents the variation of the SC associated with each area, where the optimal partition is identical K opt = 5. The maximum values were: SC (MA) = 0.599, SC (NA) = 0.669, and SC (PA) = 0.660. The representation of the clusters associated with the EDSs and obtained for each area is shown in Figs. 7, 8 and 9. Tables 1 and 2 present the statistical parameters (mean, standard deviation, and quintiles) of each cluster from each area. The signification of the notations from tables are the following: Q1, Q2, Q3, and Q4 are quintiles which divide the data sets associated with each cluster from the considered areas into equal parts nk /4, where nk is the number of the elements from each cluster k, k = 1, …, 5; m represents the mean; σ corresponds the standard deviation. The data analysis from Table 1 indicates values of the voltage deviation below by the maximum limit (+ 10% compared to the rated power) for all clusters, regardless of the area, which highlights an appropriate voltage quality. Clusters C1 and C2 have a reduced deviation (less than 5%), and cluster C5 integrates the EDSs where the voltage deviation is very close to the higher value (10%). Regarding the data from Table 2, these express the levels of the voltage quality. It can be observed that the means indicates a Very Good quality for clusters C1 (all areas), cluster C2 (Peripheral Area) has a Good quality, clusters C2 (Middle and Nearby Areas), C3 (all areas), and C4 (Nearby and Peripheral Areas) present a Moderate level of the quality, and for remaining clusters, the quality is Unsatisfactory. Thus, if the supply voltage is further away from the rated value (the deviation is higher) then the irregularity degree will be higher, having negative consequences on the quality level. To provide a better understanding and comparison, Figs. 10, 11 and 12 show the boxplots that contain the quintiles associated with the voltage deviation for the clusters of each area and Figs. 13, 14 and 15 for the irregularity degree.

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Fig. 6 The silhouette coefficient of the three analyzed areas (Middle, Nearby, and Peripheral)

The boxplot represents the content of the distribution through the five statistical parameters: the minimum value, the first quintile (Q1, 25%), the second quintile (Q2, 50%) representing the median, the third quintile (Q3, 75%), and the fourth quintile (Q4, 100%) representing the maximum value. It will also show atypical values outside the distribution, the so-called outliers.

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Fig. 7 The clusters associated with Peripheral area

Fig. 8 The clusters associated with Nearby area

The range between quintiles (also called interquartile) is the range between Q3 and Q1, represented in the boxplot by a blue rectangle. The red line represents quintile Q2. It divides the series into two equal parts so that 50% of the terms of the series have values lower than the median and 50% higher than the median.

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Fig. 9 The clusters associated with Middle area

4.2 Analysis of Continuity in the Electricity Supply The database contains the records performed during a year associated with 21 MV feeders from a rural area. Figure 16 presents the primary characteristics of the feeders. The statistical analysis of data highlighted that the mean of the length is 61.9 km, with a maximum by 109.4 km and a minimum by 9.6 km. Also, the mean of the installed power in the transformers from the EDSs is 7.1 MVA, with a maximum by 12.6 MVA and a minimum by 1.8 MVA. The mean of the rated power associated with an equivalent transformer is 175 kVA. All these values obtained from the statistical analysis are normal in the rural areas from Romania. The System Average Interruption Duration Index (SAIDI) has been calculated with relation (2), and the unplanned interruptions have been quantified for each feeder from the records. There were no atypical data in the records, so all data have been stored in the warehouse. Each indicator was normalized using relation (7) such that the values are located in the range [0, 1]. The K-means clustering algorithm has been applied for K = 2, …, K max , where K max is 4, the value calculated with relation (8). The optimal partition has corresponded to K = 3, with SC having a value of 0.73. Figure 17 presents the variation of the SC associated with the optimal partition. Figures 18 shows the representation of the clusters obtained according to the indicators of continuity in the electricity. The partition quality is given by the compact structure, without bordered elements or outliers, of each cluster.

PA

0.74

1.91

1.33

0.56

1.81

2.98

2.22

1.76

MA Middle area, PA Peripheral area, NA Nearby area

0.74

σ

3.34

3.50

2.12

Max (Q4)

m

2.52

2.68

Q3

1.22

1.95

1.48

2.17

Q1

Q2

0.95

0.52

4.22

5.13

4.64

4.27

3.81

2.74

MA

NA

MA

0.69

Area

Area

0.59

C2

C1

Clusters

Min (Q0)

Statistical parameters

NA

0.53

4.30

5.01

4.80

4.31

3.85

3.06

PA

0.45

3.72

4.57

4.05

3.68

3.35

3.00

0.45

5.72

6.56

6.07

5.69

5.36

4.67

MA

Area

C3

Table 1 The statistical parameters of the clusters from each area for voltage deviation

NA

0.33

5.49

6.30

5.70

5.41

5.23

5.01

PA

0.47

5.27

6.17

5.61

5.21

4.86

4.59

0.45

7.21

8.06

7.54

7.09

6.86

6.48

MA

Area

C4 NA

0.46

6.94

7.70

7.27

6.73

6.59

6.48

PA

0.57

6.86

7.74

7.30

6.74

6.39

6.22

0.35

8.85

9.40

9.06

8.93

8.72

8.16

MA

Area

C5 NA

0.43

8.49

9.12

8.74

8.51

8.18

7.98

PA

0.29

8.75

9.07

9.00

8.75

8.51

8.44

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PA

7.9

11.4

9.8

13.4

6.57

3.69

Q3

Max (Q4)

m

σ

2.1

2.7

2.7

4.9

10.3

6.9

4.4

MA Middle area, PA Peripheral area, NA Nearby area

3.3

5.1

4.6

3.4

6.5

Q1

Q2

1.3

3.7

20.3

27.3

23.9

20.3

17.6

13.5

MA

0.6

MA

NA

Area

Area

0.6

C2

C1

Clusters

Min (Q0)

Statistical parameters

NA

4.1

20.5

25.3

24.6

21.0

16.6

14.0

PA

3.4

16.1

22.8

19.0

15.7

13.3

10.7

4.7

34.8

43.8

38.3

34.3

30.6

27.8

MA

Area

C3

Table 2 The statistical parameters of the clusters from each area for irregularity degree

NA

3.8

31.5

40.9

33.6

30.8

28.4

26.3

PA

4.7

30.3

39.0

34.4

29.5

26.5

23.5

6.7

54.4

68.0

60.8

52.5

48.7

45.3

MA

Area

C4 NA

6.0

50.0

59.6

54.1

47.6

45.7

42.6

PA

7.1

49.8

60.8

54.6

49.1

42.7

42.6

6.1

80.9

90.3

83.8

83. 5

78.0

69.4

MA

Area

C5 NA

6.9

74.4

84.4

78.2

74.9

69.3

66.1

PA

5.3

80.0

85.0

84.6

79.8

75.4

75.3

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Fig. 10 The boxplots of the clusters associated with Peripheral Area for voltage deviation

Fig. 11 The boxplots of the clusters associated with Nearby Area for voltage deviation

Table 3 presents the statistical parameters (mean, standard deviation, and quintiles) of each cluster for the two indicators (unplanned interruptions and SAIDI). The data analysis indicates values of the unplanned interruption in the range [2, 5] for cluster C1 and between 5 and 9 for the clusters C2 and C3. If the mean is considered, C1 has an average value of about 4, followed by C2 with 7, and C3 with 8 interruptions.

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Fig. 12 The boxplots of the clusters associated with Middle Area for voltage deviation

Fig. 13 The boxplots of the clusters associated with Peripheral Area for the irregularity degree

Regarding the SAIDI, the cluster C1 presents the smallest values, below 4 min/ year, and the clusters C3 has the highest values, between 10 and 14 min/years. A better framework can be provided by the quintiles through the boxplots, see Figs. 19 and 20. The medians, Q2, have very close values to the means for cluster C2. Cluster C3 has the minimum value of the unplanned interruptions equal with the

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Fig. 14 The boxplots of the clusters associated with Nearby Area for the irregularity degree

Fig. 15 The boxplots of the clusters associated with Middle Area for the irregularity degree

median, which indicates an asymmetry of the data. A similar case meets for cluster C1, where quintile Q1 is very close to Q2. The values associated with cluster C1 are the closest to the average value registered in Romania for distribution networks (2.05 h at the level of 2019 [29]). However, the values obtained for all feeders are much higher than the European average, which is about 1.2 h by 2019.

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Fig. 16 The primary characteristics of the analyzed feeders

Fig. 17 The silhouette coefficient for the optimal partition (K max = 3)

In a ranking associated with the SAIDI indicator presented in [29], Romania ranks 26th out of 30 countries, which means that the DNOs must consider and take measures to improve the continuity of the electricity supply, see Fig. 21. All information helps the DNO to establish a ranking of the feeders subject to attention. Thus, the feeders assigned to cluster C3 must be integrated into the “hot”

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Fig. 18 The 2-D representation of the obtained clusters

Table 3 The statistical parameters of the clusters Statistical parameters

Unplanned interruptions

SAIDI

Clusters

Clusters

C1

C2

C3

C1

C2

C3

Min (Q0)

2

5

7

1.3

5.0

10.1

Q1

2.8

6.8

7

2.1

6.1

10.4

Q2

4

7

7

2.3

7.1

11.2

Q3

5

8

8.5

3.3

8.0

13.3

Max (Q4)

5

9

9

4.1

8.5

14.0

m

3.7

7.1

7.7

2.7

7.0

11.8

σ

1.2

1.2

1.2

0.9

1.2

2.0

area, where the DNO must apply the strategies to improve the continuity of the electricity supply. In these conditions, all causes which led to these high values must be identified, and the necessary measures for minimization of unplanned interruptions and SAIDI to be applied.

5 Conclusions The “power quality” term, quite ambiguous a few decades ago, has been reanalyzed and redefined following the current requirements of electricity consumers. In the conditions of the liberalization of the energy markets and the expansion of electricity production from renewable sources, the power quality and the quality of the supply

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Fig. 19 The boxplots of the unplanned interruptions associated with the three clusters

Fig. 20 The boxplots of the SAIDI associated with the three clusters

service represent challenges addressed to the distribution operators and the electricity suppliers. Thus, some technical and economic issues should address by DNOs to find the most effective solutions. In these conditions, power quality is particularly topic for all categories of consumers, not only at medium and large industrial enterprises. The unsatisfactory voltage quality represents the main factor that affects the proper function of equipment of from the consumers, including households. The standards adopted in the power quality field define a whole series of disturbances, which can occur in the electrical networks and contain recommendations to

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Fig. 21 SAIDI at the level of the European countries in 2019 [29]

avoid their occurrence. Based on the standards, things have changed, with possible comparisons between measurements made in different locations and consistent data analysis, regardless of the tools used to measure and record the electricity parameters. Monitoring of the parameters will allow the DNOs to determine whether the source of the disturbances is external (belongs to the consumer) or own (due to the operating regime of the operated networks). The detection of the causes that generate the power quality problems (for the consumer and the operator), their understanding, and identification of the best solutions represent the main challenges for the Decision-Maker. The data from various locations from the electrical network leads to large-size databases, seen by a Decision-Maker as multi-dimensional records with combined possibilities to be analyzed. The authors analyse all disturbances from files containing the current and voltage measurements (CVMs), downloaded from the power analysers installed in the EDSs, to find the "hot" areas where there are problems with power quality. A clustering based-Data Mining methodology, including the extraction of the features associated with voltage variations and the continuity of the electricity supply from the CVMs. The methodology has been tested in the power quality analysis based on the performance indicators regarding the voltage variations, SAIDI, and unplanned interruptions. The analysis has been performed using the large-size databases belonging to a Romanian DNO. The obtained results have highlighted the effectiveness of the new approach to identify the areas with power quality issues being able to help the DM in the planning and operation process of electric distribution networks.

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Even if there may be more theoretical measures, they must be specific to each EDS and consumption point, and their implementation is done only based on a cost– benefit analysis. The DNOs should not think how to improve the continuity of the electricity supply or the voltage quality, but must ensure optimal power quality to all consumers.

References 1. Bollen M, Etherden N, Yang K, Chang G (2012) Continuity of supply and voltage quality in the electricity network of the future. In: IEEE 15th international conference on harmonics and quality of power (ICHQP), Hong Kong, China, pp 375–377 2. Galbau S, Grigoras G, Neagu B, Scarlatache F, Lucache D, Hustiuc V (2022) Characterization of the power quality in the electric distribution networks using data mining with K-means clustering. In: 10th International conference on systems and control (ICSC), Marseille, France, pp 131–136 3. Pit A (2023) Voltage events. https://www.janitza.com/whitepaper-about-voltagedrops.html. Last accessed 21 Jan 2023 4. EN 50160 (2023) Voltage characteristics of electricity supplied by public distribution systems. A power quality standard. http://admin.copperalliance.eu/docs/librariesprovider5/power-qua lity-and-utilisation-guide/542-standard-en-50160-voltage-characteristicsin.pdf?sfvrsn=4&sfv rsn=4. Last accessed 19 Jan 2023 5. Mallajoshula R, Naidu IES (2019) Power quality issues in power systems. Int J Innov Technol Exploring Eng 9(2S3):308–313 6. Remigio-Carmona P, González-de-la-Rosa JJ, Florencias-Oliveros O, Sierra Fernández JM, Fernández-Morales J, Espinosa-Gavira MJ, Agüera Pérez A, Palomares-Salas JC (2022) Current status and future trends of power quality analysis. Energies 15(7):2328 7. Zeraati M, Esmail M, Golshan H, Guerrero J (2019) Voltage quality improvement in low voltage distribution networks using reactive power capability of single-phase PV inverters. IEEE Trans Smart Grid 10(5):5057–5065 8. Alkahtani AA, Alfalahi S, Athamneh AA, Al-Shetwi A, Mansor MB, Hannan MA, Agelidis V (2020) Power quality in microgrids including supraharmonics: issues, standards, and mitigations. IEEE Access 8:127104–127122 9. Lumbreras D, Gálvez E, Collado A, Zaragoza J (2020) Trends in power quality, harmonic mitigation and standards for light and heavy industries: a review. Energies 13:5792 10. Alam MR, Bai F, Yan R, Saha TK (2021) Classification and visualization of power quality disturbance-events using space vector ellipse in complex plane. IEEE Trans Power Deliv 36(3):1380–1389 11. Vinnal T, Janson K, Järvik J, Kalda H, Sakkos T (2020) Supply voltage quality in low voltage industrial networks of Estonia. Est J Eng 18(2):102–126 12. Markiewicz H, Klajn A (2023) Voltage disturbances. Standard EN 50160 voltage characteristics in public distribution networks. https://copperalliance.org.uk/uploads/2018/03/542-standarden-50160-voltage-characteristics-in.pdf. Last accessed 20 Jan 2023 13. Mozaffari M, Doshi K, Yilmaz Y (2022) Real-time detection and classification of power quality disturbances. Sensors 22(20):7958 14. Neagu BC, Grigora¸s G (2017) The assessment of power quality in electric distribution systems from Romania. Adv Energy Res 28:157–211 15. Furlani Bastos A, Santoso S (2021) Optimization techniques for mining power quality data and processing unbalanced datasets in machine learning applications. Energies 14(2):463 16. Romanian National Energy Regulatory Agency (2023) The performance standard for the electricity distribution service. https://www.anre.ro/ro/legislatie/norme-tehnice/standarde-deperformanta1387201290. Last accessed 21 Jan 2023. (in Romanian)

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17. Kriukov A, Grigoras G, Gavrilas M, Scarlatache F, Vicol B (2014) Use of fuzzy techniques in reliability assessment of electric distribution systems. In: IEEE 15th international conference on harmonics and quality of power (ICHQP), Bucharest, Romania, pp 689–693 18. Prettico G, Marinopoulos A, Vitiello S (2022) Guiding electricity distribution system investments to improve service quality: a European study. Utilities Policy 77:101381 19. Klai´c Z, Nikolovski S, Kraus Z (2011) Voltage variation performance indices in distribution network. Tech Gaz 18(4):547–551 20. Grigoras G, Neagu BC, Adascalitei A (2017) On the assessment of slow voltage variations in electric distribution networks using K-means clustering algorithm. In: 22nd IMEKO TC4 symposium and 20th international workshop on ADC modelling and testing (IMEKO), Iasi, Romania, pp 139–143 21. Neagu B, Grigoras G (2015) Assessment of slow voltage variations from the electric distribution systems with fuzzy techniques. In: 7th International conference on electronics, computers and artificial intelligence (ECAI), Bucharest, Romania 22. Iordache M, Conecini I (1997) Electric power quality. Ed. Tehnica, Bucharest. (in Romanian) 23. Grigoras G, Scarlatache F, Neagu BC (2016) Clustering in power systems applications. Lambert Academic Publishing, Saarbrücken, Germany 24. Li MJ, Ng MK, Cheung Y, Huang JZ (2008) Agglomerative fuzzy K-means clustering algorithm with selection of number of clusters. IEEE Trans Knowl Data Eng 20(11):1519–1534 25. Sinaga K, Yang MS (2020) Unsupervised K-means clustering algorithm. IEEE Access 8:80716–80727 26. Halkidi M, Batistakis Y, Vazirgiannis M (2001) On clustering validation techniques. J Intell Inf Syst 17(2/3):107–145 27. Rousseeuw P (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20:53–65 28. Hassani M, Seidl T (2017) Using Internal evaluation measures to validate the quality of diverse stream clustering algorithms. Vietnam J Comput Sci 4:171–183 29. Eurelectric (2023) Distribution grids in Europe. Facts & figures. https://cdn.eurelectric.org/ media/5089/dso-facts-and-figures-11122020-compressed-2020-030-0721-01-e-h-57999D1D. pdf. Last accessed 22 Jan 2023

Machine Learning and Deep Learning Approaches for Energy Management in Smart Grid 3.0 Amitkumar V. Jha , Bhargav Appasani , Deepak Kumar Gupta, Srinivas Ramavath, and Mohammad S. Khan

Abstract Energy management systems (EMS) in smart grid (SG) are complex and dynamic systems that require intelligent decision-making to optimize energy usage and reduce costs. Integrating renewable energy sources, energy storage systems, and SG technologies has significantly increased data volume and complexity in EMS. Machine learning (ML) and deep learning (DL) techniques are increasingly being applied to the EMS to address these challenges and to make them more efficient and reliable. ML algorithms can be used to analyze large amounts of data from smart meters, IoT devices, and other sources to identify patterns and trends. This can help predict energy demand and supply and identify areas of inefficiency that can be improved. On the other hand, DL techniques can be used to model complex relationships between variables and make accurate predictions. For example, neural networks can be used to predict energy consumption based on historical data and weather patterns. Overall, ML and DL techniques can help optimize energy usage, reduce costs, and improve the efficiency of EMSs in smart grids. This chapter systematically reviews various ML and DL algorithms covering their critical applications, advantages, disadvantages, research gaps, and solutions. The several ML and DL strategies employed to meet various restrictions and achieve various EMS objectives are also compared and critically analyzed in this review study. It also discusses potential research directions and recommendations for implementing ML and DL techniques in EMS for SG 3.0. Keywords Smart grid · Next-generation smart grid · ML · DL · Communication technologies · Computation technologies A. V. Jha · B. Appasani · S. Ramavath School of Electronics Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar 751024, India D. K. Gupta School of Electrical Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar 751024, India M. S. Khan (B) Department of Computing, East Tennessee State University, Johnson City 37614-1266, USA e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 B. Appasani and N. Bizon (eds.), Smart Grid 3.0, Power Systems, https://doi.org/10.1007/978-3-031-38506-3_6

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Acronyms A3C AI ANN BEMS BESS CNN DBSCAN DDPG DER DL DNN DQL DQN DRL DRL DT EMS GAN HEMS IEMS IoT KNN MADRL MEMS ML MLP NB PCA REINFORCE RF RNN SARSA SG SOM SVM SVPG TPRO T-SNE WLAN

Asynchronous advantage actor critic Artificial intelligence Artificial neural network Building energy management system Battery energy storage system Convolutional neural network Density-based spatial clustering of applications with noise Deep deterministic policy gradient Distributed energy resources Deep learning Deep neural network Double DQ-learning Deep Q-network Deep reinforcement learning Duel DQ-learning (DRL) Decision tree Energy management system Generative adversarial networks Home energy management system Industrial energy management system Internet of things K-nearest neighbor Multi-agent DRL Microgrid energy management system Machine learning Multi-layer perceptron Naïve Bayes (NB) Principal component analysis REward increment = Nonnegative factor × Offset reinforcement × Characteristic eligibility Random forest Recurrent neural network State-action-reward-state-action (SARSA) Smart grid Self-organizing map Support vector machine Stein variational policy gradient Trust region policy optimization T-distributed stochastic neighbor embedding Wireless local area network

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1 Introduction The smart grid (SG) is an intelligent electrical network that controls electricity production, distribution, and consumption using cutting-edge technologies and communication networks [1]. The SG has gone through multiple generations, each incorporating new technology and features to boost its dependability, sustainability, efficiency, reliability and resiliency. For e.g., the reliability aspects and other approaches that can be followed to enhance the sustainability and resiliency of the SG communication network is comprehensively analyzed in [2]. Recently, advanced technologies such as machine learning (ML) and deep learning (DL) are increasingly being used in the Energy Management Systems (EMS) in SG to optimize energy usage, reduce costs, and increase grid reliability. In this section, we briefly introduce the concept of next-generation SG known as smart grid 3.0, EMS, and the roles of advanced technologies such as ML and DL in EMS within the context of smart grid 3.0.

1.1 Smart Grid 3.0 The SG is an intelligent electricity network that uses advanced technologies and communication systems to manage electricity generation, distribution, and consumption. Over the years, the smart grid has evolved into several generations, incorporating new technologies and features to improve its efficiency, reliability, and sustainability [3]. Smart Grid 3.0 is the latest and most advanced generation of the smart grid. It is characterized by its ability to integrate a wide range of renewable energy sources, energy storage systems, and electric vehicles into the grid, providing a more decentralized and sustainable energy system. The development of Smart Grid 3.0 is driven by the need to address the challenges associated with traditional centralized energy systems, including the reliance on fossil fuels, limited grid capacity, and vulnerability to natural disasters and cyber-attacks. Smart Grid 3.0 offers a more flexible, reliable, and sustainable energy system, which can support the transition to a low-carbon economy and improve the quality of life for people worldwide. Smart Grid 3.0 also incorporates advanced technologies such as artificial intelligence (AI), and the Internet of Things (IoT) to enhance the grid’s performance, security, and resilience [4]. By leveraging these technologies, Smart Grid 3.0 can anticipate and respond to changes in energy demand and supply in real time, reducing energy waste and promoting clean energy sources. Overall, Smart Grid 3.0 represents a significant step towards a more sustainable, decentralized, and intelligent energy system, and its adoption is critical for the future of energy.

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1.2 Energy Management System An energy management system (EMS) is a computer-based system designed to monitor, control, and optimize energy usage in buildings, industrial facilities, and other energy-intensive operations. EMS collects and analyses energy consumption and production data, identifies inefficient areas, and makes recommendations to reduce energy usage and costs. Integrating renewable energy sources, energy storage systems, and electric vehicles in smart grid 3.0 has created new challenges for energy management. The EMS is critical in managing and optimizing energy usage in SG 3.0. The EMS uses advanced technologies such as machine learning (ML), deep learning (DL), artificial intelligence (AI), and the Internet of Things (IoT) to collect and analyze data on energy consumption, production, and distribution. EMS in SG 3.0 can optimize energy usage by balancing energy demand and supply, predicting energy demand based on historical data and weather patterns, and identifying areas of inefficiency in the grid. EMS can also be used for fault detection and diagnosis, load forecasting, and demand response, among other applications. Adopting EMS in SG 3.0 is essential for achieving a sustainable, decentralized, and intelligent energy system. EMS can help to reduce energy waste, improve the reliability and efficiency of the grid, and promote the use of renewable energy sources.

1.3 Role of Machine Learning and Deep Learning in EMS The EMS is crucial in optimizing energy usage and reducing energy waste in SG 3.0. In recent years, there has been a growing interest in leveraging advanced technologies such as ML and DL in EMS to improve energy efficiency, reduce costs, and increase grid reliability [5]. Consequently, ML and DL have become integral components of EMS in SG 3.0. ML and DL are used to process and analyze vast amounts of data generated by sensors, smart meters, and other devices in the grid. ML algorithms can be used to analyze large amounts of data generated by sensors, smart meters, and other devices in the grid. ML algorithms can learn from historical data, predict energy demand, and adjust energy usage in real-time. This can lead to more efficient energy usage, reduced energy costs, and improved grid reliability. Additionally, ML can be used for demand response, load forecasting, fault detection and diagnosis, and energy efficiency optimization. On the other hand, DL algorithms, a subset of ML algorithms, can analyze complex data sets such as images, videos, and sound. DL algorithms are particularly useful in analyzing data from distributed energy resources (DERs) such as solar and wind power systems. Integrating ML and DL in EMS in SG 3.0 can potentially revolutionize the energy sector. With ML and DL, EMS can learn from historical data, predict energy demand, and adjust energy usage in real-time, thereby improving grid efficiency and reducing

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energy waste. Nevertheless, using ML and DL in EMS can help optimize the use of renewable energy sources, making smart grid 3.0 more sustainable and reducing carbon emissions.

2 Energy Management System: Architecture, Levels, and Applications In SG 3.0, the EMS plays a crucial role in the reliable and efficient operation of the SG. Recently, the research in the paradigm of EMS has attracted many researchers covering various application domains, including monitoring and control, load forecasting, demand response, renewable energy integration, energy storage management, fault detection, and efficiency optimization. The statistical representation of different application aspects of the EMS is represented in Fig. 1 where the last ten years (2013 to 2022) data have been taken from the Scopus database. The publication statistics indicate that demand response is one of the highly researched applications of EMS. In contrast, fault detection is the least explored application of the EMS. Nevertheless, the steady growth in all domains of EMS indicates the importance of EMS in SG 3.0.

Fig. 1 Researchers interest towards various applications of EMS

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2.1 Architecture of EMS The EMS is a complex architecture resulting from highly digital twined between the prosumer, power utility, and operator. This type of control architecture manages electricity and energy in order to enhance the EMS’s techno-economic performance. The most generic architecture of the EMS is shown in Fig. 2. The SG 3.0 comprises various domains such as generation, transmission, distribution, consumer, market, and operation [6]. The generation domains include both conventional and renewable generation sources. Distribution domains in SG 3.0 follow distributed energy resources approach where several microgrids are envisioned to improve flexibility and operability. Customers are basically prosumers in SG 3.0. The distributed EMS is used to integrate customer and SG 3.0 infrastructures. The distributed EMS stores, utilizes, and maintains electricity the customers contribute. During peak time, such contributions can be supplied to the customers through the main SG infrastructure. The real-time data monitoring, pricing, load forecasting, demand-response analysis, etc., are performed by gathering, analyzing and processing voluminous data through EMS at the operation and control centre of the SG.

Fig. 2 Description of EMS in SG 3.0

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2.2 Levels of EMS in SG 3.0 The EMS is discussed in literature from different perspectives and under different scopes. The energy management operations performed at the home level is referred as home EMS (HEMS). The energy management operations performed at the building level are referred to as building EMS (BEMS). On the other hand, the energy management operations performed at the industry level are known as industrial EMS (IEMS). The energy management operations are also applied at the microgrid level in the SG, which can be referred to as microgrid EMS (MEMS). The aspects of different level of EMS is presented in the following sections.

2.2.1

Home Energy Management System

These are used in residential homes to manage energy usage and optimize energy consumption. Developing federated microgrids as HEMSs can greatly aid the future SG system solution. HEMSs encourage cost reduction by enabling energy efficiency, making microgrids more commercially viable. Operators can leverage the extensive data HEMSs give about household energy use via neighbourhood and wide area networks to improve the grid’s safety, efficacy, and resilience. HEMS typically consist of a smart energy meter, home automation systems, and other smart devices. A panoramic survey on various aspects of HEMS is presented in by Zafar et al. in [7]. For HEMS, the network architecture, including components, appliances, control elements, interfacing devices, etc., are presented in [8]. The models described here cover many smart technology concepts, mechanisms, and HEMS-related schemes.

2.2.2

Building Energy Management System

These are used in commercial and industrial buildings to optimize energy usage and reduce energy costs. The prime objective of the BEMS is to monitor and control building energy for its conservation. A BEMS emphasizes several IoE technologies and their uses to lower building energy usage and reduce GHG emissions. A comprehensive survey on various aspects of BEMS is presented in [9], where several challenges, recommendations and potential research directions are emphasized. Typically, BEMS include HVAC systems, lighting, other building automation systems, IoT devices, sensors, actuators, etc. Specific to EMS applications, the BEMS is comprehensively reviewed by Mariano et al., in [10]. This authors focus on monitoring and control, load forecasting, energy storage management, and demand response aspects of the EMS covering residential and non-residential buildings.

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Industrial Energy Management System

The instability and volatility of renewable energy sources, the unknown surrounding plug-in electric vehicles, the price of electricity, and the time-varying load pose additional difficulties for power engineers to establish a demand–supply balance for reliable SG operation. The SG can operate reliably, securely, and efficiently when the EMS can effectively coordinate the energy sharing and trading among all available energy supplies and supply loads economically under all circumstances. The comprehensive review of the framework, architecture, challenges, recommendations and future directions on EMS for the SG is presented in [11]. The mathematical modelling, analysis, and challenges of IEMS for load forecasting and demand response are discussed in [12]. The IEMS are used in manufacturing and industrial facilities to manage energy consumption and improve efficiency. IEMS typically include monitoring and control systems for production equipment and other industrial processes.

2.2.4

Microgrid Energy Management System

Applications for microgrid technology can be found in a variety of settings, such as university campuses, military bases, and isolated islands. An integrated energy system, a microgrid, consists of several interconnected distributed energy supplies and loads located in a specific local area with a distinct electrical boundary. Microgrids function as small-scale local power systems, and they can switch between grid-connected and island modes. The MESMs are used to manage energy in smallscale power systems that can operate independently or be connected to the larger power grid. The MESM operates within the microgrids for EMS. A novel technique for MEMS based on distributed energy resources is proposed in [13]. The MEMS can include renewable energy sources such as solar and wind power, energy storage systems, and other control and monitoring systems.

2.2.5

Multi-level Energy Management System

With the penetration of electric vehicles (EV) and the integration of renewable energy sources, the need for multi-level EMS (MLEMS) has been observed. The MLEMS envisages providing operator centric benefits such as monitoring and controlling of the SG and prosumer’s centric benefits such as demand-response, minimization of cost, energy usage optimization, etc. Contrary to the other EMS, the MLEMS aims to improve the performance of the SG in terms of its techno-economics aspects and end user experience. A comprehensive review on MEMS emphasizing state-of-theart developments is presented in [14]. Whereas, from the computational aspects, the role of different computational techniques for MEMS are discussed in [15].

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2.3 Applications of EMS The objective of EMS is to monitor and control the various parameters of the SG, analyze demand and response in real-time, load forecasting based on historical data, integrate renewable energy sources with traditional sources, locate faults and act accordingly to diagnosis in real-time, manage energy contributed by the prosumer at small scale, optimization of energy usage for efficiency enhancements, etc. Overall, EMS have the potential to improve energy efficiency, reduce costs, and promote sustainability in the energy sector. The specific application of EMS depends on the needs of the system and its goals which can be broadly classified into the following categories. • Energy monitoring and control—EMS can monitor energy usage in real-time and control energy consumption to optimize efficiency and reduce costs. • Demand response—EMS can manage energy demand during peak usage periods by adjusting energy usage in real time. • Load forecasting—EMS can use historical data to forecast energy demand, which can help utilities plan and manage energy supply. • Renewable energy integration—EMS can integrate renewable energy sources such as solar and wind power into the grid. • Fault detection and diagnosis—EMS can be used to detect and diagnose faults in the grid, helping to improve reliability and reduce downtime. • Energy storage management—EMS can be used to manage energy storage systems, optimizing the usage of energy storage and reducing energy waste. • Energy efficiency optimization—EMS can optimize energy usage to improve efficiency and reduce energy waste, resulting in cost savings and reduced carbon emissions. The different priority applications of EMS with objectives are shown in Fig. 3.

3 Key Technologies for EMS in SG 3.0 Different computation and communication technologies exist to the backbone of the EMS. Integrating these technologies in EMS can help optimize energy usage, reduce energy waste, and promote sustainability in the energy sector. By leveraging these technologies, EMS can improve energy efficiency, reduce costs, and enhance the reliability of SG 3.0.

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Fig. 3 Major applications under EMS umbrella

3.1 Key Technologies Used in EMS Architecture Several technologies are used in EMS architecture. Some of the key technologies are briefly summarised below.

3.1.1

Smart Meter

Smart meters are digital meters that record energy usage and provide real-time data on energy consumption. The smart meter is one of the most crucial components of the SG 3.0. The smart meter is an advanced energy meter that collects data from the load devices of end users. These data are analyzed, and several recommendations can be made based on the analysis. The users are aware of the real-time energy consumption and pricing-related information. The smart meter also delivers additional data to the utility company or system operator. A smart meter uses several sensors and control devices, all supported by specialized communication infrastructure. An overview of smart meter design, the architecture of EMS, and the monitoring system is presented by Zheng et al. [16]. In [17], the EMS architecture for monitoring and control through

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smart meters is developed where consumer and distributor-owned smart meters are designed separately. A review of technologies for the smart meter is presented in [18], covering diverse aspects of it.

3.1.2

Internet of Things Devices

In the last few years, a drastic change is observed in internet connected devices due to the phenomenal advancement in the IoT paradigm. The SG 3.0 is equipped with a large number of IoT devices which serve various purposes. Particularly, the EMS applications use IoT devices for various purposes, such as monitoring the SG’s health, data communication between the end user and system operator, real-time metering information, pricing regulations, fault identifications, etc. One of the IoT devices’ prime objectives is to ensure connectivity among the EMS devices. By giving information on electricity management, IoT is employed in EMS to balance regions with unreliable power supply and those with abundant electricity. IoT provides data as a reserve in power plants to control demand and supply by optimizing the generation technology. Pawar et al. [19] proposed an EMS where IoT integrates end devices for seamless and effective communication. This article dealt with load forecasting and demand-response applications in more detail.

3.1.3

Cloud Computing

Cloud computing is a technology which offers simple, universal, on-demand access to a configurable pool of computer resources, such as servers, networks, applications, storage, and services. Resources can be provisioned and released without much management work. It is one of the most efficient and cost-effective solution to handle large voluminous data generated during the various applications of the EMS. It offers provisions to utilize infrastructure, platforms, and software across several levels of the EMS. Its advantages include on-demand self-service, broad network access, resource pooling, rapid elasticity, etc. Since EMS is envisioned to be climate-oriented, resource conservative, affordable, and economical, cloud computing can be regarded as an integral part of the EMS. The two most celebrated surveys on cloud computing for the EMS paradigm are presented in [20, 20]. In the former survey, cloud computing for different SG objectives is surveyed. Whereas in the later survey, more focus is given to EMS applications of the SG in which the potentials of cloud computing are thoroughly analyzed.

3.1.4

Artificial Intelligence

SG is also powered by artificial intelligence (AI), which acts as the “intelligent agent” that constantly monitors its surroundings and takes action to achieve its objectives. AI is essential for integrating renewable energy, stabilizing energy networks,

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and lowering financial risks brought on by infrastructure instability. To address the intermittent nature of renewable energy, for instance, AI’s self-learning, flexibility, and computation capabilities have tremendous potential. Moreover, the AI technologies such as Machine Learning (ML) and Deep Learning (DL) are used to analyze energy data and optimize energy consumption in EMS. Some benefits AI can offer include resiliency, reliability, precise load forecasting, better monitoring and control, more sophisticated outage alerts, better resource allocation and utilization, etc. A panoramic survey on various aspects of AI in EMS is presented by in Aguilar et al. in [22].

3.1.5

Energy Storage System

The word “prosumer” is a combination of “producer” and “consumer.” Consumers of energy often keep their connections to the main grid. But, they can also generate and even store energy, often using EV batteries and photovoltaic solar panels. In the SG, an energy storage system is required to store the excess energy generated by the consumer which the main grid of the system can utilize. In this direction, one of the prominent technology is battery energy storage system (BESS). One of the pioneer work in this direction is presented in [23] where grid connected BESS is analyzed from the control and operation perspective. The integrations of more and more renewable energy sources are encouraged in BESS where batteries batteries and flywheels are used to store energy for later use. This helps in reducing energy waste and improves energy efficiency. Nevertheless, decarbonizing the electricity grid and cutting greenhouse gas emissions depend heavily on energy storage system. Many energy storage options are now available, however lithium-ion batteries are now the preferred technology because of their affordability and excellent efficiency.

3.1.6

Renewable Energy Sources

Due to the enormous growth in electricity consumption, generating electricity using renewable energy sources has been encouraged in the last few years. However, due to the complexity of the SG existing architecture, it is challenging to incorporate renewable energy sources into the mainstream grid. While such integration is crucial for full fill current and futuristic electricity demands, on the other hand, continuous monitoring and control of the SG is important to ensure the parameters of the SG remain within the desirable limits. Hence, EMS plays a crucial role in achieving optimum SG performance. A review in this direction is presented in [24].

3.1.7

Energy-Efficient Devices

Energy efficient devices are another important technology that can be integral to the EMS architecture. Some energy-efficient devices that can be part of the EMS

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architecture are LED lighting and smart thermostats. These energy-efficient devices can be used to reduce energy consumption and improve energy efficiency. A review in this direction is presented in [25], where authors analyzed the impact of devices on energy management and proposed different energy-efficient devices for enhancing energy conservation. The comprehensive review of various aspects of different key technologies which are indispensable to the architecture of EMS in SG 3.0 is presented in Table 1. Table 1 A comprehensive review on technologies used in EMS architecture Technologies

Key application domains

Benefits

Smart meter

Monitoring and control, efficiency optimization

Real-time energy Vulnerable to consumption, pricing, and security threats efficiency enhancement and attacks

IoT

Demand response, fault detection and diagnosis

Low-cost solution to interact between different components of SG 3.0 seamlessly

Cloud computing

Demand response, monitoring and control, load forecasting energy storage management

Distribution of processing The optimize power requirements to the algorithms and network (Internet) tools are required

[20, 21]

AI

All

Reduction in human interference and enhancement in system capability

[22]

Energy storage system

Energy storage management

Improvement in Requires flexibility, availability and efficient energy economy of the SG storage devices through open and flexible trading of energy

[23]

Renewable energy integration technologies

Renewable energy integration, efficiency optimization

Reduce burden of electricity production from conventional energy generation sources

Requires optimum technologies and tools for efficient integration

[24]

Energy efficient devices

Efficiency optimization

Save energy by using energy-efficient devices in across all levels of EMS in SG

Prone to failure, [25] security threats, and lack of standardization

Limitations

References [16–18]

Vulnerable to [19] security threats, attacks, and robustness

Lack of specialist algorithms for SG

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4 Communication Technologies for EMS in SG 3.0 In addition to these technologies, some other technologies focusing on the communication perspective, which play a crucial role in EMS, are briefly discussed below. These communication technologies are necessary to enable data exchange between devices and systems.

4.1 Wireless Communication Technologies The National Institute of Standards and Technology (NIST) has recommended various wireless and wired communication technologies that can be utilized to different extent for various applications of the SG. The wireless communication technologies for EMS applications can be summarised with the following points. • WLAN: The IEEE 802.11 standards, popularly known as WiFi can be used to create WLAN to facilitate network connectivity for different devices on the network. It is based on noninterfering three technologies: FHSS, DSSS, and OFDM. The various end devices can be connected to the network seamlessly using WLAN. It offers several advantages such as high data rate, remote connection, seamless integration, connectivity during mobility, flexibility, etc. Using WLAN, data rate can be achieved up to 900 Mbps. The WLAN communication technologies can be used to connect smart meters, IoT devices, other sensors and actuators in the EMS. The EMS for using WLAN technologies is presented in [26], where end devices are interfaced using WLAN technologies. Further, WiFi based service is created for EMS applications [27]. • ZigBee: Based on an open international standard, the ZigBee home area wireless network is developed, which is a dependable, economical, and low-power solution to provide network connectivity to the end devices. It has been standardized under IEEE 802.15.4 standard. Using DSSS modulation, ZigBee uses the unlicensed frequency bands of 868 MHz, 915 MHz, and 2.4 GHz. A low data rate of 25–250 Kbps can be achieved using ZigBee, which is possible for a typical range of 10–100 m. ZigBee devices have varying transmission ranges and battery lives depending on the topology chosen. For security, ZigBee uses 128-bit AES encryption. It can be used to provide end-device connectivity for different applications of EMS in SG 3.0. In [28], authors proposed EMS where ZigBee communication technologies are used for interfacing end devices to the EMS infrastructure. • WiMAX: To provide worldwide interoperability based on microwave communication, Worldwide Interoperability for Microwave Access (WiMAX) was developed in 2001. It was standardized under IEEE 802.16 standard. It can provide connectivity up to 48 km and achieve data rates up to 70 Mbps. Thus, it can be seen as one of the potential communication technologies for EMS in SG 3.0. It can be used to create the backbone communication network for SG. Some of the specific EMS applications where WiMAX can be used include monitoring and

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control, fault detection and diagnosis, demand response analysis, etc. The synergy of WiMAX and ZigBee is considered for EMS applications [29]. • Bluetooth: The Bluetooth is another low-cost solution developed to provide network connectivity to end devices. It was standardized under IEEE 802.15.1 standards under the wireless personal area network umbrella. It is a radio frequency communication standard for low-power, short-range communication. It utilizes the unlicensed ISM frequency between 2.4 and 2.4835 GHz. It provides a 721 Kbps data rate and covers distance typically in the range of 1–100 m. Devices configured for Bluetooth include the full OSI 7-layer communication stack. Both point-to-point and point-to-multipoint communication configurations can be facilitated by it. Bluetooth can connect end devices over the network to achieve the objectives of different EMS applications. In [30], Bluetooth technology is utilized for end device interfacing where demand response and load forecasting applications are analyzed in SG. • Satellite: The use of artificial satellites to establish communication links between different locations on Earth is referred to as satellite communication. It establishes a communication connection between a source transmitter and a receiver at various locations on Earth by relaying and boosting radio telecommunication signals via a transponder. Satellite communication is standardized under digital video broadcasting for satellite, 2nd generation (DVB-S2) standards reported in 2005 and further updated in 2015 by DVB-S2X to provide network connectivity over the Internet [31]. Concerning the return link, DVB-RCS2 standard was developed. One of the most applauded satellite based internet access network was developed under the standard Sat3Play. The main role of satellite technologies in EMS can be to create long haul backbone network where traditional terrestrial networks cannot be implemented. Nevertheless, it can be used in EMS to enable remote monitoring and control of energy usage in remote areas. In particular, it is useful in off-grid areas where traditional communication networks are unavailable [32]. • Cellular: Contrary to the radio waves used in WiFi, the cellular networks are used in cellular communication technology. It is based on cellular towers to provide network connectivity among mobile end devices. The rapid advancements from 1 to 5G offers manifold growth in communication capability, reliability, affordability, etc. It is capable to offer data rates up to 100 Mbps. It can be used to connect various end devices such as sensors, actuators, smart meters, IoT devices, etc. for several applications of EMS in SG 3.0 [33]. Moreover, cellular communication technologies such as 4G and 5G are extensively used in EMS to enable remote monitoring and control of energy usage. Cellular networks provide reliable connectivity, enabling remote monitoring of energy usage and control devices in real-time. The comprehensive discussion on requirements, challenges and recommendations for various applications of SG including EMS is included in [34].

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4.2 Wired Communication Technologies • Ethernet: Ethernet is a communication technology which uses cables for creating networks. It is developed by IEEE and standardized under IEEE 802.3 standards [35]. It can be used to create wired LAN where physical layer interface can be provided by wired communication technology used in EMS to connect devices to the internet. Ethernet provides fast and reliable connectivity, making it possible to transfer large amounts of energy data in real-time. The different types of cables can be used for interfacing and creating Ethernet based network. These include twisted pair cable, coaxial cable, and fiber optic cable. The choice of these cables depends on specific EMS applications based on data transfer rate, reliability, security, etc. Among all, optical fiber based Ethernet network is more robust, secure, reliable, and capable of transferring the data at the rate of 2.5 Gbps [36]. However, implementation cost is high in this. The communication infrastructure based on optical fiber is presented in [37] and such infrastructure is also analyzed form reliability and resiliency perspective of SG. • Power line communication: One of the first trustworthy communication channels that electric utilities had access to was power line carrier (PLC), which could not be susceptible to the intolerance and unreliability of leased (common carrier) telephone circuits. The radio frequency signal in varying from 30 to 500 kHz can be transmitted using the well-established power lines. The communication interoperability for EMS under SG paradigm is standardized by ISO/IEC 149083 standards. It can support the data rate up to 200 Mbps. PLC can be used in EMS to connect smart meters, IoT devices, and other devices to the internet. However, it is more prone to interference as low SNR is observed. On some of the 220/230 kV, 110/115 kV, or 66 kV linked power transmission networks, voice, telemetry, SCADA (supervisory control and data acquisition), and relaying communications are provided by PLC systems that can be exploited for several applications of EMS. The PLC based communication infrastructure for HEMS and BEMS is proposed in [38]. The comprehensive survey on communication technologies for SG is included in [39] and in this, some specific challenges pertaining to the EMS applications of the SG are also elaborated. The more general classification of different communication technologies that can be used for EMS application in SG 3.0 is as shown in Fig. 4 (Table 2).

5 Machine Learning and Deep Learning Approaches for EMS in SG 3.0 Several computing technologies are used in EMS in a Smart Grid 3.0, such as ML, DL, cloud computing, edge computing, big data analytics, blockchain, IoT, etc. The integration of these computing technologies in EMS enables efficient energy

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Fig. 4 Communication technologies viable for EMS applications

management, optimization of energy usage, and better decision-making to promote sustainability in the energy sector [40]. The comprehensive review on ML and DL technologies as a part of AI with in the paradigm of EMS for SG 3.0 is presented in the subsequent sections. Electrical grid management and operation are inherently complex decisionmaking processes, which are made even more difficult by the growing use of renewable energy sources, smart sensors, IoT devices, etc. As a result, this introduces additional variability and uncertainty into the functioning of the SG. Using traditional analysis methods to address the operation, maintenance, and planning of the SG can be time-consuming and not necessarily an optimum solution. To address these challenges, AI is being implemented across all sectors generation, transmission, distribution, operation, customer of the SG [41]. The comprehensive represented of various AI techniques is presented in Fig. 5. In the further section, we concentrate on ML and DL sectors of the AI and comprehensive discussion covering various their aspects are presented for EMS application of the SG 3.0.

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Table 2 Different communication technologies used for EMS in SG 3.0 Communication technologies

Category

Data rate

Standards

Preferable application domain

References

WLAN

Wireless

54–900 Mbps

IEEE 802.11x

All

[26, 27]

ZigBee

25–250 Kbps

IEEE 802.15.4

Monitoring and control, load forecasting

[28]

WiMAX

70 Mbps

IEEE 802.16 Monitoring and control, fault detection and diagnosis, efficiency optimization

[29]

Bluetooth

721 Kbps IEEE 802.15.1

Monitoring and control, demand response

[30]

Satellite

256 Kbps–10 Mbps

DVB-S2, DVB-S2X, DVB-RCS2

Energy storage management

[31, 32]

Cellular

14.4–100 Mbps

LTE, 4G, 5G Monitoring and control, load forecasting, demand response

Ethernet

PLC

Wired

[33, 34]

172 Mbps IEEE 802.3

Monitoring and control, fault detection, demand response

[35–37]

200 Mbps ISO/IEC 14908-3

Monitoring and control

[37–39]

Fig. 5 Overview of ML and DL for EMS applications

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5.1 ML for EMS in SG 3.0 ML is one of the sub-field of AI which enables a machine to perform certain task by training a model. Depending upon the training used to develop model, ML can be broadly classified into following categories.

5.1.1

Supervised Learning

In supervised learning, the algorithm learns from labeled data, where the input and output variables are known. The goal is to create a model that can accurately predict the output variable for new input data. There are three techniques used in the supervised learning algorithm which are classification, regression, and forecasting. In classification technique, the ML must draw a conclusion from observed values and decide which category new observations fall into while doing classification jobs. The ML model estimates and comprehends the relationships between variables in regression technique. Regression analysis is very helpful for prediction and forecasting since it concentrates on one dependent variable and a number of other changing factors. Regression can be used to establish a relationship between the dependent and independent variable. Forecasting, which is frequently used to study trends, is the act of making predictions about the future based on the evidence from the past and present. Some of the popular algorithms under supervised learning based on classification technique which are highly researched within the paradigm of EMS applications include, multi-layer perceptron (MLP), random forest (RF), support vector machine (SVM), K-nearest neighbor (KNN), naïve Bayes (NB), decision tree (DT). Whereas, the regression based highly appreciated supervised ML algorithms used in EMS applications are SVM, DT, Polynomial, MLP, RF, KNN, etc. [42]. A systematic review in ML techniques for load forecasting and demand response analysis of EMS application is presented in [43].

5.1.2

Unsupervised Learning

The unlabeled data sets are used in this type of machine learning to train the model. The model tries to find patterns and relationships in the data without being given specific output labels. Using the unlabeled data, it tries to establish a pattern that can solve the problem. We have the input data but no corresponding output data in unsupervised learning. Hence, it cannot be used to solve a regression or classification problem directly like supervised learning where data are labelled. Discovering the underlying structure of a dataset, classifying the data into groups based on similarities, and representing the dataset in a compressed format are the objectives of unsupervised learning.

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The unsupervised learning can be classified as clustering and dimensionality reduction (association) based on the techniques used in training the ML model. Using the clustering technique, items are grouped into clusters so that those who share the most similarities stay in one group and share little to none with those in another. The data objects are classified based on the existence or lack of commonalities discovered by cluster analysis. The phrase “it is a way of converting the higher dimensions dataset into lesser dimensions dataset, ensuring that it provides similar information” can be used to describe the technique of “dimensionality reduction.” These methods are frequently used in machine learning to solve classification and regression issues while producing a more accurate predictive model. Under unsupervised category, some of the popular algorithms based on clustering technique which are highly researched within the paradigm of EMS applications include, density-based spatial clustering of applications with noise (DBSCAN), K-means, self-organizing map (SOM), etc. Whereas, dimensionality based unsupervised ML algorithms which are highly explored in the paradigm of EMS are principal component analysis (PCA), T-distributed stochastic neighbor embedding (T-SNE), etc. [44].

5.1.3

Semi-supervised Learning

The main drawback of supervised learning is that it costs a lot to process and necessitates manual labelling by ML experts or data scientists. Furthermore, the range of applications for unsupervised learning is constrained. The idea of semi-supervised learning is presented to address these issues with supervised learning and unsupervised learning methods. Semi-supervised learning is a combination of supervised and unsupervised learning, where the algorithm learns from both labeled and unlabeled data. It makes use of both label data (like supervised learning) and unlabeled data (like unsupervised learning) to train the model. The three types of semi-supervised learning that are frequently used are clustering, dimension reduction, and classification. The system learns from labelled data using a supervised learning model, producing a response key that the evaluation algorithm can use to assess the consistency of the training results. The load forecasting based on semi-supervised ML model is designed and analyzed by Aslam et al. in [45]. The different semi-supervised algorithms, ML model, in-depth analysis, and scope for potential EMS applications are comprehensively reviewed in [46] by Van Engelen et al.

5.1.4

Reinforcement Learning

In addition to supervised learning and unsupervised learning, reinforcement learning (RL) is one of the types of machine learning paradigms. To conduct activities, it employs agents that pose as human domain experts. Instead of requiring labelled data, RL learns from experiences by interacting with the environment, watching what happens, and reacting to the outcomes. In reinforcement learning, the algorithm learns

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by interacting with an environment and receiving feedback in the form of rewards or penalties. The goal is to learn a policy that maximizes the cumulative reward. While deep neural networks are used by the majority of reinforcement learning algorithms, different techniques are better suited for various kinds of environments. According to the states and action types present in the environment, RL algorithms can be divided into three major categories: (1) A finite number of states and discrete actions, (2) an infinite number of states and discrete actions, and an infinite number of states and continuous actions [47]. Some of the most popular techniques in RL are Q-learning, State-Action-RewardState-Action (SARSA), Deep Q-network (DQN), REward Increment = Nonnegative Factor × Offset Reinforcement × Characteristic Eligibility (REINFORCE), Trust Region Policy Optimization (TPRO), Stein Variational Policy Gradient (SVPG), etc. [48]. Specifically, EMS is performed for electric vehicle connected to the grid infrastructure in this article (Table 3). Table 3 Applications of ML for EMS in SG 3.0 ML category

EMS applications

Supervised learning

Challenges

Recommendations

References

Monitoring and Lack of labeled control, load data, overfitting, forecasting, interpretability demand response, fault detection and diagnosis

Collect and annotate high-quality data, use regularisation techniques, develop explainable models

[42, 43]

Unsupervised learning

Renewable energy integration, demand response, load forecasting

Difficulty in evaluating model performance, need for domain expertise

Choose appropriate evaluation metrics, incorporate domain knowledge, use ensemble methods

[44]

Semi-supervised learning

Monitoring and control, demand response, efficiency optimization, fault detection and diagnosis

Limited labeled data, dependence on data representation

Incorporate unlabeled data, use diverse representations, optimize hyperparameters

[45, 46]

Reinforcement learning

Monitoring and control, demand response, load forecasting, renewable energy integration fault detection and diagnosis

Sample inefficiency, safety concerns

Develop safe and efficient exploration strategies, incorporate domain knowledge

[47, 48]

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5.2 DL for EMS in SG 3.0 A number of achievements have been made in the presentation of ML methods for various SG applications [49]. However, because of the numerous difficulties they provide, shallow neural networks and simple ML models are hardly used for EMS applications. These difficulties are largely caused by two facts: high-dimensional representations and unrealistic complexities. To address these issues, the learning paradigm switches to DL, which is the most impressive flagship of ML which can handle the voluminous data in a fast, efficient, and effective manner. The DL is subset of ML and so of the AI. It uses numerous layers to gradually extract higher-level features from the input’s raw data. In recent times, DL techniques of the ML are mostly explored when greater insights based on input and output data are required. DL integrates high intelligence in supervisory and operational decision making, resulting in a more intelligent and decentralized energy paradigm. DL is comprised of two paradigm which are ANN and representational learning. Some of the popular DL techniques are presented in subsequent sections.

5.2.1

Multilayer Perceptron

The most effective technique in the DL is the deep neural network (DNN). It acquires the depth of the networks and thus it is regarded as the extended version of ANN having more number of layers. In DNN, there are several layers which is used for data processing and learning. The multilayer perceptron (MLP) is a classical example of DNN where densely populated layers are found. The multiple connected layers provides strong insights between input and output. The application of MLP for nonlinear system is supported by a number of properties, including strong generalization of results, distributed representation and computation, mapping capabilities, and fast information processing. The MLP model has a number of benefits, particularly in higher-dimensional environments, but there are also drawbacks. These drawbacks include algorithm complexity, a lengthy training period for big MLPs, and a heavier computational load. Building and training this DNN model may be computationally expensive because to the several stacked layers. In [50], authors proposed a multilayer perceptron based DL model for load forecasting applications of EMS.

5.2.2

Recurrent Neural Network

The recurrent neural network (RNN) is a type of neural network that is commonly used for sequential data analysis. In RNN, the network is fed back to the input. Using a feedback loop and hidden layers, RNN’s recursive processing can reveal useful information about prior states.

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The RNN’s internal memory is one of its strengths. RNNs are vulnerable to the disappearing and growing gradient problem, though. Such shortage can be removed by Long Shor term Memory (LSTM) or Gated Recurrent Unit (GRU) when deployed for EMS applications. In EMS, RNN can be used to model the time-series data of energy consumption and production and make accurate predictions of the future energy demand and supply. RNN can also be used to capture the dependencies and correlations between the energy and weather data, and improve the accuracy of the load forecasting models. In this direction RNN based DL model is presented by Kamoona in [51] for electric vehicle connected SG network.

5.2.3

Convolutional Neural Network

The convolutional neural network (CNN) is a type of neural network which captures the spatial features from raw data by convolutional operators. The convolution operations on multiple-dimensional data which forms basis of the CNN layers creates slice-wise representation by extracting the semantic correlations of underlying spatial features. The K filters are distributed geographically onto several channels in CNN’s feature mapping. The pooling technique is used to reduce the feature map’s breadth and height. CNNs use filters to capture the semantic correlations through convolution operations in multiple-dimensional data, pooling layers for scaling, shared weights for memory reduction, and shared weights to analyze the hidden patterns. As a result, CNN architecture gains a significant potential for comprehending spatial aspects. However, CNN’s model suffers from its inability to capture distinctive traits despite its potential. The CNN is commonly used for image recognition tasks. In EMS, CNN can be used to analyze the spatial and temporal patterns of energy consumption and production, and predict the future energy demand and supply. CNN can also be used to detect anomalies and faults in the grid, and identify the root causes of the problems [52].

5.2.4

Deep Reinforcement Learning

DRL is a type of DL that combines reinforcement learning with neural networks. The fundamental principle behind DRL is to provide an agent rewards and penalties in order to influence its policies. The DRL process involves creating an autonomous agent that is capable of navigating the search space and offering the best course of action. The various versions of DRL are in the literature. Deep Q-learning (DRL), Double DQ-learning (DQL), and Duel DQ-Learning (DRL) are examples of valuebased models. Deep Deterministic Policy Gradient (DDPG) and Asynchronous Advantage Actor Critic (A3C) are examples of policy-gradient-based models [53].

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In EMS, DRL can be used to optimize the energy consumption and production by learning from the past experiences and feedback. DRL can also be used to control the energy storage systems, such as batteries and capacitors, and improve the efficiency and reliability of the grid. Multi-Agent DRL (MADRL) model for EV charging stations with Energy Storage Systems (ESSs) and photovoltaic (PV) systems was introduced in [54]. This was capable to deal with voluminous real-time dynamic data in order to cater demands of active and intelligent EMS. In addition to these highly appreciated DL techniques, there are some other techniques which are explored in the context of various applications of SG up to certain extent. For e.g., Generative Adversarial Networks (GAN) is a type of deep learning that consists of two neural networks, a generator and a discriminator, that are trained to compete with each other. In EMS, GAN can be used to generate synthetic data that can be used to augment the real-world data, and improve the accuracy and robustness of the models. GAN can also be used to generate realistic scenarios for testing and validation purposes. The autoencoder is a type of neural network which is based on auto-associative feedforward method. It can handle the unlabeled data since it learns from the raw data based on unsupervised manner. It is composed of three elements which are encoder, decoder, and coding layer. These three elements are used for dimensionality reduction and feature reductions in autoencoder. The DL techniques has shown great potential in addressing the challenges of EMS in the smart grid. It can handle large-scale and complex data, learn from the historical patterns and trends, and make accurate predictions and decisions in realtime. A comprehensive summary on cited DL techniques with its applications in EMS, challenges, and recommendations are presented in Table 4.

6 Future Research Directions and Challenges 6.1 Future Research Directions The future research directions were identified by performing a bibliometric analysis searching for the key terms: Machine Learning, Deep Learning, and Energy Management System in Web of Science collection. The resultant 2087 articles were analyzed using the VOSviewer software, and the results are sown in Fig. 6. From Fig. 6, it can be identified that the EMS research community is shifting its interest towards battery management, which is expected due to the popularity of EVs. So, battery management of EVs using ML will be a hot topic of research in the near future. The use of digital twins for demand response applications along with ML will be another potential future research topic. Deep reinforcement learning for microgrid grid control, and deep learning for forecasting applications, will continue to be hot research topics in the near future. Finally, an integration of blockchain with ML is becoming popular choice for SG applications.

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Table 4 Review on DL for EMS DL techniques

EMS applications Challenges

Recommendation

References

MLP

Demand response, load forecasting, efficiency optimization

requires tuning a number of hyperparameters such as the number of hidden neurons, layers, and iterations

To work upon non-conves loss function of hidden layers

[50]

RNN

Energy storage management, fault detection

Limited memory, training Incorporate external instability information, use attention mechanisms

CNN

Monitoring and control, load forecasting, fault detection

Lack of labeled data, interpretability

Collect and annotate [52] high-quality data, develop explainable models

DRL

Demand response, energy management, control of energy storage systems

Sample inefficiency, safety concerns

Develop safe and efficient exploration strategies, incorporate domain knowledge

Fig. 6 Bibliometric analysis for identifying the future research directions

[51]

[53, 54]

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6.2 Major Challenges in Communication Technologies To realize the vision of the EMS applications of the smart grid, two-way seamless connectivity is essential. A numerous wired and wireless communication technologies have been developed to achieve this objective. The communication standards and protocols under both wired and wireless communication technologies offer their own advantages as well as plagued with certain limitations. In this section, the several challenges causing hindrances in proliferation of wired and wireless communication technologies are covered in a comprehensive manner. The WLAN is low cost, seamless, and feasible solution for communication between devices. However, it plagues with reliability and availability because its components are prone to network failure. Further, some of the EMS applications require low latency in which WLAN technologies can not be used [55]. ZigBee is a low cost solution for end device connectivity. Due to its limited batter, capability, processing power, it can be used for various applications of EMS where continuous operations and high computing power is required. On the other hand, Bluetooth is requires high power for end device connectivity and data exchange. The security is a major concern in Bluetooth based connectivity in EMS. Moreover, the Bluetooth based connectivity in EMS applications, particular with use of smart meter, may lead to security threats and attacks [56]. This may result in adverse results causing huge economical losses. The WiMAX has comparatively higher installation cost due to its costly radio frequency devices. The WiMAX technology must be used with in frequency lesser than 10 GHz as frequency more than this cannot penetrate the wall. Thus WiMAX technology becomes costly wireless solution for EMS applications as leasing of frequency bands is required since frequency less than 10 GHz are already licensed. Despite providing well remote connectivity and covering larger geographical area, the satellite communication cannot be seen as the ultimate solution for wireless communication technologies. This is because it suffers with high delay. Some of the mission critical applications of EMS such as load forecasting and microgrid managements require very less communication delay which cannot be achieved by the means of satellite communication. Moreover, it requires high installation cost. The communication infrastructure for IP over satellite communication is still in nascent phase. Cellular communication operates in the licensed bands and its installation cost is higher. Further, it may affect the operation of EMS for various applications due to its susceptibility of call drop probability, and higher call setup time. The wired communication technologies perform better than most of the wireless communication technologies, however, it suffers with many challenges such as installation cost, flexibility, remote accessing, etc. the most effective solution for EMS applications is optical fiber as it provides high data rate, high reliability, high availability and also offers better security. The only drawback it suffers with is the high initial installation cost. Thus, in most cases, optical fiber can be used to create long haul backbone network of the SG 3.0 for EMS applications. Contrary to the

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optical fiber, PLC is also popularly used in many applications of the SG 3.0 as it requires almost no installation cost due to its utilization of preexisting power system infrastructure. However, PLC based communication system plagues with several challenges such as high electrical interference, signal fading effect, low data rate, lack of interoperability and standardization, etc.

6.3 Major Challenges in ML Techniques for EMS Despite the potential benefits of ML techniques for EMS in the SG 3.0, some challenges still need to be addressed. Some challenges include the lack of high-quality data, inadequate training data, underfitting, overfitting, lack of skilled resources, the need for interpretable models, security and privacy concerns, and the ethical and social implications of the technology. Specifically, supervised learning suffers from overfitting due to lack of labelled data generated from several EMS applications, including load forecasting, demandresponse, etc. The need for domain experts is essential for deploying unsupervised ML models in EMS applications. Further, semi-supervised ML models lack sufficient labelled data and corresponding dependency metrics. On the other hand, RL requires voluminous data and lots of processing. Costs for maintenance are considerable in RL. An overload from excessive reinforcement may taint the outcomes. Thus, RL is recommended for addressing complicated issues instead of basic issues. With the increasing complexity and variability of the SG, traditional EMS techniques are no longer sufficient. Deep learning, which is a subset of machine learning that uses neural networks with multiple layers, has shown great potential in addressing the challenges of EMS in the smart grid. Deep learning can learn highly complicated, non-linear relationships and correlations between the input and output data, unlike conventional, “shallow” techniques. It has been demonstrated that deep learning algorithms typically outperform conventional methods like SVR, shallow ANNs, and Random Forests in terms of prediction accuracy. This adaptability, nevertheless, has a price. In particular, deep learning architectures are computationally expensive to train and difficult to interpret, and they need many data to perform better than other approaches. Furthermore, it should be understood that improving an ANN’s depth at random may not necessarily produce optimal outcomes. Moreover, the neural network used as the DL’s brain is a black box, which may degrade the system’s performance. Overfitting and underfitting are also commonly observed challenges in DL. DL is also plagued with a lack of flexibility and data sets.

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7 Conclusion The energy management system in smart grid 3.0 is one of the dominant applications. The EMS envisages to conserve, optimize energy usage, and to enhance efficiency and efficacy of the SG 3.0. It aims to benefit both end consumer as well as various stake holders of the power system. In this chapter, a generic architecture of EMS is presented and various potential applications of the EMS are systematically reviewed. Being backbone of the SG system, the different communication technologies are comprehensively analyzed in this chapter. The various applications of EMS, particularly, real-time monitoring and control, load forecasting, demand response require handling, processing, and analyzing of voluminous data. Nevertheless, application such as fault detection requires prompt analysis and response for ensuring availability of the EMS services. In this direction, computing technologies such as ML and DL play vital role. The in-depth analysis on such computing technologies with challenges and recommendations are comprehensively included in this chapter. The research trends, and consequences are also analyzed through bibliometric graph. Based on the analysis, it is observed that a lot of effort is required in computing technologies to accurately model, and predict the system behaviors. We envisage that the research outcome presented herein provides synergy between communication and computing technologies to proliferate the EMS and contribute positively to bring the SG to the next level.

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Evolutionary Algorithms for Load Frequency Control of Renewable Microgrid Nilesh Kumar Rajalwal and Deep Shekhar Acharya

Abstract With the advancement in renewable energy technology, the concepts of smart grids and microgrids are becoming more popular. A smart grid utilizes bidirectional digital communication techniques to identify and respond to the network’s dynamic changes. A smart grid can also incorporate several microgrids in a large area. A microgrid combines distributed energy resources (DER) such as solar, wind, and diesel generators, energy storage devices, and loads. A merger of different microgrids at the distribution level gives a concept of a multi-microgrid. During islanded mode, the multi-microgrid experiences heavy fluctuations in voltage and frequency due to dependency on DER, which drives the multi-microgrid towards instability. An efficient load frequency control (LFC) strategy is required to enhance the multimicrogrid’s dynamic performance. LFC is used for regulating the output frequency of the microgrid within a specified limit after a disturbance. An effective LFC technique reduces frequency fluctuations and improves the microgrid’s dynamic performance within acceptable limits. Evolutionary algorithms are one of the efficient LFC methodologies during island and grid-connected modes. In this chapter, four popular evolutionary algorithms, such as Gravitational Search Algorithm (GSA), Particle Swarm Optimization (PSO), Teaching Learning-based Optimization (TLBO) and Grey Wolf Optimization (GWO), are used for optimizing the parameters of PID controller for LFC of microgrid during different operating scenarios. In the multimicrogrid test system, non-linear DERs such as photo-voltaic and wind energy generators and diesel and battery storage systems are incorporated. It is observed that LFC using evolutionary algorithms, effectively reduces the frequency fluctuations that are observed during dynamic conditions in the microgrid. The results of all four evolutionary algorithms are also compared for designing a suitable load frequency controller.

N. K. Rajalwal Department of Electrical and Electronics Engineering, Birla Institute of Technology, Mesra, Deoghar, India D. S. Acharya (B) Department of Electrical and Electronics Engineering, Birla Institute of Technology, Mesra, Ranchi, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 B. Appasani and N. Bizon (eds.), Smart Grid 3.0, Power Systems, https://doi.org/10.1007/978-3-031-38506-3_7

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Keywords Multi-microgrid · Load frequency control · Optimization · Grey wolf optimization · Teaching learning-based optimization · Particle swarm optimization · Gravitational search algorithm

Abbreviation LFC DER DG WTG SPV BESS GWO PSO GSA TLBO PID FOPID ISE

Load Frequency Control Distributed Energy Resources Diesel Generators Wind-Turbine energy source Solar Photo-Voltaic energy source Battery Energy Storage System Grey Wolf Optimization Particle Swarm Optimization Gravitational Search Algorithm Teaching Learning-Based Optimization Proportional-Integral-Derivative Fractional Order PID Integral Square Error

Symbols KEj TEj Rj T12 Kwtj Twtj Kpj Kij Kdj ΔPL Kpvj Tpvj Kbessj Tbessj Kdegj Tdegj λj μj tsj

Gain of the power system transfer function in j-th area Time constant of the power system transfer function in j-th area Speed Regulation constant in j-th area Tie-Line coefficient Gain of wind turbine transfer function in j-th area Time constant of wind turbine transfer function in j-th area Proportional Gain of PID/FOPID controller in j-th area Integral gain of PID/FOPID controller of j-th area Derivative gain of PID/FOPID controller of j-th area Load change Gain of solar PV transfer function in j-th area Time constant of solar PV transfer function in j-th area Gain of BESS transfer function in j-th area Time constant of BESS transfer function in j-th area Gain of DEG transfer function in j-th area Time constant of DEG transfer function in j-th area Non-integer order of integral action of FOPID controller in j-th area Non-integer order of derivative action of FOPID controller in j-th area Settling time of frequency deviation of j-th area

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1 Introduction The global electricity demand is predicted to rise by 9%, adding another 2500 terawatt-hours, which would take the overall demand to 29,281 terawatt-hours by 2025 [1]. Although the electricity demand is anticipated to expand at a comparable rate until 2023, the increase in fuel prices and pollution levels may increase the renewable power generation capacity at a higher rate. Globally, electric power utilities are investing in the addition of renewable power generation technologies for the benefits such as sustainability, clean energy production, and eco-friendly. Due to these benefits, power generation through renewable energy sources (RES) is expected to increase from 29 to 35% by the end of 2025 [1]. In the deregulated electrical industry, the RES is not only connected with the transmission utilities. Small-scale RES are directly integrated into the distribution utilities, initiating the concept of a microgrid in the modern power system. A microgrid is an interconnection of distributed energy resources (DER) such as diesel generators, wind turbine energy sources (WTG), solar-photovoltaic energy sources (SPV), and battery energy storage systems (BESS) supplying small loads through low/medium voltage feeders [2]. A microgrid works under two different operating modes, i.e. grid-connected and islanded. In the islanded mode, the microgrid is disconnected from the main grid and the local loads of the microgrid are supplied through the DER incorporated in the microgrid. In the grid-connected mode, the bidirectional power flow between the microgrid and main grid is possible based on the power generation in the microgrid through the point of common coupling (PCC) [3]. Due to multiple energy sources and bidirectional power flow, a microgrid consists of advanced communication, metering infrastructure, and modern control techniques. It makes the operation of microgrids more reliable, efficient, and flexible [4]. Incorporating the DER at different locations of distribution system is converting a conventional distribution system into a multi-microgrid. Figure 1 depicts the simplified form of a multi-microgrid. As the penetration of DER in the multi-microgrid is increasing, the upgradation in the control techniques is also required. The penetration of DEGs is small in a microgrid and the power generation through SPV and WTG is intermittent. This leads to low inertia in the microgrid. A low inertia microgrid may observe severe voltage and frequency fluctuations during dynamic conditions. The RES connected to the microgrid may not be utilized for controlling the frequency of the microgrid due to low inertia. The response time of DEG is low, and thus dynamic control of frequency in microgrids by controlling DEG alone is difficult [5]. Considering the above points, a reliable load frequency control (LFC) strategy is required to reduce frequency fluctuations and improve the dynamic performance of islanded microgrid within acceptable limits. However, the evolutionary algorithm-based LFC is a suitable solution for improved and efficient dynamic performance of a microgrid. An evolutionary algorithm is a search algorithm that is designed to determine the best solution to a complex and challenging optimization problem [6]. Evolutionary algorithms have been utilized for the last two decades to solve different engineering problems.

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Fig. 1 Schematic of multi-microgrid

It opens the door to applying the evolutionary algorithm for LFC in microgrids. Table 1 presents various algorithms that are used for LFC in renewable smart grid. In [7, 8], the LFC is utilized for single-area grid in [10–20] LFC is applied in multiarea smart grids and microgrids and in [21–24] LFC is used in specialized cases such as AGC of ST-thermal plant, two area non-reheat thermal plant and hydrothermal wind power plant. Various evolutionary algorithms are used for LFC of renewable smart grid. In [7, 10, 11] GA and improved version of PSO are applied for tuning the parameters of the controllers. Other evolutionary algorithms such as Mine blast algorithm [8], Bee optimization algorithm [9], Social-spider optimization algorithm [12], BBO algorithm [13], GWO [24] and improved version of GWO [24], symbiotic optimization search [19], Backtracking search algorithm [21], Jaya algorithm [22] and gravitational search algorithm [23] is also utilized for LFC of single and multiarea microgrid. PSO is also combined with artificial bee colony optimization in [25] and fuzzy logic in [26] to enhance the system’s LFC performance. The type of controllers tuned using evolutionary algorithms also affects LFC’s performance. In literature, mostly PI and PID controllers [8, 12–15, 18–24] are tuned using the algorithms to regulate the frequency of the renewable smart grid. However, special controllers such as Scaling factor fuzzy logic PID controller in [9], FOPID Controller in [10, 11], two-stage FPIDN-FOI controller in [16], two-stage adaptive Fuzzy PI controller in [17], radial basis neural network based sequential fuzzy system in [25] are also incorporated to improve the performance of LFC. In this chapter, the frequency control of a multi-microgrid in islanded mode is done by optimizing the parameters of PID and FO-PID controllers of BESS and DGs. Different optimization methods such as gravitational search algorithm (GSA),

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Table 1 Evolutionary algorithms used for LFC in renewable smart grid Ref

Grid used

Algorithm

Controller type

Effects

[7]

Single area

GA based PSO

Linear active disturbance rejection control

Improved performance compared to fuzzy and conventional PI controllers

[8]

Single area

Mine Blast Algorithm (MBA)

PID Controller

Notable improvements in settling times and peak overshoot values

[9]

Isolated hybrid wind-diesel power system

Bee optimization algorithm

Scaling factor fuzzy logic PID

Superior performance to a conventional PID and SF-FLPID

[10]

Multi-microgrid

GA

FOPID controller Better performance compared to GA based PID controller

[11]

Multi-microgrid

Dynamic PSO

FOPID controller Improved results compared to conventional PSO

[12]

Two area microgrid

Social-spider optimization algorithm

PID controller

Results are compared with GA

[13]

Islanded hybrid microgrid

BBO algorithm

PID controller

Both transient and steady state performance are compared with conventional approach

[14]

Hybrid power system model

Quasi-oppositional harmony search algorithm

PID controller

Improvement in settling time, undershoot and overshoot, and power deviation

[15]

Multi-microgrid

GWO algorithm

PID controller of DG and BESS

Compared with conventional algorithm

[16]

Automatic-generation control

Imperialist Competitive (ICA) Algorithm

Two-stage FPIDN-FOI controller

Improvement in settling time, overshoots and undershoots (continued)

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Table 1 (continued) Ref

Grid used

Algorithm

Controller type

[17]

Multi-area

PSO & GWO

Two-stage Superior adaptive Fuzzy PI performance in controller settling time, peak overshoot and peak undershoot

Effects

[18]

Multi-area

Hybrid bacterial foraging particle swarm optimization

PI Controller

Better settling times, overshoot and undershoot values, very fast and consistent convergence values

[19]

Multi-area

Symbiotic Optimization Search

PID Controller

Increased in dynamic stability, better than PSO

[20]

Multi-area wind energy Harmony search, control system TLBO and sine-cosine algorithm

PID and PIDA Controller

Performance comparison of the given algorithm

[21]

Two-area non-reheat thermal power plant

Backtracking Search Algorithm

PI and PID Controller

Better dynamic performance compared to conventional techniques

[22]

Automatic generation control of two area ST-thermal power system

Jaya Algorithm

Integral, PI and PID Controller

Better dynamic performances like settling time, overshoot, undershoot is achieved

[23]

Two-area multi source power system

Gravitational search algorithm

PID Controller

Lower settling time

[24]

Hydrothermal wind power plant

Improved Grey wolf optimization

PID Controller

Dynamically adjusts parameters to follow wind power disturbance, frequency deviation, and tie-line power deviation

[25]

Microgrid

Artificial bee colony and PSO

Radial basis neural network based sequential fuzzy system

Improvement in dynamic performance

[26]

Islanded microgrid

Fuzzy logic and PSO Fuzzy logic

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particle swarm optimization (PSO), teaching learning-based optimization (TLBO) and grey wolf optimizations (GWO) are used for tuning the parameters of PID and FO-PID Controllers, and results are compared in the end.

2 Mathematical Model of Multi-microgrid Figure 2 presents the block diagram of a two-area microgrid system that consists of SPV, BESS, WTG and DGs. The SPV and WTG are used to supply the load in the microgrid. DGs are provided in case of shortage of generation due to RES, and BESS is utilized for enhancement of the dynamic performance of the multi-microgrid. The output of PID controllers in the microgrid is connected to DGs and BESS, to mitigate the frequency deviation.

2.1 Diesel Generators The transfer function of first-order DG is shown in Fig. 3 [15]. The DG supplies the power difference between generated power through RES and load demand with the attached speed governor controller. The tuning of PID parameters is done using different optimization techniques.

Fig. 2 Block diagram of multi-microgrid system

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Fig. 3 Transfer function of diesel engine Fig. 4 Transfer function of BESS

ΔPBES Δf

2.2 Battery Energy Storage System (BESS) The second part of the microgrid is a BESS that regulates the microgrid frequency during the dynamic state. The microgrids may have frequency fluctuations due to sudden load changes. The PID controllers tuned by various optimization methods are used to maintain the frequency fluctuations by maintaining BESS output. The transfer function of first-order BESS is shown in Fig. 4 [15].

2.3 Wind Turbine Generator (WTG) Model The mechanical output of the wind turbine is given by Eq. (1). [27–29]. PO_W T =

1 3 ρ AC p (λ, β)Vwind 2

(1)

where, ρ is the air density factor, β is pitch angle of the blade, λ is the tip speed ratio, C p . is the power coefficient, and Vwind is the wind speed. The output of the wind turbine is provided to the generator for electricity generation. The first-order transfer function of WTG model is presented by block diagram in Fig. 5. Fig. 5 Transfer function of WTG

ΔPWTG ΔPWT

Evolutionary Algorithms for Load Frequency Control of Renewable … Fig. 6 Transfer function of SPV

161 ΔPSPV

ѱ

Δ

2.4 Wind Turbine Generator (WTG) Model The first-order transfer function of the SPV model is presented by the block diagram in Fig. 6 [5].

3 Evolutionary Algorithms for Load Frequency Control This section discusses a brief idea about the four optimization algorithms GSA, PSO, TLBO and GWO.

3.1 Grey Wolf Optimization This algorithm was created by Seyedali Mirjalili and colleagues after studying the behaviour of grey wolves [27]. Grey wolves are the finest predators at finding prey and have a rigid dominance hierarchy, The alpha (α) wolf is the dominating wolf in the pack and takes all the decisions regarding hunting, feeding, sleeping, and mitigation of the pack. The beta (β) wolves are at the next level after the alpha wolves. They aid the alpha wolf in making a judgement and take charge of the pack when the alpha is unwell. Delta (δ) wolves are third in the hierarchy and are scouts, hunters, and caregivers. Omega (ω) is the last level of wolves in the pack and is treated as a scapegoat. Alpha (α) is regarded as the best solution in the GWO approach, and beta (β) is regarded as the next best solution. Delta (δ) is considered the third-best solution, and all the remaining solutions are counted as omega (ω) [28].

3.1.1

Mathematical Model and Steps of GWO Algorithm

The GWO technique is based on the hunting technique of grey wolves for the prey. The mathematical model is divided into three parts i.e. Encircling, Hunting, Attacking, and Search For Prey. 1. Encircling Before hunting, the grey wolves incircle the prey by randomly relocating themselves in the search space. In the encircle and hunting phase, it is assumed that the prey stopped moving. In GWO optimization, the encircling process is modeled by

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updating the wolves’ location based on the previous location and prey location. It can be defined as P(t + 1) = P(t) − A.D.

(2)

where P(t) and P(t + 1) are the current and new locations of the wolf, and A is the coefficient matrix. The value of A is obtained by. A = 2.a.r1 − a

(3)

Here vector a has its value in linear decreasing order from 2 to 0 during the simulation. The value of vector a is updated as follows a =2−i

( ) 2 I

(4)

where, I is the maximum iteration and i is the current iteration. D depends on the prey’s position Pp and defined as | | D = |C.Pp (t) − P(t)|

(5)

where, C = 2.r2 r1 in Eq. (3) and r2 in Eq. (5) are randomly generated vectors between 0 and 1. 2. Hunting After encircling, the hunting phase started. In this phase, the hierarchy of the pack plays an important role. The best three solutions during the simulation are the alpha, beta, and delta. The alpha is considered the closest solution from the prey, followed by beta and delta. Based on the location of alpha, beta, and delta, other wolves update their position with the following mathematical model. P(t + 1) =

P1 + P2 + P3 3

The value of P1 , P2 and P3 is obtained as follows: P1 = Pα (t) − A1 .Dα P2 = Pβ (t) − A1 .Dβ P3 = Pδ (t) − A1 .Dδ

(6)

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| | where, Dα = |C1 .Pα − X |, Dβ = |C2 .Pβ − X | and Dδ = |C3 .Pδ − X | The GWO method begins the optimization process with random solutions as a swarm intelligence algorithm. Each solution has a vector that preserves the values for the problem’s parameters. Calculating the objective value of each solution comes first in each cycle. As a result, each solution has a variable to hold its objective value. There are three additional vectors and three other variables in addition to the ones already mentioned to save essential information in memory when dealing with GWO. These vectors and variables store the locations and objective values of the alpha, beta, and delta wolves in the memory. Before the process of changing positions, these variables should be adjusted. 3. Attacking As mentioned, the grey wolves encircle and hunt their prey once it stops moving. In the next step, the grey wolves attack their prey and bring the hunt to an end. By lowering the value of a, which further limits the fluctuating range of A, in a random interval [−2 2], the mathematical model of approaching the prey is modelled. The value of A decides the phase of the algorithm. If the value of A > 1 or A < −1, the prey is attacked by the pack and the phase is termed as exploitation phase. If the value of A is between −1 < A < 1, the pack will move in search of prey, and the phase is termed as exploitation phase. In the exploration phase, the primary goal is to find new portions in the search space by applying abrupt changes in the solutions. By doing so, new promising portions in the search space may be obtained, and the solutions will not stagnate at a local optimum. On the other hand, in the exploitation phase, the main aim is to find the nearest of each solution obtained during the exploration phase. Based on the locations of α, β and δ wolves, as well as the values of A, D and a parameters, the wolf updates their position to assault the prey, and the algorithm move towards the local optimum. 4. Search for Prey Apart from the above-listed parameters, C is another parameter that encourages the search for prey in the algorithm. The value of C varies randomly in the range [0, 2] and determines the next position of the wolves. The wolves are more drawn towards the prey when parameter C has a value greater than 1. Since the method generates ‘s value at random, the search for the prey or exploration phase is prioritized from the first to the last iteration, avoiding local optimum. The flow chart of the GWO is displayed in Fig. 7 [28].

3.2 Particle Swarm Optimization PSO is based on the intelligent movement of birds, fishes, or swarms. If a group of birds or fishes searches randomly for food, all the birds/fishes in the group can share

164 Fig. 7 Flowchart of GWO [27]

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their detection and help the entire group to get the best food. In this way, the social interaction concept of swarms is used to solve the optimization problem [29]. In PSO, several particles initiate the swarm movement in the search space to find the optimum location. Each particle search for the best position in the search space and is associated with its personal best position i.e. Pbest which is achieved so far by the particle. The PSO also tracks for global best or G best which is the best position obtained so far by any nearby particle [30].

3.2.1

Mathematical Model and Steps of PSO Algorithm

The mathematical model of PSO is obtained below: 1. First, create a population of particles and assign the particles with position, velocity, and fitness value. 2. Based on the objective function, determine the particle’s new position. Update the particle’s position if the new position beats the old best position. 3. Determine the best particle based on the particle’s last best places. 4. Update the velocity of the particles based on the following equation. ( ) ( ) vit+1 = ω.vit + c1 U1t Pbt1 − Pit + c2 U2t gbt − Pit

(7)

where, vit+1 is the particle’s velocity in the next iteration and ω is inertia. The random scaling factor U and weight c are defined for both social and cognitive components. The difference between an individual best of a particle and current position is termed a a cognitive component. However, the difference between the global best and current positions is termed a social component. The cognitive ( ) component is presented as c1 U1t Pbt1 − Pit and the social component is presented as ( ) c2 U2t gbt − Pit in Eq. (7). 5. After the velocity update, change the particle’s position to a new one based on the equation. Pit+1 = Pit + vit+1

(8)

where, Pit and Pit+1 are the particle’s position in the current iteration and next iteration respectively and vit+1 is the particle’s velocity in the next iteration. 6. Again, move to step 2 till the convergence criteria is satisfied. the flowchart of the PSO algorithm is shown in Fig. 8.

3.3 Teaching Learning Based Optimization TLBO is another optimization algorithm that uses a population of solutions to reach the global solution. TLBO treats the population as a group or class of learners.

166 Fig. 8 Flowchart of PSO [29]

N. K. Rajalwal and D. S. Acharya

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167

Apart from the population, various optimisation algorithms also consider many design parameters. In TLBO, these design parameters will be comparable to different subjects provided to the students, and the student’s performance will be comparable to their “fitness” [31]. The teacher is thought to be the best solution so far. TLBO algorithm is a two-phase process. The “Teacher Phase” is the first phase, and the “Learner Phase” is the second phase. The “Teacher Phase” refers to learning from the teacher, and the “Learner Phase” refers to learning among the students through interactions [32].

3.3.1

Mathematical Model of Teacher Phase

In Fig. 9, the marks obtained by learners of a class are modeled in curve-A, which has the mean value of M A . The best learner is replicated as a teacher because the teacher is regarded as the most informed person in society, as indicated by T A in Fig. 9. The teacher seeks to spread knowledge among the students, which will raise the level of knowledge in the entire class and assist students in receiving high grades. As a result, a teacher raises the class mean to the best of their ability. To raise the learners’ level to a new mean M B , the teacher T A will attempt to move the mean M A toward their level following their capabilities. Although the teacher T A will make every attempt to teach the class, the caliber of the students and the quality of the teaching will determine how much the students learn. The teacher assistant works hard to raise the caliber of the students for a mean M A to M B . Practically, a teacher can only move the mean of the class up to some extent depending upon the quality of students in the class. The teacher will update the mean of the class in a random process that depends on many factors. If Mi is the Fig. 9 Distribution of marks

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existing mean of a class and Ti is the teacher at an interaction i. Now, Ti will increase the knowledge of the class, and the updated mean of the class will be Mnew . The following equations obtains the mean difference. Mean_Di f f er encei = ri (Mnew − TF Mi )

(9)

where is a random number in the range of [0,1] and is the TF teaching factor which value is selected either 1 or 2 randomly with the same probability. The existing solution is modified based on the mean difference obtained in equation. X new,i = X old,i + Mean_Di f f er encei

3.3.2

(10)

Mathematical Model of Learner Phase

Learners’ knowledge increases through input from the teacher and interactions among themselves. The interaction between learners is a random process, and the learner’s knowledge is modified as follows Fori = 1 : Pn X i and X j , two learners are selected randomly ( ) I f f (X i ) < f X j The existing solution is modified such that ( ) X new,i = X old,i + ri X i − X j Else ( ) X new,i = X old,i + ri X j − X i End I f End For If X new gives better result, accept the value. 3.3.3

Steps of TLBO Algorithm

1. Initiate the optimization parameters along with optimization problem. 2. Initiate the population size (number of learners) and design variables (courses offered). The population matrix will be represented with rows equal to population size and colums equal to design variables. 3. In the teacher phase, calculate the mean of all the columns of the population matrix. This will give the mean vector M, D . The best solution is treated as a teacher X teacher and he will make efforts to shift the mean of the learners from the mean vector to the teacher.

Evolutionary Algorithms for Load Frequency Control of Renewable …

M_new, D = X teacher.D

169

(11)

4. The difference in the two means is determined using Eq. (1) and the solution is updated as per Eq. (2). If the X new gives the better value of the objective function, we accept the X new for the learner’s phase. 5. Learners will increase their knowledge by interacting among themselves. The mathematical model of learner phase is already shown in Sect. 3.3.2. Run the simulation till the convergence criteria are satisfied. Figure 10 shows the flow chart of the TLBO algorithm.

3.4 Gravitational Search Algorithm In GSA, a system of isolated mass is considered in which the masses follow the Law of gravity and Law of motion. As per GSA, the solutions are taken as objects, and their fitness is measured based on their masses. All the objects attract each other by gravitational force, and the objects attracted towards the heavier masses cause global movement. Heavy-mass objects move slowly, while light-mass objects move fast [33]. The object has four design variables, i.e. position, inertial mass, active and passive gravitational mass. The fitness function provides the inertial, active and passive gravitational mass and the position of the mass provides the solution of the problem. As the algorithm progress, light-mass objects are attracted by heavier mass, and these heavier mass are considered a good solution for the objective problem [34].

3.4.1

Mathematical Model of GSA

1. First the gravitation constant G is computed at iteration t is calculated as G(t) = G 0 e

−αt/ T

(12)

where α and G 0 are initiated at the start of the search, and T is the total number of iterations. 2. The objects are following the law of gravity, which can be expressed as F=G

M1 M2 R2

(13)

where F is the force between two objects, G is the gravitational constant, M1 and M2 are the masses of two objects, and R is the distance between M1 and M2 .

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Fig. 10 Flowchart of TLBO [32]

3. As per the Law of Motion, a=

F M

(14)

where a is the acceleration of object, F is the aplied force and M is the object’s mass.

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171

4. Three types of masses that are already discussed are the active gravitation mass Ma , passive gravitation mass M p and ineritial mass Mi . Based on these masses, the gravitational force Fi j due to mass j on mass i can be expressed as Fi j = G

Ma j X M pi R2

(15)

where Ma j is the active gravitation mass and M pi is the passive gravitation mass of the objects j and i, respectively. 5. Object i Acceleration is Computed by ai =

Fi j Mii

(16)

where Mii represents the inertia mass of object i 6. The Velocities and Positions of the Masses Are Updated by Eq. (17) and (18)

3.4.2

Vi (t + 1) = randi X Vi (t) + ai (t)

(17)

X i (t + 1) = randi X X i (t) + ai (t)

(18)

Steps of GSA

• In the first step, initiate the value of α, T and G 0 . • N number of objects are randomly considered, and the position of each object is defined as. ( ) X i (t) = xi1 (t), xi2 (t) . . . xin (t) , i = 1, 2, . . . N • • • •

(19)

In step 3, all sub-steps are repeated. All objects are evaluated to find the best and worst objects. The value of G is updated as per Eq. (12). The force for iteration i is calculated as. Fidj (t) = G(t)

) M pi (t)X M pj (t) ( d x j (t) − xid (t) Ri j (t) + ε

(20)

• The total force on an object i at iteration t is calculated as follows. Fid (t) =

∑

rand j Fidj (t)

j∈K best, j/=i

where K best is the set of K objects with biggest mass.

(21)

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• The inertial mass is calculated as follows m i (t) Mi (t) = ∑ N j=1 m j (t)

(22)

• The acceleration of object i is calculated as ai (t) =

Fi (t) Mii (t)

(23)

• From Eqs. (6) and (7), the velocity and position of the object i is computed. • The algorithm is run until the convergence criteria is satisfied. • Fig. 11 shows the flowchart of the GSA.

4 Results and Discussions 4.1 Tuning of the Controllers and Time Response Analysis This section presents the tuning of the parameters of the PID and fractional-order PID (FOPID) controllers using the four evolutionary algorithms discussed previously: PSO, TLBO, GSA and GWO in Sect. 3. The tuning task has been formulated into an optimization problem, for which the fitness function has been defined as: t

F(K ) = ∫[(Δ f 1 )2 + (Δ f 2 )2 ]dt

(24)

0

where, Δf1 and Δf2 represent the frequency deviations of the area-1 and area-2, respectively and K is the set of controller parameters. The fitness function signifies the integral square error (ISE) of the two-area LFC with respect to the deviations in the frequencies of each area when subjected to load perturbations. The simulation model of the two-area LFC was developed as per the block diagram shown in Fig. 2. The values of the various parameters in the model considered for this work are illustrated in Table 2. The analysis of the LFC system has been done based on three cases, namely, Case-1: Step Load change of 0.1 pu in both areas. Case-2: Step Load change of 0.1 pu in one area and time-varying irregular pulse load perturbation in the other. Case-3: Time-varying irregular pulse load perturbation in both areas. The time-varying pulse load perturbations used in cases 2 and 3 are different and have been illustrated in Figs. 12 and 13, respectively. The performance of the controllers has been tested in all three cases.

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Fig. 11 Flowchart of GSA [33]

173

174 Table 2 Parameters of the LFC model developed in MATLAB simulink

N. K. Rajalwal and D. S. Acharya

Parameter

Area

Value

Gain and time constant of power system

1

Kp1 = 60 and Tp1 = 18 s

Gain and time constant of power system

2

Kp2 = 58 and Tp2 = 16 s

Droop characteristic slope parameter

1, 2

R = 5 Hz/MW

Wind turbine gain and time constant

1

Kwt1 = 1 and Twt1 = 1.5 s

Solar PV gain and time constant

2

Kpv2 = 0.0075 and Tpv2 = 0.03 s

BESS gain and time constant 1

Kbess1 = 1 and Tbess1 = 0.1 s

BESS gain and time constant 2

Kbess2 = 1.05 and Tbess2 = 0.15 s

DEG gain and time constant

1

Kdeg1 = 1 and Tdeg1 =3s

DEG gain and time constant

2

Kdeg2 = 1.2 and Tdeg2 = 3.1 s

Tie-line coefficient

T12 = 0.0322 MW/ Hz

Fig. 12 Time varying load change used in case 2

The LFC model was developed and simulated in MATLAB Simulink 2021a. All four evolutionary algorithms have been implemented in MATLAB 2021a. The parameters of the EAs are listed in Table 3. The upper and lower bounds of the parameters of the PID and FOPID considered for this work are mentioned in Table 4. All the algorithms have been executive of 30 independent runs. The maximum number of iterations in each run was set at 500 for all four algorithms. The optimum values of the controller gains, obtained from the four algorithms after tuning the PID controller by minimizing the objective function (Eq. 24), are listed in Table 5. Similarly, the optimum values of the parameters of FOPID controllers are

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Fig. 13 Time varying load change case 3

Table 3 Values of parameters of the evolutionary algorithms

Table 4 Upper and lower bounds of the controller parameters

Algorithm Parameters PSO

ω = 0.53, c1 = 1.49, c2 = 1.49, population size = 20

TLBO

TF = 1 or 2, population size = 20

GSA

G0 = 100, α = 20, population size = 20

GWO

a0 = 2, population size = 20

Controller PID controller

FOPID controller

Parameters

Lower bound

Upper bound

Kp

0

20

Ki

0

20

Kd

0

20

Kp

0

20

Ki

0

20

Kd

0

20

λ

0

1

μ

0

1

shown in Table 6. The tuning of the controllers has been done only for case-1. These tuned controller parameter values have also been used for the other two cases. Case-1: In this case, the LFC is subjected to a step load change of 0.1 pu in both area-1 and area-2. The ISE values and the settling times of the frequency deviation curves of area-1 (ts1 ) and area-2 (ts2 ) are listed in Table 7. The table shows that FOPID controller exhibits better performance than the PID controller, in the case of all the algorithms. When the algorithms are compared, it is found that the Grey Wolf Optimization (GWO) approach outperforms the other algorithms, in tuning both PID and FOPID controllers. Figure 14a and b illustrate the frequency deviations of both areas, with PID controller. Similarly, the frequency deviations of both areas, with PID controller, are illustrated in Fig. 15a and b. The model was simulated for 10 s.

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Table 5 Results of tuning the PID controller Algorithm

Kp1

Ki1

Kd1

Kp2

Ki2

Kd2

GWO

14.51

3.502

0.005

10.106

3.11

0.005

GSA

13.82

2.926

0.008

10.162

3.261

0.0048

PSO

12.322

2.628

0.001

11.013

2.917

0.003

TLBO

11.876

1.992

0.0035

10.892

2.933

0.0041

Table 6 Results of tuning the FOPID controller Algorithm

Kp1

Ki1

Kd1

λ1

μ1

Kp2

Ki2

λ2

Kd2

μ2

GWO

14.772

3.721

0.006

0.98

0.95

10.323

3.236

0.0049

0.89

0.91

GSA

13.618

2.617

0.007

0.99

0.99

10.207

3.184

0.0049

0.91

0.92

PSO

11.968

1.995

0.002

0.97

0.92

10.822

2.115

0.0026

0.98

0.93

TLBO

11.123

1.757

0.004

0.97

0.98

10.367

2.008

0.0019

0.99

0.91

Table 7 Comparison between performance of PID and FOPID controllers tuned by the EAs Controller

Algorithm

ISE

ts1 (sec.)

ts2 (sec.)

FOPID

GWO

9.057e-5

2.03

1.91

PID

GSA

9.127e-5

2.39

1.93

PSO

9.151e-5

2.37

2.083

TLBO

9.137e-5

2.41

2.16

GWO

9.078e-5

2.163

1.95

GSA

9.189e-5

2.445

1.944

PSO

9.751e-5

2.572

2.207

TLBO

9.45e-5

2.986

2.212

It can be inferred from Figs. 14 and 15 that the GWO performs better than the GSA, PSO and TLBO. It can be observed that the PID (Fig. 14) and FOPID (Fig. 15) controllers perform better when tuned with GWO. It can be observed that the oscillations are less and decay off quickly in the case of GWO. Also, from Table 7, it is observed that PSO performs better than TLBO in terms of the settling time of the frequency deviations of both areas. The FOPID controller tuned with GWO exhibits a settling time of 2.03 s (area-1) and 1.91 s (area-2), which are the least compared to all the others. Case-2: In this scenario, area-1 is subjected to a time varying load change as shown in Fig. 12 and area-2 is subjected to a step load change of 0.1 pu. The simulation was run for 15 s. The frequency deviations of both areas with the PID controller are illustrated in Fig. 16a and b, and those with the FOPID controller are depicted in Fig. 17a and b. It is observed that the frequency deviation, with GWO, exhibits lesser

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Fig. 14 a Frequency deviation of area-1 with PID controller for case-1. b. Frequency deviation of area-2 due with PID controller for case-1

oscillations and a faster decay to zero. It is also observed that, with GWO, the peak of the first overshoot (undershoot), is lesser than the other algorithms. Case-3: In this case, the two-area LFC is subjected to time-varying load change (Fig. 13) in both the areas. The simulation was run for 50 s. The frequency deviations of both areas in the presence of the PID controller are illustrated in Fig. 18a and b, and those in the presence of the FOPID controller are shown in Fig. 19a and b. It is observed that the frequency deviation, with GWO, exhibits reduced oscillations and settles to zero faster than with other algorithms.

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Fig. 15 a Frequency deviation of area-1 with FOPID controller for case-1. b Frequency deviation of area-2 with FOPID controller for case-1

4.2 Effects of Parameter Variation The performance of the controllers has also been tested under parameter variation due to specific causes or external disturbances. For this purpose, the plant time constants (Tp1 and Tp2 ) of both areas and speed regulation R have been varied by + 20%, and the effects on frequency deviation have been observed. The results of the experiment are summarized in Table 8. It is observed that the FOPID controller exhibits better performance than the PID controller, as the percentage change (signifying the effect of parameter variation) in the settling time of frequency deviation is lower when the FOPID controller is used in the LFC.

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Fig. 16 a. Frequency deviation of area-1 with PID controller for case-2. b Frequency deviation of area-2 with PID controller for case-2

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Fig. 17 a Frequency deviation of area-1 with FOPID controller for case-2. b Frequency deviation of area-2 with FOPID controller for case-2

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Fig. 18 a Frequency deviation of area-1 with PID controller for case-3. b Frequency deviation of area-2 with PID controller for case-3

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Fig. 19 a Frequency deviation of area-1 with FOPID controller for case-3. b Frequency deviation of area-2 with FOPID controller for case-3 Table 8 Effect of parameter variation on performance of the controllers Parameter Change (%) PID

FOPID

% change in ts1 % change in ts2 % change in ts1 % change in ts2 Tp1, Tp2

20

2.13

2.11

2.06

2.047

R

20

2.056

2.012

2.033

2.01

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5 Conclusion The application of nature-inspired evolutionary algorithms (EA) for tuning PID and FOPID controllers for a two-area LFC is demonstrated in this chapter. The parameters of the controllers have been tuned using four optimization algorithms, namely, TLBO, PSO, GSA and GWO. The tuning task has been formulated into an optimization problem by using ISE as the objective function. The performance of the algorithms and the controllers, have been tested and compared subjected to three different scenarios. It was found that the FOPID controller outperforms the PID controller by exhibiting faster response and better control over the frequency deviation of the two areas of the LFC. Also, it has been established that the GWO algorithm can find better solution to the optimization problem, thereby providing better values for the controller parameters, which aids in extracting the optimum performance from both the controllers. The controllers also demonstrated stable responses when subjected to 20% variation in system parameters such as plant time-constants and speed regulation due to certain reasons or external disturbances. However, the effect of real-time variations in solar PV and wind generations on the frequency response of the LFC, with various controllers or control strategies, needs to be explored and forms the future scope of this work.

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Agents-Based Energy Scheduling of EVs and Smart Homes in Smart Grid Muhammad Waseem Khan, Guojie Li, Keyou Wang, Muhammad Numan, Linyun Xiong, Sunhua Huang, and Muhammad Azam Khan

Abstract With the advancement of the smart grid (SG), it has become suitable for energy consumers to handle and control their consumption. The ingenious practice of the incorporation of renewable energy sources in the SG environment, along with electric vehicles (EVs) and energy storage systems (ESSs) in smart homes (SHs), is a popular approach to minimizing electricity outlays and improving grid stability. Therefore, this chapter presents optimal energy scheduling techniques for EVs and SHs in SG-connected operations to control energy flow in SH that comprises solar photovoltaic generation (SPV), fuel cell (FC), wind turbine (WT), ESS, EVs charging points, SH lightening loads, and protection and control systems. Several energy management and optimization techniques have been presented to minimize overall costs allied with domestic energy consumption and EV charging during different market prices and ESS degradation costs. The finest energy scheduling for both the SH and EVs charge has been studied, and a suitable power distribution structure and cost uncertainty in SG is demonstrated. The EV scheduling is employed via linear programming (LP) based and shaped on the agents-based technique. Autonomous smart agents operate optimally, accomplish their tasks independently, and improve M. W. Khan · G. Li (B) · K. Wang Department of Electrical Engineering, Shanghai Jiao Tong University, Minhang, Shanghai 200240, PR China e-mail: [email protected] M. W. Khan e-mail: [email protected] M. Numan U.S.-Pakistan Centers for Advanced Studies in Energy, National University of Sciences and Technology, Islamabad 44000, Pakistan L. Xiong School of Electrical Engineering, Chongqing University, Chongqing 400044, PR China S. Huang School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, PR China M. A. Khan HITEC University, Cantt, Taxila, Rawalpindi 47080, Punjab, Pakistan © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 B. Appasani and N. Bizon (eds.), Smart Grid 3.0, Power Systems, https://doi.org/10.1007/978-3-031-38506-3_8

185

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the SG’s operational competency. Also, the energy scheduling technique expanded that permits power charging at maximum level to entirely charge the EVs batteries and encourage the vehicle owners to charge the EV batteries at conceivably lower charges and pollutant emissions, while the SG lower and surplus power generation from the available energy sources, robust charging and discharging of EVs and power trading are studied. Keywords Energy scheduling · Electric vehicle · Renewable energy sources · Smart grid · Vehicle-to-home · Vehicle-to-grid · Smart home

1 Introduction Due to the surge in energy consumption in both commercial and non-commercial sectors, the power network faces the problem of smooth energy flow to the consumers, especially the domestic/residential sector, due to the urbanization and population growth along with the practice of numerous high-power motors and appliances by several homes unanimously, which eventually leads to higher energy demands and so-called peaks load [1, 2]. The implementation of electric vehicles (EVs) as a novel technology within the power grid has decreased emissions of harmful gases within the transportation sector. Additionally, this implementation has facilitated the ability to share power efficiently with the utility grid (UG). However, due to the dependency of EVs on the power network, energy demands and consumption have increased [3]. In [4], it has been reported that 13 million EVs were operated in the year 2021, and expected that this number will exceed 73 million by 2025. It is noticeable that the rise in the number of EVs will upsurge the peak load demands, specifically in the domestic sector, because EVs are measured amongst energy loads of higher consumption. The peak energy demands disturb the grid suppleness and produce an imbalance between power generation and demands [5]. Hence, the integration of smart homes (SHs) within the smart grid (SG) applications has the potential to align power flow and effectively manage peak loads, providing assistance to both power companies and energy consumers alike.

1.1 Introducing SGs and SHs SG is an electrical network that empowers a two-way data and electricity flow with high-end digital communication technologies that allow it to sense, respond and pro-act to deviations during operations and other issues to enhance reliability, efficacy, power delivery safety, and usage [6]. The SGs have automation, information technology and communication systems that can monitor the flow of power from the generation point to the power consumption (even lower most consumers’ end

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level i.e., appliances level) and control the power stream or limit the electrical load to balance generation in near real-time. A by-product of the SG comprises the connection of smart meters, which can remotely read the energy consumptions, sense outages, connect and disconnect service, recognize electricity theft, and distribute prepaid electrical energy to consumers who need this service. Moreover, SGs can be attained by employing well-organized transmission and distribution schemes, network operations, integrations of renewable energy resources (RERs), and consumer integration [7]. Therefore, by using DR [8], the consumers can shift their consumption from a peak/high energy pricing period to an off-peak/low pricing period which depends on the amounts set by the utility companies such as realtime pricing (RTP) and TOU, etc. Thus, the SG solutions support efficiently monitoring, accurately measuring, and actively controlling power streams in real-time that can subsidize losses identification, and thereby suitable supervisory and technical arrangements can be taken to handle the losses. SH is an energy consumer residence that follows internet-based unified devices to enable the remote supervision and monitoring of connected loads/equipment within the SH systems. SH technology, also known as home automation, offers owners convenience, comfort, security, and energy efficiency by permitting them to control connected smart devices by SH application or another networked device. As part of the internet-of-things (IoT), SH devices often run together, sharing customer usage information and systematizing arrangements based on the owners’ preferences. Figure 1 presents the architecture of an SG that contains various renewable power generation sources such as SPV, fuel cell (FC), wind turbine (WT), ESS, EVs charging points, SH lighting loads, and protection and control systems. The SH is connected with the SG where the advanced metering infrastructure offers two-way communication networks to support data exchange between energy consumers and utilities. Utilities can access the meter data from SHs and consumers can collect the current and future predicted energy pricing signals. To fulfill different SH energy demands, the applications of power electronics have been used to convert/invert one form of energy to another (such as AC/DC, AC/AC, DC/AC, and DC/DC) to enhance the grid reliability and minimize the energy costs [9]. Moreover, considering both AC and DC buses is to exchange electrical power between the busbars during peak and off-peak periods to make the SG self-sufficient to overcome the energy gaps internally to evade purchasing power from the neighbor grid with additional charges. The key features that SG offers are: 1. DR: Anticipating energy consumption in real-time to adjust power generation accordingly and consequently evade excess energy usage and or creation of fossil fuels (FFs) based generation capacities. 2. Renewables-based Smart Energy Generation • Renewable power generation plants i.e., SPV, WT, FC, etc. • Decentralized in nature (generate power by the end-users themselves). • Mid-term positive impacts on decreasing CO2 emissions.

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Fig. 1 Renewables-based SG architecture and connected loads

3. EV and ESS • • • • •

Power storage and transport during peak and off-peak intervals. Positive impact on the environment as it decreases CO2 emissions. Cost-effective and availability. Intelligent electrical infrastructure. Main challenges for adoption are costs, batteries/battery banks, safety, and accessibility.

4. Flexible Energy Distribution • Distribution needs to become more automated, protected, and efficient. • More flexible to accomplish the challenge of integrating RERs while adjusting power capacity and demand. 5. Active Energy Efficiency • • • •

Making energy useful and visible. Providing resources to improve energy consumption. Submission of new technologies and machinery that are newly available. Completing up to 30% energy savings with fast payback sources.

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In the context of SHs, all devices, including EVs and Energy ESS are interconnected through a Home Area Network (HAN) utilizing Wi-Fi or ZigBee technology [17]. Due to the inadequate progression capability of the smart meters in SH and other deliberations counting privacy and security, a supplementary apparatus called a home energy controller (HEC) is usually essential to display the energy consumption and schedule the SH utilizations and ESS processes when required. The scheduling is normally based on the energy consumption and the associated cost signal, the SH utilizations’ characteristics, the loads’ significance, and the ESS [10]. It is better to have the HEC create the scheduling toward price saving repeatedly, while the consumer should be able to override certain decisions and limits, possibly remotely.

1.2 Literature Review Optimizing the scheduling of energy generation, storage, and consumption in smart homes (SHs) has been recognized as a challenging optimization problem. Therefore, multiple research studies have been conducted to explore optimization approaches that can achieve energy-efficient scheduling in SHs. In [11], the genetic algorithm (GA) was used to reduce overall energy prices. In the home energy management system (EMS), the context of the mixed-integer LP prototype was offered to limit the highest power with the prospect of the bi-directional practice of the EVs and energy storage systems (ESSs). Small-scale SPV power production, two-way ESS, vehicleto-home, and vehicle-to-grid (V2G) abilities, and diverse demand response (DR) approaches were combined into the planned EMS model. Similarly, mixed-integer nonlinear programming (NLP) was defined for SH appliances and battery SS under the time-of-use (TOU) tariff in SH to lessen peak shaving, energy costs, and filling valley [12]. Moreover, the study in [13] inspected the stochastic energy management (EM) for the SH connected with renewables-based energy provisions and the local ESS opportunity provided by EV electrification. Random-variable mock-ups like solar photovoltaic (SPV) generation and SH energy consumption predictive models and Markov Chain prototype of EV movement are established. The stochastic EM problem was stated using optimal stochastic dynamic programming to accomplish power flow among energy sources and decrease the electrical energy price under TOU while sustaining EV charging necessities and SH power requests. In [14], an optimization context for sizing apparatuses and effectual energy usage in a domestic building with EV, ESS, and SPV generation was created. The optimization strategy was outlined as a convex programming problem to proficiently optimize and control the ESS constraints. Home-to-vehicle mode, vehicle-to-home mode, and purchasing of electrical energy to charge the ESS from the UG were efficiently measured. Moreover, the shuffled frog leaping algorithm was considered for optimum scheduling of SPV generation, ESS, EV, and the electric heater in the SH through home EMS [15]. The proposed algorithm lessened consumption costs of the daily electrical energy and ensured proper supply to both the thermal and electrical loads. Similarly, via a multi-agent system, an efficient real-time algorithm was proposed [16] to improve

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electricity generation without disruption in the connected hybrid system confined solar system with FC backup. The incorporation of the EV as an ESS with an uninterruptible power supply under the DR provision was presented to minimize electricity costs while preserving manipulator comfort in the SH. In addition, a robust grey wolf optimization was considered to overwhelm power scheduling difficulties in a SH. It is realized as a multi-objective optimization problem to decrease SH consumed power costs, balance power consumptions during a time horizon, mainly at peak load periods, and maximize the consumers’ satisfaction level [17].

1.3 Motivation and Scope The unpredictable nature of RERs and instability problems associated with the grid energy costs significantly disturb the viability of domestic energy consumers and EV charging station demands, which always leads to a decline in earnings. The centralized approach is insignificant to handle such issues as they require the flow of huge amounts of data and setup to operate the entire system by a single controller and conceivably lead to some anomalous operations and affect the grid reliability. The employment of EVs in the power industry is rapidly rising since EVs are a single entity but not the greatest solution to diminish carbon emissions and ease the power grid operation efficiently with the highest saturation of renewables. Conspicuously, an EV charging station supports the power industry with energy request and send electricity proposals and transport facilities in day-ahead and intra-day electricity market intervals to lessen the vehicle charging costs while sustaining the EVs’ irregular energy requests optimally. Therefore, it is required to design a suitable energy scheduling technique and cost uncertainty model on the customers’ side for both the SHs and EVs connected with the SG to distribute electricity to the customers in a well-organized and consistent way. Hence, a promising distributed agents-based EM scheme via LP approach for energy scheduling of EVs and SHs has been considered to resolve their EM issues while offering the highest independence for varied distributed generating units (DGUs), EVs, and SHs domestic loads. The agents operate and complete their tasks autonomously and communicate with the adjacent agents to supervise the EM tasks optimally, which guarantees that the agents-based supervision scheme is more consistent, flexible, and economical to implement [18].

1.4 Novelty and Contributions The key novelties and contributions of this chapter are briefly summarized as follows: 1. Optimum energy scheduling for both the domestic load and EVs aggregator are studied, and a suitable power distribution structure and cost uncertainty in SG is demonstrated for the domestic loads and EVs.

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2. Energy scheduling of EV stations is employed via LP-based and created on the agents-based technique. Autonomous smart agents operate optimally and accomplish their tasks autonomously, which improves the SG’s capability. 3. Model EV charging station that permits power charging at maximum level to entirely charge the EVs batteries and encourage the vehicle owners to charge the EV batteries at conceivably lower charges and pollutant emissions. 4. The SG lower and surplus power generation from the available energy resources, robust charging and discharging of EVs, and power trading is attained. The allocated agent recurrently monitors the flow of power and charges and makes the trading jobs to improve the grid’s reliability.

1.5 Chapter Organization This chapter mainly provides a complete study of different optimal energy scheduling techniques that helps in charging the EVs and managing the SH load demands efficiently connected to the SG network. The abbreviations used in this chapter are listed in detail in Table 1, and the chapter is organized as follows: Section 1 presents a wide-ranging updated literature review on SG, SH, and EV charging strategies, and different energy scheduling techniques and control strategies have been presented that support and explain the chapter’s scope and contributions. Section 2 provides EM scheduling strategies for EVs and SHs in SGs. SG technology, optimization approaches, optimized energy scheduling technique, and its implementation in SG have been discussed. Moreover, the key benefits and challenges associated with the consumer loads have been elaborated. Section 3 covers the SG structure, EV charging infrastructure, and problem formulations. Deterministic-based and agents-based optimal scheduling of EV charging stations and SHs have been provided. In addition, the importance and scope of the RERs in SG, EV technology and charging options, and charging infrastructure and associated challenges have been explained. Section 4 deliberates some results and effectiveness studies that help in the validation and scrutiny of the optimal energy scheduling strategy in the application of EVs and SHs in SG, and Sect. 5 concludes the chapter.

2 EM Scheduling Strategies of EVs and SHs in SGs 2.1 SG Technology and Its Role in EM SG technology is a modern, advanced power system that facilitates bi-directional communication and control between the electricity grid and end-users. The SG system is designed to provide a reliable, efficient, and sustainable energy supply by optimizing energy consumption and improving the grid’s operational efficiency

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Table 1 List of abbreviations Acronym

Definition

Acronym

Definition

EV

Electric vehicle

UC

Unit commitment

SG

Smart grid

V2G

Vehicle-to-grid

UG

Utility grid

LP

Linear programming

SH

Smart home

NLP

Non-linear programming

GA

Genetic algorithm

PSO

Particle swarm optimization

EMS

Energy management system

DTMF

Dual-tone multi-frequency

TOU

Time-of-use

GSM

Global systems for mobile

ESS

Energy storage system

GUI

Graphical user interface

SPV

Solar photovoltaic

SOC

State of charge

EM

Energy management

DG

Distributed generator

RERs

Renewable energy resources

GA

Generation agent

IoT

Internet-of-things

CA

Charging agent

FC

Fuel cell

TA

Trading agent

WT

Wind turbine

MA

Market agent

FF

Fossil fuel

LA

Load agent

HEC

Home energy controller

BEV

Battery EV

DGU

Distributed generating unit

BMS

Battery management system

DR

Demand response

TEV

Transportation EV

DSM

Demand side management

PEV

Passenger EV

PHEV

Plug-in-hybrid EV

LTEV

Large-scale transportation EV

MG

Microgrid

EREV

Extended-range EV

CPP

Critical peak pricing

Q4EV

Quad-four wheels EV

PLP

Peak load pricing

PGEV

Plug into grid EV

RTP

Real-time pricing

[19]. In SGs, the EMS is crucial in managing energy demand and supply. The EMS integrates various technologies and algorithms to optimize energy usage and reduce energy wastage. The EMS can use different techniques to manage energy consumption, such as DR and scheduling algorithms [20]. The SG is an advanced energy delivery network that employs intelligent and autonomous controllers, advanced software for data management, and two-way communications between power utilities and consumers. Its goal is to improve power systems’ efficiency, reliability, and safety. The key objective of the SG is the transition of an energy-efficient power grid by managing volatile demands and RERs using scalable information processing architectures [21]. Demand side management (DSM) is a critical component of the SG, and it involves altering consumer demand profiles to match supply while incorporating RERs. DSM also facilitates the integration of distributed generation, reduces operational costs, and decreases CO2 emissions. DR is a subset of DSM that motivates changes in electricity use by end-users in response to changes in electricity prices or

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to induce lower electricity use during high market prices or when grid reliability is at risk [22]. Efficient DR programs are vital for the successful deployment of the SG, and the study of DR schemes is essential. DR schemes can be classified into three categories based on the control mechanism, motivations offered to consumers, and decision variables used to identify task-scheduling and energy-management-based DR schemes.

2.1.1

DR Methods Based on Control Mechanism

This class of DR schemes can be classified further into centralized and distributed programs according to where the response decision is made. In centralized programs, the power utility is responsible for response decisions for load activation or load scheduling, considering that a group of users forms a group [23]. However, the operation and control of the grid in a centralized manner are difficult in complex and large grids. As an alternative, communication between energy suppliers and consumers can be distributed. In such distributed schemes, the power utility’s main contribution is the transmission of price signals. Users can coordinate directly with each other to achieve an aggregated load reduction. The following sections deliberate the details on centralized and distributed DR schemes in SGs network. 1. Centralized DR Control Scheme: In a centralized scheme, a central controller gathers demand information from consumers and makes decisions for the demand schedule. This type of management is effective for controlling buildings and charging stations for plug-in-hybrid EVs (PHEVs). Central controllers are used in islanded microgrids (MGs) to simplify the integration of DGUs into the utility network. In MGs, multiple power micro-sources operate together as a single system to provide energy to a cluster of loads in a local area. The MGs primary function is to maintain power balance independently of the main grid. 2. Distributed DR Control Scheme: Distributed DR control programs do not centrally collect demand information. Consumers can access indicators of the grid’s state and react if the system’s state is critical. Researchers have taken inspiration from the distributive nature of the internet to provide efficient control mechanisms in SG environments. Consumers receive pricing information from the utility, a function of the current aggregated load, and use it to adapt their loads. Distributed schemes are used in conjunction with other mechanisms targeting crucial system parameters. 2.1.2

DR Methods Based on Offered Motivations

The motivational methods offered to consumers encourage them to shift or reduce their power demands. There are two main categories within this group, including time-based DR and incentive-based DR. Time-based DR schemes offer customers varying electricity prices based on the cost of electricity during different time periods. Different pricing structures are available, including retail price structures

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and DR-based programs. Retail pricing structures offer customers fixed prices or consumption-based electricity rates to reduce their usage. However, customers do not have a say in determining the prices, and there are no economic incentives to encourage them to respond to hourly changes in electricity prices. On the other hand, DR-based programs encourage customers to reduce their electricity usage through customer contributions in response to signals sent by the energy provider. Different pricing schemes are available, such as flat pricing, TOU pricing, critical peak pricing (CPP), and peak load pricing (PLP). Flat pricing is the traditional energy pricing scheme, while TOU pricing is applied in different time periods. CPP has variable prices, and at least one period can change regularly or due to occasions of system stress. PLP divides the day into several periods, with different prices announced to the customers ahead of each day. Similarly, incentive-based DR programs are more suitable for industrial consumers, and load reductions are achieved through explicit bill credits or payments. Incentives can also take the form of load reduction targets, which are compensated by the energy provider if achieved. This method has been proven to be more effective than other DR pricing schemes, particularly for large-scale consumers. RTP is a pricing scheme that requires active customer participation. In this scheme, the energy provider announces electricity prices before the start of each time period, and customers use an energy management controller to make smart decisions about modifying their energy usage in response to the prices. The day ahead RTP is an alternative RTP-based solution that addresses some of the challenges associated with RTP. Predicted real-time prices for the next day are announced beforehand, and customers are billed based on the day ahead price. Incentive-based DR programs offer fixed or time-varying incentives to customers that reduce their electricity usage during periods of system stress. Customer enrolment and response are voluntary, although some programs penalize customers that fail to respond when events are declared. Classical programs such as direct load control enable the power utility to remotely cycle or turn off consumers’ electrical equipment, while interruptible/curtailable load programs offer upfront incentives to customers, who agree to reduce load during system emergencies. These programs have been considered in various DR programs, including those targeting power consumption reduction in SHs environment.

2.1.3

DR Methods Based on Decision Variables

Depending on decision variables, DR methods can be further categorized into two main groups. The first group controls when requested loads should be activated, while the second group determines how much energy should be allocated to each consumer or appliance during each time period. DR programs that fall under the task scheduling group control the activation time of loads and aim to reduce power consumption during peak-demand hours by shifting loads to off-peak hours. This is achieved by using a target power level that should not be reached at peak-demand hours. Various DR programs based on communication protocols, power demand control policies, direct load scheduling algorithms, multiple knapsack methods, and

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TOU pricing schemes are presented in the literature to optimize task scheduling procedures. DR programs under the energy-management group aim to reduce the power consumption of specific loads during system stress by controlling the appliance’s operation to consume less power. Various DR programs, including a waterfilling-based scheduling algorithm, cooperative scheduling approach, and distributed incentive-based algorithm, are available in the literature to optimize EM. These DR programs determine when to activate a specific set of appliances and how much energy to allocate to appliances during each time slot. The satisfaction level of EM is studied in the literature to develop power-scheduling schemes driven by the qualityof-experience factor, which describes the consumer’s satisfaction degree on the grid’s performance and defines the social welfare of the system.

2.2 Optimized Energy Scheduling Strategies and Their Implementation in SGs The EMS strategy involves multiple interconnected sub-systems that must be effectively controlled to achieve desired goals and ensure safe operations within the SGs. An efficient EMS facilitates the optimal distribution of power across the powertrain components, resulting in improved system performance, reduced operating costs, increased component lifespan, and decreased CO2 emissions [24]. In [25], the supervisory control system is used to manage the EM strategy and can be classified as rule-based or optimization-based. Rule-based control techniques, also known as metaheuristic control, are suitable for real-time decision-making because they can accurately solve complex optimization problems without requiring complex mathematical equations or future data. As shown in Fig. 2, the EM strategies in SGs are divided into online and offline approaches to enhance the utilization of RERs, balance the electrical loads, and maximize the economics. Therefore, deterministic and fuzzy logic are the two classes of high-level control techniques used in rule-based control. The former includes four categories: thermostat (on/off) control strategy and state machine-based strategy, while the latter includes fuzzy predictive strategy, convectional fuzzy strategy, and fuzzy adaptive strategy. Optimization-based control is divided into real-time and global optimization. The former uses an online strategy with a global positioning system and information technology systems, and an algorithm to minimize fuel consumption, while the latter is an offline strategy that requires prior knowledge of the driving cycle and can be implemented using various algorithms, such as stochastic dynamic programming, control theory approach, and adaptive fuzzy rule-based.

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Fig. 2 EM strategies in SGs

2.3 Optimization Algorithms and Their Approaches Several nonlinearities and uncertainties affect the achievement of multiple contradictory objectives involved in the power system. In addition to these objectives, many additional constraints limit the power system. The random and dynamic behavior of EVs increases the complexity of the power system. To implement V2G, optimization techniques are used to manage the proper power flow between the grid and EVs [26]. These optimization techniques are intelligent methods to optimally utilize EV batteries’ energy for various benefits in different fields [27]. Many nonlinear variables are involved due to the complex power system integration with V2G. EV mobility constraints and power system limitations introduce unpredicted variables. The unit commitment (UC) determines the optimal dispatch schedule for the available generation resources in the power system. Several optimization algorithms have been used to solve the UC problem with V2G technology. Conventionally, quadratic programming and LP are used to solve the UC problem. Although they can find the best solution for the problem, they are limited to linear and simple objectives. NLP and mixed-integer NLP are used for more complex UC optimization. However, these techniques also face challenges in handling uncertain variables and require larger computational resources while solving real-world optimization issues/problems. Another method is the priority list optimization method, which has a fast computational speed but requires more research for its implementation. The Lagrange relaxation method reduces the duality gap while determining the proper coordination technique to provide the optimal solution. One difficulty of this method is finding feasible solutions. Artificial intelligence is an alternative solution for solving complex V2G system problems. Particle swarm optimization (PSO) and GA are the best optimization solutions for solving complex V2G system problems. PSO is a computationally intensive algorithm that searches for global optima in populated random solutions by updating generations. GA is an iterative way of searching the global optimal solution

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under a fixed-time limit for its operation. PSO takes less memory and computational time, which is its advantage.

2.4 SHs and EVs in SGs EVs and SHs play a critical role in modern SG. Both EVs and SHs are part of the DERs that can interact with the grid and provide flexibility in the management of the energy system. EVs are a key component of the SG as they offer a new load source and energy storage capacity. The integration of EVs in the SG can have significant benefits, including reducing greenhouse gas emissions and promoting RERs [28]. EVs can be used not only as a source of energy demand but also as a source of energy supply through V2G technology. This technology enables EVs to feed energy back into the grid during peak demand or to help balance the grid during supply disruptions. SHs, on the other hand, are residential buildings equipped with a range of intelligent systems and devices, including smart thermostats, smart lighting, and smart appliances. These devices can communicate with the SG to optimize energy use and provide DR. SHs can adjust their energy consumption based on energy prices, TOU rates, and the availability of RERs. By using these devices, SHs can reduce energy consumption and save money on energy bills. In the SGs, EVs and SHs can interact with each other to optimize energy usage and provide flexibility in the management of the energy system. For instance, an SH can adjust its energy consumption based on the charging status of an EV. The SH can prioritize the charging of the EV during times of low energy demand and reduce energy consumption during times of high energy demand. This can help to balance the grid and reduce energy costs for the homeowner. Similarly, EVs can interact with the SG to optimize their charging and discharging schedules based on energy demand and availability. EVs can charge during low energy demand or high renewable energy availability, and discharge energy back into the grid during peak demand or supply disruptions. This can help balance the grid, reduce the need for energy storage, and promote using renewable energy sources. SHs are residences equipped with technology that allows for automation and remote control of various electronic devices and systems, such as lighting, heating and cooling, security systems, appliances, entertainment systems, and more. The working principle of an SH is based on the IoT, where all devices are connected and communicate with each other and with the homeowner through a central hub or app. The architecture of a SH typically includes several components, such as sensors, controllers, and actuators. Sensors detect changes in the environment, such as temperature, humidity, or movement, and send this information to the controllers. Controllers process the data received from sensors and send commands to the actuators, which perform actions such as turning on or off lights, adjusting the temperature, or opening and closing doors. There are two types of SHs: standalone and integrated. Standalone SHs use individual devices that are not interconnected but can be controlled by a central hub or app. Integrated SHs, on the other hand, use a

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central platform that integrates all devices and systems into a single network. This allows for greater automation and control and more efficient use of resources. The applications of SHs are vast and varied. They can improve energy efficiency by automatically adjusting lighting and temperature settings, reducing energy waste [29]. They can also improve home security by monitoring for unusual activity and alerting homeowners or authorities of potential threats. SHs can also provide convenience and comfort by allowing homeowners to remotely control devices, set schedules, and automate routine tasks. Overall, SHs are becoming increasingly popular as technology advances, and more homeowners seek to improve their quality of life through automation, energy efficiency, and security. The following sub-sections categorize popular SH systems based on their function, gadgets, and hardware-software combinations.

2.4.1

Dual-Tone Multi-frequency Based SHs

Such SHs system utilizes the dual-tone multi-frequency (DTMF) tones, generated from a mobile phone, to remotely control appliances. The signal generated consists of two frequencies, allowing the system to perform pre-specified tasks. A DTMF transmitter is required to send the signal through a channel to the receiver. The signal weakens as it travels, so it is amplified before decoding. The receiver circuit includes a condenser microphone and an amplifier, and the signal is decoded and sent to the microcontroller to perform its specified operation. However, noise reduction is crucial for DTMF-based systems to avoid false data transfer and false appliance switching.

2.4.2

GSM-Based SHs

Global systems for mobile (GSM) based SHs require a mobile phone, GSM module, microcontroller board, and control circuit. Commands are sent as SMS to the GSM module, which receives the message and sends it to the microcontroller board to execute the command. The microcontroller board utilizes a relay module to turn on/off specific appliances. The accuracy of GSM-based systems is over 98%, and sending and receiving messages takes only 2 s. A liquid crystal display shows the important messages and a smart app provides a flexible graphical user interface (GUI) to the user.

2.4.3

Voice Recognition-Based SHs

Zigbee-based SHs are the most popular in voice recognition-based automation. The system comprises a microphone module, Zigbee coordinator (central controller), and terminals (appliance controller). The Zigbee coordinator is connected to different terminals that can perform various tasks, including monitoring and controlling

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temperature, gas, humidity, and appliance switching. Zigbee utilizes the Microsoft speech application programming interface for voice recognition, and radio frequency Zigbee modules establish wireless networks. Bluetooth-based SHs are easy to install and operate, with a smart app featuring a user-friendly GUI, Bluetooth module, and appliance control circuits.

2.4.4

Wi-Fi and Internet-Based SHs

SHs based on Wi-Fi and the internet are versatile and have a wide range of functions, including intelligent appliance control, lighting, intrusion alarm systems, and gas/ smoke detection. These systems support 3G and 4G internet and can be controlled by an android app. Firewall protection ensures security, and the home gateway provides data translation among the internet, router, and ethernet shield.

2.5 Associated Key Benefits and Challenges with EVs and SHs in SG EVs and SHs are expected to play a vital role in the future of the electricity grid. With the integration of EVs and SHs into the grid, several benefits can be achieved, such as cost savings, reduced carbon emissions, etc. However, some challenges need to be addressed. The key benefits and challenges associated with the integration of EVs and SHs in SG environments are highlighted below.

2.5.1

Benefits of EVs and SHs in SG

1. Reduced Carbon Footprints: EVs and SHs can significantly reduce the carbon footprints by using RERs, such as solar or wind energy, to deliver power to EVs and SHs. This reduces the dependence on FFs, which emit greenhouse gases and significantly contribute to climate change. 2. Energy Cost Savings: SHs with EV charging systems can use TOU electricity rates to charge the EVs during off-peak hours, when electricity is cheaper. This can result in significant cost savings on electricity bills. 3. Improved Grid Stability: EVs and SHs can help balance the grid by providing energy storage and DR capabilities. The batteries in EVs and SHs can store excess energy generated during off-peak hours and release it back into the grid during peak hours, reducing the strain on the grid. 4. Increased Renewable Energy Integration: EVs and SHs can help integrate more renewable energy into the grid. SHs can be designed to use RERs to power the homes and charge the EVs, reducing dependence on non-renewable sources.

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5. Enhanced Grid Resiliency: EVs and SHs can increase grid resilience by providing backup power during power outages. The batteries in EVs and SHs can be used to power critical loads in homes and support the grid during emergencies. 6. Reduced Air Pollution: EVs produce zero emissions, which can significantly reduce air pollution, especially in densely populated areas where air pollution is a major concern. 7. Improved Energy Efficiency: SHs can be designed to be energy-efficient, with features such as energy-efficient lighting, high-voltage AC systems, and appliances. This can help reduce energy consumption and lower electricity bills. 8. Increased Energy Independency: EVs and SHs can provide energy independency by reducing the dependence on the UG. With the ability to generate their electricity using renewable sources, SHs, and EV owners can reduce their reliance on the grid and have greater control over their energy use. 9. Greater Consumer Engagement: SHs and EVs can increase consumer engagement by providing real-time information about energy use and costs. This can help consumers make informed decisions about their energy use and reduce their consumption. 10. Reduced Peak Demand: EVs and SHs can help reduce peak demand on the grid by charging the EVs during off-peak hours and reducing energy consumption during peak hours. This can help prevent blackouts and brownouts, which can be costly and disruptive. 2.5.2

Challenges of EVs and SHs in SG

1. Grid Integration: One of the main challenges of EVs and SHs in the SG is grid integration. As more EVs and SHs are deployed, the grid must be able to handle the increased demand for electricity. This requires upgrading and expanding the existing grid infrastructure to ensure it can handle the additional load. Additionally, SG technologies must be developed to enable the grid to manage and control the flow of electricity to and from EVs and SHs. 2. Cybersecurity: With the increased connectivity and reliance on digital systems in the home and in EVs, cybersecurity threats become a significant concern. Ensuring the security of EVs, charging stations, and SH devices is critical to maintaining the integrity and stability of the grid. 3. Consumer Adoption: Consumer adoption is another challenge for EVs and SHs in SG. While the potential benefits of EVs and SHs in the SG are significant, consumer adoption remains a key barrier to their widespread deployment. Consumers must be willing to invest in EVs and SH technologies and change their energy consumption habits to take advantage of these technologies’ cost savings and other benefits. 4. EM: One of the primary challenges is managing the energy demands of EVs and SHs, which can fluctuate rapidly and unpredictably. This requires complex algorithms and sophisticated control systems to optimize energy use and ensure grid stability.

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5. Infrastructure: EV charging infrastructure is still in the early stages of development, and there is a need for widespread deployment of charging stations to support EV growth. Similarly, SH infrastructure also needs to be developed to enable communication with the grid and allow for remote control of devices. 6. Interoperability: With multiple manufacturers and technology providers in the market, ensuring interoperability between different EVs, charging stations, and SHs devices can be challenging. Standardization efforts are ongoing, but there is still a long way to go. 7. Data Management: The integration of EVs and SHs generates vast amounts of data, which must be collected, analyzed, and acted upon in real-time. This requires robust data management systems and analytics capabilities to make the right decisions at the right time. 8. Regulatory Challenges: Integrating EVs and SHs into the grid raises regulatory challenges related to ownership, liability, and privacy. Regulations must be put in place to address these issues and ensure that the interests of all stakeholders are protected.

3 SG Structure and EV Charging Infrastructure 3.1 Structure of an SG The structure of an SG is shown in Fig. 3, which comprises different DGUs (SPV system, small scale WTs, FCs), distributed generation backup power (battery SS), EVs charging slots, commercial and domestic loads (SHs and EVs), and normally a protection and control system associated with supervising the entire setup optimally. The power generation from the renewables is boosted through power transformers before the long transmission to the distribution/utility network to minimize power loss and energy costs. The utility system transfers the power to end users in both AC and DC forms to overcome their electricity needs. In addition, to exchange powers within the grid, two busbars are vital to be considered to fulfill different forms of energy demands easier during peak and off-peak periods, which consequently leads to making the grid self-sufficient to complete the energy gaps internally and avoid to purchase electrical power from the connected grid with supplementary charges. For energy scheduling of EVs and SHs in the SG environment, a distributed agents-based technique has been considered to supervise the generation capabilities, protect the SG, and communicate with the electricity market to achieve the current and predict future billing costs. The local agents frequently check the units’ current states to create power balancing selections with the consumers’ load since the electrical power generation from renewables mostly depends on climatic and weather differences (such as temperature, solar irradiance, wind speed, etc.).

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Fig. 3 SG structure and presentation of power transmission, distribution, and consumption

3.2 Problem Formulation To regularize an appropriate unit initiation and dispatch of the existing resources referring to the EMS, the best suitable approach is provided by assigning all the connected information of operating units, the batteries’ state of charge (SOC), EVs charging, electricity market charges, and SH loads within the SG given is given in Eq. 1 [9]. min

T  N  

      deg i,t i,t i,t i,t PDG + Pbat + D tE P + ηCbat x Ei V t · C DG · Cbat

(1)

t=1 i=1 min,t max,t i max Subject to: 0 ≤ x Ei,tV ≤ Pbat and PDG ≤ PDG ≤ PDG , where; i i i = PS P V , PW T , PFC . i,t where, x Ei V = charging rate of the corresponding ith EV. PDG = power generation i,t of the distributed generator (DG), C DG = maintenance cost of the ith DG in time i,t i,t t, Pbat = ith battery power in time t, Cbat = ith battery maintenance cost in time i t, PS P V = power generation from the SPV, PWi T = power generation from the WT, deg i PFC = power generation from FC, D tE P = electricity price in time t, Cbat = battery degradation charges, η = efficiency in percentage, and t = time interval in hour. The momentous aspects that have been measured in valuation of the batteries power are: i PDG

1. Daily required power, 2. Planned backup power, and 3. Maximum capacity of the battery.

Agents-Based Energy Scheduling of EVs and Smart Homes in Smart Grid

⎧ ⎪ ⎨ t = 1, 2, 3, . . . , T ; where 0 ≤ t ≤ T ∀ i = 1, 2, 3, . . . , N ; where 0 ≤ i ≤ N ⎪ ⎩ T ≤ 24

203

(2)

t t where, P+ve = battery charging in time t, P−ve = battery discharging in time t, Vu = DC bus unit voltage level, Bc = battery storage capacity (AH), S OC 0 = initial battery SOC, S OC min = battery minimum SOC, and S OC max = battery maximum SOC. For battery health, the initial SOC must be subjected to the limits specified in [9] to evade the battery’s health issues and guarantee full charge of EVs; all EVs should be coupled to the charging point upon the EV SOC reaches 20%.

3.3 Deterministic-Based Energy Scheduling of EV Aggregator In this part, the model of a deterministic-based energy scheduling of EV aggregator is studied to solve the scheduling issues allied with the EVs followed by the agent-based finest scheduling approach of EV aggregator in the domestic area. The projected model contains the EV collector, which frequently participates in the energy market to exchange power between EVs and the power grid. Secondly, the model also covers the purposes for satisfying the SHs load demands either from the installed DGUs or buying power from the UG.

3.3.1

Objective Function

The main objective purpose of this model comprises four parts. 1. The revenues of the contracted electrical energy between the EV user and the charging station. 2. The cost and income during charging and discharging of vehicles from/to the grid. 3. The EV battery collector’s preservation costs during the agreed usage year. 4. The reimbursement fee to the EV holder is due to the declining EV battery lifecycle. The best suitable energy scheduling in a residential zone for both EV and SH consumers has been designed aim to offer a consistent, economic, and effective energy distribution plan with low pollutant emissions to fulfill consumers’ load demands in the domestic sector and is specified below [9]

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max O B J = F(O) =

T   t=1

deg

t D tE V PEt V − DUt G PEt V −Cbat PEt V − Cbat PDt E V



× t

∀t ∈ T



(3)

T ≤ 24

where, max O B J = F(O) = The objective function, D tE V = agreed electricity price of the EV owner in time t, PEt V = power exchange among the UG and EV aggregator in time t, PDt E V = EV discharge power in time t, DUt G = energy price of the UG in t = EV batteries maintenance costs in time t. time t, and Cbat 3.3.2

Batteries Charging and Discharging

The EV batteries’ minimum and maximum discharge and charge limits are defined in Eqs. (4) and (5), respectively. Similarly, Eq. (6) indicates the EV power exchange, t t is the maximum charge of EV and P−ve,max is the maximum discharge where, P+ve,max of EV in time t. t t 0 ≤ P−ve ≤ P−ve,max ; ∀t

(4)

t t 0 ≤ P+ve ≤ P+ve,max ; ∀t

(5)

t t PEt V = P+ve − P−ve ; ∀t

(6)

t P+ve,max =

J I  

i P+ve,max u tj ; ∀ t

(7)

i P−ve,max u tj ; ∀ t

(8)

i=1 j=1

t P−ve,max =

J I   i=1 j=1

where, i represent the EV category and j shows the EV index. u tj is denotes a binary value equal to 1 (EV is connected) and 0 (EV is not connected).

3.3.3

EV Energy Rises and Falls Calculation

EV charging energy rises on the energy of EVs arrival for charging, while energy loss occurs due to EVs’ departure from the charging station during time t is calculated in Eqs. (9) and (10), respectively.

Agents-Based Energy Scheduling of EVs and Smart Homes in Smart Grid v Arr  I   i,t

E tArr

=

 S OCimax − S OC 0 v i,t Arr ; ∀ t

205

(9)

i=1 v i,t =1 Arr v i,t

E tDep =

Dep I   

S OCimax ; ∀ t

(10)

i=1 v i,t =1 Dep

where, E tArr = rise in energy due to arrival of EVs to the charging station, v i,t Arr = ith number of EV arriving at the charging station at the time t, E tDep = drop in energy due to departure of EVs from charging station, v i,t Dep = ith number of EV departing from charging station at time t.

3.3.4

Power Storage in Charging Station

To store the charging station electricity, the total stored power of the charging place is E t . The E t at time t is equivalent to the summation of charging place preserved power at time t − 1 plus added power by EV arrival at charging place and EV battery charging at the time t minus EV battery discharging power and the compact energy by the departure of EV, which is calculated as: E =E t

t−1

+

E tArr

+η

+ve

 t P+ve

× t −

E tDep

 t P−ve × t ;∀t + η−ve

(11)

E0 = Et

(12)

t 0 ≤ E t ≤ E max ; ∀t

(13)

3.4 Agents-Based Energy Scheduling Strategy for EVs and SHs in Domestic Area Agents-based energy scheduling technique is one of the promising approaches used in different fields to reach efficient scheduling outcomes with minimum possible difficulties. The fundamental purposes include; the finest EV charging scheduling strategy, fulfillment of the SHs electricity demands in the domestic zone, abating EV charging cost (known as owner profits growth), and reducing pollutant emissions are the key targets to be achieved. To achieve these targets, the introduction of local agents must operate autonomously and recurrently monitor the SG operational activities to make the arrangement efficient and reliable. The five agents i.e., (generation

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agent (GA), charging agent (CA), trading agent (TA), market agent (MA), and load agent (LA)) complete step-wise operational process in the SG environment is given in Fig. 4. Where, the SG entire power generation competences have been scrutinized by the installed GA and checked for the restraint’s optimization occupation, while the CA and LA monitor the mandatory load demands and direct commands to the connected GA to satisfy the electricity demands competently. After the power matching work over the demands achieves, the excess power is stored in the ESS and then directed to the UG, respectively.

Fig. 4 Step-wise operational flow of the agents-based energy scheduling strategy

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3.4.1

207

LP-Based Optimization Approach

To handle the grid optimization problem, the LP procedure can be used. The total costs of the electricity in t = 1, 2, 3, …, T is stated below in Eq. (14). The optimization development has been prepared in three stages i.e., 1. DGs power generation capabilities, domestic load, and EV charging status and been highlighted in the EV charging procedure. By using the LP method, Eq. (1) can be extended and the modified form can be formulated as:     deg i,t i,t i,t i,t + Pbat + ηCbat Ptot (t) = PDG · C DG · Cbat (14)

3.4.2

Domestic Load and EV Status in Charging Station

The calculation of precise connected electrical loads such as domestic energy load (Pdlt ) and initial EVs SOC has a prodigious position. Therefore, the total load associated with the SG together with EV optimal charging to exactly measure and distribute the energy to the consumers’ load is specified below:   t t Pload (t) = Pdlt + Pbat + P+ve,max S OCi0 =

i K Arr i K max

i i K Arr = K max − ξi · m

(15)

(16) (17)

The SOC is subject to the EV regular driving mileage, which can be attained via i i = capacity of the ith arrived EV (kWh), K max = Eqs. (16) and (17). Where, K Arr i maximum capacity of the ith EV (kWh), ξ = EV energy consumption (kWh/km), and m = daily driving distance of the EVs (km).

3.5 Importance of RERs in SGs Many harmful gases that cover the globe and trap solar heat are caused by burning FFs to produce heat and electricity. FFs such as oil, coal, and gas are largely responsible for universal climate change, accountancy for over 75% of the worldwide harmful gases’ productions and almost 90% of the total CO2 emissions. It is a serious concern that to evade the worst influences of climate alteration, emissions must need to be minimized by nearly 50% by 2030 and achieve complete net-zero emissions by 2050. To reach this target, the reliance on FFs should be ended, and a huge investment in alternative energy sources is required that are sustainable, clean, affordable, accessible, and

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reliable. RERs are abundantly available in nature, provided by the water, wind, sun, geothermal, and waste are refilled by nature and release slightly to no harmful gases’ pollutants or emissions into the surroundings. Therefore, five main reasons why accelerating the conversion to clean energy is the way to a liveable healthy planet today and aimed at generations to arise.

3.5.1

Availability and Accessibility of RERs

RERs are available and accessible in all regions around the globe, and their potential is still under investigation to be entirely harnessed. The International Renewable Energy Agency estimates that about 90% of global electrical energy can come from renewables, and should be achieved by 2050. Renewables take a way out of dependency on FFs imports, allowing regions to protect themselves from the erratic price blows of FFs and expand their economies while driving comprehensive economic evolution, new career opportunities, and easing poverty.

3.5.2

Lower Energy Costs

Nowadays, renewable-based energy production is actually the economic power assortment in most regions of the world as renewable-based energy technologies prices are dropping rapidly. It has been noted that electricity costs from SPV power dropped by 85% between 2010 and 2020. Prices of offshore and onshore WT energy also fell by 48% and 56%, respectively. Dropping costs make RERs more attractive all around the globe together with low- and middle-income regions, where the supplementary new energy demand will come from. Moreover, with dropping prices, there is always a factual chance for the new-fangled power stream in the next few years to be provided by low-carbon sources. Although wind and solar power costs are projected to remain higher in 2022 and 2023 than in pre-pandemic stages due to universally elevated service and freight charges, their affordability essentially improves due to considerably sharper rises in coal and gas prices [30].

3.5.3

Healthier Energy in Nature

According to the World Health Organization, up to 99% of individuals in the world breathe bad air that surpasses air quality confines and seriously damages their health. It has been reported that more than 13 million human losses occur yearly due to avoiding environmental issues, mainly air pollution. Air pollution mainly causes by adopting FF-based energy generation and using traditional FF-based vehicles in the transportation sector. In 2018, air pollution from FFs caused about 8 billion dollars a day in economic and health outlays, which equals 2.9 trillion dollars annually. Therefore, replacing the traditional FFs-based generation with clean energy sources (such as RERs) will help address environmental change and health and air pollution.

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3.5.4

209

Job Opportunities and Career Developments

The investment in renewables generates three times more career opportunities than in the FF industry. The IEA predicts that the progression towards net-zero emanations will initiate an overall rise in energy division jobs, while around 5 million jobs in FF production could be lost by 2030, an expectable 14 million new career professions would be formed in the clean energy sector, resultant an increase of overall 9 million occupations. Similarly, energy-related industries would need additional employees to yield new roles in the trade and manufacturing of EVs and efficient appliances using advanced low-emissions technologies.

3.5.5

Role of Renewables in Economic Growth

In 2020, for about 5.9 trillion dollars was consumed on subsidizing the FF industry, counting through tax breaks, explicit subsidies, and environmental and health compensations that were not evaluated into the overall cost of FFs. In comparison, a yearly investment of about 4 trillion dollars needs to be devoted to renewable energy (investments in infrastructure, new technology, and backups, etc.) until 2030 to permit us to achieve net-zero pollutant emissions by 2050. The decrease in pollution and climatic influences alone could save the globe per year, equal to 4.2 trillion dollars by 2030. In addition, efficient and consistent advanced renewable technologies can form a system less prone to energy market shocks and recover suppleness and energy safety by diversifying power transfer possibilities.

3.6 EV Technology and Charging Options EV technology has swiftly advanced since its launch in the market, and currently, numerous PHEV and battery EV options are accessible. Enter the EV drive-train! Unlike core combustion technology-which practices pressure and combustion to drive a vehicle, or EVs, are driven by electromagnetism. These EVs use electricity, classically stored in an ESS (battery), to power an electric motor. EV technology is cast-off in hybrid EVs, PHEVs, and battery electric vehicles (BEVs).

3.6.1

Hybrid EVs Technology

The hybrid EV was the primary EV technology to grasp the modern automobile market. These vehicles combine the electric motor and internal combustion engine with a low-rating battery for storage purposes, which primarily comes from recapturing energy via regenerative braking and was the main reason hybrid EVs become more fuel-efficient than a typical internal combustion vehicle.

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Plugged-In-Hybrid EVs Technology

Secondly, the PHEV is driven by an electric motor and an internal combustion engine. However, the PHEV has a high-power ratting battery pack that the EV supply apparatus can charge. This empowers the vehicle to run in all-electric modes wherein the vehicle is driven only by the electric motor till the battery is typically depleted. At this stage, the EV runs in hybrid mode until the fuel in the vehicle tank is completely depleted. Increasing the size of a battery and operating a vehicle on electricity decreases tail-pipe discharges and rises the EVs’ energy and fuel efficiency.

3.6.3

Battery EVs Technology

Lastly, battery EVs have no inner combustion engine and are only powered by the electric motor and battery. Battery EVs don’t practice gasoline fuel and are solitarily stimulated by the EV supply equipment. A Battery EV has the highest rating battery cells of all vehicle types, has the utmost energy efficiency, and produces zero tail-pipe emissions.

3.6.4

EV Battery Management System

The EV battery must be efficiently used and could be guaranteed by an intelligent/smart battery management system (BMS), which controls the discharging and charging of the EV batteries and confirms the ideal practice of the battery cells. The BMS regularly monitors each separate cell and dynamically balances their charging. This effectually surges the range and life of the EV battery. Normally, the installed sensors and microcontrollers supervise the charging level and functionality, ensuring the optimal charge with low costs.

3.6.5

EV AC Charging

As shown in Fig. 5a, with an AC EV charger, the electricity needs to be transformed to DC first and then directed to the battery, while in DC, the direct current can stream into the EV battery directly. Charging an EV with an AC power source, the car’s onboard charger/system is cast-off to take care of the alteration of outlet current to the battery current. It subsequently collects AC and changes it into DC, and then sends it to the EV battery.

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211

Fig. 5 AC, DC and wireless charging infrastructures; a AC charging—AC power is supplied from the charging station to the onboard charger, which supplies DC power to the EV battery. b DC fast charging—the charger is off-board the vehicle and supplies DC power directly to the EV battery. c Wireless charging

3.6.6

EV Fast DC Charging

As deliberated in Fig. 5b, the fast DC chargers adjust AC to DC power within the charging lot and transport DC power straight to the EV battery to charge faster. The three main concerns in DC fast charging development are: 1. 2. 3. 4.

Abating cooling efforts, Providing high power compactness, Reducing the system size, and Minimizing the overall costs.

Recently, a traditional high power density entails forced-air conserving, which will no more be required in the next generation of charging solutions, as they will entail liquid cooling driven by the structure power density rise. Compact plans must deliberate higher converting speeds to reduce the size of magnetic apparatuses.

3.6.7

EV Wireless Charging

In EV wireless charging, the power is transferred by magnetic fields via inductive coupling among wire coils (inductive charging), or through electric fields via capacitive coupling amongst metal electrodes (capacitive charging). Getting rid of cables and plugs with wireless power stream is a vast demand in current infrastructures that

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are seen by wireless charging schemes. As shown in Fig. 5c, the inductive indicting permits the vehicle to be charged through energy conceded from a coil in the ground of the vehicle parking space to a coil unified into the EV. Different power conversion techniques can be practiced to shape a DC-DC phase. The resonant network topologies are highly chosen since they decrease the circuit switching losses. The DC-DC stage is mounted in the vehicles to align battery requirements and output voltage.

3.7 EV Charging Infrastructure and Associated Challenges to Widespread Adoption Utilities worldwide are investing in building different charging infrastructures required for EVs, such as private cars, public buses, and logistics trucks, in reducing carbon emissions and imports of high FFs costs. The global rise in the EV charging (fast and slow) infrastructures presented by the IEA [31] is graphically shown in Fig. 6. In 2021, over 1.8 million charging stations were accessible to the users globally in which 0.5 million chargers were mounted in 2021 counted for about 37%, which exceeds the over-all available chargers in 2017. In 2021, fast charging increased slightly more than in 2020 (48% compared with 43%), and slow charging much slower (33% compared with 46%). Moreover, between 2015 and 2019, the average annual growth rate was almost 50%. In 2021, the fast-charging points increased by about 5%, and slow-charging was much slower than in 2020. China contributed a much higher number of widely available chargers, accounting for about 85% and 55% of the global fast and slow chargers, respectively. The investments in EV charging infrastructures are enormously important for private and public transportation agencies, industries and businesses, and individuals who participate in purchasing an EV but are incapable of mounting a charging slot at their homes. Innovation and advanced technology are lashing the EV space with stimulating behaviors to charge extended-range batteries and fast charging abilities to get consumers back on the road swiftly. The grid networks, local governments,

Fig. 6 Global fast and slow charging infrastructures

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213

and legislation are falling behind in approaching a sophisticated technique for car owners to charge their EVs. EV charging equipment classically has some intelligence that upkeep consumers’ authentication, data collection, vehicle communication, data monitoring, and transactions. In some cases, the bi-directional control feature will be prepared that permits a communication system to regulate the power level injected into the vehicle battery in response to cost signals or supplementary dispatch reasons. Some of the foremost challenges that EV charging networks facing are: 1. 2. 3. 4. 5. 6. 7. 8.

Poor high-tech charging infrastructure, V2G interoperability, Charging slot downtime and performance, Reliable payment method incorporation, Power issues during vehicle charging, Inadequate charging station communication protocols to network, Stability attainment between fast charging points and on-road units, Ease-of-use during vehicle charging.

4 Results and Performance Evaluation of the Energy Scheduling Strategy 4.1 Input Data and System Setup The individual residential consumer base energy demand is shaped by using opensource tools consuming the CREST model and the results of [9] are further elaborated for better understanding of the overall chapter idea. As shown in Fig. 7a, the entire electrical energy costs for the optimal scheduling strategy of the EVs aggregator can be premeditated for each time slot. The domestic load in the form of SH and six different EVs types (transportation EV (TEV), passenger EV (PEV), largescale transportation EV (LTEV), extended-range EV (EREV), quad-four wheels EV (Q4EV), and plug-in to the grid EV (PGEV)) have been measured, while the parameters of these selected EVs types have been given in Table 2. Similarly, the installed DGUs capacity within different operating intervals to fulfill the domestic and EV charging demands has been deliberated in Fig. 7b. max Similarly, the EVs’ maximum energy consumption (E cpm ) is the vehicle energy that gains from the overall distance travelled before they are tied to the station. As shown in Fig. 7c, the capacity of a total 100 number of EVs could be connected to the charging station at any interval during 24 h. Hence, Fig. 7d shows that the rise and drop in energy consumption occur in the arrival E tArr and departure E tDep of the EVs to/from the charging station during 24 h period, respectively.

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Fig. 7 a Domestic SHs load demands and elasticity costs. b DGUs power generation capabilities. c Day ahead numerical results of the EV energy consumption. d Energy rise and drop relationships with the EV arrival and departure

Table 2 Parameters of different categories of EVs [9] EV parameters

PEV

TEV

LTEV

EREV

Q4EV

PGEV

Proportion

0.1

0.05

0.05

0.1

0.2

0.5

max,t P−ve (kW)

1.5

1.5

5

1.5

1.5

1.5

max,t P+ve (kW) S OC max (kWh)

3

3

10

3

3

3

8.2

23

85

17

8.7

29

max E cpm

0.156

0.185

0.586

0.253

0.112

0.161

(kWh/km)

4.2 Simulation Results and Discussion It is important to supply a satisfactory level of electricity to the residential area to fulfill the SHs’ energy demands and deliver sufficient power to the charging station to charge EVs. Thus, the installed DGUs run together to produce sufficient energy to accomplish such demands. As depicted in Fig. 8, the DGUs generate more power than the total connected (domestic and EV) required load demands from morning 08:15 to 20:08 h. Due to the use of EVs during the day, the energy demands are lower than

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Fig. 8 Total available power and required EVs and domestic loads

at night, as the EV owners bring their vehicles for charging during night time. During different operational intervals, the grid has shortfalls due to the variations in RERs generation and the highest amount of charging demands as occurred during 02:00– 08:00 h for about 661 kW. Similarly, the lowest and highest electricity demands have been noted at 14:00 h and 03:00 h for about 19 kW and 124.5 kW, respectively. The lowest domestic energy demand happened during the night between 00:00 and 07:00 h, but because of charging the highest number of EVs, the total domestic and EVs electricity demands were highly recorded, with an average of 101.98 kW. During the operation and scheduling periods, the SG first delivers excess power to the ESS for higher load intervals and then shares the rest power with the UG to reduce electricity costs. In the power trading between SG and UG, the purchased and sold powers from/to the UG are considered positive and negative, respectively. This study considers the battery minimum and maximum SOCs as 20% and 80%, respectively. Figure 9a presents the power trading among the SG and UG using an uncoordinated energy scheduling technique, while the battery SOCs have been shown in Fig. 9c. Similarly, the power trading and battery SOCs using the coordinated agents-based technique have been deliberated in Fig. 9b and d, respectively. In the agents-based coordinated scheduling technique, due to the presence of autonomous agents and a higher amount of generation contribution from RERs, optimal energy scheduling could be achieved to fulfill the load demands efficiently, which leads to lower electricity costs and cleaner surroundings. On the other hand, in an uncoordinated technique, the electricity generation and distribution directly occur with the consumer loads and UG, which mostly leads to higher electricity costs.

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Fig. 9 a Power trading scheduling with UG using the uncoordinated technique. b Power trading scheduling with UG using the coordinated agents-based technique. c Batteries SOC in uncoordinated technique. d Batteries SOC in coordinated agents-based technique

4.3 Validation of Optimal Energy Scheduling Strategy The simulation results have been properly compared with the stochastic technique to validate the effectiveness of the anticipated agents-based optimal energy scheduling strategy. As shown in Fig. 10a, the domestic load (in the form of SHs) in both coordinated and uncoordinated techniques are equal, but EV charging loads are changed due to the arrival of EVs at the charging place during different periods. It could be realized that via coordinated agents-based technique, the SG delivers 3.40% higher power in average per hour ratio compared with the stochastic approach. This means that the total electricity costs for both SHs and EVs charging consumers are also reduced and can save electricity costs by 16.92%, shown in Fig. 10b.

5 Conclusions The UG’s voltage variances and cost uncertainties are significant problems for the SH load and EV charging. Therefore, a reliable energy scheduling technique can help in handling such deficiencies. The SG system that is unified with the control

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Fig. 10 Comparison between stochastic and coordinated agents-based techniques. a Delivered power. b Energy costs

system and utilizes the RERs to deliver electricity to the consumers and handle the price uncertainties helps achieve the robust energy scheduling of the EVs and SHs consumptions. The wide-ranging context of optimal energy scheduling approaches that helps charge the EVs and manage the SH load demands efficiently is important in reaching zero emissions and lowering electricity costs. The structure of the SG charging strategy and different energy scheduling techniques have been elaborated that support and explain the energy scheduling issues associated with the EVs and SHs in SG. Robust optimization practices and the integration of RERs in current EV charging infrastructures and SG are vital, while the EV technology and charging options could be upgraded to handle the challenges associated with the EV charging stations. The optimal energy scheduling technique that permits power charging at a maximum level to entirely charge the EVs batteries and encourage the vehicle owners to charge the EV batteries at conceivably lower charges and deliver electricity to the SHs with low emissions has great significance in the power networks.

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Advanced Control Functionalities of Smart Grids from Communication and Computational Perspectives A. Paspatis, E. Pompodakis, I. Katsigiannis, and E. Karapidakis

Abstract Smart grids encompass advanced control functionalities, aiming to accommodate large shares of renewable energy resources in the power system. The latter is critical for reducing the environmental emissions arising from the operation of power systems, which traditionally have been one of the largest emitters. However, the complexity of the smart grid operation, when compared to that of the conventional bulk power system, needs to be addressed through sophisticated control schemes. Moreover, as these control schemes become more and more advanced, their needs regarding communication and computational requirements are also increasing. Particularly advanced computational requirements arise with adopting control approaches based on machine learning, artificial intelligence, deep learning, neural networks etc., while advanced cooperative control schemes, e.g. distributed control, need advanced communication channels. This chapter reviews the advanced control functionalities of the main building blocks of the smart grid, i.e., transmission and distribution system, microgrids, distributed energy resources and smart homes. Particular emphasis is given to the technologies that require advanced computational and communication capabilities. Keywords Smart grids · Control schemes · Power systems · Hierarchical control · Optimization · Distributed energy resources · Renewable energy resources · Smart homes · Microgrids

Acronyms ACE ANN

Area control error Artificial Neural Networks

A. Paspatis · E. Pompodakis · I. Katsigiannis · E. Karapidakis Hellenic Mediterranean University, Heraklion, Crete, Greece A. Paspatis (B) Manchester Metropolitan University, Manchester, UK e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 B. Appasani and N. Bizon (eds.), Smart Grid 3.0, Power Systems, https://doi.org/10.1007/978-3-031-38506-3_9

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AVR BESS DSO GAN HVAC HV I IBRs ML MPPT PMUs PLC P PI PID RL TSOs

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Automatic Voltage Regulator Battery energy storage systems Distribution system operator Generative adversarial network Heating, ventilation and air-conditioning High voltage Integral Inverter-based resources Machine learning Maximum power point tracking Phasor measurement units Power-line communication Proportional Proportional-integral Proportional-integral-derivative Reinforcement learning Transmission system operators

1 Introduction The emergence of the smart grid concept in recent decades has been driven by the need to transition towards a more environmentally friendly and digitally enhanced power system operation. This transition aims to achieve objectives related to reducing environmental emissions and electricity costs [1]. Notable transformations in legacy power systems are observed in terms of power system control and market mechanisms, accompanied by a shift towards utilizing a diverse range of renewable energy sources for electricity generation [2]. At the same time, new tools and concepts are introduced to analyze and monitor such smart grids’ operation under normal and abnormal conditions, such as synchrophasors [3]. Moreover, other energy vectors, such as heating, cooling and mobility, are also shifting to “green” approaches, partly through their electrification. This fact creates new challenges in the next generation smart grids, which will have to host electrical loads originating by other energy vectors, while the optimal interconnection of those different vectors is also becoming essential [4]. The realization of the above smart grid scenario relies partially on the “smartness” of the next generation smart grids, where advanced control approaches, including both classical control approaches as well as modern optimization, machine learning, artificial intelligence, model predictive control and deep learning approaches are applied to meet the required tasks [5]. The application of those intelligent control schemes is evident in all layers of the power system control structure, while the entities of smart homes, distributed energy resources and microgrids are also expected to play a significant role in the operation of the smart grid of the next decades.

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Integrating intelligence across a broad spectrum of entities, ranging from household devices to large-scale power plants and intra-area control, assumes a pivotal role within the context of Smart Grid 3.0. This significant aspect accentuates the extensive scope and relevance of the present chapter. In particular, this chapter analytically introduces and discusses the intelligent controls required in those various levels of the smart grid architecture and their advanced communication and computational needs. The key feature of the chapter corresponds to an introduction to the advanced control schemes applied to all layers of the smart grid, indicatively: (i) transmission and distribution system, (ii) microgrids, (iii) distributed energy resources, and (iv) smart homes/buildings, bringing the required proactive intelligence, which is required to meet the operational tasks of the next generation smart grids, e.g. voltage and frequency regulation, congestion management, fault-ride-through, virtual inertia, black start, optimal operation, etc. Apart from the control-oriented review and descriptions, a discussion of the computational and communication requirements of such approaches will be discussed, which directly connects this chapter with the scope of the present book. Particularly, Sect. 2, reviews the conventional hierarchical control of power systems, while Sect. 3, discusses the advanced control requirements for the building blocks of the smart grids. Finally, Sect. 4, concludes with regard to the advanced communication and computational requirements of the presented control schemes.

2 Hierarchical Control of Power Systems Traditionally, control of bulk power systems has been established through the control of the generation system, the generating units and the transmission system, as shown in Fig. 1 [6]. In this scenario, generation system control is responsible for the load frequency control and the economic dispatch (also known as secondary and tertiary control, respectively). At the same time, the generation units regulate the dispatched power commands through their local unit controls (also known as primary control). In the lower level, reactive power and voltage control is accomplished in the transmission system control level, through compensation devices and tap changers [6]. On top of the described functions, the curtailment of large renewable energy sources plants and load (load shedding) may be required in certain conditions. Over the years, this hierarchical control of the power system has been proven capable to realize a stable voltage and frequency and a satisfactory power quality, while the curtailment of RES plants has been massively utilized in non-interconnected power systems to guarantee the power balance and avoid issues arising from the stochasticity of those sources [7]. Regarding the control realization of the conventional hierarchical control of power systems, this is mainly achieved through standard proportional-integralderivative (PID) controllers in all three levels. For the generation unit control (primary

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Fig. 1 Hierarchical control of power systems

control), the Automatic Voltage Regulator (AVR) and the Governor operation of the synchronous generators adjust the real and reactive power output of the generators through the speed and voltage control of the unit in a way that proportionally shares the power generation amongst them, as well as supporting the local voltage and frequency. Then, the secondary load frequency control is again accomplished through integral (I) control functions to minimize the area control error (ACE). Finally, the tertiary level control governs the exchange of power between different TSOs areas and considers reserve and financial objectives [8], usually involving solving an optimization problem, rather than classical PID control functionalities [9]. Even though the above-described control approach has been proven efficient enough to maintain a stable power system operation, its applicability is being challenged today due to the ongoing transformation of power systems and their gradual evolution to the smart grid era [2]. Particularly, components such as smart homes, smart buildings, and microgrids are now being installed in the power system, accompanied with their advanced control functionalities, while the active distribution feeders modify the legacy operation of transmission and distribution systems, also challenging their control schemes operation. Moreover, renewable energy sources with high stochasticity are increasingly inserted in the power grid, and classical curtailment approaches are not enough anymore to guarantee a stable and economical operation. Those issues are analytically discussed in the next session, where the advanced control schemes required for all the building blocks of smart grids are reviewed, aiming to maintain a stable power system operation in the future.

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3 Advanced Control Schemes for the Smart Grid Scenario As the building blocks of smart grids are equipped with advanced control schemes, it is of great importance for smart grid engineers to be familiar with them. In the remainder of this section, the main building blocks of smart grids will be presented, along with the main control schemes that are required for their optimal operation, aiming to realize smart grids with advanced functionalities sustainably. Additionally, the latest advances in the control of those components will be briefly discussed.

3.1 Advanced Control and Energy Management Systems in Bulk Power Systems Transmission systems have historically corresponded to the skeleton of power systems, acting as the entity interconnecting the large generation plants with the distribution system and the end-users therein. As discussed above, the control rooms of the transmission system operators (TSOs) have been in charge of maintaining a proper power flow around the transmission networks, as well as making sure that power balance is achieved, equalizing generation with consumption, in order to maintain a stable and tight frequency regulation. However, up to recently, most of the control requirements were based on the operation of large power plants and disregarded the presence of DERs. However, the Grid Codes of the TSOs worldwide have now introduced multiple control algorithms as proposals for the generation plants to be connected, expanding such requirements to DERs too, e.g. the fault-ride-through requirements required in Germany from 2007 [10]. In addition, with the rapid increase of renewable energy sources penetration, and especially due to their interconnection at the distribution system level, the role of the distribution system is not any more limited to just supplying the required electrical energy to the loads. However, it can also act as an active node of the system, with the capacity of generating power, when the load is low, and the weather conditions lead to significant electricity generation by DERs. This has led to an extra need for coordinated transmission system operator/distribution system operator (DSO) operation [11], with the appropriate control and communication systems for establishing a useful interconnection massively being researched in the previous years, with regards to stability, market operation and flexibility [12–14]. Finally, as the energy transition does not only focus on the electricity vector, improvements need to be made in utilizing energy resources of other energy vectors, such as heating, cooling, natural gas, mobility and others. This has led to significant efforts in optimizing the operation of all those energy vectors. However, those energy vectors are coupled, for example through electric boilers, electric vehicles chargers etc.; hence, the optimum solution is the one that considers all the vectors, i.e., forming a multi-vector optimisation problem [15]. Such optimization approaches consider the cost of different energy production approaches and the forecasted load and RES

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generation and plan the system operation to operate with the minimum possible cost and the highest possible energy originating from renewable energy sources [16]. Then, with regards to the lower level of modern hierarchical control schemes, advanced secondary control schemes have been proposed utilizing advanced communication and control approaches, as well as new technologies such as phasor measurement units (PMUs)/synchrophasors, which allow synchronized measurements [17]. Finally, regarding primary control, further discussion is provided in the subsection regarding DERs equipped with smart inverters. It is noteworthy that apart from the classical control approaches, i.e., based on PID control and optimization, advanced control schemes of the bulk power systems have also adopted neural networks approaches, e.g. for the load frequency control [18], deep-reinforcement-learning techniques, e.g., for voltage control applications [19] and artificial intelligence approaches for the fault detection [20], to merit from their capabilities when it comes to optimal and adaptive operation.

3.2 Hierarchical Control of Microgrids Microgrids, according to their definition, are clusters of loads, distributed generators, and storage devices, operating in a coordinated way, either connected to the main grid or in islanded mode. Microgrids are expected to pave the way towards smart and environmentally friendly grids, due to their dependence mainly to renewable energy resources, and their offerings with regards to system reliability, as they can continue operating in islanded mode when a grid failure takes place, while they can also support remote and critical areas with their advanced islanded operation capabilities [21]. As it is easily understood, the control of microgrids becomes an important topic as, in the absence of the main grid, they need to coordinate voltage and frequency regulation and the power balance in a usually small-scale power system [22]. Similarly to the bulk power system control, the hierarchical control of microgrids has also attracted attention, especially for larger microgrids, e.g. medium-voltage microgrid applications [23]. The conventional approach of microgrid hierarchical control presented in [23] and Fig. 2 follows the established hierarchical control of transmission systems, discussed in the previous section. However, advanced solutions have been proposed in recent years, focusing on the accurate reactive power sharing problem [24], the exact voltage and frequency regulation to the nominal points, as well as applying distributed control schemes, such as consensus control [25], and local control actions [26]. Modern approaches, such as MPC control, have also been proposed to improve the objectives’ effectiveness [27]. Additional functionalities which are usually taken into consideration in the hierarchical control scheme of microgrids may have to do with their seamless mode transition [28, 29], i.e., between the grid-connected and islanded modes, as well as the configuration management of multi-microgrids [30], which correspond to enabling technologies for the increased reliability offered by the microgrids technology.

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Fig. 2 Hierarchical control of microgrids

Even though classical control approaches and optimization problems are used most of the times to address the above objectives, artificial intelligence, with selflearning capability and low dependency on mathematical models, may be employed for the optimal dispatch of sources in microgrids [31–34], which eventually may require advanced communication and computational capabilities. Artificial intelligence algorithms can be applied in all control layers to enhance their performance, as explained below [34]: (A) Primary control layer: The scope of the primary control layer is the real-time power-sharing between the inverter-based resources (IBRs), maximum power point tracking (MPPT) and inertia control [34]. Artificial intelligent algorithms can offer many opportunities to enhance all the aforementioned applications. For example, conventional MPPT algorithms suffer from slow tracking speed and poor performance under certain circumstances [35]. Conversely, neural network-based algorithms [35, 36] can perform MPPT more accurately and faster. (B) Secondary control layer: The secondary control layer restores the voltage and frequency to their nominal values after the action of primary control. Conventional secondary control methods suffer from inaccuracies and lagging responses while requiring extensive communication infrastructure [34]. Moreover, communication failures can significantly affect the synchronization between the IBRs, thus, the voltage and frequency restoration [37]. Artificial intelligent algorithms can be applied to improve the performance of secondary control. Indicatively, in [38] ANN and Genetic Algorithm is applied to control the voltage and frequency deviations at the secondary layer, while in [39], a

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novel reinforcement learning (RL) algorithm is developed to compensate for the reactive power, unbalanced load currents and harmonics. A distributed machine learning secondary controller has been proposed in [40] without the need for a communication channel. An extreme learning machine algorithm is proposed in [41] to treat communication failures between the IBRs. A data-driven and distributed heuristic dynamic programming-based secondary controller is also presented in [42] without the need for accurate model parameters. (C) Tertiary control layer: The tertiary layer involves many functionalities such as power and energy management, autonomous market participation, consumer/ prosumer segmentation, load and renewable’s forecasting [34]. Artificial intelligent algorithms can be used to improve all the aforementioned functionalities. Indicatively, data-driven stochastic energy management for isolated microgrid based on a generative adversarial network (GAN) is proposed in [43], while in [44], a fuzzy logic controller with low complexity is proposed to minimize the grid power profile fluctuations. In [45], a bee colony and ANN algorithm is applied, minimizing the production cost, increasing the convergence speed and improving the efficiency. In [46], a reinforcement learning and dynamic programming algorithm is proposed to maximize reliability, self-sustainability, environmental friendliness, battery life, and customer satisfaction. Artificial intelligent algorithms, e.g., ANN, have also been effectively used for load and renewable forecasting [47].

3.3 Distributed Energy Resources Equipped with Smart Inverters Even if the hierarchical control of smart grids and microgrids poses an interesting problem in the optimal system operation and the control objectives for the realization of the next generation of power systems, significant emphasis is today given to the specific control functions of the inverter units of distributed energy resources, with the concept of smart inverters arising [48]. Smart inverters utilize the capability of power electronic devices to operate in a fast manner and primarily based on the digital control algorithms implemented in their control card, hence, stability issues of the modern smart grid are expected to be partly addressed through the control of those devices [22]. Indicatively, a major issue met in modern smart grids is the reduction of the system inertia, as National Grid has showcased for the UK [49]. In fact, as inverter-based resources do not have any mechanical rotating parts to store energy, frequency events may be more adverse in the scenario of a grid fault, leading to a risk for the proper system operation and restoration following to a fault. With this regard, the virtual inertia introduction through the control algorithm of the IBR has been proposed and has recently moved into the implementation stage [50, 51]. Additionally, the ancillary services provision through droop control as well as fault-ride-through schemes has been extensively discussed in the literature, aiming to mimic the operation of the

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conventional synchronous generators during the normal (voltage and frequency regulation, power sharing) and abnormal grid conditions (e.g. reactive power provision) [52–55]. The above functionalities can be accomplished through both grid-following and grid-forming inverter schemes. In fact, most of the IBRs in the power system today correspond to grid-following units, meaning that they require an already established grid to connect to, while grid-forming inverters are mostly deployed in microgrid applications, and they can control the voltage and frequency at their output. However, as grid-forming inverter units usually require storage devices to govern the power balance, they mostly focus on battery energy storage systems (BESS). Different grid-forming control schemes have been reviewed in [26]. The traditional approaches include the double loop voltage control schemes, while the droop control is also employed usually to mimic the operation of synchronous generators, guarantee power sharing between the grid-forming units, and possibly provide virtual inertia to the usually inertia-less microgrids. Particularly for the grid-forming inverter units, advanced control schemes that go further than the voltage and frequency regulation and the power-sharing requirements may be needed to address black-start functionality provision, a fact that is also expected to be required in future smart grids, where multiple generation units of limited rated capacity will exist rather than limited black-start units of large rated capacity [56]. The major solution for all the above approaches is the classical PID control where most commonly the proportional (P) or proportional-integral (PI) are the base for all different control loops. In fact, for the control loops depicted in Fig. 3, droop control usually utilizes proportional control schemes, while by utilizing the synchronous reference frame, inner voltage and current control loops are based on the PI control scheme to regulate the output voltage and current to their reference values. Additionally, to address the robustness of the control schemes performance under different conditions, sliding-mode control schemes have been proposed [57], while other nonlinear schemes like the bounded integral control scheme [58] have been proposed to address an inherent current limitation through the control design [22, 55]. Moreover, alternative approaches based on MPC have been proposed for the droop control implementation [59] and the inner control loops implementation [60]. Finally, artificial intelligence-based approaches have recently been proposed for the control realization or tuning [61–63]. Another major issue of IBRs is their influence on the protection system of power networks [64–67]. Specifically, power networks are protected using overcurrent relays based on the assumption that the current flows to the customers from the high voltage (HV) substation. The bulk connection of IBRs causes a reverse of power flow affecting the operation of protection systems. Effects like blinding of protection equipment, sympathetic tripping of relays, islanding, loss of relay’s coordination, auto-reclosing problems need to be carefully examined before the connection of IBRs and can restrict the massive connection of IBRs [66]. Several solutions have been proposed, such as restricting the fault current of DGs through fault current limiters [66], virtual impedance current limiters [65] and adaptive protection schemes [68]. Last years, artificial intelligent-based protection schemes have emerged as promising

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Fig. 3 Control structure of smart inverters

solutions in networks with IBRs, such as Artificial Neural Networks (ANN) [69, 70], intelligent differential relay [71], decision tree-induced fuzzy rule-based differential relaying [72], rule-based protection with machine learning (ML) techniques [33], centralized protection based on fuzzy logic and graph algorithms [73] and hybrid fuzzy-optimization for adaptive relay setting and optimal coordination [74].

3.4 Smart Home and Smart Buildings Conventionally, automation systems were not present in homes and buildings, where control functions were limited to the temperature setpoints of air-conditioning and heating systems. However, such operation was deemed insufficient, and advances were required to increase energy efficiency. For example, the European Union’s 2020-20 targets required that by 2020, an energy requirement reduction of 20% would be achieved through the increased energy efficiency [75]. Given that the electricity sector significantly contributes to the total energy demand, homes and buildings naturally correspond to a significant target group for improved energy efficiency. Hence, the smart home and smart building has emerged, where the provided smartness suits both the energy-efficient operation and the increased amenities for the residents [76], with the rest of this subsection focusing on the former. The control of Smart Homes and Smart Buildings has conventionally focused on automation systems governing the heating, ventilation and air-conditioning (HVAC) systems [77]. Additionally, in recent years such automation has increased, covering shading systems, lightning systems, electric vehicle charging systems, and the coordination within the local electricity generation systems [78, 79]. In particular interest for the topic of this book, in buildings with sufficient amount local generation systems, the smart building concept maybe lead to the adoption of an advanced optimization

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system that not only can optimize the energy consumption of the building but also allows it to further participate in the smart grid scenario, through the provision of ancillary services and the optimal coordination with the optimizers of the distribution network, effectively acting as microgrids [80, 81]. Furthermore, financial schemes and advanced control tools, such as the net metering and the zero feed-in (selfproduction) control approach, can also enhance the capability of residential homes and buildings to participate in the energy transition, without any effect to the many times congested (with regards to RES penetration) distribution grid [82]. Apart from the classical approaches, artificial intelligent approaches have also been proposed to manage the monthly consumption of smart homes [83], as well as machine learning for both the automation of the home [84] as well as its interoperability with the overall smart grid [85], with such approaches aiming to efficiently handle and act on the vast amounts of data involved in such applications. Additionally, the supervisory control aiming to automate all procedures in a smart building environment, needs advanced communication platforms [86, 87].

4 Discussion Many of the control schemes discussed above do not only rely on the sophisticated control design and its proper turning to maintain closed-loop system stability but also need the appropriate establishment of communication channels to exchange the required information and data, as well as advanced computational capabilities to realize efficient real-time control of the smart grid. For example, voltage and frequency regulation require continuous monitoring of power flows and system conditions, which must be communicated to control centers in real-time to ensure timely responses. Similarly, fault-ride-through and black start capabilities require rapid detection and isolation of faults, as well as coordinated actions across different parts of the grid. To support these requirements, smart grids rely on a range of communication technologies, such as 5G, power-line communication (PLC), fiberoptic communications, satellite enabled 6G networks, etc. Also, significant efforts are undergoing in order to establish the required communication protocols for the smart grid era [88]. This especially holds true for the centralized control schemes, such as optimizers implemented in TSOs control rooms [89], as well as the distributed control schemes, which may be utilized in microgrids and smart building applications, that need information from the closest nodes of the communication network. Furthermore, advanced sensor networks may further improve the overall operation of Smart Grid 3.0, which relies on accurately measuring the quantities of interest to decide on its appropriate actions. These communication technologies and protocols enable high-speed data transfer between different components of the grid, allowing for real-time monitoring and control. However, they also introduce new challenges related to security, reliability, and interoperability. In terms of computational requirements, advanced control schemes often rely on complex algorithms that require significant processing power. For example,

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optimal operation algorithms use mathematical optimization techniques to determine the most efficient way to allocate resources across the grid. These algorithms can be computationally intensive and require specialized hardware or cloud-based computing resources. Artificial intelligent approaches or deep learning algorithms may also require a platform with extensive computational capabilities [90] to realize real-time control of power systems through handling based on big data analysis. In addition, there are several other communication and computational requirements that pose challenges for advanced control schemes in smart grids: • Scalability: Smart grids are characterized by a large number of interconnected devices, sensors, and control systems. Ensuring scalability of communication networks and computational infrastructure to handle the increasing volume of data and control signals is a significant challenge. • Latency and Delay: Real-time control in smart grids demands low latency and minimal delay in data transmission. However, the inherent characteristics of communication technologies, network congestion, and data processing times can introduce delays that impact the responsiveness of control schemes. • Reliability and Resilience: Communication networks in smart grids must be highly reliable to ensure continuous data exchange and control operations. Challenges include mitigating the impact of communication failures, network disruptions, and cyber-attacks to maintain the integrity and reliability of control systems. • Interoperability: Smart grids incorporate diverse technologies, devices, and vendors, making interoperability a critical challenge. Ensuring seamless communication and compatibility between different components, protocols, and standards is essential for efficient control and coordination across the grid. • Security and Privacy: With the increasing connectivity and data exchange in smart grids, ensuring robust security measures and protecting the privacy of sensitive information becomes crucial. Control systems and communication networks must be designed to withstand cyber threats, unauthorized access, and data breaches. • Data Management and Analytics: The growing volume of data generated by sensors and devices in smart grids presents challenges in data storage, processing, and analytics. Efficient management of big data, including data filtering, compression, and real-time analysis, is essential for effective control schemes. • Edge Computing: As the complexity of control schemes increases, there is a need for localized computational capabilities at the edge of the grid. Edge computing can enable faster decision-making and reduce reliance on central processing resources, but it also introduces challenges related to resource allocation, synchronization, and load balancing. • Standards and Protocols: The development and adoption of standardized communication protocols, data formats, and interfaces are crucial for interoperability and seamless integration of control schemes in smart grids. Aligning different stakeholders and industry players around common standards is a significant challenge. Addressing these communication and computational challenges requires ongoing research, technological advancements, and collaboration among researchers,

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industry experts, and policymakers. Overcoming these hurdles will contribute to the efficient and reliable operation of control schemes in smart grids, enabling the realization of their full potential in supporting sustainable and resilient power systems.

5 Conclusions This chapter reviewed the advanced control functionalities of the main building blocks of the smart grid, i.e., transmission and distribution system, microgrids, distributed energy resources and smart homes. At the same time, particular emphasis was given to the control techniques that need advanced computational and communication capabilities for their proper operation, which corresponds to the main topic of this book. Particularly, it was shown that modern control approaches in all smart grid components might lead to an increased computational burden and therefore require advanced processing units, while centralized or distributed control approaches that may need an overall more optimal operation of the smart grid, significantly rely on communication channels for measurements and control signals exchange. Hence, a thorough understanding of such components is required by smart grid engineers, a topic covered in the sequel of this book.

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Multistage PD-(1+PI) Controller Design for Frequency Control of a Microgrid Considering Demand Response Program Hossein Shayeghi and Alireza Rahnama

Abstract This chapter examines the load-frequency control (LFC) problem for an islanded fully-renewable microgrid (MG) that meets all users’ power demands with renewable energy sources (RESs). Islanded MGs with substantial RESs penetration face uncertainty. If we want to reap the advantages of RESs, we must equip the MG with a strong and effective control system since these sources, notably wind turbines and photovoltaic systems, are weather-dependent. For LFC, a multistage controller is designed. It removes PID controller flaws and operates quickly and reliably. The proposed controller is a Proportional Derivative (PD) cascaded with One + Proportional Integral (1+PI). The PD controllers operate as filter to speed up controller response, while PI controllers overcome steady-state error. This control approach combines these two controllers in the first and second stages to decrease steady-state error and achieve system stability faster to increase system responsiveness. Demand response programs (DRPs) make up for the MG’s lack of auxiliary services. This chapter discusses how a frequency-based control method for responsive loads in smart MGs may involve in the LFC issue. The DRP presence or absence and response loads involvement amount have been examined. The MG faces uncertainty and nonlinear variables, making LFC controller design challenging. Metaheuristic algorithms are used to find optimum controllers for the LFC issue. The particle swarm optimization with nonlinear time-varying acceleration coefficients (PSO-NTVAC) is used to find the best controller settings. To adjust for frequency variations in a 100% renewable MG, the cascade PD-(1+PI) controller is evaluated under a number of situations, including system modelling uncertainty and nonlinearity, and the existence of the DRP. Keywords Frequency regulation · Hybrid microgrid · Multistage controller · PSO-NTVAC algorithm

H. Shayeghi (B) · A. Rahnama Energy Management Research Center, University of Mohaghegh Ardabili, Ardabil, Iran e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 B. Appasani and N. Bizon (eds.), Smart Grid 3.0, Power Systems, https://doi.org/10.1007/978-3-031-38506-3_10

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Abbreviations BDEG BGTG CSP DRP IAE ISE ITAE ITSE LFC MG MHP MUS PSO RES ST STP WTG

Biodiesel engine generator Biogas turbine generator Concentrated solar power plant Demand response program Integral of absolute error Integral of square error Integral of time-weighted absolute error Integral of time-weighted square error Load-frequency control Microgrid Micro-hydro power Maximum undershoot Particle swarm optimization Renewable energy sources Settling time Solar thermal power Wind turbine generator

1 Introduction There are still areas without access to electricity in the twenty-first century, despite its pervasiveness. Because of their ability to operate independently and in islanded mode, MGs can be considered a suitable solution for satisfying consumers’ electrical demands placed in geographically remote areas or where supply using the utility grid is uneconomic or unreliable. Besides, supplying electricity locally and on a small scale could correct voltage sag and reduce energy losses and power transmission problems [1, 2]. On the other hand, the traditional power generation units that meet most electricity demand are at the opposite end of the green environment [3]. Extensive CO2 gas production by thermal units and radioactive materials in nuclear power plants cause major environmental problems. RESs features such as environmentally friendly nature and their good availability have led to the widespread use of these power resources in MGs. The aforementioned make MGs a clean option in line with addressing environmental concerns and eliminating the dependency on fossil fuels [4]. MGs, despite their benefits, will have their drawbacks because of the existence of the RESs. The intermittent nature of RESs and the uncertainties inherent in these sources’ nature may cause a generation-consumption mismatch, eventually leading to the MG’s blackout. In addition to the mentioned subjects, in the case of un-isolated MGs, the grid’s connection could guarantee the system’s stability. However, in islanded MGs, sudden change either in load, wind speed, or in the solar irradiations will cause the system’s frequency and, hence, the output power

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fluctuates, and eventually, they can lead to systems instability. To cope with these issues, designing and using a robust intelligent controller for the LFC task of the MG will be necessary. The concept of MG and its LFC methods have been the subject of various studies [5, 6]. In MGs, which generally have different renewable units, different control methods are used; For example, in an MG containing distributed generation resources and in the presence of storage devices, a fuzzy type PD-TID controller is used [7]. The traditional PID controller has been one of the most widely used in MG frequency control. In [8], the PID controller optimized by a reinforcement learning (RL) method is used in an MG with renewable units, storage elements, and system nonlinearities. As is clear, the existence of integral gain in traditional controllers such as proportional integral derivate (PID) controller is necessary for handling the steady-state error [10]. The key problem with these kinds of controllers is that the integrator’s activities during the transient period negatively affect the system’s performances. One approach to solve this problem involves the use of cascade controllers [11]. Cascade controllers are a modification of the conventional controllers that usually consist of two (or even more) different stages to overcome their single-stage forms obstacles. Recently, various studies have been done that these kinds of controllers are taken in charge of LFC tasks in power systems. The use of the PI-PD cascade controller in terms of electric vehicle models has been reported in [12], that the controller was designed by the Gray Wolf Optimization (GWO) algorithm. The multistage TDF-(1+FOPI) controller is used in an MG containing storage devices and is optimized using the bonobo optimization (BO) algorithm [13]. Although the electricity demand is growing unstoppable, some technical, monetary, and environmental issues prevent utility companies from expanding their generation capacities. This fact impels to shift from the “load-following” paradigm to “generation-following,” and it caused smart grids and MGs development. Demand response is a concept that within the generation-following scope. Each DRP mainly consists of a controller part and an appliance part [14]. This chapter takes into account HEV charging stations that might serve as appliances in DRP and analyses how they affect the MG’s LFC task [5]. Using demand-side managements in the DRPs form is also rapidly gaining ground in modern MGs LFC. It has also been looked at how DRP performs in the management and scheduling of modern microgrids in [15]. In [16], a DRP along with storage resources for MG frequency control has been introduced. Optimal tuning of MG’s controller is a vital and complicated issue, especially when the MG faces nonlinearities and high penetration of RESs with the mentioned features. There can exist more than a single response to an optimization problem, so using an efficacious algorithm will lead to reaching or getting very close to the best answer. Swarm intelligence is one of the most powerful methods for achieving solutions to an optimization problem, and the particle swarm optimization (PSO) algorithm is well-known among them [17]. Usually, these kinds of algorithms contain some coefficients which overshadow their performance [18]. Hence, finding the best value for these coefficients is important, and some efforts have been taken about this. PSO with nonlinear time-varying acceleration coefficients (PSO-NTVAC) is taken from normal PSO and is applied in this work, where its coefficients change nonlinearly during the iterations.

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In this chapter, we try to accomplish the LFC task by designing and applying a robust cascade controller to improve the utilization efficiency of various renewable energy forms in an isolated MG where renewable plants will meet the total electricity demand. Introduced Proportional Derivative cascaded with (1 + Proportional Integral) (PD-(1+PI)) controller, besides providing the system’s stability, improves the performance by its tow-stage structure, where there is no integrator in the first stage of the controller. In other words, clearing the steady-state error in the PD-(1+PI) controller is left to the second part, which leads to the controller’s effective and fast responses. Novelties and contributions of the chapter can be summarized as below: 1. Proposing a multi-stage PD-(1+PI) controller for frequency control of an entirely renewable MG. 2. Using a DRP with frequency fluctuations-based control law as a support for the secondary controller to solve the LFC problem in an isolated fully-renewable MG. 3. Optimizing the controller gains using the PSO-NTVAC algorithm to reduce system costs while achieving the desired degree of stable performance. 4. Considering system modelling uncertainties and nonlinearities in controller synthesis in the presence of the DRP. The rest of the chapter is arranged as follows: In Sect. 2, the case of the study is described and an overview of the studied MG is provided. Different renewable power plants are introduced in Sect. 3. The DRP, which consists of two parts, control and responsive loads, is also described as a unit in this section. In Sect. 4, the proposed framework for the controller is outlined. In Sect. 5, the method of achieving the optimal controller, namely the PSO-NTVAC optimization algorithm, is introduced. The proposed control strategy is analyzed in Sect. 6. Finally, in Sect. 7, the results are discussed and reviewed.

2 Case of the Study In addition to the power supply, traditional central power plants have other facilities to support frequency stability, power regulation, voltage stability, and so on. The traditional plants can adjust their generation by changing the amount of fuel consumed. Renewable units typically interface with the grid by power electronics and generally deliver all their available power to the net, so they cannot provide support services like traditional units. The conventional fossil fuel-based power plants synchronize with the grid and provide rotational inertia to stabilize system frequency [10]. Using RESs with their benefits instead of centralized power plants leads to the disappearance of traditional units’ features in MGs. Smart grids by using DRPs can significantly be a suitable replacement for ancillary services of the traditional plants. Most devices used in DRP can turn ON/OFF instantly, and this feature makes them respond much faster

Multistage PD-(1+PI) Controller Design for Frequency … ΔΦ

ΔVW Set Point

Σ -

ΔPLFR

Solar-Thermal Power Plant

ΔPw

Wind Turbine Generator

ΔPSTP

ΔPCL

Biogas Turbine Generator

ΔPBDEG

1/R1

Σ

`

Biodiesel Engine Generator

Crucial Loads

ΔPWTG

ΔPBGTG Microhydro Turbine Generator

Σ

Controller

245

ΔPMHP

-

Σ

ΔPS

System Dynamics

Δf

Model

ΔPDRP

Demand Response Controller

1/R2

Fig. 1 General scheme of the studied MG

than traditional units. This is one of the main reasons for using DRP in frequency control [11]. Most forms of renewable energy sources are very susceptible to environmental factors. Therefore, their power generation may change abruptly depending on weather conditions. So, in MGs with high penetration of renewables, it is necessary to use a vigorous control strategy. In addition to disturbances caused by weather conditions, various sources such as radiofrequency interferences, electromagnetic interferences, and telemetry systems in communication lines may generate noises in the system that will overshadow the performance of the controllers. Accordingly, in the studied MG, a two-stage controller is embedded. In addition to being robust, the first part of the proposed controller acts as a filter that enables the controller to respond quickly against successive disturbances in a short period. All the resources of the system studied in this chapter for MG are renewable. Total MG demand will be met by renewable hydropower/wind/solar/biogas and biodiesel based units with the support of a DRP. A two-stage controller is considered for the system. This controller’s gains in the presence of nonlinear factors of the time delay of 20 ms are determined by applying the PSO-NTVAC optimization algorithm. Figure 1 shows an overview of the studied MG. The controller in this MG is responsible for setting up three units under its supervision to solve the LFC problem. In the next section, the different units of the islanded MG are studied. The linear transfer functions of each power plant are also stated at the end of their respective subsections. An HEV-appliance-based DRP with a frequency deviation-based control rule has also been presented as a LFC loop support.

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3 Power Plants of the MG and DRP Configuration 3.1 Solar Thermal Power (STP) Plant Choosing the right size and technology to exploit solar radiation and generate electricity depends on several factors. Concentrated solar power plants (CSPs) are among the most popular solar energy technologies [19]. CSP is a technology where the temperature of heat transfer fluids (HTFs) (steam, thermal, oil, molten salts, etc.) is increased by collecting solar power. Thereupon, a power generation section can transform high-temperature thermal energy to mechanical work. Large-size CSPs (often in the range of 20–50 MW) need large areas in addition to high investment costs. Using medium-sized CSPs around 1–5 MW would be a more reasonable option in an islanded MG. Furthermore, small and medium-size CSPs are a better alternative for the diffusion of distributed power generation [19, 20]. There are three main sections in CSPs [19] as shown in Fig. 2. (1) Solar field: This part determines how solar energy is concentrated. The four technologies used in solar fields are parabolic trough collector (PTC), solar tower, linear Fresnel reflector (LFR), and solar dish systems. Figure 3 illustrates these configurations.

Power Block

G

Regenerator

Pump

Solar Field

Turbine

Vaporizer

Thermal Condenser

Energy Storage Heater

Cooling Tower

Fig. 2 Process flow of an ORC-based CSP

Fig. 3 Four main configurations of solar fields. a Parabolic trough, b linear Fresnel reflector, c solar tower, d dish concentrator

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Due to its cheapness and good thermal efficiency, interest in LFR technology has increased. LFR can generate efficient heat at medium or high temperatures (.< 500 .◦ C). A comparison between STP plants using LFR and PTC technologies has been performed in [20], which shows that where the other conditions are the same, STP with LFR needs 56% less land-area than PTC type SPT units to produce the same amount of power. Consequently, the use of LFR technology is preferred in situations where we have limited usable land-area or in densely populated areas. (2) Thermal energy storage (TES): It can be active, passive, two-tank, thermocline, etc. In the absence of TESs, during the sunniest hours of the day, the collectors will adjust to prevent overheating the HTFs and defocus extra sunlight. TESs will prevent the loss of excess sunlight. Also, fuel-based backup systems (BSs) in CSPs configurations (with or without TESs) are common. BSs help to have a nearly constant generation. TES and BSs increase the potential of CSPs [19]. Figure 2 shows a configuration of CSPs with TESs. (3) Power block: It can be based on steam Rankine and organic Rankine cycles, Stirling engines, combined cycles, etc. There are significant differences in the structure and technologies of medium-sized CSPs and large-size CSPs. Steam turbines in CSPs are not suitable when the generated MWe is low because steam cycles require higher pressures and temperatures. Therefore, higher installed capacity will be required for proper efficiency. An appealing alternative is to use high-molar-weight organic fluids instead of steam in the Rankine cycle. Organic Rankine Cycles (ORCs) input thermal energy temperature is in the range of 250–350 .◦ C, and its conversion efficiency is about 20–25%. ORC heat conversion technologies are well-suited for collaborating with renewable energy due to the feasibility of building decentralized power plants with lower-power capacity and low-grade heat recovery [21]. Sahoo et al. [5] introduced a linearised model for LFR type STP with ORC conversion cycle where the HTF is thermal oil. This transfer function is as follows: ( G ST P (s) =

KLFR 1 + sTL F R

)(

K O RC TH X 1 + sTH X

)(

1 1 + sTST

) .

(1)

This linearized transfer function models the actions between different parts of the STP plant. In this equation, thermal gain.(K O RC ) and time delay,.TH X , are considered 100% and 0.1 s, respectively.

3.2 Micro-hydro Power (MHP) Plant Hydroelectric power is an important renewable resource that can be implemented in remote communities in off-grid form. The hydropower plants can be classified into large, medium, small, mini, and micro in terms of generation capacity. While the large hydropower plants can produce more than 100 MW of power, micro-hydro power plants have a capacity inferior to 5 kW [22]. Without the requirement for dams

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Fig. 4 MHTG schematic diagram

or storage facilities, MHP plants are attractive for their inexpensive maintenance and installation costs, the potential of local administration, and their little environmental effect. Water is not wasted but rather used in the production of electricity by MHP facilities. As shown in Fig. 4, the water directed to a valley is poured into a turbine through a pipeline, thus providing the mechanical work required to rotate the generator turbine. Hydropower plants can be classified into three types according to the technology of construction and operation: impoundment, diversion, and run-of-river. The second and third types often do not require a dam, but the first type used for large hydropower plants will need a dam to store water. In the diversion type hydropower plants, a facility diverts an apportionment of a river through a canal or penstock. Run-of-river type MHP plants are mainly small-scale plants in which there is just a weir, and the water is generally not stored, or the volume of the stored water is low. The power plants’ intaking water level is adjusted through civil works, which in MHP plants can be medium (head .> 15 m) or low (5 m .< head .< 15 m) [22, 23]. Numerous factors play a role in the efficiency, success, and sustainability of hydropower plants, such as site selection, installation, equipment, estimation and calculation of demand, cost-effectiveness, power plant size, and canal length [22]. From a profitability point of view, studies have also been conducted on the use of MHTGs for matters other than home lighting in remote areas in a way that generates revenue [24]. In [25], a linearized transfer function is introduced for a low-head MHP plant as follows: )( )( ) ( 1 + sTR S 1 − sTH T 1 (2) . G M H P (s) = K M H P 1 + sTH G 1 + sTR H 1 + 0.5sTH T where . K M H P shows the participation coefficient of the MHP plant, which is considered to be 0.5 in this work. In this equation, which is obtained by considering the hydro turbine’s operation, penstock, and governor speed, there are stable and unstable zeros. Therefore, (2) represents a non-minimum phase system. The Bode diagram of

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Bode Diagram Magnitude (dB)

0 -10 -20 -30

Phase (deg)

-40 360 270 180 90 10 -3

10 -2

10 -1

10 0

10 1

10 2

Frequency (rad/s)

Fig. 5 Bode diagram of the MHT unit linear transfer function Fig. 6 Step response of the MHT unit linear transfer function

this MHP plant is depicted in Fig. 5. As expected, the existence of the nonminimum phase (NMP) term in the transfer function (refers to hydro turbine operation), introduces extra phase lag. This term also will lead to the system’s opposite-direction responses, which can be seen in the system step response shown in Fig. 6. These features will make the control process more complicated.

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Fig. 7 The general process taking place in the biogas plants

3.3 Biogas Turbine Generator (BGTG) Unit Traditional diesel generators may utilize biogas as a fuel source. Methane (CH4 ) makes up between 60 and 70% of biogas, which is extracted from the bio-chemical degradation of organic components in oxygen-free conditions. In other words, biogas results from a high-efficiency natural microbial process that results in the conversion of biomass to CH4 . Compost, agricultural waste, and animal manure are just a few examples of the types of biodegradable waste that may be used in this procedure. Hence, using these renewable sources to produce fuel required in traditional diesel generators is a good option, particularly in rural and remote areas. Most biogas plants have the same structure except for design preferences and features that influence their success or failure [26]. Figure 7 shows the general flow of extraction and exploitation of materials used in biogas plants. The biogas inlet-valve, speed governor, gas turbine, and combustor all play roles in the biogas plant’s overall functionality. Here we express the linearized transfer function for a BGTG facility as below [5]: ( G BGT G (s)BG

1 + s Xc (1 + sYc )(1 + sb B )

)(

1 + sTC R 1 + sTBG

)(

1 1 + sTBT

) .

(3)

In (3), . K BG indicates the contribution of the BGTG to the nominal loading.

3.4 Biodiesel Engine Generator (BDEG) Unit Fossil fuels can be replaced with biodiesel produced from animal fats or vegetable oils. The transformation of vegetable oils or animal fats to monoalkyl ester or biodiesel occurs in a process called transesterification. The term biodiesel is used to illuminate that transesterified vegetable oils are used as diesel fuel [27]. Renewable resources are used to produce biodiesels. Their lack of harmful substances, including sulphur and carcinogenic compounds, as well as their biodegradability and ease of

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disposal, all pique interest. However, currently, biodiesel cost is mainly influenced by the feedstock price and is 1.5–3 times as much as fossil diesel [28]. From an environmental perspective, different blended fuels, including 80% of biodiesel blending with 20% of kerosene (B80&K20), 80% of biodiesel with 10% of kerosene, and 10% of diesel fuel (B80&K10&D10) and D2,1 have been compared in the same diesel engine with various loading conditions [28]. The results show that although the cylinder pressure curves for these fuels were similar, B80&K20 and B80&K10&D10 had lower CO emissions per unit of energy. Also, in the biodiesel blends with kerosene, the emission of NOx has been significantly reduced. A study by Ramadhas et al. [30] examined the closeness performance of the pure and blended biodiesels to diesel fuels. In this study, the rate of change in cylinder volume is introduced as a function of the crank angle as follows: ] [ 0.5 sin 2θ dV = 0.5Vdisp √ − sin θ dθ 2(L/S)2 − sin2 θ

(4)

where . L is the length of the connecting rod and S is the stroke length both in m. .Vdisp indicates the displacement volume (m3 ), and .θ is the angular displacement for BDC2 (degree). According to (4), if the universal gas constant (. ) is 8.314 (kJ/kmol K) and instantaneous temperature at each crank angle shown by .T (Kelvin), so the rate of mechanical work performed by the crank can be expressed as below: ] [ 0.5 sin 2θ dV = 0.5ReT Vdisp √ − sin θ dθ 2(L/S)2 − sin2 θ

(5)

El-Fergany et al. [31] provide a transfer function for a standard diesel engine generator (DEG) that may be utilized for BDEG can be used for BDEG. The relationship between the inlet valve and the engine may be described by the transfer function given by (6). ( G B D E G (s) = K B D

KV A 1 + sTV A

)(

KBE 1 + sTB E

) .

(6)

3.5 Wind Turbine Generator (WTG) Unit Wind turbines are intermediaries for wind’s kinetic energy conversion into electrical or mechanical energy. Wind Energy Conversion Systems (WECSs) can be classified into (1) horizontal-axis converters, (2) vertical-axis converters, and (3) upstream power stations. A various number of options for wind machine design and construc1 2

Diesel fuel with 50 ppm sulfur content. Bottom Dead Centre.

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Fig. 8 The front view of a three-bladed horizontal axis wind turbine

Swept area of blades A= πr 2

Radius

Tower

Underground Electrical Connections

tion are available and affect their performance. Front-view illumination of a common kind of wind energy converter, the three-blade horizontal axis converter, is shown in Fig. 8 [32]. Consider the wind turbine in Fig. 8; wind power will come from the wind passing through the swept area (. A). Assume that the wind blows perpendicular to the . A. With the SI system, the wattage of wind power is represented as follows: Pw =

1 ρa AV 3 2

(7)

In (7), .ρa (=1.25 kg/m3 ) is the mass density of air. If .r (=23.5 m) is the radius of the circular cross-sectional area, then the swept area will be . A (=1735 m2 ). V is the wind velocity in m/s. The energy in the wind is kinetic. As the wind passes through the turbine blades, its velocity (and consequently its kinetic energy) decreases and the turbine blades accelerate. Theoretically, whenever the wind speed reaches onethird of its original value after passing through the turbine blades, the blades have captured the highest fraction of usable wind energy. Equation (7) will improve by a coefficient, namely .C p , which identifies a measure of the amount of wind power that a wind turbine can use. In other words, .C p is the turbine power coefficient that describes the power conversion efficiency of the wind turbine. .C p is a function of the wind turbine’s tip speed ratio (.λ) and blade pitch angle (.β) [32, 33]. The expression for approximating .C p is as follows:

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Pw

Shutdown speed

Rated Power

Rated speed Cut-in speed

Wind Speed

Fig. 9 A general power curve for a wind turbine

(

π(λ − 3) C p = (0.44 − 0.0167β) sin 15 − 0.3β

) − 0.0184(λ − 3)β

(8)

where the wind turbine’s tip speed ratio is defined as below: λ=

r ×Ω vw

(9)

The radius of the turbine is shown as r is the turbine radius, .Ω indicates the turbine blades’ tip speed, and .vw is the wind speed. So, the improved version of the (7) can be express as: 1 (10) Pw = ρa AV 3 C p 2 The output power of a wind turbine and their performance is highly dependent on wind speed. The minimum wind speed that can turn the turbine blades and produce usable power is called cut-in speed. The maximum power produced by a wind turbine is called the rated power, and the minimum speed that leads to the production of this output power is called the rated speed. The maximum speed at which a wind turbine can operate is called the shutdown speed or furling speed. Operating a wind turbine at speeds higher than this level may cause serious damage to the system [33]. This information is generally provided by the manufacturers, along with other details. Wind generators’ power curve is one of the important characteristics that display the above information. Its general shape is shown in Fig. 9.

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The first-order linearized transfer function of the wind turbine is defined as follows [5, 33]: KW T (11) G W T G (s) = 1 + sTW T where . K W T (= .1.0) is the gain and .TW T (= .1.5 s) is the time constant of the WTG unit.

3.6 Load-Generator Dynamic Model The aggregate available instantaneous power-changes of the MG can be written as (12), and the frequency deviation in terms of the load-generator dynamics model of the system is estimated as (13) [5]. ΔPs = ΔPST P + ΔPM H P + ΔPB D E G + ΔPBGT G + ΔPW T G ± ΔPD R P − ΔPC L (12) Δf =

1 ΔPs D + sM

(13)

3.7 Demand Response Program (DRP) Smart grids empower consumers to participate actively through DRPs. DR is a demand-side management method in which consumers change their consumption according to need or various motivating factors. In modern grids, load shedding will be a method to maintain system stability. In a nutshell, one method to dampen frequency variations is to get non-essential loads involved in addressing the LFC task by cutting down on or increasing their power use. In general, there are a number of electrical appliances in each DRP that are governed by a control rule [14]. Employing frequency-based control logic to switch on/off responsive loads is an approach to system frequency restoration. In frequency-based DRPs, if the system frequency deviations are less than an allowable limit, often delimited by the utility, it will lead to some appliance’s activation and vice versa. Most of the loads used that participate in DRPs are unnecessary loads whose temporary decrease or increase in their power will not cause much inconvenience to the consumers. Loads such as refrigerators, freezers, water heaters (which act as batteries for thermal energy), hybrid electric vehicles (HEVs), and air conditioning loads can be in the category of unnecessary loads and accommodate a frequencybased demand control. Since most DR loads are dual state (on/off), most DRP configurations have discrete responses. Threshold frequency (.Δ f max ) adjustment has a significant effect on

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Fig. 10 Frequency response characteristic of aggregate DR

DRP performance. Proper adjustment of .Δ f max for each DR appliance will result in almost continuous and smooth frequency responses by aggregate DR. In fact, DRP-participating loads are not like the traditional generators that produce more or less energy by changing the amount of fuel consumed. An electric vehicle may, for example, consume energy in one hour and delivery the energy in the later hours. On the other hand, demand-side devices can switch almost instantly between on and off modes. With all this, it can be said that regardless of how much responsive load flexibility is used, the total net energy consumption over one year will be roughly the same. Finally, with these DRP features, demand-side devices with rapid on-off capability are a good option for primary frequency control without actually delivering power. An aggregator can collectively provide a primary reserve delivery in such a way that the frequency has a soft and suitable characteristic by managing the portfolio of responsive loads [14]. The frequency response characteristic of the aggregate DR is shown in Fig. 10, which is magnified for detail. In Fig. 10, the frequency thresholds of .kth and (.k + 1)th DR appliances are labeled as .Δ f max (.k) and .Δ f max (.k + 1), respectively. The total amount of DR appliances (. PD R ) that can get activated by frequency deviation-based control logic is as follows [5]:

ΔPD Ri

⎧ Δ f > Δ f max i ⎨ −PD R max i Δf ΔP = |Δ fmax i | D R max i −Δ f max i ≤ Δ f ≤ Δ f max i ⎩ PD R max i Δ f < −Δ f max i

(14)

where the index.i indicates that there may be different DR units in the network that the aggregator will coordinate. However, the tolerable frequency perturbation threshold .Δ f max is generally determined by the utility. In this chapter, the network frequency is equal 50 Hz and .Δ f max is equal to 0.05 Hz. Figure 11 shows the control logic in frequency-based DRP for an isolated MG. As it is clear, the maximum and minimum participation of responsive loads occurs when the frequency disturbances transcend the limit.

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Fig. 11 DRP control flowchart

About a quarter of CO2 emissions come from the transport sector. Three-quarters of these emissions are correlated to cars and trucks. Using batteries/super capacitors to supply vehicles is an approach to ameliorate global concerns over environmental matters and petroleum paucity. HEVs are vehicles that can be driven with both fuel and electricity, so they are a good alternative for long distances. Vehicles drive on electricity, in addition to being independent of fossil fuels, have less noise, high energy conversion efficiency, and zero tailpipe emissions. These vehicles are used in grid to vehicle (G2V) and vehicle to grid (V2G) modes. In G2V mode, the vehicle absorbs power during off-peak hours, and vice versa in V2G mode, which is in peak hours. In [5], plug-in HEVs are intended as appliances for DRP, and the linear transfer function of HEV is expressed as below: G H E V (s) =

K H EV 1 + sTH E V

(15)

where . K H E V (.= 1.0) and .TH E V (= .0.02 s) are the gain and time constant of the HEV, respectively.

4 Structure of the Proposed Controller A control strategy’s main goal in the LFC task of a MG is to maintain the system frequency at a constant value or achieve a zero steady-state error in the shortest time since a disturbance occurs. There is always a specific limit to the frequency fluctuations of power systems. Exceeding this limit will not be accepted as it may cause significant detriments to the consumer end or damage expensive industrial equipment. On the other hand, a network’s power consumption will not be static due to changes in consumers’ and industries’ loads. Also, RESs such as wind turbine generators and solar power plants, which depend on weather conditions, will produce

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Fig. 12 Structure of the PID controller

a different amount of power as weather changes. So, it is necessary to properly adjust the controllable power plant inputs (water for hydro generators, steam for turbo generators, etc.) to establish the system’s stability quickly. While having a simple configuration and clear functionality, the controller used for this aim should make the power system robust to uncertainties and nonlinearities and counteract disturbances [34]. The PID controller is still one of the most widely used controllers in different industries due to its easy use and understandable structure. Copious attempts have been made to perform an optimal PID, especially in the LFC field. However, optimizing the conventional PID controller’s gains in the presence of higher-order systems and nonlinearities such as time delays or uncertainties can be a cumbersome process. Besides, since the PID’s three parts—derivative, integral, and proportional work together, their results are intertwined. This controller’s proportional element is responsible for lowering the system’s error. With the help of the integrator, the system’s overall error may be minimized. Simultaneously, the derivative component has the task of controlling the other two terms in swift output changes that will help control the overshoot and ringing [35]. When the PID controller is used for the LFC task, the input signal will be frequency fluctuations (.Δ f ), and its output control signal (.ΔU ) will be obtained through the following transfer function. The structure of the PID controller is illustrated in Fig. 12 [10]. ( ΔU =

KP +

) KI + sKD Δf s

(16)

The term . K P in (16) has come to be used to refer to proportional gain, . K I refers to integrator gain, whereas . K D refers to the operations of derivative gain of the controller. In the event of an error, the integrator gains of the PID (. K I ) will increase to reduce the steady-state error, which will weaken the controller’s performance in the transient period and increase the error clearing time. Therefore, it is better to use a structure that improves the weakness of the PID performance in the transient period. As has been noted, one approach to solving the conventional controllers’ problem during the transient period is the use of cascade controllers. The PD-(1+PI) controller is a

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Fig. 13 Structure of the proposed PD-(1+PI) controller

restructured version of the traditional PID controller that does not have an integrator in the first stage. Figure 13 depicts the schematics for this controller. As seen in the figure, there are two stages in this controller’s structure: firstly, the PD stage, which works as a filter, and secondly, the PI controller, where a unit gain is added with it. It is possible to consider each component of this controller as a separate controller. As a result, it combines the characteristics of these controllers to increase the system’s dynamic reaction speed by reducing steady-state error and establishing stability. Also, the proposed controller has two more gains than the PID controller, one for adjusting the derivative filter (. N ) in the first stage and the other for the second stage’s proportional gain (. K P P ). It can be said that this controller has two additional levers for better and more optimal adjustment. The transfer of this controller can be written as follows: ) ( KI (17) Δf ΔU = (K P + s K D ) 1 + K P P + s Power systems may experience high-frequency noises. The origin of these noises may be due to various factors, such as radiofrequency interferences (radar, arc welding, distant lightning, etc.), electromagnetic interferences (produced by heaters, air conditioners, etc.), and telemetry systems in communication lines. The suggested controller’s first stage employs a filter based on first-order derivatives, guaranteeing the noises’ suppression.

5 Optimization Problem-Solving Procedure 5.1 Standard PSO Algorithm A particle swarm optimization algorithm is one of the nature-inspired optimization methods and acquired from the social behavior of bird thronging and fish school-

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ing [36]. The PSO mimics the individual’s behaviors of a swarm. In the PSO, an initial “population” of particles has been considered, which individuals fly through problem space with given velocities. According to the particle’s historical best memory and neighborhood best memory, velocities get adjusted haphazardly at each iteration. The particle’s movements through the problem hyperspace are in a way that eventually leads to a problem’s optimal or near-optimal solution. Three simple behaviors can define an individual’s actions in a swarm. First is separation, which points to avoiding local flockmates and means each particle tries to find its own way through its knowledge. The second behavior is following the average direction of crowded flockmates, called alignment, while the third behavior is cohesion, which points to orientation and movement in the average position of local neighbors [17, 37]. From a mathematical point of view, there is a vector like . x i ∈ R n which shows the position of the .ith particle at .t. All particles at each iteration accelerate to their next position using their velocity vector that could be identified as .v i ∈ R n for .ith particle. Therefore, it can be said: x i (t) = x i (t − 1) + v i (t)

(18)

Individual’s own experiences and neighborhood knowledge are two main factors that determine the velocity vector in (18). The first factor is depended on the decisions that have been made so far by each particle. Suppose a decision (movement to a new position) has led to a better solution than their previous experiences. In that case, it will be considered a success, and the particle’s best memory (pbest) will get updated. The neighborhood knowledge, also known as social knowledge, is the second influential factor on the velocity vector and can be considered global or local. In the case of using global knowledge, the .ith particle at each iteration will be attracted to the best position through all particle’s positions, which are known as global best (gbest). It is worth noting that the best position is the one that brings the optimal value to the OF at each iteration. It is also possible to use the local bests (lbest) or even every particle’s best experiences instead of gbests. So it turns out that the particles in a population need to interact with each other to achieve optimal solutions, and this is why the PSO is known as an algorithm based on social behaviours. In a situation where .ith particle knows the gbest, the velocity vector will be as follows: v i (t) = wv i (t − 1) + c1 rand1 (0, 1) ( P i (t) − x i (t − 1)) ( ) + c2 rand2 (0, 1) P g (t) − x i (t − 1)

(19)

In (19), there are three components: (1) Inertia: The first term in (19) refers to .ith particle’s habit, which indicates the particle’s tendency to move in its previous direction, which has been moving so far. This parameter controls the particles’ exploration in the problem space so that the higher values for .w allow particles to move quicker and find the optimal positions around them faster, while small values for .w will make the moving intervals narrower. There are modified versions of the PSO that this component

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Fig. 14 The process of updating the .ith particle’s velocity vector in .tth iteration

of the velocity vector gets scaled with a constant or variable coefficient like .w, which is a positive number [38]. In the original version of the PSO, the value of .w was set to 1, but later Shi and Eberhart [39] introduced a version of the PSO with time-varying inertia weight (TVIW-PSO) in which the value of .w in each iteration decreases linearly as follows: w = wmax −

(wmax − wmin ) t tmax

(20)

where .t demonstrates the current iteration and .tmax refers to the maximum iteration number. .wmin and .wmax are the beginning and ending values of the inertia, respectively. Damping the population member’s velocity is essential to prevent them from wandering around the optimal position in the later iterations. The introduced descending function adjusts the values of .w with this approach. (2) Self-knowledge: Refers to a linear attraction to the best position founded by itself until .tth iteration. It is also known as the “nostalgia” of a particle. (3) Social-knowledge: Finally, social-knowledge, known as “cooperation,” is a linear gravitation to the best position ever founded. There are two acceleration coefficients in (19), namely .c1 and .c2 , which show the contribution of self-knowledge and social-knowledge in the particle’s movements. Because in each iteration, the importance of the second and third parameters can be different, so two different random coefficients will change the contribution of these two parameters. These random coefficients with uniform distribution are shown as .rand1 and .rand2 in the range of [0.0, 1.0]. Figure 14 displays the process of updating the .ith particle velocity vector at .tth iteration. During the PSO algorithm’s execution, the particles’ updating rules are the same. Each updating is based on the former values of the particle’s own and its neighbors, and updating are parallel. Figure 15 shows the execution flowchart of the PSO algorithm to solve an optimization problem.

Multistage PD-(1+PI) Controller Design for Frequency … Fig. 15 PSO flowchart

261

Start Determining algorithm parameters and coefficients: Population size, c 1, c 2, ω Initialize particles with random positions ( x ) and velocities ( v)

Calculate fitness of each particle according to the target problem update individuals’ best fitness ( pbest ) and global best (gbest ) Update velocity and position of each particle

Termination criterion satisfied?

No

Yes

End

Due to its simplicity and computational effectiveness, PSO has attracted much attention. Various efforts have been made to make it more effective. However, most of these efforts are philosophically the same performance, modeling member’s behaviors trying to reach their aim as a group [17, 38, 40]. Specifically, most of the studies are focused on how the algorithm’s parameters change [17, 37, 39]. However, achieving the appropriate variation process of social and cognitive participation is essential to avoid getting caught up in optimal local solutions and prevent premature convergence, especially when facing complicated and multimodal problems like LFC issues. The PSO with variable acceleration coefficients is one approach to achieve this purpose, discussed in the following section.

5.2 PSO with Non-linear Time-Varying Acceleration Coefficients Algorithm The PSO parameters’ undeniable impact on this algorithm’s performance quality has made the optimal adjustment of these coefficients the subject of discussion in

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many studies. A PSO-TVAC algorithm is an approach to create a reasonable balance between variations of self-knowledge and social knowledge during the algorithm’s execution in order to search every corner of the search space. According to the lack of social experiences in the initial stages, from the start of the algorithm to its end, the individual’s reliance on self-knowledge should be decreasing, while on the other hand, the impact of social-knowledge increases. In other words, the purpose is to prevent individuals from clustering in local optima in the early stages of optimization. By getting close to the end of the optimization process, members must be converging to the global optima to find the best possible solution. Thus, the process of changing acceleration coefficients can be nonlinear. The nonlinearity of the changes reduces the possibility of particles getting stuck in local optimums. According to the iterations in the PSO-TVAC, acceleration coefficients (.c1 and .c2 in (19)) change linearly. These changes are formulated as follows: ) t ( c1 = c1 f − c1i + c1i tmax ) t ( + c2i c2 = c2 f − c2i tmax

(21) (22)

Equations (21) and (22) show the changes in the self-knowledge and socialknowledge impact the particles’ velocity according to the iterations, respectively. Eslami et al. [41] introduced a type of PSO-NTVAC in which the changes in c1 and c2 are nonlinear. In this algorithm, while changing the inertia weight according to (20), .c1 and .c2 change according to the exponential function as follows: ) [ ] ( c1 = c1i − c1 f exp − (4t/tmax )2 + c1 f ) [ ] ( c1 = c2i − c2 f exp − (4t/tmax )2 + c2 f

(23) (24)

here,.c1i and.c2i are preliminary values, and.c1 f and.c2 f are final values of the acceleration coefficients, respectively. Accordingly, the velocity update equation mentioned in (19) for the PSO-NTVAC algorithm can be upgraded as follows:

v i (t) = wv i (t − 1) ) [ ] ) (( + c1i − c1 f exp − (4t/tmax )2 + c1 f rand1 (0, 1) ( P i (t) − x i (t − 1)) (( ) [ ] ) ( ) + c2i − c2 f exp − (4t/tmax )2 + c2 f rand2 (0, 1) P g (t) − x i (t − 1) (25) The nonlinear changes of the .c1 , .c2 , and the linear changes of the .w during the optimization of a problem with 1000 iterations (.tmax = 1000) are shown in Fig. 16. Other relevant parameters are given in Table 1. Nonlinear changes of the PSO-NTVAC parameters provide a better and more reasonable harmony between exploration and exploitation of individuals than the original PSO version and improves the algorithm performance.

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Fig. 16 Variation of PSO-NTVAC parameters with iteration

5.3 Objective Function (OF) Since in solving an optimization problem, the value of the OF is the measure of optimality, it is necessary to determine an appropriate OF to achieve the suitable solutions. The proper OF should be such that it avoids unnecessary complexity and incorporates the desired vital parameters. Integral of time-weighted absolute error (ITAE), expressed in (26), is one of the OFs that most widely used in the LFC task and We have also used ITAE as the OF in this chapter. Although (26) inherently includes the settling time (ST ) of the error signal, however, to better control of this parameter’s effect during optimization, we have separately added ST with a weight factor of.w2 to OF. Besides, for better performance and appropriate responses, especially in the transient period, the maximum overshoot (MOS) of the error signal is entered into the OF formula as another additional term. Consequently, the following incommensurable OF will be used in this optimization issue to achieve optimum controller design: ∮tsim (26) I T AE = t |Δ f | dt 0

min O F = w1 I T AE + w2 ST + w3 M O S

(27)

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⎧ ⎪ ⎪ K P min ≤ K P ≤ K P max ⎪ ⎪ ⎨ K I min ≤ K I ≤ K I max s.t. : K D min ≤ K D ≤ K D max ⎪ ⎪ K P P min ≤ K P P ≤ K P P max ⎪ ⎪ ⎩ Nmin ≤ N ≤ Nmax Each term’s contribution to the resultant OF is specified by its weight coefficients w1 , .w2 , and .w3 . It is possible to determine the appropriate values of the weighting coefficients (.w1 , .w2 , and .w3 ), by an optimization algorithm. Here we find that the values 1000, 5, and 1200 are suitable for .w1 , .w2 , and .w3 , respectively. Solving the OF minimization problem with the help of PSO-TVAC algorithm will cause the population members to search for the optimal position. The dimensions of the search space are exactly equal to the number of decision variables. Finally, the member whose position brought the lowest value for OF will be introduced as the answer to the optimization problem.

.

6 Simulation and Performance Review In this section, the proposed controller’s performance with DRP support is evaluated and compared with the results obtained when the PID controller is used. The islanded MG detailed in Sect. 3 has been implemented in the Simulink environment of MATLAB software using the unit’s linear transfer functions. The maximum available power to the DR unit for participation in DRP is 20%. The controller is designed with 10% load perturbation applied, and the simulation time (.tsim ) is 15 s where .ΔVwind = 0 and .Δϕ = 0. In Table 1, the numerical values of the transfer function parameters and other information used in the simulation are listed. Additionally, Fig. 17 shows an overview of the controller’s design process for the proposed isolated MG by the PSO-NTVAC algorithm. The derivative filter parameter (. N ) in the first stage of the controller control is fixed and equal to 50. After determining the optimal gains of the controller, its performance has been evaluated in different situations. Case 1 studies the effect of the DRP specified in the previous section. Case 2 evaluates the performance of the controller during 12 months of the year with moderate amounts of sunlight and wind. In case 3, the effect of the nonlinear factor (delays) is investigated. In every mentioned case, some of the essential time-domain characteristics are reported in the related table. Besides, the frequency responses of the MG are plotted for each situation. To study the superiority of the PSO-NTVAC algorithm, two other algorithms are used to find the best values for the controller coefficients. One is the standard PSO, and the other is the spotted Hyena optimization (SHO) algorithm [42]. By applying 10% perturbation to the demand side, the controller design is done using three metaheuristic algorithms. Also, to study the next scenarios and make comparisons, in addition to the proposed controller, the PID controller has been designed with the help of the same three algorithms for the studied MG. The system frequency

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Table 1 Nomenclatures, symbols, and values Symbols Nomenclatures

Values

.c1i , .c2i

2.5, 0.5

.c1 f , .c2 f . Max I t .n Pop .wi , .w f

STP plant . K L F R , . T L F R, . T ST

“Self-knowledge” and “social knowledge” initial values “Self-knowledge” and “social knowledge” final values Maximum number of iterations Number of population members Initial and final values of the inertia coefficient

0.5, 2.5 100 30 0.9, 0.4

The gain of the collector, time constant, time constant of the ORC-based thermal turbine

5.0, 0.42 s, 0.3 s

Gain, time constant

1.0, 1.5 s

Combustion response delay, biogas delay, valve actuator Lead time, lag time, delay of the discharge and

0.01 s, 0.23 s, 0.2 s

WTG plant . K W T , . TW T

BGTG plant TC R ,TBG , TBT X c , Yc , b B BDEG plant K V A , TV A , K B E , TB E

MHP plant TH G , TRS , TR H , TH T

HEV unit K H E V , TH E V

. D, . M

Time delays T D1 , T D2 DRP unit Δ f max , ΔP max

0.6, 1.0 s, 0.05

Valve gain, delay of the valve actuator, time constant and, engine gain

1.0, 0.05 s, 0.5 s, 1.0

Duration of delay, reset time, transient droop of MHP plant and its delay, respectively

0.2 s, 5 s, 28.75 s, 1.0 s

Gain of the HEV unit and its time constant, respectively Rotating mass unit Damping constant and droop constant of the MG

1.0, 0.02 s

Time delays for output of the DR unit and control signal

0.01 (p.u. MW/Hz), 2.0 (Hz/p.u. MW) 20 ms, 20 ms

Maximum frequency 0.05, 20% deviations in DRP control strategy, and maximum amount of available responsive loads

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First stage proportional gain

KP

Set point

1

Σ

KD Derivative gain

Σ -

1

KI

S

Σ

TD2

ΔU

1

MHP

Σ -

N S

Crucial Loads

BDEG

Σ

Time Delay

Integral gain

Σ

RESs

-

Σ

Rotating mass

-

BGTG

K PP

TD1

1/R1

Second stage proportional gain

ΔΦ

STP

ΔV W

WTG

PSO-NTVAC Algorithm

Time Delay

DR Appliance

Δf

DR Controller

DRP Unit

Objective Function (OF) Two-stage Controller

Fig. 17 General scheme of the MG and formation of the proposed PD-(1+PI) controller

fluctuations in the face of this perturbation are depicted in Fig. 18. To clarify the superiority of the proposed control strategy, some of the important time-domain system characteristics including time integral of absolute error (IAE), time integral of time-weighted absolute error (ITAE), time integral of square error (ISE), and time integral of time-weighted square error (ITSE) are listed in Table 2. The mathematical equations for these indicators are given in (26) and (28)–(30). To show the advantage of the PD-(1+PI) in establishing the system’s steady-state stability more quickly, the settling time (ST ) characteristics of the system are also recorded in Table 2. To better demonstrate the performance quality of the PD-(1+PI), the percentage of improvement in the characteristics listed in Table 2 are plotted in Fig. 19, where the optimal proposed controller designed using PSO-NTVAC is compared to PID controller. In addition, Fig. 20 shows the improvement percentage in system characteristics using the PSO-NTVAC algorithm to determine the proposed controller’s optimal gains than the PID controller. ∮tsim (28) I AE = |Δ f | dt 0

∮tsim I SE =

Δ f 2 dt

(29)

tΔ f 2 dt

(30)

0

∮tsim I AE = 0

As it turns out, in using both controllers, the PSO-NTVAC algorithm performed better with the proposed OF. Therefore, the rest of the studies and evaluations will be based on the design done with this algorithm. In addition, the speed of perturbation clearing and establishing steady-state stability is better by applying the proposed

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Fig. 18 System dynamic response with 10% load perturbation using a PD-(1+PI) and b PID controllers. Solid: PSO-NTVAC, dotted: standard PSO, dashed: SHO Table 2 Performances indices’ values by applying three optimization algorithms Controller Optimization Performances indices’ values algorithm OF IAE ITAE ISE ITSE 4 4 4 4 .×10 .×10 .×10 .×10 PD-(1+PI)

PID

PSO SHO PSO-NTVAC PSO SHO PSO-NTVAC

35.1195 70.1539 9.8996 19.7723 59.1288 15.5888

178.3155 262.9675 68.5950 117.3124 235.1314 105.3894

126.7902 198.0574 64.5843 131.2248 313.7476 117.3081

Fig. 19 Improvement percentage of the MG’s characteristics

4.9443 6.5640 1.3448 1.8285 2.9991 1.8334

1.08579 2.2812 0.1582 0.3972 1.4055 0.3181

ST 0.9050 2.4332 0.5246 1.17156 4.18220 0.7167

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Fig. 20 Improvement percentage of MG characteristics in controller design using PSO-NTVAC algorithm compared to SHO and standard PSO Table 3 Performances indices’ values by applying three optimization algorithms Algorithm Controller PID PD-(1+PI) KP KI KD KP KI KD PSO SHO PSONTVAC

5.4493 1.3946 6.2271

9.6060 9.1069 9.9887

0.1837 0.2126 0.1625

3.0836 2.5425 3.1149

4.2738 7.6867 6.1131

0.6459 0.2926 1.1917

KPP 0.0001 1.5361 0.0032

controller. In Table 3, the obtained gains of the two controllers by applying the mentioned algorithms are listed. Case 1: DRP Effect To better illustrate the impact of the DRP unit and responsive loads, here we have reviewed the performance of the controller in various participation of responsive loads in the LFC task. In the controller design, the maximum participation of the DRs is considered to be 20%. Here the participation of 15 and 10% is evaluated with the same controller. Figure 21 depicts the system’s dynamic behavior under various conditions. Some time-domain characteristics of the system are also listed in Table 4. The results confirm that the DRP unit’s attendance significantly improves system exposure to perturbation. It is also found that there is a lower limit to the maximum load available for the DRP. In other words, for the effectiveness of DRP, the amount of .ΔPmax is of particular importance, so it must be considered before implementation. This value should be taken into account in the prevailing situation and potential expected expansion of the MG. Case 2: Wind Speed and Solar Irradiation Effects Wind speed (Vwind ) and solar radiation (.ϕ) are stochastic factors affecting system behavior, especially where all power demand is supplied by RESs. Here, in addition to the 10% load disturbance, it is assumed that over a year, the changes in wind speed (.ΔVwind ) and solar radiation

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Fig. 21 Dynamic response of the system with the proposed controller and applying various DR participations. Solid: .ΔPmax = 20%, dashed: .ΔPmax = 15%, dotted: .ΔPmax = 10% Table 4 Case 1 DR partici- Performances indices’ values pation % OF

IAE .×104

ITAE .×104

ISE .×104

ITSE .×104

MOS.×104 MUS .×104

ST

10

91.9024

325.4155

312.0256

7.4179

3.3117

360.0306

492.2615

3.4992

15

25.0878

101.3919

73.8532

2.2564

0.3352

106.0717

395.3680

0.9947

20

9.8996

68.5950

64.5843

1.3448

0.1582

6.7187

335.2088

0.5246

Fig. 22 Power coordination of the STP and WTG plants

(.Δϕ) are such that the output power changes of the WTG and STP units are as shown in Fig. 22. Changes in solar radiation and wind speed are in line with the real average monthly values for the Bhubaneswar region of India [5]. The MG frequency responses are depicted in Fig. 23, and Some of the important time-domain indicators of the system are summarized in Table 5. In addition, the rate of improvement of system time-based indicators using the proposed controller compared to the PID controller is shown in Fig. 24. The results show that the value of the introduced OF is 45% better than the PID controller when the PD-(1+PI) is applied. Also, the time-domain characteristics of the system, including IAE, ITAE, ISE, and ITSE, have improved about 50%.

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Fig. 23 Frequency responses of the MG solid: proposed controller, dashed and dotted: PID controller Table 5 Time-domain characteristics of the MG in case 2 Controller Performances indices’ values OF IAE .×104 ITAE .×104 ISE .×104 PID PD-(1+PI)

5333.116 3144.199

1197.810 679.7079

47,425.16 25,693.38

10.0174 4.5898

ITSE .×104

ST

157.8015 56.24694

293.771 195.0401

Fig. 24 Comparing the application of the suggested controller to the PID controller in case 2

Case 3: Delay Effects Nonlinear factors such as signal sending/reception delays complicate the LFC process. The controller was initially designed with a delay of 20 ms, which can be completely random. Here, the behavior of the controller in case of delays of 15 and 10 ms is also examined. The MG frequency response with the proposed and PID controllers for different time delays is shown in Fig. 25. Also, as in previous cases, Table 6 lists system time-domain indicators.

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Fig. 25 Effects of different time delay factor in the MG frequency response: a 20 ms, b 15 ms, and c 10 ms time delays. Solid: proposed controller, dashed: PID controller Table 6 Performances indices’ values by applying three optimization algorithms Delay time Controller (ms)

Performances indices’ values IAE .×104

ITAE.×104 ISE .×104

ITSE.×104 ST

PD-(1+PI) 9.6291

68.4389

64.3321

1.1536

0.1427

0.4694

PID

106.5568

123.4610

1.7260

0.3306

0.7535

PD-(1+PI) 9.6797

68.4393

64.5590

1.2369

0.1478

0.4924

PID

106.1437

123.4912

1.7769

0.3267

0.7270

PD-(1+PI) 9.8996

68.5950

64.5843

1.3448

0.1582

0.5246

PID

105.3894

117.3081

1.8334

0.3181

0.7167

OF 10 15 20

16.4373 16.2899 15.5888

The MG frequency 50 Hz; applying the time delays of 20 ms means that the DRP and the controller responses are always one step behind in terms of the time cycle. However, it is observed that by considering different time delays, the ST will improve by about 30% with the proposed controller, and other evaluation indicators is improved about 40% using the PD-(1+PI) controller.

7 Conclusion This chapter presents a control strategy for the LFC task, topped by a multistage controller called PD-(1+PI). Using this two-stage structure is to eliminate the undesirable performance of the traditional controllers like PID controllers in the transient period due to the integral component’s presence. Designing a suitable controller for LFC depends not only on the controller structure but also on how it is optimized. In this chapter, in addition to presenting a cascade controller’s structure, a social knowledge-based optimization algorithm called PSO-NTVAC was also discussed.

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The proposed controller was designed with two other social intelligence-based algorithms (SHO and standard PSO) to compare algorithms’ efficiency. Optimizing a controller for LFC requires defining a particular OF suitable for problem aims. Therefore, to achieve the desired results and also due to the impact of the OF on the optimization algorithm’s responses, an incommensurable OF was introduced, and optimizations were performed. With the PSO-NTVAC algorithm’s help, the ST is on average 60% better than when optimizing with PSO or SHO algorithms. Other evaluation indicators, including ITAE, IAE, ISE, and ITSE, have all about 50% better on average. Evaluations show that using the proposed controller, in which there is no integrative factor in the first part, can improve ST by up to 50%. This result was achieved when the nonlinear factor of 20 ms delays in the control signal and DRP response was considered. In the proposed isolated MG, DRP has been used as an alternative to traditional ancillary services. Studies show that the attendance of DRP has a significant impact on improving system performance. Also, it was found that the mere use of DRP is not enough, and for good efficiency, it is necessary to set a lower limit for the participation of responsive loads (.ΔPmax ). Different levels of .ΔPmax have been evaluated to confirm this. With a 5% reduction in participation (.ΔPmax = 15%) from the set point (20%), the ST increases by about 90%, despite maintaining system stability. This shows the importance of the amount of available load for participation in DRPs.

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Solid State Transformer: Topologies, Design and Its Applications in a Smart Grid Selami Balci, Saban Ozdemir, Necmi Altin, and Ibrahim Sefa

Abstract The Solid State Transformers (SST), also known as Power Electronic Transformer (PET), combine power electronic converters and medium or highfrequency transformers. The SST provides the same features of the conventional Line Frequency Transformers (LFTs), such as voltage matching and galvanic isolation. Besides, it provides additional features such as improvements in size and efficiency, advanced monitoring and control features, active/reactive power support capabilities, etc. With these advanced control and monitoring functions, SSTs are considered a candidate to enable remarkable improvement in the new grid system and have attracted the attention of many researchers, especially in smart grid and microgrid studies. The SST design process can be explained in two parts, power electronic converter and control design and medium or high-frequency transformer design. The medium or high-frequency transformer is the key component of SST applications. The operating frequency, core material and core form, the number of turns value, resulting magnetizing and leakage inductance, parasitic capacitance values and insulation requirements are important parameters that affect the transformer’s efficiency and power density and should be carefully considered during the design phase. This chapter presents the SST concept, components and Finite Element Analysis (FEA) based modern design technique for high and medium frequency transformer design. Terms and definitions are explained in detail, including the power converter topologies, core material and wire specifications. In addition, the Energy Internet Concept and role of the SSTs in the Energy Internet as Energy Routers is discussed. The S. Balci Department of Electrical and Electronics Engineering, Faculty of Engineering, Karamanoglu Mehmetbey University, Karaman, Turkey e-mail: [email protected] S. Ozdemir · N. Altin (B) · I. Sefa Department of Electrical and Electronics Engineering, Faculty of Technology, Gazi University, Ankara, Turkey e-mail: [email protected] S. Ozdemir e-mail: [email protected] I. Sefa e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 B. Appasani and N. Bizon (eds.), Smart Grid 3.0, Power Systems, https://doi.org/10.1007/978-3-031-38506-3_11

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Multiport Solid-State Transformer (SST), capable of connecting to multiple sources and/or loads, is introduced as a solution for Energy Router applications, showcasing its simplicity, reliability, and high power density. Keywords Solid state transformer · Multi-port solid state transformer · FEA · High frequency transformer · Energy internet · Energy router

Nomenclature Symbol fs Lab L1 , L2 , L3 L12 L23 L13 Va Vb μr σ ρ ϕa ϕb ϕab V1 , V2 , V3

Defination Switching frequency The equivalent inductance between the port-a and port-b Phase self inductances for the delta connection The equivalent inductance for the delta connection The equivalent inductance for the to delta connection The equivalent inductance for the to delta connection Voltage of the port-a Voltage of the port-b \ Relative Permeability Resistivity Electrical Conductivity Phase shift angles of the converters Phase shift angles of the converters Phase shift angles between the port-a and port-b Phase voltages for the delta connection

Acronyms AC DAB DC DER DRL EV ESS FEA FEM FPGA GA GaN HFT

Alternative current Dual-active bridge Direct current Distributed energy resources Deep reinforcement learning Electric vehicles Energy storage systems Finite element analysis Finite element method Field programmable gate arrays Genetic algorithm Gallium nitrate High frequency transformer

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IoT LFT MF MPC MPSST MV PET PV PSFB PSO PWM SiC SST SVM TAB

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Internet of things Line frequency transformer Medium frequency Multi-port power converter Multi port solid state transformer Medium voltage Power electronic transformer Photovoltaic Phase-shifted full bridge Particle swarm optimization Pulse Width Modulation Silicon carbide Solid state transformers Support vector machine Triple active bridge

1 Introduction Transformers which are mainly used for voltage matching and/or providing isolation between the load and source, have been used in the distribution and transmission systems for more than 140 years. Although there have been some performanceimproving developments on the main components of a transformer (the core, windings and insulation materials), the idea of operating principal has not changed since 1886 when the first commercial transformer based on Stanley’s patent was presented. However, with the advances in power electronics, medium frequency (MF) power transformers have become attractive as a replacement for Line Frequency Transformers (LFTs) [1]. The most important advantage of MF transformers is the reduced dimensions and weight [2]. This MF transformer can be used for different purposes such as voltage matching, providing bidirectional power flow and isolation. Thus, the new power system designs with reduced size and volume can be applied in vehicles, ships and aircraft without compromising important specifications such as efficient and safe operation [3]. Use of MF transformers in common power electronic applications such as grid interfaces of renewable energy resources, energy storage and traction systems has made them more common and introduced new design specifications and requirements. Since these MF transformers are used with suitable power converters, they are called Solid-State Transformers (SSTs). These power converters bring additional and important advantages to the SSTs, such as advanced monitoring and control features, active/reactive power support capabilities, voltage requlation, etc. Therefore, SSTs are considered essential components of the future smart grids. The SST design process can be explained in two parts, power electronic converter and control design and medium or high-frequency transformer design. Different power converter topologies, including resonant converters, Phase-Shifted Full Bridge

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(PSFB) converters and Dual-Active Bridge (DAB) converters, can be used in the power converter stage of the SST to achieve high power density and efficiency with their soft switching capabilities. Various control and modulation strategies have been proposed to obtain high steady state and transient performance. The transformer is the key component of the SST applications. An increase in operating frequency offers significant improvements in transformer size, weight and power density. However, the increase in operating frequency may also increase core and copper (winding) losses. Therefore, core materials, winding options, conductor structures and insulation materials are very important. The core material and type should be selected according to the desired power level and density. The specifications of the soft magnetic materials used in the transformer core are also very important and define the transformer’s efficiency and size. Therefore, many studies have been presented in the last two decades. Even with the same core type and material, different core cross-sectional areas and the number of turn values can be used for the same power level, resulting in different power density and efficiency numbers. Therefore, a precise calculation should be made between the number of turns and the core cross-sectional area for maximum efficiency. On the other hand, the transformer magnetizing inductance and the leakage inductance are important parameters and affect the power electronic converter and overall system performance. In addition, parasitic capacitance is one of the important parameters at higher frequencies which also affects losses. Furthermore, insulation requirements are another topic that should be considered, especially in medium and high voltage applications. Therefore, a medium or high-frequency transformer design is a multi-objective optimization problem. Some analytical and artificial intelligence-based optimization studies have been proposed to obtain an optimal medium/high-frequency transformer design. However, since the aforementioned parameters are highly dependent on geometrical shapes and have nonlinear relations, these studies require high computational effort. Fortunately, Finite Element Analysis (FEA) method is very useful for calculating and simulating these parameters and medium/high-frequency transformer design. Furthermore, the FEA-based software’s co-simulation features enable simulating the power converter, control scheme and electromagnetic design together. The recent improvements have reduced the specific power loss and increased the flux density, operating frequency and temperature values of soft magnetic materials used in transformer cores. Along with the new generation wide-bandgap (such as SiC and GaN), power switches with higher switching frequency and lower loss capability, SSTs seem to have the potential to replace the conventional LFTs due to reasons listed below [4]: • The conventional LFT is too large and bulky in size. • Possible leakage of oil used in conventional transformers may cause environmental pollution. • The conventional LFT cannot improve input voltage conditions and cannot regulate unbalanced voltage or high-or low-voltage situations at the output. It also reflects any frequency variations or disturbances to the secondary side.

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• If a nonlinear current is drawn, the core can be saturated, and losses of LFT gets increase. • The input current waveform of the LFT is highly dependent on the output current waveform. If the output current is high and not symmetrical, it may cause saturation in the core and an increase in losses. • The LFT cannot control the value and waveform of the drawn current instantaneously. Besides, SSTs provide more compact, efficient and flexible power conversion systems for renewable energy applications, full-electric and hybrid vehicles, trains, electrically driven ships and aircraft applications. In addition, they make an important contribution to improving power quality by minimizing the effect of instabilities to modernize the AC and DC grid structures, creating a reliable grid structure [1].

2 Solid State Transformer The idea behind the SST is to operate the transformer at higher frequencies. Thus, above mentioned reduction in size and weight and improvement in efficiency and power density can be achieved. Since the grid does not provide this required highfrequency AC voltage, it should be generated by power converters. Therefore, a DC/ AC inverter must be designed to supply the transformer. This high frequency supplies the transformer and generates high-frequency voltage at the secondary winding. Since it is not useful, this high-frequency voltage should be rectified. Depending on the power flow requirement, a unidirectional or bidirectional AC/DC converter is used at the secondary part. This configuration is the core structure of the SSTs [4]. An SST can be used in AC/AC, AC/DC, DC/DC or DC/AC applications, and according to this situation, different configurations can be designed by adding AC/DC and DC/ AC power converters. Although SST structures can be classified depending on their different specifications, they generally grouped into three groups: single-stage SST, two-stage SST and three-stage SST based on the number of power converter stages, as given in Fig. 1 [1]. It can be easily seen that the topology given in Fig. 1a has AC/AC frequency conversion at both sides of the transformer. The AC/AC converter on the left side is required to convert the low-frequency grid voltage to the medium/high-frequency AC voltage. Similarly, the AC/AC converter on the right side converts the medium/highfrequency AC secondary voltage to the low-frequency AC voltage. This topology is called a direct conversion. The secondary side can be designed with two stages, an AC/DC stage, rectifying the medium/high frequency and a DC/AC inverter stage generating low-frequency AC voltage as shown in Fig. 1b. In the topology given in Fig. 1c, the bidirectional isolated DC/DC converter, which can be designed as a single-phase or three-phase, are used as the main element [5–7]. The input side of this topology has an AC/DC rectifier stage to generate DC voltage from the AC grid voltage. The isolated DC/DC converter provides voltage matching and isolation with

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Fig. 1 Different SST Topologies a Single stage SST, b two-stage SST, c three-stage SST

its medium/high frequency transformer and power converters. Finally, in order to be connected to the low frequency medium voltage (MV) or low voltage (LV) AC grid, DC/AC inverter stage is employed [8].

2.1 An Overview of Soft Magnetic Materials If alloys and composite materials are grouped according to their properties, four materials are used as the core material of medium/high-frequency transformers: silicon steel, amorphous, nanocrystalline and ferrite [9]. Steel alloys (SiFe) are frequently used in low-frequency transformer designs. The silicon additive affects the steel’s crystallised structure and increases the material’s electrical resistance. Thus, the specific core loss of the magnetic material due to the eddy currents decreases [10]. Accordingly, the hysteresis losses and magnetostriction of the core material also decrease. In addition, the saturation flux of the core material increases. However, the silicon density makes the material rigid and difficult to process, such as cutting. In recent years, non-oriented SiFe core materials

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have been manufactured in 0.05 mm and 0.10 mm thicknesses and suitable up to 2 T saturation flux density to increase the operating frequency and reduce losses [11]. However, its usage is limited at low frequencies ( 15%, while numerical methods such as FEA simulations offer high accuracy but with a high computational burden. In addition, since the SST combines a power electronic converter and medium and high-frequency transformer, the complex

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magnetic circuitry and nonlinear semiconductor switch system should be considered together. Generally, the experience obtained from past studies, trial and error applications, and co-simulation studies are used in the design process. However, since this design process is a multi-objective optimization problem, this method may not have the optimal design. Therefore, researchers have been working on machine learning applications in the SST design process in recent years. These studies can generally be grouped into three groups. The first group focuses on the optimal design of the medium or high-frequency transformer. These studies use machine learning methods to determine the design parameters to achieve optimal performance according to the design requirements. For instance, a genetic algorithm (GA) a deep reinforcement learning (DRL) approaches are applied to find the optimal parameters of the transformer, such as the number of phases, the number of turns, and the core size [26, 27]. It is validated that machine learning algorithms effectively improve the efficiency and power density of the transformer. In the second group, researchers focus on the SST, a combination of medium or high-frequency transformer and power electronic converters. While optimizing the transformer design, they also consider the power converter and control dynamics. For instance, GA has been used to optimize the transformer topology and to select the optimal power electronic devices [28]. Similarly, particle swarm optimization (PSO) techniques have been used to optimize the control strategy for SSTs [29]. The last group is the modelling studies. Since modelling of the SST is too complex, machine learning techniques are used to improve SST models’ accuracy and develop new models that can predict SST performance under different conditions. For instance, neural networks have been used to model and predict the behavior of SSTs. In addition, reinforcement learning techniques have been applied to control the power electronics in SSTs to improve their efficiency and reliability [30]. A support vector machine (SVM) algorithm is also used to train the model using data from an SST prototype. Using this model, the efficiency and power factor of the SST can be accurately predicted [31]. The machine learning algorithms are useful in modelling and design phases.

3 New Trends in SSTs: Multi-Port SSTs The widespread use of distributed energy systems also raises new requirements, such as integrating energy storage systems (ESSs) into local generation and load units to improve energy reliability. A multi-port converter is a good solution for integrating more than two sources/loads into each other. However, this requires multiple converters and transformers when galvanic isolation is required between the ports, resulting in increased volume, weight, cost, and lower efficiency. Especially when galvanic isolation is necessary, multiport SST is a good choice for fulfilling the abovementioned needs. It is also an economical solution for medium voltage integration [32–35]. In Fig. 5a; conventional two-port SST, in Fig. 5b; three-port SST and in

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Fig. 5 Multi-port SST; a Conventional two-port SST, b Three-port SST c General form of the multi-port SST

Fig. 5c; a general form of the multi-port SST are given. Although it is shown that the number of ports can be increased as much as desired, they are generally designed as three-port and rarely four-port SST due to control difficulties. The multi-port SST is actually an extended version of the DAB converter. While the energy flow is from one port to another in DAB converters, it can be from any port to any other ports in multi-port SSTs in a bidirectional way. Moreover, this energy flow can be partially provided by some number of ports. While the phase shift inserted between the active bridges controls the power flow from one port to another in DABs, it is more complex in the multiport SST. The flowing power from one port to others is dependent on the phase-shift angles among the related active bridges as in (1) [34]: Pab =

Va' Vb' φab (π − φab ), φab = φa − φb 2π 2 f s L ab

(1)

here a and b represent port names which is selected randomly, Pab is the power transferred port-a to port-b; φ a and φ b are phase shift angles of the converters, Va ' and Vb ' are the port voltages that transferred to the AC side, and L ab is the equivalent inductance between port-a and port-b. Unlike DAB, a change in the phase-shift of one port affects all ports even if their phase-shift angles are kept constant. Therefore, it is very difficult to control, and this difficulty increases immensely as the number of ports increases. Some effective control schemes, such as the decoupled control structure [34] are proposed to facilitate the control.

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4 SST Applications in Smart Grids 4.1 Energy Internet Concept A smart grid is a modernized version of the traditional power grid that uses advanced technology to improve the electricity supply’s efficiency, reliability, and security. This includes using devices such as smart meters, sensors, and advanced analytics to monitor and control the flow of electricity and integrating renewable energy sources like solar and wind power [36–38]. Integrating smart meters and bidirectional communication between the power company and consumers allows for real-time energy consumption monitoring, enabling dynamic pricing and demand response programs. This allows consumers to make informed decisions about their energy usage and allows the power company to better manage the supply and demand of electricity. Additionally, advanced analytics can be used to detect and diagnose power outages and to predict and prevent equipment failures [37]. Smart grids also enable the integration of renewable energy sources, such as solar and wind power, into the power grid. This allows for a more sustainable and resilient power supply and reduces greenhouse gas emissions. Furthermore, the integration of advanced communication technologies, such as the Internet of Things (IoT) and wireless networks, allows for better management of distributed energy resources (DERs) and enables the integration of electric vehicles (EVs) into the power grid [36]. Overall, implementing a smart grid aims to make the power supply more flexible and resilient while reducing costs and greenhouse gas emissions. It also provides more options for consumers and enables the integration of renewable energy sources, contributing to a more sustainable energy future. This new generation power grid with bidirectional power flow has emerged new concepts to reach the desired advantages. One of the new concepts is the Energy Internet. Energy Internet, also known as the Internet of Energy, refers to integrating advanced technology and digitalization in the energy sector. The energy internet concept is based on creating a decentralized and digitalized energy system where energy is generated and consumed locally, rather than relying on large central power plants. This is achieved by connecting DERs, such as solar PV modules and wind turbines, to the power grid and using ESSs to store excess energy. Besides, It can enable dynamic pricing, demand response and real-time energy consumption monitoring and control [37–39]. The energy internet concept needs a component that can enable the integration of these various energy sources, storage devices and loads efficiently and cost-effectively as depicted in Fig. 6. This component is called Energy Router. The energy router is a device or system used to manage and optimize energy flow in a distributed energy system, such as a smart grid or microgrid. It is designed to intelligently balance the supply and demand of energy by routing energy from

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Fig. 6 The energy internet concept

different sources, such as solar panels and wind turbines, to different loads, such as homes and commercial or industrial facilities [40, 41]. The energy router dynamically adjusts the energy flows by using the data gathered through real-time communication. On the other hand, it also has some software components, such as advanced algorithms and control systems, which are responsible for managing and optimizing energy flow. The functions expected from a router can be listed as follows: • To provide a suitable power flow by interacting multiple energy sources and loads, using communication protocols such as WiFi, Ethernet, etc. • To ensure the safety and security of the systems. • To be suitable for plug-and-play systems. • To be able to control the power level and/or direction quickly.

4.2 Use of the SST as an Energy Router The power electronic converters are key components of the Energy Router because they are critical for achieving a reliable, high power dense and efficient system. For example, traditional interconnections of sources in a DC system use separate DC-DC or AC-DC converters, as shown in Fig. 7a, to connect each source to a common DC bus. Using a multi-port power converter (MPC), as shown in Fig. 7b, to efficiently integrate different energy sources eliminates the need to use separate converters and decreases the system size and cost. Different converter topologies have recently been proposed to interconnect different sources such as PV, energy storage and load [42]. However, the presented topology does not provide isolation between the sources and the load, which is essential for many applications. The SST, which consists of an HFT transformer and provides isolation, have been proposed as a solution for energy routing in the context of the energy internet. The

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Fig. 7 a Multiple power electronic converters based energy router, b The multi-port converter based energy router

SST-based energy router is shown in Fig. 8. This allows them to actively manage and optimize the energy flow, making them well-suited for use as energy routers. They can also interface with DERs such as solar PV modules, wind turbines, ESSs, and EVs. This provides voltage matching between the DERs and the power grid and manages the power flow between the grid, ESSs, and EVs, ensuring that the energy generated by DERs is utilized to the fullest extent possible [43]. Furthermore, SSTs have high power density and high efficiency, allowing for a smaller and more compact design than traditional transformers, making them well-suited for use in microgrids

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Fig. 8 The SST based energy router

and other distributed energy systems. However, the necessity of using an SST for each system causes both an increase in size and cost and a decrease in efficiency. As a remedy, multi-port SSTs are presented. Multiport SSTs have advantages such as compact size, lower component requirements, and higher efficiency over the other solutions. The use of multi-port SSTs as energy routers is becoming increasingly common and attracts the attention of researchers.

4.3 MPSST as an Energy Router In order to meet the insulation requirement, an SST-based multi-port systems, known as multi-port SSTs, have been propsed and attracted attention of researchers in recent years [8–12, 44–47]. Three-port SST, also called Triple Active Bridge (TAB), is shown in Fig. 9. This topology offers higher power density, bidirectional power transfer between ports, and higher efficiency and reliability. Additionally, it offers galvanic isolation, a crucial component of many safety standards. These aspects distinguish the MPSST from traditional multi-port DC-DC converters [48]. The AC power transfer is the foundation of the topology operation in Fig. 9. Thus, the phase shift and equivalent inductances between the source and load side voltages determine the amount of power that is transferred. Power flows from a leading to a lagging bridge depending on the phase shift between the bridges, which determines power’s amount and direction. Thus, a bidirectional power flow, in other words, bidirectional converter is obtained. Figure 10a depicts the equivalent circuit for TAB circuit. To analyze current flows between terminals, the converter’s equivalent circuit can be converted to a delta equivalent model as shown in Fig. 10b to simplify the process. Equivalent inductance values L12 , L23 , L13 for the delta model can be obtained as follows: L 13 =

L1 L3 + L1 + L3 L2

(2)

L 12 =

L1 L2 + L1 + L2 L3

(3)

L 23 =

L2 L3 + L2 + L3 L1

(4)

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Fig. 9 The three-port SST

Afterwards, currents between the ports of the TAB converter are obtained as below: I12 =

V2 [(ϕ3 − ϕ2 )(π − (ϕ3 − ϕ2 ))]. 2π 2 f s L 12

(5)

Fig. 10 a The Y equivalent model of the three-port SST. b The delta equivalent model of the three-port SST

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I23 = I13 =

V3 [ϕ2 (π − ϕ2 )] f s L 23

(6)

V3 [ϕ3 (π − ϕ3 )] 2π 2 f s L 13

(7)

2π 2

where the phase shifts φ3 and φ2 regulate the amount of power flowi¸sng between ports 1 and 2, respectively. The phase shift angles φ3 and φ2 with respect to port-1 are measured here, by considering port-1 as the reference port. Here, the transformer turns ratio is considered to be 1 (1:1:1). The net currents I1 and I2 drawn from ports-1 and -2 can be obtained using (5)-(7): I1 = I12 + I13

(8)

I2 = I23 − I12 .

(9)

The power flow can be controlled by applying the appropriate current reference and corresponding phase shift. The number of pots can be increased according to the requirements and number of sources/loads. A five-port SST is proposed to provide a direct MV interface, as given in Fig. 11 [49]. Similarly, a six-port SST, depicted in Fig. 12, is discussed for the locomotive traction systems and electric multiple units, marine propulsion, wind power generation, and grid distribution applications [50]. However, it is worth noting that increasing port number results in exponantial increase in the control complexity. For three-port SST system, there are 24 different sets of equations for |ϕmn | ≤ π/ 2 condition. By considering the bidirectional power transfer, the number of cases can be reduced to four [51]. This helps control and analyze the converter. However, even after these assumptions, the resulting number of equations is still high for a higher number of ports, making the converter’s control and analysis complex. Many researchers accept the four-port system as a limit in these terms.

4.4 A Case Study—Four-Port MPSST as an Energy Router This section examines a four-port SST for use as an energy router. In this case study, one port is considered to be DER port (for instance, a PV system is connected to this port), another port is connected to the battery-based ESS, third one is connected to the load, and finally the last is connected to the grid, as depicted in Fig. 13. Since the four-port SST inherently provides bidirectional power flow from any port to any other port, there are many different power flow scenarios. However, for this case study, since one port is connected to the PV system and the other one is connected to the load, and the load is assumed to be a non-generating load, it can be deduced that the four-port energy router has two unidirectional ports and two bidirectional ports as in Fig. 14a. The possible power flow options are shown in

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Fig. 11 The five-port SST

Fig. 14b for this case. The theoretical background of the power flow, and control design can be found in [34] in detail. In Fig. 14a, the transformer core can be considered as a common energy-sharing structure. Power sharing is performed according to the availability/requirement of the power of these ports. The energy support/request patterns between ports depend on whether the ports are unidirectional or bidirectional and the number of ports. Accordingly, the number of patterns can be estimated according to the probability calculation. As mentioned, this system includes two unidirectional ports (PV and load ports) and two bidirectional ports (the grid and battery ports). While determining the

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Fig. 12 Six-port SST

patterns, the ports do not always need to demand or supply energy. For example, PV cannot energize the system at night and in cloudy weather. In another example, when the battery is fully charged, and there is no need for energy, the battery port does not need to operate. According to this explanation, obtained possible power flow scenarios for the four-port SST-based energy router are depicted in Fig. 14b. This figure consists of 18 different power flow scenarios. Here, if any port participates in energy transfer, this situation is shown in a red line, and the arrow on the line shows the direction of power flow. Similarly, the black dashed lines between the MPSST ports and the core indicate that that port is inactive and not supplying and/ or requesting power from any other port. According to this explanation, the first figure in Fig. 14b indicates that the power flows from the grid to the load, while the PV and battery do not contribute to any power flow. Similarly, in the second figure, the PV generates enough power to supply the load and inject to the grid. For this figure, it is assumed that the battery system is completely charged and cannot accept any energy. If the battery’s charge level is suitable to get charge, there may be two different scenerio: (i) The PV can supply the load and charge the battery. (ii) The PV can supply the load and charge the battery with its maximum charge power and surplus power can be exported to the grid. On the other hand, if the PV power is

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Fig. 13 The four-port SST as an energy router

insufficient to supply the load demand, the PV and battery ports can supply power to the load port. If the battery port achieves its maximum discharge power and the load power demand cannot be supplied by PV and battery ports, the PV, grid and battery ports can supply the load together. Along with the other scenarios, these scenarios are also depicted in the figure.

5 Discussion The SST, a combination of the medium/high frequency transformer and power electronic converters, has been initially considered for mobile applications like locomotives because of improved power density and efficiency. With the improvements in wideband-gap semiconductor switches, microcontrollers, FPGA, and soft magnetic core materials, today they are considered a general replacement for conventional LFTs. The key idea behind the SST concept is using a medium/high-frequency transformer to provide voltage matching and galvanic isolation. The medium/highfrequency transformer design is more complex than the conventional LFT design. The core material selection, core form, conductor and winding design are all very

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important and should be done accordingly. In addition, electrical, electromagnetic and thermal characteristics of the transformer, such as maximum flux density, core temperature, magnetizing and leakage inductances, parasitic capacitances, and core and winding losses, should be considered in the design. The FEA software provides a very helpful tool to design medium/high frequency transformer. They also enable designers to combine the electromagnetic model of the designed transformer with

Fig. 14 a The power flow defintions for the four-port energy router b The possible power flow options of the four-port energy router

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Fig. 14 (continued)

a power electronic converter and control scheme and thus provide more accurate designs. In this study, along with the SST concept, and components, a new application area for the SSTs is discussed. Within the energy internet concept, devices that can manage the power flow, called energy routers, are major components. The SST, especially MPSST, are inherently attractive solutions for energy router applications. They provide a power-dense and efficient solutions with flexible power flow capabilities. In conclusion, SSTs are a promising energy routing solution in the energy internet context. They have the ability to actively manage and optimize energy flow, interface with DERs, and have high power density and high efficiency. The implementation of SSTs in distributed energy systems can contribute to the goal of creating a more intelligent and efficient energy system. Further research is needed to fully understand the potential of SSTs as an energy router and to develop and test practical implementation of SSTs in real-world scenarios.

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Emerging Communication Technologies for V2X: Standards and Protocols Yasin Kabalci and Ural Mutlu

Abstract The objective of the early Intelligent Transportation Systems (ITS) was to improve road and traffic safety and to facilitate the management of the transportation system by providing Vehicle-to-Everything (V2X) communications to the vehicles and the entities surrounding it. With the recent advances in V2X technologies and the increasing popularity of Electric Vehicles (EVs), the EVs are envisaged to become part of the ITS. Moreover, V2X communications enable the integration of the EVs as interconnected entities into the Smart Grid ecosystem. Thus, the Smart Grid can perform enhanced monitoring and control capabilities by collecting data not only from the vehicle but also from the overall road infrastructure. Therefore, this chapter presents the V2X communication standards and protocols enabling the EVs to interact wirelessly with the grid infrastructure and other Road Side Units (RSUs). First, the existing Dedicated Short Range Communications (DSRCs), European Telecommunications Standards Institute (ETSI) ITS, and Long-Term Evolution (LTE) Cellular V2X (C-V2X) wireless protocol stacks and requirements, as well as the protocols used in the Vehicle-to-Grid (V2G) communications are outlined. Next, the emerging V2X wireless communication technologies of IEEE 802.11bd and New Radio (NR) V2X designed to provide high reliability, low latency, and high throughput communications to the new generation of autonomous vehicles and autonomous driving use cases are presented in detail. Keywords Vehicle-to-everything (V2X) · Intelligent transportation systems (ITS) · Dedicated short range communication (DSRC) · Wireless access in vehicular environments (WAVE) · 802.11p · 802.11bd · Long term evolution (LTE) V2X

Y. Kabalci (B) Department of Electrical and Electronics Engineering, Faculty of Engineering, Nigde Ömer Halisdemir University, 51240 Nigde, Turkey e-mail: [email protected] U. Mutlu Bor Vocational School, Nigde Ömer Halisdemir University, 51240 Nigde, Turkey e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 B. Appasani and N. Bizon (eds.), Smart Grid 3.0, Power Systems, https://doi.org/10.1007/978-3-031-38506-3_12

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Abbreviations 3GPP 5G NR BPSK BS BSM BTP BWP CA CAM CCSA CRL CS CSMA/CA C-V2X D2D DCM DENM DFT DMRS DoS DSMP DSRC EDCA e-MIP ETSI EV EVSE FCC FNTP FR HARQ HTTP ICE IEC IEEE IP ISO ITS JSON LDPC LTE MAC

3rd Generation partnership project Fifth generation new radio Binary phase shift keying Base station Basic safety message Basic transport protocol Bandwidth part Certificate authorities Cooperative awareness messages China Communications Standards Association Certificate revocation list Charging station Carrier sense multiple access with collision avoidance Cellular V2X Device-to-device Dual-carrier modulation Distributed environmental notification messages Discrete Fourier transform Demodulation reference signal Denial of service Dedicated short message protocol Dedicated short range communications Enhanced distributed channel access E-Mobility interoperation protocol European Telecommunications Standards Institute Electric vehicle EV supply equipment Federal Communications Commission Fast networking & transport layer protocol Frequency range Hybrid automatic repeat request Hypertext transfer protocol Internal combustion engine International Electrotechnical Commission Institute of Electrical and Electronics Engineers Internet protocol International Organization for Standardization Intelligent transportation systems JavaScript object notation Low-density parity check Long-term evolution Medium access control

Emerging Communication Technologies for V2X: Standards and Protocols

MBMS MCS MIMO NGV NR OBU OCB OCHP OCPI OCPP OFDM OpenADR OSCP OSI PHY PKI PLC PLS PRB PRR PSCCH PSFCH PSID PSSCH QCI QoS RAT RB RRI RSU SAE SC-FDMA SCI SDAP SIFS SNR SOAP SoC TB TCP UDP UE V2G V2I V2N

Multimedia broadcast multicast services Modulation and coding schemes Multiple-input multiple-output Next generation V2X New radio On-board units Outside the context of basic service set Open clearing house protocol Open charge point interface Open charge point protocol Orthogonal frequency division multiplexing Open automated demand response Open smart charging protocol Open systems interconnection Physical layer Public key infrastructure Power line communications Physical layer security Physical RB Packet reception ratio Physical sidelink control channel Physical sidelink feedback channel Provider service identifier Physical sidelink shared channel QoS class identifier Quality of services Radio access technology Resource block Resource reservation interval Road side unit Society of automotive engineers Single carrier-frequency division multiple access Sidelink control information Service data adaptation protocol Short interframe space Signal to noise ratio Simple object access protocol State of charge Transport block Transmission control protocol User datagram protocol User equipment Vehicle-to-grid Vehicle-to-infrastructure Vehicle-to-network

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V2P V2V V2X WAVE WPT WSMP XML

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Vehicle-to-pedestrian Vehicle-to-vehicle Vehicle-to-everything Wireless access in vehicular environments Wireless power transfer WAVE short message protocol Extensible markup language

1 Introduction The advances in communications, electronics and information technologies, and transport engineering in the 1990s led to the development of Intelligent Transportation Systems (ITS). The initial aim of the ITS was to improve road and traffic management, reduce vehicle collisions and traffic accidents, reduce driving times, improve fuel efficiency, and prevent air pollution, among others. Following the definitions of the ITS, U.S. Federal Communications Commission (FCC) allocated 75 MHz of the frequency spectrum in the 5.9 GHz band for the use of ITS in 1999. The allocation of the spectrum triggered the research and development in ITS and its enabling Vehicle-to-Everything (V2X) communication standards around the world [1, 2]. The V2X, the enabling communication technology of ITS, provides the means to exchange data between vehicles equipped with On-Board Units (OBU) and the other transportation actors such as traffic management infrastructure, pedestrians, Road Side Units (RSU), Smart Grid, etc. V2X encompasses four communication modes; Vehicle-to-vehicle (V2V), Vehicle-to-Infrastructure (V2I), Vehicleto-Pedestrian (V2P), and Vehicle-to-Network (V2N), illustrated in Fig. 1 [1, 3]. V2V technology allows vehicles to share traffic information and other sensory data such as current speed and location, to achieve traffic safety, cooperative awareness, autonomous driving, and platooning, amongst other use cases. V2I mode enables the vehicles to communicate with the RSUs so that data can be shared with the infrastructure, which is made up of traffic lights, municipal authorities, and, recently the Smart Grid. V2I communications contribute towards autonomous and remote driving, platooning, smart charging of Electric Vehicles (EV), etc. V2P mainly deals with the safety of pedestrians. The V2P sends traffic status alerts to the user terminals to warn pedestrians of potential dangers or accidents. V2N communications provide vehicles with Internet connectivity to access cloud services, Video on Demand, billing, etc. In the last decade, the car industry is gradually shifting from internal combustion engine (ICE) based vehicles to EVs. The ever-growing concerns related to environmental pollution and climate change have led to a sharp increase in EV sales, with 26% of all new car sales worldwide being EVs in 2021 [3]. The EVs are expected to be equipped with V2X OBUs enabling them to participate in the ITS [3–5]. Different from the ICE vehicles, one of the main requirements for the EVs is the ability to interact with the Smart Grid. The interaction between the EV and the Smart Grid

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Fig. 1 V2G, V2V, V2I, V2P, and V2N communication modes

is particularly important because the need to charge the EVs can impact the distribution grid, while the Smart Grid can manage and coordinate the energy flow in the grid. The interaction between the EVs and the Smart Grid is carried out through Vehicle-to-Grid (V2G) communications. The term V2G refers to a set of bidirectional communication standards, where besides charging the EV batteries by the grid, at peak demand hours, the power from the EV battery may also be delivered back to the grid [6]. Although, V2G has its own set of protocols different from the V2X communication protocols, the interaction between EVs and Smart Grid can be significantly improved by using V2X technologies [3, 4]. Figure 1 depicts the V2G as a component of the V2X communications. With the combination of V2G and V2I, various EV and traffic parameters, such as State of Charge (SoC), driving range, vehicle’s location and direction, and traffic conditions can be aggregated by the Smart Grid, thus smart charging coordination, load balancing, charge scheduling, and reliable authentication and billing decision can be taken by the Smart Grid [3, 4]. For example, the Smart Grid may decide to reserve future energy usage at a certain Charging Station (CS) for an EV depending on the EVs route and SoC. In the literature, separate studies [5, 7] show the use of the IEEE 1609 protocol stack, a V2X protocol, in conjunction with the IEC 61850 protocol to manage EV charging. In [8] Cao et al. propose using V2V protocols to exchange expected charging times and locations, and the cost of charging to coordinate charging and reservations between EVs. In other words, V2X standards and protocols are essential for the future success of Smart Grids. There are currently two prominent communication technologies adopted for use in V2X. Dedicated Short Range Communications (DSRC) is the first set of V2X standards to be fully developed and released. DSRC is defined by the IEEE and based on the IEEE 802.11p technology, adapted from the existing 802.11a standards [9]. The IEEE 802.11p standard defines Physical layer (PHY) and Medium Access Control (MAC) layer procedures. IEEE 802.11p has also been accepted as the communication protocol by the European standard ETSI ITS-G5 [10]. However, the deployment

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of DSRC has been limited and rather slow due to its poor scalability and issues with dense vehicular networks and high-mobility environments [2]. The second V2X communication technology was developed by the 3GPP and is based on cellular communications; hence it is termed Cellular-V2X (C-V2X) [11]. Cellular networks require communication to traverse a base station (BS), leading to an inherent network delay. Moreover, the mobility of the vehicles may lead to scenarios with no cellular network coverage. Therefore, to meet the ITS requirements, besides the infrastructure-based communication, C-V2X should also provide direct sidelink communications without the support of the cellular network. The direct Device-toDevice (D2D) communications introduced by the 3GPP in Rel. 12 specifications provide the direct communication that is needed for V2X, but it was not specifically developed for V2X communications. The first official support for V2X by the 3GPP started with Long Term Evolution (LTE) Rel. 14, and these standards are termed LTE V2X. LTE V2X also suffers from similar issues as DSRC [12]. Although, both IEEE 802.11p and LTE V2X-based communications successfully meet the basic safety requirements of the initial definition of the use cases, they both perform poorly in dense network scenarios. Moreover, with the advances in the automotive and telecommunications industries, there was a need to define a new set of use cases that can support Autonomous Vehicles and Autonomous Driving. Therefore, to address shortcomings and the new use cases, both IEEE 802.11p and LTE V2X standards are being updated. IEEE 802.11 Next Generation V2X Study Group was formed in March 2018 [13, 14], which resulted in the definition of IEEE 802.11bd standards as enhancements to IEEE 802.11p. On the other hand, 3GPP defined a new set of enhanced V2X use cases in Rel. 15. To support the enhanced V2X applications, New Radio (NR) V2X standards were specified in Rel. 16 based on the Fifth Generation New Radio (5G NR) specification [15, 16]. This chapter extensively reviews the existing and emerging technologies and standards developed to support V2X communications. The chapter emphasises the Radio Access Technology (RAT) standards of IEEE 802.11p and its enhanced version IEEE 802.11bd, and the cellular standards of LTE V2X and NR V2X. Therefore, the PHY layer modifications made to the 802.11 families of protocols and LTE and 5G NR families of protocols are discussed. In addition, to understand why the PHY layer modifications were needed, the chapter also reviews the protocol stacks of DSRC/ WAVE and ETSI ITS-G5 together with their use cases, which are the classifications for the communication requirements. The chapter is organized as follows. Section 2 outlines the ETSI ITS protocol stack and protocols, IEEE 1609 protocols, the protocol stack in C-V2X, and EV communication protocols. Section 3 starts with the properties of IEEE 802.11p standards and the second part of this section discusses the enhanced features of IEEE 802.11bd standards. Section 4 gives a detailed overview of the LTE V2X and NR V2X standards with some emphasis on resource allocation. Section 5 discusses and outlines the security algorithms used in V2X. Finally, Sect. 6 is the conclusions section.

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2 V2X Protocol Stacks, Use Cases, and Requirements In order to understand the protocol families used in ITS, this section gives an overview of the protocol stacks employed in the different protocol families. The ITS standards are usually classified with the V2X access technology, such as DSRC or CellularBased C-V2X standards. In the DSRC group, there are the EU standards of ITS G5 developed by ETSI [17, 18] and the US standards of IEEE 1609 family, also known as Wireless Access in Vehicular Environments (WAVE) developed by IEEE [19]. Furthermore, there are also local standards for China and Japan. On the other hand, the cellular-based C-V2X is divided according to the mobile communication network generation, i.e. LTE-V2X and 5G NR V2X. The protocols are defined so that both ETSI C-ITS and the IEEE 1609 family of standards may operate with either IEEE 802.11p/bd or C-V2X as access technologies [20]. The last part of the section briefly outlines the EV to Smart Grid messaging and communication protocols.

2.1 ETSI Cooperative-ITS (C-ITS) Reference Architecture In order to standardize V2X application interoperability over various systems, ETSI defined a reference model common for all ITS-compliant nodes. The ETSI reference protocol stack follows the principles of the ISO/OSI reference model as it defines interfaces or service points between the layers. The protocol stack consists of four data plane layers, a management layer, and a security layer, with Application, Facility, Network and Transport, and Access layers making up the data plane [18, 21]. ETSI reference architecture is shown in the upper parts of Fig. 2, which also symbolically shows that different access technologies can be used by the applications. Figure 3 shows the protocols used by the reference model. Application Layer: The application layer defines the type of messages that can be exchanged between V2X applications based on the use cases. Three classes of applications have been identified; Road Safety, Traffic Efficiency, and Other Applications [17]. On the other hand, the use cases can be classified into one of the classes. The applications are also defined according to the communication resources they require. The communication resource requirements are given in terms of communication pattern, V2X mode, periodicity of messages (Hz), latency (ms), communication range (m), and message reception reliability (%) [17, 22]. For example, the Road Safety Cooperative Awareness use case has a periodicity of 10 Hz, between 300 and 1000 m, a data rate of 10–100 kbps, a message size of 60–1500 bytes, and a reliability range of 90–95%. Facilities Layer: It generates support services or “facilitates” applications. In other words, this layer collects information from V2X application services, generates the messages, and manages to send and receive the messages. The most important function of the facilities layer is the management of the Cooperative Awareness

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Fig. 2 C-ITS protocol stack with different access technologies

Fig. 3 ETSI ITS protocols

Messages (CAM) and Distributed Environmental Notification Messages (DENM) [23, 24]. CAMs are periodically transmitted messages, while DENM are only sent as notifications when a certain event occurs. Network and Transport Layer: this layer is responsible for the end-to-end delivery of messages. Besides the IP and TCP/UDP protocols, the layer also defines proprietary protocols of Basic Transport Protocol (BTP) and GeoNetworking at the transport and network layers. The Basic Transport Protocol (BTP) provides an end-to-end, connection-less transport service similar to UDP. The main objective is to multiplex and demultiplex messages between the GeoNetworking protocol and the facilities layers. GeoNetworking provides node-to-node delivery based on geographical addressing and geographical forwarding. GeoNetworing is particularly

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useful because it supports three modes of packet delivery, unicast, broadcast, and topologically-scoped broadcast [25, 26]. Access Layer: initially, the ETSI ITS standards were defined to work with IEEE 802.11p RAT, but with the development of C-V2X, both LTE V2X and NR V2X are supported. PHY and MAC procedures of the RATs are given in the next section.

2.2 DSRC and IEEE 1609 Protocol Family The DSRC architecture consists of two main standards, IEEE 802.11p in the PHY and MAC layers and IEEE 1609 family in the upper layers [27]. The IEEE 1609 is part of the WAVE family of standards and the terms DSRC and WAVE can be used interchangeably. The protocol stack of WAVE is given in Fig. 4. In the application layer, each V2X application broadcasts its core state information in a Basic Safety Message (BSM) format, with a nominal periodicity of 10 Hz [28]. Upon receiving a BSM message, a V2X application can build a model of a vehicle’s trajectory, assess the threat to itself, and warn the driver if it is serious. In the network and transport layer, besides using IPv6 and TCP/UDP protocols, WAVE architecture defines WAVE Short Message Protocol (WSMP), also known as IEEE 1609.3 protocol. WSMP is especially designed for optimized operation in a wireless vehicular environment. In WSMP, messages are addressed based on application ids and MAC addresses. In the protocol, the address info field contains the V2X application Provider Service Identifier (PSID) identifying the source–destination application and the MAC address to identify the intended node for the transmission. The address field can be both unicast and group address. In the PHY and MAC layers, the WAVE stack is originally designed to work with IEEE 802.11p, which is given in detail in the next subsection. Unlike ETSI ITS,

Fig. 4 WAVE consisting of IEEE 802.11p and IEEE 1609 protocol family

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where the access stratum was completely independent from the other protocols, the WAVE standards IEEE 1609.4 protocol resides above IEEE 802.11p and deals with physical channel access parameters related to multi-channel. The management plane procedures are defined in IEEE 1609.1 and the security protocol is defined in IEEE 1609.2.

2.3 LTE V2X and NR V2X Protocol Stack The protocol stack for NR V2X PC5 interface user plane and control plane is given in Fig. 5. It should be noted that the stack is the same for both LTE and NR V2X. LTE V2X and NR V2X should be treated differently in the application layer. In the LTE V2X application layer, the use cases are defined in conjunction with ETSI ITS and US Society of Automotive Engineers (SAE) [29]. Therefore, the use cases are similar to the DSRC and ETSI ITS. The following service requirements are expected: • Latency: the latency requirements vary from 20 ms for V2V communication over PC5 interface to 1000 ms in V2N communications. • Message Size: Periodic messages should be in the range of 50–300 bytes, while event-triggered messages should be 1200 bytes. • Frequency: a periodic message should be transmitted at 10 Hz. • Range: a number is not specified, but a driver should be able to respond within 4 s. • Speed: for V2V communications, 500 km/h should be supported, while for the other transmission modes, the requirement is 250 km/h. NR V2X use cases are designed to support autonomous vehicles and autonomous driving and are 3GPP-specific use cases [16]. NR V2X defines four new enhanced

Fig. 5 LTE V2X and NR V2X protocol stack for PC5 interface

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Table 1 Requirements for enhanced V2X applications Use case class

Maximum latency (ms)

Packet size (bytes)

Packet reliability Data rate (%) (Mbps)

Minimum range (m)

Vehicle platooning

10–500

50–6000

90–99.99

50–65

80–350

Advanced driving

3–100

300–12,000

90–99.99

10–50

360–500

Extended sensors

3–100

1600

90–99.99

10–1000

50–1000

Remote driving

5

–

99.999

Uplink: 25 Downlink: 1

.

use cases: Vehicles Platooning, Advanced Driving, Extended Sensors, and Remote Driving. The communication requirements of these four use cases depend on the level of automation that the V2X application could support. The levels of automation are given in [30] and are in the following order; 0—No Automation, 1—Driver Assistance, 2—Partial Automation, 3—Conditional Automation, 4—High Automation, 5—Full Automation. Depending on the level of automation in use, the requirements for the enhanced V2X use cases are given in Table 1 [16]. In the networking and transport layers, both LTE V2X and NR V2X support IPv6 with TCP/UDP as well as non-IP networking. If the networking protocol is Non-IP, then as an alternative protocol, C-ITS, WSMP, ISO-defined FNTP, and CCSA-defined DSMP can be used [31]. The RATs of LTE V2X and NR V2X communications are presented in Sect. 4.

2.4 EV to Smart Grid Protocols For successful integration of the EVs into the Smart Grid, a communication link between the EVs and the Smart Grid is required so that the Smart Grid can collect information about the SoC and other energy management-related sensory information. Several application-level messaging protocols based on open standards that manage the data flow and the authentication/billing procedures have been defined. Table 2 [3, 32] shows some of the widely used protocols and their use cases. These messaging protocols use a mixture of HTTP, XML, Web Services, SOAP, and JSON technologies and rely on available TCP/IP level connectivity. The interaction between the EV and the Smart Grid takes place at the communication controllers of the EV and the EV Supply Equipment (EVSE), which is the equipment that connects the EV to the grid for charging purposes. Various wired and wireless communication protocols have been successfully deployed to connect the EV and the EVSE [4]; however, standardization of the protocols is essential for the growth of the EV market and the successful deployment of the V2G-enabled vehicles.

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Table 2 Charging stations and power grid messaging protocols Protocol—standard Use cases

Reference

OCPP

Authorizing and billing charge sessions, grid management, smart charging, operating charge points, reservation

[33]

OCHP

Authorizing and billing charge sessions, smart charging, charge point information, reservation, roaming

[34]

OCPI

Authorizing and billing charge sessions, charge point [35] information, reservation, smart charging, roaming, app-based charging

OSCP

Smart charging by communicating capacity forecasts, handing out capacity budgets, managing grid capacity

[36]

OpenADR

Smart charging, handling registrations, load demand, pricing signal, managing grid

[37]

eMIP

Authorizing and billing charge sessions, providing smart charging features, roaming, platform monitoring

[38]

IEEE 2030.5

Exchanging metering, billing and tariff data, Smart grid applications, demand response, reservation, sending SMS

[39]

IEC 61850

Parameter modelling, standardizing message structures, interoperability of applications, virtual DER operation

[40]

ISO 15118—2

Authorizing and billing charge sessions, schedule-based charging, certificate handling

[41]

ISO 15118—20

Authorizing charge sessions, dynamic charging, compulsory use of TLS certificates, wireless and pantograph charging

[42]

IEC 61851 family of protocols provides control signalling using pulse-width modulation between an EV and EVSE during conductive charging. On the other hand, the ISO 15118 family of protocols is a full suite of protocols specifically defined by the IEC and ISO to serve the needs of the EVs and function as a bidirectional V2G communication interface between EVs and an EVSE [43]. Figure 6 shows the ISO 15118 protocol stack. At the Physical and Data Link layers, both Power Line Communications (PLC) and wireless communications are supported [44, 45]. HomePlug Green PHY is used when an EV is plugged into an EVSE and conductive charging is carried out. HomePlug Green PHY is based on the HomePlug AV standards, with the main difference being in the data rates supported; to ensure reliability HomePlug Green PHY is limited to 10 Mbps. When Wireless Power Transfer (WPT) or pantographic charging is in use, or the EV needs to communicate with the EVSE without being plugged in, the IEEE 802.11n wireless protocol is used to transmit data between the EV and the EVSE. The IPv6, TCP, TLS, and UDP protocols are described by their respective standards in the upper layers. ISO 15118 Part 2 was defined in 2014 and does not support bidirectional V2G, but ISO 15118 Part 20 was published in April 2022 and supports new use cases such as WPT, pantographic charging, bidirectional power transfer, and dynamic charging [41, 42]. V2GTP manages the session between the EV and EVSE, while XML provides data formatting for V2G application messages. For security,

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Fig. 6 ISO 15118 protocol stack

the TLS certificate provides the authentication of the EVs and the EVSEs and the confidentiality and integrity of the messages. Further protection for sensitive information, such as credit card details for billing, is provided by XML encryption and signatures.

3 DSRC—IEEE 802.11p and IEEE 802.11bd 3.1 IEEE 802.11p The IEEE 802.11p standards specify the PHY and MAC layers of the DSRC and are largely based on the IEEE 802.11a standards [46]. The main design requirements of the IEEE 802.11p standards were to support warning and awareness message transmission frequency of 1–10 Hz over a range of 100–800 m with data rates of up to 10 Mbps and end-to-end latency of less than 10 ms for vehicles moving in a varying vehicular environment [22]. Like in 802.11a, the PHY layer of IEEE 802.11p is also based on OFDM but with modifications in some parameters. In order to mitigate intersymbol interference that higher speeds can cause, the timing parameters of the OFDM waveform are scaled by a factor of 2, while the bandwidth and sub-carrier spacings are reduced by half [47]. Thus, the symbol and guard durations of IEEE 802.11p are increased to 8 µs and 1.6 µs, respectively, whereas the bandwidth and the sub-carrier spacing are decreased to 10 MHz and 156.25 kHz respectively. The main difference of the IEEE 802.11p MAC layer is the Outside the Context of Basic Service Set (OCB) operations. OCB operations allow data transfer without using authentication, association, and data confidentiality services used in the other IEEE 802.11 networks. Sending data without applying the security procedures reduces the connection establishment time, which can be prohibitively long for V2X

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communications. The security services in DSRC are provided by WAVE IEEE 1609.2 standards. The MAC protocol used in IEEE 802.11p is Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) [48]. Unlike other protocols, the CSMA/CA algorithm in IEEE 802.11p MAC protocol does not use exponential back-off and acknowledgement mechanisms. Each IEEE 802.11p transmitter checks if the channel is idle and waits for a fixed time period before transmitting to reduce the probability of collisions. There are two reasons for using a fixed value, the first reason is that an exponential back-off can result in large Contention Window sizes leading to unwanted high latencies, and the second reason is that IEEE 802.11p is a broadcast system and it does not implement ACK algorithm. The MAC protocol also implements Enhanced Distributed Channel Access (EDCA), which prioritizes channel access to allow time-critical and safety messages to be transmitted with lower contention delays.

3.1.1

Problems with DSRC

In relatively low vehicle density situations, the performance of IEEE 802.11p standards in terms of reliability and latency is satisfactory [48, 49]. However, when the vehicular density causes the channel occupancy rate to reach 50–60% or more, the protocol’s performance declines rapidly [34]. In this high-density scenario, the likelihood of packet collisions due to simultaneous transmissions increases. Likewise, the number of hidden nodes that attempt to transmit over the same channel resulting in packet collision, is also likely to increase. These packet collisions lead to data loss, delayed transmission, and reduced efficiency. V2X communication systems based on the IEEE 802.11p standards cannot realize the requirements of low latency and low packet loss ratio that are required by the new V2X applications such as autonomous driving and platooning. Therefore, the adoption of the IEEE 802.11p standards in vehicular communication has been limited.

3.2 IEEE 802.11bd Since the introduction of the IEEE 802.11p standards, some advanced features have been added to the IEEE 802.11n/ac/ax family of protocols that could also be used to enhance the performance of the DSRC protocols [2]. Therefore, the IEEE 802.11bd Task Group was formed in January 2019 to define new and improved PHY and MAC layers for Next Generation V2X (NGV) communications. Following the initial feasibility studies, the main design goals of IEEE 802.11bd can be summarized as doubling the MAC throughput of IEEE 802.11p for relative velocities up to 500 km/h, doubling the communication range of IEEE 802.11p, providing vehicle positioning in affiliation with V2X communications, and reduced latency [2, 14, 50]. Additionally,

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IEEE 802.11bd conformance must satisfy the conditions of interoperability, coexistence, backward compatibility, and fairness with IEEE 802.11p. In other words, IEEE 802.11bd and IEEE 802.11p-enabled devices must be able to communicate with each other over the same channel and decode each other’s frames while both protocols get the same priority over the channel resources. At the time of writing, the IEEE 802.11bd standards are being finalized and are expected to be published at the end of 2022. The draft standards include the improvements outlined in [2, 51], which are tabulated in Table 3. PHY Layer: In the PHY layer, the finalized IEEE 802.11bd standards would use the same OFDM timing and frequency parameters as IEEE 802.11p, which would ensure backward compatibility. In order to increase transmission efficiency, the channel coding scheme of Low-Density Parity Check (LDPC) coding has been adopted by the IEEE 802.11ac and is also going to be used in IEEE 802.11bd. However, it has been shown that changing the timing parameters by a factor of 2 reduces the effectiveness of the LDPC codes in the fast-varying wireless environment [22]. Therefore, similar to the Demodulation Reference Signal (DMRS) training sequences inserted within an LTE or 5G OFDM frame, the IEEE 802.11bd standards propose using midambles, which have the same form and function as the preambles but are placed within a frame. The draft specifications specify three midamble periodicities, i.e. every 4, 8, and 16 OFDM symbols, the choice of which is determined by the modulation scheme and the channel conditions [52]. Midambles provide robustness against high Doppler frequencies in high-speed environments, i.e. vehicles moving at 500 km/h. For efficient transmission, the interval between the midambles should not exceed the coherence time of the channel. According to [53], at a 5% packet error rate, the combination of LDPC codes and midambles would improve Signal to Noise Ratio (SNR) by 2.5 dB. Figure 7 shows the use of preambles and midambles in the frame; it should be noted that IEEE 802.11bd preamble and IEEE 802.11p preambles are the same. Bandwidth: The use of the same OFDM parameters as in IEEE 802.11p implies that the bandwidth of IEEE 802.11bd is also 10 MHz. However, to meet the goal of Table 3 Comparing IEEE 802.11p and IEEE 802.11bd parameters Parameter

IEEE 802.11p

IEEE 802.11bd

OFDM sub-carrier spacing

156.25 kHz

156.25, 78.125, 39.0625 kHz

FEC coding

BCC

LDPC

Channel estimation

Preamble

Midamble

Modulation scheme

Up to 64-QAM

Up to 256-QAM

Channel bandwidth

10 MHz

10, 20 MHz

Retransmission

No

Yes, up to 3 times

Fast BSS transition

No

Yes

Error correction

Part of data packet

Dedicated packet

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Fig. 7 Midambles in IEEE 802.11bd frames

twice the throughput of IEEE 802.11p, the IEEE 802.11bd specifications consider using 20 MHz bandwidth divided into two 10 MHz channels, one a primary and the other one a secondary channel with channel access mechanism similar to the one implemented in IEEE 802.11n/ac protocols [2, 54, 55]. If an IEEE 802.11bd device needs to use the whole 20 MHz, the transmitter first senses the primary channel for an inter-frame interval followed by a back-off period. Meanwhile, before the back-off period of the primary channel expires, the secondary channel is sensed for a shorter time interval. This algorithm ensures that 10 MHz channels can be used by both the IEEE 802.11bd and IEEE 802.11p devices so that the compatibility and fairness requirements are met while doubling the number of access channels and throughput. Besides the increased bandwidth, IEEE 802.11bd would also introduce higher order Modulation and Coding Schemes (MCS) of up to 256QAM modulation with an LDPC coding rate of 5/6, which is the same as in IEEE 802.11ac [52]. Considering that the MCSs in IEEE 802.11p is up to 64QAM modulation with a coding rate of 3/4, the maximum data rate will be relatively higher in IEEE 802.11bd. Dual Carrier Modulation: Dual-Carrier Modulation (DCM) is an algorithm first introduced in 802.11ax [2]. In DCM, the same symbol is transmitted on two sufficiently spaced apart OFDM sub-carriers. In IEEE 802.11bd, DCM is carried out as Binary Phase Shift Keying (BPSK) DCM. In IEEE 802.11bd BPSK DCM, a BPSKmodulated OFDM symbol is placed on one subcarrier, and a rotated version of the first modulated symbol is in a second subcarrier [52]. The duplicate subcarriers are on two adjacent 5 MHz channels, which make up the 10 MHz channel. The redundancy and frequency diversity introduced by the DCM technique results in a 3 dB or higher performance gain. Retransmission: In order to improve reliability, IEEE 802.11bd introduces adaptive retransmissions, which can prevent frame losses at the MAC layer and avoid delays that may be caused by higher-layer retransmissions. The protocol allows up to 3 retransmissions with a time interval equal to Short Interframe Space (SIFS) in between the transmissions [55, 56]. The protocol is adaptive because a device can decide whether to re-transmit or not and how many frames to re-transmit depending on the channel congestion level. To ensure backward compatibility, a frame includes a training field that can be decoded by both IEEE 802.11bd and IEEE 802.11p devices. However, IEEE 802.11p devices treat the original data frame and its retransmissions as independent frames.

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4 Cellular V2X—LTE V2X and NR V2X The 3GPP standards of 5G NR have already been deployed and in the near future are expected to provide ubiquitous cellular connectivity not only to consumer devices but also to a wider range of devices, including vehicular systems. Before D2D communications were defined in Rel 12, cellular communication was assumed too slow to be feasible for V2X communications because all the communications had to be relayed through BSs and the network. D2D enabled neighboring devices to set up direct communication, which could lower the latency of the transmissions. Since the inception of the 3GPP V2X study group in 2015 [29], two different sets of specifications, LTE V2X in Rel 14 and NR V2X in Rel 16, have been published [57].

4.1 LTE-V2X In order to take advantage of the widespread LTE cellular infrastructure, the LTE V2X standards were developed on the existing LTE standards. LTE-V2X standards are based on the idea that a cellular infrastructure cannot always be present and relied upon. Therefore, besides the Uu interface between a User Equipment (UE) and an eNodeB, LTE-V2X communications between UEs can also be established directly through the new PC5 interface with or without network assistance [58]. Figure 8 shows the difference between PC5 and Uu interfaces.

4.1.1

Physical Layer of LTE V2X

Most of the PHY layer procedures of the LTE-V2X PHY layer are the same as LTE PHY layer procedures with some minor modifications [2]. As a waveform, LTE uses DFT-spread Single Carrier FDMA (SC-FDMA) that can support both 10 and 20 MHz channels. The time and frequency resource structures of the LTE-V2X are the same as LTE. A resource block (RB), the smallest unit of resources that can be allocated to a UE, is a sub-frame consisting of 14 OFDM symbols in 1 ms time period with 12 subcarriers of 15 kHz subcarrier spacing each. Although, traditional LTE uses only 2 symbols out of the 14 symbols for DMRS, to deal with the effect of mobility

Fig. 8 Sidelink as PC5 interface and downlink and uplink as Uu interface

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and Doppler frequency, LTE-V2X RB contains 4 DMRS symbols spread out evenly on the 3rd, 6th, 9th, and 12th symbols in the sub-frame. The last symbol is used as a guard symbol, leaving only 9 symbols for data transmission. Turbo code channel coding is applied to the transport data before either QPSK or 16-QAM modulation is applied [12]. With 3GPP Rel 15, 64 QAM modulation has also been added to the standards to increase the data rate [59]. LTE-V2X Uu Air Interface The transmission mode over the Uu interface in LTE-V2X is developed to support V2X applications tolerant to delays and to provide various V2N services through the cellular network. The LTE-V2X Uu interface is based on the transmission mode between an eNodeB, which is a BS in LTE, and a UE with some modifications. In order to reduce the latency due to scheduling overhead, the eNodeB employs semipersistent scheduling. In semi-persistent scheduling, communication resources are allocated for the next transmission and for several consecutive transmissions [2, 60]. Semi-persistent scheduling is especially advantageous in the uplink establishments since a UE does not have to wait for a new resource allocation. In this mode, all the messages must traverse at least one eNodeB and V2X applications do not have to be in the same cell or close to one another. The transmission from a UE to the network employs a V2X server, which receives the V2X messages as unicast messages through the LTE uplink transmissions. The replies to a UE can be either unicast or broadcast in the downlink transmissions via Multimedia Broadcast Multicast Services (MBMS) [1, 61]. LTE-V2X PC5 Air Interface The PC5 interface provides direct communication between V2X nodes without requiring the packets to go through the network. In LTE, the PC5 interface can be established both with and without network assistance. Hence, PC5 is a direct communication, and the data delivery method in the LTE PC5 interface supports only broadcast communications. Data in LTE-V2X PHY layer is transmitted as transport blocks (TBs), which are essentially mapped on groups of RBs [62]. Besides data, two consecutive RBs also include Sidelink Control Information (SCI), which conveys information about the MCS and the resources for current and future transmissions. In LTE PC5, data is transmitted on the Physical Sidelink Shared Channel (PSSCH) and SCI is transmitted on the Physical Sidelink Control Channel (PSCCH). The PSSCH and PSCCH channels are not transmitted over separate channels but are multiplexed in the frequency domain in the same sub-frame. Depending on whether the communication resources are allocated by a serving eNodeB or not, the transmission mode in PC5 can be either Mode 3 or Mode 4. Mode 3 is the network-assisted communication mode applicable in situations where eNodeB connectivity is available. In this mode, the eNodeB monitors and collects channel quality information from the UEs and allocates resources accordingly. Semipersistent scheduling, UE report-based scheduling, and cross carrier scheduling can be applied as a scheduling mechanism. Mode 4 is employed when eNodeB connectivity is not available. In this mode, a UE reserves the communication resources

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autonomously by using the resource reservation algorithm, a two-part algorithm comprising of sensing and semi-persistent scheduling [62]. In the sensing part, a selection window duration is chosen based on the application’s latency requirement, and the channel’s state is sensed. Channel resources for the transmission are then reserved semi-persistently for a duration in the range of 0–1000 ms. Comments on LTE-V2X As already mentioned, the Uu interface is not designed for latency-sensitive applications, but the improvements of the PC5 interface are specifically defined to support low-latency use cases. Studies have shown that in Mode 4, in terms of Packet Reception Ratio (PRR), the LTE-V2X performs better than the IEEE 802.11p standards while also improving the communication range [12, 63]. Furthermore, according to [46], the centralized eNodeB control of the resource in PC5 Mode 3 results in efficient use of the frequency resources leading to better performance guarantees compared to IEEE 802.11p. On the other hand, because the resource reservation in PC5 Mode 4 depends on the sense and reserve method similar to IEEE 802.11p, in high-density traffic scenarios, the performance of LTE-V2X also decreases [12]. To increase spectral efficiency, frequency reuse similar to that in cellular networks is also introduced in PC5 Mode 4.

4.2 NR V2X The aim of 3GPP Rel 16 is to specify NR V2X communication standards based on 5G NR specifications in order to support the service requirements of the enhanced V2X use cases defined in [16]. The service requirements of the enhanced use cases of NR V2X are not a replacement for the basic safety services defined in LTE V2X but a complement to the LTE V2X standards. In other words, the two sets of services will coexist [64]. While LTE V2X-enabled devices can only handle the basic safety services and requirements, NR V2X-enabled devices are expected to support both basic safety and enhanced services. Therefore, 3GPP Rel 16 requires different and stricter design aims and architectural factors than LTE-V2X. The objectives of NR V2X communication are defined as enhanced sidelink design, Uu interface enhancements, Uu interface based sidelink allocation and configuration, RAT selection, Quality of Services (QoS) management, and coexistence. Key PHY layer differences of the PC5 interfaces between LTE V2X and NR V2X are given in Table 4. The 5G system architecture supports two operation modes for V2X communication, namely V2X communications over the Uu interface and the PC5 interface. Most of the V2X-specific modifications in the PHY layer are in the PC5 interface, largely based on the Rel 15 NR Uu design, with some concepts of the LTE V2X from Rel 14 also used. In Rel 16 NR V2X, the Uu interface only supports unicast communications, while PC5 procedures support unicast, broadcast, and groupcast transmissions [15, 60]. A UE is not limited to a single type of communication and can establish communications of multiple types simultaneously. For example, in a given

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Table 4 Comparing LTE V2X and NR V2X RAT parameters Parameter

LTE V2X

NR V2X

Sidelink modes

Mode 3 and 4

Mode 1 and 2

Fast sidelink scheduling

No

Yes

Preemptive resource scheduling

No

Critical messages

OFDM sub-carrier spacing

15 kHz

15, 30, 60 kHz

PSCCH and PSSCH multiplexing

Frequency

Time

Retransmission

Blind

Adaptive, HARQ

FEC coding

BCC

LDPC

Modulation scheme

Up to 64-QAM

Up to 256-QAM

DMRS per sub-frame

4

Flexible

Frame types

Broadcast

Unicast, groupcast, broadcast

Slot duration

Fixed

Min-slot and multi-slots

Sensing window

Fixed

Adaptive

platoon, the platoon leader can communicate with the other group members using groupcast while using unicast to communicate with a specific vehicle. In order to support efficient unicast, broadcast, and groupcast transmissions, the PC5 interface in NR V2X has undergone extensive changes compared to the LTE V2X. Therefore, the modifications described in the following subsection are all related to the PC5 interface.

4.2.1

PHY Layer Modifications

The PHY and Data Link layer procedures of NR V2X are inherited from 5G NR. The major modifications in the PC5 interface are outlined below. The channel coding scheme in 5G NR is LDPC codes, just like in the IEEE 802.11bd. NR specifies modulation schemes of up to 256 QAM in Rel 16, compared to 64 QAM for LTE V2X in Rel 15. LTE V2X specifies 4 DMRS symbols per sub-frame, but the only constraint in NR V2X is that a slot must contain at least 6 data symbols, i.e. the number of DMRS symbols is not a fixed number and would depend on the channel conditions. 1. OFDM—Numerologies and Slots OFDM is a mature waveform that has been successfully deployed in LTE and WiFi communications. Rather than defining a new waveform, 5G NR uses OFDM and adds advanced features to the OFDM to support higher performance with minimal changes. In 5G NR terminology, the smallest unit of resources that can be scheduled is also an RB, but the term slot is also used interchangeably. Different from the LTE, where an RB or sub-frame is always 1 ms, in 5G NR, the duration of a slot depends on the numerology. LTE uses a fixed subcarrier spacing of 15 kHz, while NR supports

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different numerologies or sub-carrier spacings, which are multiples of 15 kHz. For the frequency band of 5.9 GHz in the Frequency Range 1 (FR1), NR V2X specifies support for sub-carrier spacing of 15, 30, and 60 kHz. For future applications that use frequencies above 6 GHz or NR FR2, sub-carrier spacing of 60 and 120 kHz will be supported. Sub-carrier spacing and symbol duration are inversely proportional. In LTE, data is always scheduled and transmitted in fixed-size sub-frames leading to a waste of resources when a sub-frame cannot be filled, or the transmission is delayed when the start of the next sub-frame has to be waited for. NR V2X also supports such fixed sized slot-based scheduling, but to improve performance, slot size and scheduling are flexible. When a UE has to send latency-critical messages, scheduling is carried out on a mini-slot basis, where a mini slot can be 2, 4, or 7 symbols without any slot boundaries in the OFDM frame [2]. In use cases that require large-size packets to be transmitted, slots can also be aggregated to form a multi-slot. 2. Resource Scheduling One of the main requirements of V2X communications is to facilitate low latency, which depends on timely resource allocation. Therefore, NR V2X has defined mechanisms that enable fast sidelink establishment. (A) Sidelink Modes Similar to LTE-V2X, NR V2X specifies two sidelink modes. In the NR V2X terminology, Mode 1 is the network-assisted mode and Mode 2 is the autonomous mode. A key improvement of Mode 1 over LTE V2X Mode 3 is using location and beamforming information by the gNodeB to perform spatial reuse. In other words, the future releases of NR V2X should support Multiple-Input Multiple-Output (MIMO) and beamforming algorithms in the sidelink as well as in the Uu interface. NR V2X sidelink Mode 2 is also different from LTE V2X Mode 4. The NR V2X sidelink Mode 2 consists of 2 submodes. The first submode is similar to the LTE V2X Mode 4, where each UE autonomously allocates its resources. In the second submode, the UEs cooperate by sharing resource occupation and channel quality information in order to assist each other with resource allocation. This information exchange is an improvement over the basic autonomous resource allocation method. In groupcast communications where the second submode of Mode 2 is implemented, resource allocation of the group is managed by a group lead vehicle [65]. (B) Resource Pool and Resource Reservation Sidelink resource pool is a “pool” of OFDM sub-carrier frequencies and time slots that are preconfigured for sidelink transmission. The available sidelink resources within a sidelink resource pool consist of time slots in consecutive subcarriers within a certain Bandwidth Part (BWP) [66]. The resources for the transmission and reception of PSCCH/PSSCH channels are allocated from the sidelink resource pool. The RBs within a sidelink resource pool are referred to as Physical RBs (PRB) and in the ITS spectrum, all the PRBs are reserved for sidelink communication. Since, NR V2X applications may be developed both in a spectrum dedicated to ITS and in the operator network, the standards support both the cases where all the time slots in

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a BWP are available for the resource pool or only a set of consecutive PRBs are available. Resource scheduling in NR V2X also differs from Mode 4 in LTE V2X. Mode 4 in LTE V2X follows the sense and semi-persistent allocation approach. On the other hand, Mode 2 can operate either in dynamic mode or in semi-persistent scheduling mode. In dynamic scheduling, a new RB is selected for each transport block and the scheduling is valid only for the TB being transmitted [67]. Semi-persistent scheduling is carried out by reserving resources from the pre-configured resource pool with a Resource Reservation Interval (RRI) of 0–1000 ms. Different from Mode 4, in Mode 2 the RRI in the range of 0–100 ms can be any value, thus enabling using fewer resources and faster establishment of connections. Before reserving and scheduling resources, a selection window algorithm is applied in dynamic and semi-persistent schemes. To select new sidelink resources, a UE first defines the selection window, and candidate resources within the window that are sufficient for the V2X application must also be identified [1]. Resource re-evaluation is another novel mechanism in the NR V2X sidelink Mode2 and adds flexibility to resource management procedures [68]. Wireless channels are variable by nature, and some previously undetected interferences between vehicles might make the resource reservation obsolete. In such cases, the availability of resources must be re-evaluated. In re-revaluation, a UE with reserved resources continues sensing transmission from the other users during the selection window. If the initially reserved resources become unavailable, the re-evaluation triggers the scheduling of new sidelink resources from the currently available resources in the selection window. Another resource reservation algorithm specific to NR V2X is the Pre-Emption Mechanism, which schedules traffic based on the QoS priorities [66]. In the preemption algorithm, a UE with low-priority data must free its reserved resource if it detects that another UE with higher priority requires using the reserved resources. The decision of low–high priority is made based on a threshold; a UE frees its resources to avoid a collision only if the priority of the other UEs is higher than a specified threshold. Pre-emption applies to both the dynamic and semi-persistent schemes in Mode 2. The behavior of the UE that frees resources is not defined, it may choose to re-schedule resources or not. 3. Retransmission and Sidelink Feedback Channel Retransmission in LTE V2X is blind retransmission, i.e. the source UE automatically retransmits without knowing if the initial TB has been successfully received or not. In order to avoid unnecessary retransmissions and improve reliability, NR V2X introduces a Physical Sidelink Feedback Channel (PSFCH) for unicast and groupcast communications over which Hybrid Automatic Repeat Request (HARQ) can be feedback [69, 70]. Besides the HARQ, the PSFCH can be used to transmit channel state information to the source UE, thus allowing UEs to adapt to the channel conditions by changing transmission parameters such as MCS [2].

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4. QoS Support NR V2X QoS Support is part of the new service requirements in 5G NR [16]. In LTE-V2X, the QoS model is based on QoS class identifiers (QCIs), and QoS for the PC5 interface is managed on a per-packet basis, where V2X applications are associated with specific priority and optionally with specific reliability [61]. NR V2X QoS, on the other hand, is designed to support diverse and stringent QoS requirements defined in terms of priority, transmission rate, latency, reliability, data rate, and communication range [31]. The QoS management in NR V2X is based on 5G NR QoS Flows, and each QoS flow is associated with the QoS requirements of the V2X applications defined as QoS Profiles. Enhanced V2X application requirements are mapped to specific QoS flow profiles, which are mapped to the PHY and data link layer parameters by the Service Data Adaptation Protocol (SDAP). The Access Stratum is then responsible for reserving all the necessary resources or notifying the application that the QoS requirements cannot be met. 5G NR QoS mapping model applies only when a UE is in a network coverage area and in connected state. In addition, NR V2X QoS mapping is also available in out-ofcoverage and idle RRC state, where a V2X device can use pre-configured mapping rules for the PRBs that were previously provided by the network. Moreover, QoS support is provided for sidelink unicast, broadcast, and groupcast communications. In [1], Garcia et al. describe the QoS procedures in NR V2X.

5 Security in V2X Wireless communication networks are broadcasting networks, making them vulnerable to various security threats such as eavesdropping, jamming, spoofing, and Denial of Service (DoS) attacks. Moreover, most of the V2X services are either safety–critical or sensitive to disruptions and malicious attacks; therefore, providing security for the V2X applications is essential for mitigating the risks [3, 4, 71]. For example, without the security algorithms to authenticate the RSU nodes and to ensure message integrity, a spoofer masquerading as a real RSU may send false traffic data and cause potentially fatal accidents in autonomous driving applications. Securing V2X applications is even harder considering that at the Access Stratum, the protocols in IEEE 802.11p, Sidelinks of C-V2X, and IEEE 802.11n used in V2G do not require the use of any security algorithms. In wireless communication, security algorithms are classified into Physical Layer Security (PLS) algorithms and cryptographic algorithms [4, 72, 73]. The PLS algorithms utilize the physical characteristics of the wireless channel, such as channel noise, fading, and interference to implement beamforming and MIMO techniques that make it difficult for unintended users to decode messages [74, 75]. Although beamforming and MIMO algorithms are already being deployed in 5G NR and WiFi networks, beamforming and MIMO support for V2X are expected to be included in 3GPP Rel 17 for NR V2X and in future IEEE 802.11bd standards [1, 76]. The

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cryptographic algorithms for V2X communications are applied at the transport or higher layers and adopt asymmetric cryptography mechanism and Public Key Infrastructure (PKI) approach for managing security credentials [77]. In this architecture, each vehicle is assigned an asymmetric pair of keys and a certificate, which is used to register as a valid V2X participant. On the other hand, PKI implements the certificate generation, distribution, verification, usage, and revocation functions to manage the security credentials. PKI consists of a series of Certificate Authorities (CA) to carry out all the management functions. For the DSRC, the security framework is defined in IEEE 1609.2, which specifies secure data services, secure message formats, including the processing of the messages, and certificate management. The standards are based on PKI and define the type of CAs, the structure of the certificates, and Certificate Revocation Lists (CRLs). Likewise, the security services are also based on traditional cryptography mechanisms. Digital signatures provide message authenticity and integrity, while a digital signature algorithm employs public keys to sign and verify. Moreover, symmetric encryption keys are transported by an asymmetric encryption scheme. A key requirement of the services is privacy; using a single certificate makes the driver and the vehicle vulnerable to detection and tracking. Privacy is provided by shortterm pseudonym certificates that hide the vehicle’s real identity. The pseudonyms are used for 300 s and are then revoked by the CRL. ETSI ITS security architecture is also based on PKI and is similar to IEEE 1609.2 standards [78]. In the LTE-V2X and NR-V2X networks, the UE obtains the authorization to use PC5 interfaces and the provisioning parameters for a given Geographical Area through V2X Control Function and V2X Application Server or the UE is preconfigured with the parameters. The security mechanism applied at the Access Stratum of LTE-V2X and NR-V2X networks depends on the type of interface used [31, 61]. With the Uu interface, there are no V2X-specific privacy and security enhancements; therefore, LTE and 5G NR connection establishment procedures and security mechanisms apply. The PC5 interface in LTE-V2X networks is broadcast based and connection establishment signaling is not carried out [61, 79]. The PC5 interface in NR-V2X supports broadcast, groupcast, and unicast modes [31, 80]. For broadcast and groupcast modes, the same principles as in the LTE-V2X PC5 interface apply and there is no initial security signaling. In these modes, deployment of application layer security schemes such as IEEE 1609.2 is envisaged to secure the application-level communications. In addition, to ensure that UEs are not tracked or identified, the specification requires the Layer-2 IDs to be changed and randomized after a short period of time. On the other hand, the unicast mode of NR-V2X PC5 interface supports secure connection establishment between UEs, the security specifications given in [80] outline key management and secure connection establishment procedures.

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6 Conclusions The chapter presented a review of the present and future V2X communication standards. Out of the four RAT technologies presented, IEEE 802.11p has been around the longest, and despite a very slow start and low deployment rate it has been chosen as the RAT for the Car 2 Car consortium and major European automotive leaders like Volkswagen and Volvo are actively developing C-ITS applications. C-V2X communication standards, starting with the LTE V2X seem more promising than IEEE 802.11p-based standards and have already been deployed by vehicle manufacturers like Toyota, Ford, Audi, etc. In addition, with the structured architecture presented in Sect. 2, future V2X applications can be developed on any RAT. Both families of RATs will coexist and even cooperate. The chapter showed that IEEE 802.11p communication standards are the most mature V2X protocols, but the resource allocation mechanisms can deteriorate in high vehicle density situations. IEEE 802.11bd standards will increase throughput and reliability issues of IEEE 802.11p while reducing latency. IEEE 802.11bd is an emerging standard and its overall performance is yet to be tested. The biggest advantage of IEEE 802.11bd is its backward compatibility, meaning it can work with Car 2 Car consortium’s devices. NR V2X, on the other hand, is defined from scratch and aims to support autonomous driving. Not being backward compatible might be both an advantage and a disadvantage, as NR V2X specifications define new resource allocation techniques that can improve latency and reliability drastically. Besides the high data rates, it offers the best reliability for time-critical applications. In Smart Grids, various messaging protocols enable the EVs to successfully perform charging sessions and execute authentication and billing procedures at the charging stations. With the deployment of V2X technologies and the introduction of ISO 15118 protocols, the interaction between EVs and the Smart Grid is no longer confined to the charging station. Besides providing services for enhanced use cases, the new V2X technologies can also provide wireless connectivity between the EV and the Smart Grid to achieve better energy management, thus reducing greenhouse emissions.

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Internet of Things for Smart Homes and Smart Cities Nuri Kapucu and Mehmet Bilim

Abstract With the improvement of fifth generation (5G) and beyond mobile technologies, Internet of Things (IoT) becomes more important in daily life as it provides many facilities for people in their homes and cities. The IoT can be considered a network of physical devices (“things”) used to connect and exchange information with other devices over the Internet. 5G and beyond technologies are expected to provide much more capacity and higher speed, helping the rapid growth of the IoT market. Nowadays, it is estimated that approximately 6–7 billion devices are connected through IoT technology and it is expected to increase to 20–22 billion in the near future. Smart Grid 3.0 is based on smart intelligence, automation, and data-enabled decisions, providing cost-effective electricity efficiency to consumers and reducing peak demand by enabling electrical utilities via smart technologies and improved security. So, IoT is expected to be a major technology for smart home and smart city applications and services that are also a part of the Smart Grid ecosystem. This chapter covers both IoT-based smart homes and smart cities. First, the IoT technology is introduced, and then, the IoT architecture is explained in the context of three layers included in its structure. In addition, new generation mobile technologies, namely 5G and beyond, are discussed for the IoT. After introducing the IoT technology with its architecture and enabling technologies, the chapter’s emphasis is on smart homes and smart cities by explaining protocols and architectures of smart environments with IoT-based services. In the third part of this chapter, the smart homes are presented in detail by mentioning smart home architectures, communication and medium protocols that can be used in smart homes, and several important services based on the IoT. Finally, the smart city concept alongside its architecture is given, and popular smart city services are explored. N. Kapucu (B) Department of Electrical and Electronics Engineering, Faculty of Engineering, Hitit University, 19030 Corum, Türkiye e-mail: [email protected] M. Bilim Department of Electrical and Electronics Engineering, Faculty of Engineering, Nuh Naci Yazgan University, 38090 Kayseri, Türkiye e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 B. Appasani and N. Bizon (eds.), Smart Grid 3.0, Power Systems, https://doi.org/10.1007/978-3-031-38506-3_13

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Keywords Internet of Things (IoT) · Smart homes · Smart cities · 5G and beyond · Smart applications · IoT architecture · IoT features for smart homes and smart cities

Abbreviations 5G 6G AI API BLE Gbps GHz ICT IETF IoT IP ISM LAN Mbps MHz MIMO mmWave MoCA POE QAM RF RIS SoAs THz UPB URLLC WAN Wi-Fi

Fifth Generation Sixth Generation Artificial Intelligence Application Programming Interface Bluetooth Low Energy Gigabit per second Gigahertz Information and Communication Technology Internet Engineering Task Force Internet of Things Internet Protocol Industrial, Scientific, and Medical Local Area Networks Megabit per second Megahertz Multiple-Input Multiple-Output Millimeter-Wave Multimedia over Coax Point of Entry Quadrature Amplitude Modulation Radio Frequency Reconfigurable Intelligent Surface Service-oriented Architectures Terahertz Universal Powerline Bus Ultra-reliable and Low-latency Communication Wireless Area Networks Wireless Fidelity

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1 Introduction The increasing demand for electricity, coupled with the need for a dependable, efficient, and secure power supply and the intricacy of modern electrical power systems, led to the focus on Smart Grid technology. Smart Grid technology leverages bidirectional flows of both electricity and information to enhance energy efficiency, reduce costs, and improve system performance. The underlying principles of Smart Grid rely on robust data management, monitoring, and communication capabilities. The latest iteration of Smart Grid technology, called Smart Grid 3.0, is a grid-aware system offering advanced command and control functionalities. This state-of-theart Smart Grid ecosystem is designed to be location-specific, capable of real-time processing, and tailored to specific applications. The Internet of Things (IoT) technology plays a critical role in realising Smart Grid’s objectives, particularly in the context of smart homes and cities. Figure 1 presents an overview of the Smart Grid technology, including its deployment in smart home and city environments, and illustrates the bi-directional flow of electricity and communication facilitated by intelligent nodes between the key components of the Smart Grid. The IoT is a promising communication technology for smart homes and smart cities, becoming vital to the Internet. IoT aims to connect millions of physical objects

Fig. 1 General concept of smart grid technology

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predicted to be used in our daily life over the Internet for both homes and cities in the near future. With significant improvements in mobile communications, such as the fifth generation (5G), IoT technology will be more involved in our daily life. According to [1], US$157 billion of IoT market size in 2016 has grown to $661 billion by 2021. In addition, 5G technology will become more common worldwide, and researchers are conducting extensive studies for the sixth generation (6G), enabling faster internet connection compared to 5G. Thus, in parallel with the improvements in 5G and beyond technologies, the IoT will facilitate several smart home and smart city applications such as home appliances, surveillance cameras, monitoring sensors, displays, vehicles, and new services to citizens, companies, and the public administrations and so on. So, the IoT market is expected to grow very rapidly due to cloud platforms, the improvement of new and smarter sensors, and the usage of high-speed enabled Internet connections and networks in the near future [2]. The IoT technology has great potential, bringing promising solutions and easinesses in our homes and cities as the Internet technology offers higher speeds thanks to 5G and beyond. A smart home system consists of different technologies such as mobile communication (IoT technology), control technology, signal processing and artificial intelligence (AI) [3, 4]. Based on these technologies, smart homes provide a more comfortable lifestyle with automated appliance control for home devices, including cameras, thermostats, lights, household appliances, air conditioning units and so on [5]. Additionally, the IoT technology will provide the users’ control of their home devices and provide home devices to communicate with each other thanks to higher capacity and speed features of new-generation mobile communication systems. Another usage area of IoT technology is smart city applications, in which the IoT plays a key role in making cities smarter and more secure nowadays and in the near future. A smart city is a city that is consisted of communication and networking technologies, and it provides engagements between citizens while its infrastructure is electronically connected [6, 7]. The major components of a smart city are smart people, smart economy, smart governance, smart mobility, smart environment, and smart living [8]. These components encompass various domains, including individuals, economic systems, governmental structures, transportation networks, environmental quality, and urban lifestyles. Integrating these diverse components into cohesive smart systems, enabled by the IoT technology, is the hallmark of the smart city. The use of IoT technology in urban settings relies on high-capacity, high-speed internet connections such as those offered by 5G and future generations of wireless technology. This approach facilitates the deployment of technological solutions across various urban services, including local government agencies, public transportation, healthcare providers, educational institutions, energy plants, and governance services [9]. Ultimately, the primary objective of creating smart cities is to enhance the quality of life, improve service efficiency, and meet the evolving needs of citizens through the deployment of advanced technology [10].

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2 Internet of Things (IoT) Wireless communication technology has advanced considerably in recent years, leading to a growing need to connect millions of smart devices and physical objects via the Internet. This demand has catalyzed the emergence of the IoT, a highly popular subject of study that has garnered attention from academic researchers and industry professionals. Experts suggest that IoT technology will become an indispensable part of modern life in the future [11, 12]. IoT technology has ushered in a new era of smart and automated remote management systems in various industrial domains, enabling uninterrupted communication and automation of heterogeneous devices without human intervention. This has resulted in significant benefits for society. Given the tremendous potential of IoT networks and applications, their integration with 5G mobile technology is currently being planned and commercially deployed [12]. The latest 5G technology offers a range of services that have been adapted to cater to the needs of IoT technology. These services include enhanced mobile broadband, massive machine-type communication, and ultra-reliable and low-latency communication (URLLC). They are designed to provide high-speed data transmission, low latency, and energy efficiency to IoT applications [13, 14]. It is doubtful whether 5G technology can meet the rising technical criteria, such as the rapid growth of automated and intelligent IoT networks and autonomous, ultra-large-scale, highly dynamic and fully intelligent services [15]. 6G wireless networks [16] and accompanying technological trends are coming to the fore to pave the way for the development of IoT and beyond. 6G is expected to meet and support the emerging technical criteria mentioned earlier. In parallel, it aims to improve user experiences in existing IoT systems and maintain superior service quality [17–19]. For this reason, many different studies are carried out by researchers. For example, the US Federal Communications Commission has launched the Terahertz (THz) spectrum band, which allows researchers and engineers to test 6G functions in mobile communication systems and IoT devices [20]. Such activities have encouraged researchers to accelerate their research to realize the anticipated promises of IoT. It also motivated the efforts to meet the requirements of the smart information society of the 2030s. Considering all this basic structure, it is necessary to develop different IoT technology applications and adapt them to modern life, both in the industrial field and in daily life. Numerous studies, ranging from [21–27], have discussed the role of the IoT in the context of next-generation communication technologies and related developments in great detail. Upon examining these studies, it is evident that IoT technology will continue to play a crucial role in 6G communication networks and will significantly impact the convenience of social life.

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2.1 IoT Architecture IoT technology intelligently combines heterogeneous devices wirelessly, and aims to automatically connect different electronic devices to the Internet without human intervention. This way, data detection, calculation and processing are easily performed. In the IoT architecture, data collection can be performed through personal or shared computers, smart and mobile phones, radio frequency identification, sensors and actuators to serve the users in the system [28]. The analysis in [2] shows that IoT technology will develop tremendously in the next few years due to its current potential in its architecture. From this point of view, it is planned to support nextgeneration IoT networks and applications with the extraordinary and very powerful features of 5G and beyond, providing full-size wireless coverage and the ability to operate automatically. For this reason, there are some basic requirements of IoTbased next-generation communication systems, which can be roughly summarized as [2]. • • • • •

Massive IoT Connectivity. Massive Ultra-Reliable Low-Latency IoT. Improved 5G and beyond IoT Communication Protocols. Extended IoT Network Coverage. Next-Generation Smart IoT Devices.

Three-layer architecture and service-oriented architectures (SoAs) are important architectures for IoT [29]. The first of these architectures, the three-layer architecture, typically consists of three layers [30]. The basic schematic representation of the three-layer architecture is shown in Fig. 2. Perception Layer: This layer, also called the sensor layer, is one of the lower layers of the three-layer IoT architecture [31]. This sublayer physically interacts with devices and other components thanks to smart devices such as radio frequency identification devices, sensors, actuators etc. The main purposes of this sublayer can be listed as follows: connecting physically interacting objects to the IoT network, measuring the current state information of objects through smart devices, collecting and processing the measured state information, and transmitting the obtained information to the next sublayer. Network Layer: It acts as the middle layer of the IoT architecture and carries out the transmission functions [32]. This layer receives information from the previous layer, the perception layer, and is used by its integrated networks to determine paths

Fig. 2 The basic schematic representation of the three-layer architecture

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to transmit to the IoT hub and devices or applications connected to the IoT network. It is the most important layer in the IoT architecture because devices such as a hub, switching, gateway, cloud computing and various communication technologies such as Bluetooth, wireless fidelity (Wi-Fi), and long-term evolution are integrated into this layer. This layer transmits or receives data to different objects or applications in the network using interfaces or gateways between different networks and various communication technologies and protocols. Application Layer: This layer, also called the business layer, functions as the top layer of the IoT architecture [1]. This layer receives data from the network layer to provide the necessary services. For example, this layer can provide analysis to evaluate the received data to reveal the future status of physical devices in the network, or it can perform storage by saving the data to the database. In the application layer, there are several applications, each with different features from the other. Some examples can be given as smart grids, smart cities, smart transportation, smart homes, etc. [33, 34]. Three-layer architecture for IoT is a basic architecture, and it has been designed and implemented for use in some systems [34]. However, although the three-layer IoT architecture is simple, the applications in the network and the functionality in the lower layers are diverse. In addition, the processes of these transactions contain some complexities. For example, the network layer, which is the second layer of the threelayer architecture, is not only concerned with the determination and transmission of data paths but also performs some services such as data collection and processing. In addition, the application layer must provide services to users and devices on the network and perform data services. For this reason, it is appropriate to develop a service layer to perform data services between the application layer and the network layer to reveal a general architecture for IoT. In this context, SoAs have recently been developed to support the IoT architecture [1, 35]. To make a rough definition of another architectural model, SoA is a componentbased architectural model used to connect different processing units or services of an application, thanks to interfaces and protocols. This model focuses on designing workflows for coordinated services. In this way, feasibility development is provided in the design of the IoT architecture, and it is ensured to carry out the functions of the IoT architecture in software and hardware [35, 36]. Thus, the SoA can easily adapt to the application and network layers of the three-layer IoT architecture, which has been described in detail, and create a new working order, and the service layer or interface can be very easy to use. Thus, there are four layers in a SoA-based IoT architecture: the perception layer, the network layer, the service layer, and the application layer. These layers can interact with each other and work more effectively [37]. In other words, when the three-layer architecture is compared with the SoAbased IoT architecture, an additional layer is specified as the service layer. When some studies are examined, it is stated that the service layer consists of two sublayers called service composition and service management. In order to provide the so-called complex service requests, the business layer works separately from the application layer as the upper layer of the application layer.

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The SoA-based IoT architecture, which has four layers, acts as the first layer of the perception layer architecture and is used to measure, collect and extract data associated with physical devices, similar to the three-layer architecture [38]. The second network layer is processed to support routing and data transmission with adaptive heterogeneous networks [36, 39]. Unlike the three-layer architecture, the service layer is located between the network layer and the application layer, providing support to the application layer [36]. This layer consists of service discovery, composition, management, and interface components. To explain the components here, service discovery is used to discover desired services. The service composition handles interacting with existing objects on the network and setting up service requests efficiently. Service management manages the functioning of mechanisms in the network and provides the service securely. Service interfaces are used to support interactions between all provided services. The application layer, the last layer in the SoA-based IoT architecture, is used to meet the service requests of the users in the network. This last layer can support various IoT-based applications.

2.2 5G and Beyond Technologies for IoT This section discusses the next generation of 5G and beyond technologies for IoT networks and their applications. Generally, these technologies are edge intelligence, reconfigurable intelligent surfaces, blockchain, space-air-ground-underwater communications, THz communications, and Massive URLLC, as shown in Fig. 3. Edge Intelligence: For smart 5G and beyond systems, AI functions are extended to the network edge, thanks to the computational capabilities of the edge nodes [40]. This situation creates a new structure called edge intelligence. Recently, the application of edge intelligence for IoT has been popularly discussed and studied [41–44]. The common use of edge intelligence is based on edge devices that play important roles in classifying learning tasks or generating regression processes. However, data theft is likely to occur in edge intelligence applications, and a security vulnerability exists. Therefore, researchers are expected to propose reliable edge intelligence ecosystem solutions for future 5G and beyond for IoT networks. Terahertz Communications: THz communications are among the 5G and beyond technologies that support data rates and low latency for IoT. THz communications aiming to use millimeter wave communication spectrums are predicted to be able to meet the requirements of future IoT applications [2]. Recently, in [45, 46], user localization of THz frequencies using ultra-wideband widths and THz communications in IoT-based vehicle detection networks and channel modeling in these systems are presented, respectively. All these studies show that THz communications are an important development that can be adapted to IoT networks. Space-air-ground-underwater communications: Extremely broad coverage and ubiquitous connection types are required to fully support the next generation of IoT applications [6]. To summarize, the space communication layer with different

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Fig. 3 5G and beyond technologies for IoT

altitudes [47], the air communication layer [48] that supports various air communication technologies and unmanned aerial vehicles, the ground layer with physical base stations, millions of mobile devices and computing servers [49], underwater sea underwater communication layer used to perform the operations of the vehicles [50]. Massive ultra-reliable and low-latency communications: It is stated that another important technology for IoT will be massive URLLC through low latency and reliable connectivity [51, 52]. While developments in AI are critical for URLLC, it provides complete solutions such as accurate traffic and mobility forecasting, and network control [53, 54]. The use of AI is much more important in the absence of network knowledge or when the network environment is dynamic. In addition to all these positive effects, continuous control of information packets waiting on the network causes an increase in energy consumption. For this reason, it is expected that energy-saving IoT network devices will be designed and there will be developments in this regard. Blockchain: In IoT networks based on 5G and beyond technologies, security and privacy problems may occur due to a large number of interacting users and devices

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and high attacks and threats in IoT networks. Due to the layered structure in IoT architectures, ensuring data privacy is a very important issue. In order to solve these security and privacy problems, Blockchain, a disruptive and popular technology that has been frequently mentioned recently, has come to the fore [55]. Blockchain technology can offer innovative solutions to overcome privacy and security issues in IoT networks. By definition, Blockchain technology is a decentralized, immutable and transparent database without the need for authority to manage data. This technology ensures that each IoT device is treated equally in controlling and authorizing data stored on the blockchain. Generally, blockchains are classified into two parts, public and private. In blockchain technology, everyone can transact and be involved in the process. The most well-known public blockchain works are Bitcoin and Ethereum. On the other hand, private blockchain works are managed by a central entity and have an inviting network structure [56]. The user who wants to participate in this blockchain mechanism must obtain permission from a verification center. For this reason, blockchain applications that perform successful transactions for security and privacy are costly. Therefore, these costs need to be considered when implementing future IoT networks. Reconfigurable Intelligent Surfaces: Recently, researchers have stated that reconfigurable intelligent surface (RIS) systems technology is significant for 5G and beyond next-generation communication applications. In line with this view, it is seen that some working examples for 5G beyond technology applications of RISs have taken place in the literature [57–60]. There are passive reflective surface elements in the structure of RISs, and these elements can be controlled electronically. Considering the place of IoT in next-generation communication, one might think that RISs could be used to reduce inter-cell interference and be developed to serve multi-cell IoT networks. In addition, RISs are predicted to be capable of offloading rates for IoT networks [2].

3 Smart Homes With the advancement of wireless and mobile communication technology and the availability of high-quality services, automated systems have gained popularity in households. These automated systems are integrated into homes, resulting in the creation of smart homes. The construction of smart homes involves the use of various technologies such as control systems, remote sensing, wireless and mobile communications, and signal processing. The IoT, as a fundamental part of wireless and mobile communication technology, enables users to control home automation systems remotely over the Internet. Connected devices such as smartphones, smart sensors, smart televisions, smart washing machines, and smart refrigerators can be integrated via machine-to-machine communications, making the IoT the realization of these devices in the modern world. Smart homes offer a better quality of life by enabling remote control of home devices before the user arrives home. Additionally, intelligent systems can be incorporated into home automation to monitor energy

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Fig. 4 A smart home concept [62]

consumption, optimizing electricity usage [3–5, 61]. Figure 4 depicts a smart home environment with several applications.

3.1 Architectures, Communication Medium and Protocols In a smart home environment, several sensors, home appliances, or actuators are contained, and these devices can be smart, or they do not necessarily be smart on their own. All these equipments operate smartly according to a system architecture for a home to be smart. Smart home architectures can be classified as centralized architecture and autonomous architecture: Centralized architecture: In this architecture, a centralized decision unit needs to exist, which collects data and makes correlations for the collected data from the different elements of the system, such as sensors and devices. After that, the decision unit decides according to the received data and sends messages to the proper devices. The decision unit can be a hardware-based device or cloud-based application [61, 63–65]. In addition, a software-defined radio based architecture is also available, and it is called as software-defined smart home architecture, which is shown in Fig. 5. In this architecture, smart home environment consists of three parts which are smart devices layer, controller layer and external service layer. In smart devices layer, there are sensors, smart devices and an application programming interface (API) is not known by the public. The controller layer receives and analyzes user demands and manages smart home environments such as routing and monitoring.

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Fig. 5 Software-defined smart home platform

Home service resources are integrated into the external service layer, and this layer offers efficient, high-quality services for users [66, 67]. A cloud-based smart home architecture is depicted in Fig. 6. In this architecture, the controller makes correlations and analytics using AI, IoT analytics and machine learning technologies. In a cloudbased architecture, a hub collects raw or processed data and transmits it to the cloud. Then, processed data is sent to hub, or the controller can receive data from hub via API. As seen from Fig. 6, sensors communicate with hub in a two-way transmission mode. Autonomous architecture: Each device has autonomous properties in receiving data, making decisions, sensors, and communicating with different devices in this architecture. The autonomous procedure also provides users with a remote control for some devices. Autonomous devices with local resources can process the information locally. Moreover, they can use the resources on the cloud for information processing [63]. In order to exchange information, an interconnection between smart home devices is needed, and this interconnection can be provided through a communication network. In a smart home, many sensors cooperate in setting up a smart and useful environment through several protocols. These communication protocols determine how the transmission of information is executed. According to the propagation

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Fig. 6 A cloud-based smart home architecture

medium, three types of protocols for communication are listed: wired, wireless, and hybrid. Communication protocols are decided based on the needs and priorities of the smart home environment. So, a user must decide on communication range, security issues, power consumption and network size in order to determine communication protocol for their smart home [68]. Wired communication protocols: Wired protocols transmit information via a wire, which offers advantages in terms of security, ease of use, distance, data rate, and reliability when compared to wireless transmission. However, wired communication is limited by factors such as complexity, cost, power consumption, mobility, and expansion [68]. Some well-known examples of wired protocols include Ethernet, X10, Universal Powerline Bus (UPB), INSTEON, Multimedia over Coax (MoCA), and KNX. • Ethernet: Ethernet is an IEEE 802.3 standard widely used in local area networks (LAN) and wireless area networks (WAN). Communication frequency ranges from 100 Megahertz (MHz) to 500 MHz and supports data rates between 10 Megabit per second (Mbps) and 100 Gigabit per second (Gbps). Smart home devices can be connected to an Ethernet network via cabling such as copper twisted pair, coaxial cable or fiber optics [69]. Coaxial cable, also known as coax cable, transmits radio frequency (RF) signals like television and radio signals. The twisted pair is usually utilized in telephone and computer networks [70]. Fiber optics which carry the information as light beams provide faster transmission. • X10: The X10 protocol utilizes a home’s existing electrical wiring infrastructure to transmit information signals, thereby enabling control of home appliances without the need for new cabling. However, the X10 protocol has several limitations, including slow data rates, functional restrictions, signal attenuation, command loss, and interference. Additionally, X10 data can be disrupted by noise filters

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found in newer electrical devices, and there is a risk of interference between neighboring X10 devices. To mitigate this issue, a noise block can be installed to prevent this effect [3, 70, 71]. UPB: Universal Powerline Bus also uses the power lines for information transmission, such as X10. UPB has some superiorities compared to X10, like higher data rates (although low compared to other technologies), reduction of noise caused by alternating current lines, and supporting more devices via peer-to-peer communication. Besides these, lack of encryption and small marketing are the main drawbacks of this protocol [68]. INSTEON: This communication technology makes use of the existing power lines at 132 kHz, RF transmission at 915 MHz (US)-869.85 MHz (Europe)-921 MHz (Australia) or both in order to control smart home devices remotely. INSTEON, devices communicate to each other via standard 10 bytes long messages or 24 bytes long extended messages. MoCA: Existing coaxial cables are used in this communication protocol for signal delivery, known as Multimedia over Coax. Since MoCA protocol is wired, it provides reliable, low-latency, and ultra-high-speed connections. It can be used to increase the coverage area of Wi-Fi without any data rate loss by utilizing Wi-Fi repeaters, and the maximum achievable data rate is 2.5 Gbps. For smart home security, MoCA network Point of Entry (POE) filter should be installed to block the interference of MoCA user homes. Another major advantage of the POE filter is preventing leakage of our network data to service provider or something else that shares the cable wiring. KNX: KNX technology is an open standard, and in this protocol, different communication media such as twisted pair, RF, power line, and Internet Protocol (IP)/ Ethernet are supported. The KNX Association standardizes this open standard protocol. Members of the association are device manufacturers that develop different applications for KNX-based home and building control, such as lighting control, heat control, air conditioning, energy consumption, monitoring and that sort of thing [72].

Wireless communication protocols: Wireless technology sends the information signal over free space as RF signals. Wireless transmission offers mobility, expandability, cost and flexibility advantages, and it has some disadvantages like security, data rate, interference, and coverage. Wi-Fi and Bluetooth are the most popular wireless communication technologies, but there are some other important technologies, such as Zigbee and Z-Wave. • Wi-Fi: Wireless Fidelity is known as Wi-Fi, and it is used to connect computers, smartphones, tablets and smart devices to the Internet. This communication protocol is standardized based on IEEE 802.11 protocols, as given in Table 1, and it operates in an unlicensed frequency band such as 2.4 Gigahertz (GHz) and 5 GHz. New and faster Wi-Fi protocols such as 802.11ax, 802.11n, and 802.11ac benefit from multi-user or single-user multiple-input multiple-output (MIMO) technology.

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Table 1 Wi-Fi protocols according to IEEE 802.11 family Protocol

Frequency band (GHz)

Maximum achievable data rate

Legacy 802.11

2.4

2 Mbps

802.11b

2.4

11 Mbps

802.11 g

2.4

54 Mbps

802.11a

5

54 Mbps

802.11n

2.4 or 5

450 Mbps

802.11ac wave 1

5

866.7 Mbps

802.11ac wave 2

5

1.73 Gbps

802.11ax

2.4 or 5

2.4 Gbps

The latest Wi-Fi protocol, 802.11ax, uses 1024 quadrature amplitude modulation (QAM) scheme and 8 × 8 multi-user MIMO technology and offers a 2.4 Gbps data rate at 2.4 or 5 GHz. By 2024, it is planned that Wi-Fi 7 will be in our life, and it will provide more bandwidth (up to 320 MHz) and faster data rates (approximately 5.8 Gbps) based on 4096 QAM scheme and 16 × 16 multi-user MIMO at 2.4&5&6 GHz frequency bands [73]. • Bluetooth: It is used for short-distance wireless transmission between Bluetoothcapable devices in personal area networks. It is standardized according to IEEE 802.15.1 standard and operates at the 2.4 GHz frequency band, a crowded spectrum with other wireless technologies. Data transfer between Bluetooth devices is pairing that includes user interaction such as entering a PIN code or password. The Bluetooth Classic version has low power consumption and streams information over 79 channels in the 2.4 GHz unlicensed industrial, scientific, and medical (ISM) frequency band. This version supports point-to-point device communication mainly used to provide wireless audio transmission. Therefore, it has become the main wireless technology for cordless speakers, hands-free speaking via headphones, and in-car entertainment systems. The Bluetooth Low Energy (BLE) allows data transmission over 40 channels at 2.4 GHz ISM band and is designed for operations requiring very low power. The BLE technology provides developers with great flexibility to design products with unique connectivity requirements in accordance with their market. The BLE enables using several communication topologies, such as point-to-point to broadcast, mesh, and large-scale device networks. In addition, the BLE technology provides highly accurate device positioning for indoor location services, and it is possible to determine the presence, distance, and direction of a device by another BLE-capable device. Bluetooth 5 was introduced in 2016 and developed for high demand to increase the functional features of Bluetooth for IoT. Bluetooth 5 offers a longer range for data transmission, faster data rates, and higher capacity [74, 75]. • Zigbee: ZigBee is designed according to IEEE 802.15.4 standard providing reliable, low-cost, low-rate data transmission with a limited power supply. 20–250 kilobit per second data rates are supported, and the communication range is up to

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70 m. The Zigbee wireless personal area networks operate at 2.4 GHz, 900 MHz and 868 MHz frequency bands. This protocol provides suitability for point-topoint, point-to-multipoint and mesh networks, and it has low latency, low duty cycle, and long battery life. • Z-Wave: Z-Wave is a mesh network topology-based technology, meaning each battery-free device that is included in the network acts as a signal repeater. As a result, more devices installed in a home provide a stronger network. Although ZWave has 100 m of communication range in the outdoor environment, the indoor environment reduces this range. So, installing a Z-Wave device approximately every 10 m or closer is suggested to have higher efficiency. A Z-Wave network can support up to 232 devices, allowing us the flexibility to install many devices.

3.2 IoT Enabled Smart Home Services In the earlier stages of smart home technology, homes had limited capabilities and smart home technology primarily focused on remote control and automation of homes. A decade ago, controlling blinds from a smartphone or teaching the thermostat to remember preferred temperatures was considered sufficient to label a home as “smart.“ In a smart home environment, numerous sensors, smart devices, and home appliances are included, which monitor and collect data on consumer usage habits. This data is used to optimize routines and improve the consumer’s quality of life while increasing comfort and reducing time waste. New generation smart home devices use IoT to collect data via their sensors and provide real-time adjustments based on the consumer’s location. For example, these devices monitor the consumer’s location and adjust the temperature by turning on or off the heating or air conditioning before the consumer arrives. IoT energy monitoring system creates an energy management platform for energy efficiency in our homes. This system teaches people how to use and control the home energy by monitoring electricity, electrical devices, loads and power storage. The elements of this system are smart hardware, software and data tools. A home equipped with an IoT energy monitoring system becomes a smart environment with many capabilities for monitoring and controlling energy consumption. Some of them can be summarized as follows. • Opportunity to manage and remote control of electrical circuit, which enables detection of possible irregularities, • Real-time analysis of energy consumption and approximate cost calculation based on collected data of user’s habits, • Outage detection and real-time reports are sent to the consumer’s smartphone, • Optimizing performance of energy storage and solar panels. High level of comfort is one of the most important features of IoT-based smart homes in which intelligent algorithms are used to make household devices

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autonomous for a certain level to have decision-making capabilities. In this way, window blinders adjust their position according to the daylight, and smart lights turn on and off automatically based on sensor data or a smart fridge determine and order groceries online. All these examples indicate that IoT based smart devices can provide easier and comfortable life for the users who has the main control in the home. Improved security services are provided by smart locks and security cameras alongside the monitoring systems in IoT based smart homes. Power fluctuations, water or gas leakages, and unauthorized persons are detected by all the elements of security systems and messages are sent to prevent undesirable situations [76]. On the other hand, security threats are the main problems for the security of smart homes. In addition to these, a smart home needs network security since security threats may be local or remotely over the internet connection. Mostly, security problems arise due to weak user and device authentication schemes. A security model based on products for smart home appliances is proposed by [77] in which the model involves a third-party network operator to apply security measures related to products. An authentication scheme for securing remote access is presented for a smart home network in [78] where the presented system utilizes one-time password based on a hash-based message authentication code and a hash chaining method with smart cards. The proposed scheme comprises an integrated authentication server outside the home and provides authentication, authorization, and accounting services. Healthcare services are a crucial part of IoT-based smart homes providing patients, elderly people, and healthy people with health monitoring which can be done locally or remotely. A patient is always monitored in the smart home environment to detect health problems, provide assistance services, and create local warnings or alarms if needed. An IoT-based smart home can generate long-term reports of a user’s health conditions, which a healthcare provider or the user can examine. A wireless sensor network-based intelligent system is designed to monitor elderly residents and detect unusual activities such as falls, immobility, and impaired responsiveness [79]. In this system, wearable kits are used to collect data from the inhabitant, and the gathered information is shared with a medical office-based IoT technology. In a remote healthcare monitoring system, specialized health service providers are employed to provide emergency medical support. The smart house uses physiological sensors to monitor the patient; if any crucial sign is met, the system automatically contacts the caregiver. For example, a system monitors the user’s movements via infrared radiation (IR) sensors and magnetic contacts. This system employs a temperature sensor in the main living area to measure instant temperature. Based on IoT communication, an alarm system that communicates to a remote healthcare center, the users, and their caregivers are installed to detect abnormal behaviours [80]. Another telemedicine system is designed based on physiological and generalpurpose sensors (weight sensors, motion detectors, and light sensors). The pulse oximeter and blood pressure sensors are tied to a wheelchair, which transmits information to a database server using a digital assistant or personal computer using IoT technology. Thus, the doctor examines the user’s health at his/her office [81].

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4 Smart Cities The concept of smart cities has emerged with the adaptive use of information and processing technology, which aims to manage various public works. From this point of view, IoT technology offers a very useful paradigm. Smart cities are planned to use public resources more efficiently and effectively. In this way, it aims to provide a high service quality service to the users and reduce consumption by using the resources effectively [2]. To give an example for smart city applications, an application was carried out in the city of Padova, Italy, for the optimum use of public resources. In addition, managers were supported to manage in the shortest time and most efficiently [82, 83]. IoT-based smart city applications can be realized by constructing important sub-units such as smart grid, waste management, environmental monitoring, smart health functions, smart lighting, structural condition control of buildings. In order to use public resources most optimally, all these sub-units need to be connected with an integrated communication network and IoT support to perform their functions in a comparative and informed manner. It should be noted that while implementing all these IoT-based smart city applications, attention should be paid to the security and privacy issues mentioned earlier. With the elimination of hesitations on this issue, the convenience and reliability of public administration will be increased, and it will be ensured that people live in cities where it is easier to live.

4.1 Concepts and Architecture The concept of smart cities consists of synergistically harmonizing the main industry and service sectors such as Smart Management, Smart Mobility, Smart Facilities, Smart Buildings and Smart Environment. In this sense, IoT technology can be an indispensable building block of smart cities with the efficient use of information processing techniques and may lead to the emergence of IoT-based smart cities. Thus, it can be thought that a different step with a high potential for use will be taken in the vision of smart cities [84, 85]. The main objectives in smart city planning are; It can be said that basic facts such as the controlled realization of the phenomena that operate in coordination with each other, the improvement of the service quality offered to the society, the improvement of the services providing more efficient services in terms of economy and cost etc. By achieving these goals, the improvement of living standards in smart cities and the realization of providing quality and easy living opportunities to the society will be ensured. In general, some services planned to be developed for smart cities can be summarized as follows. • • • • •

Structural Health of Buildings Waste Management Noise Monitoring City Energy Consumption Smart Lighting

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Smart Parking Air Quality Traffic Congestion Automation and Salubrity of Public Buildings.

In order to provide the smart city services described earlier, it is seen that a central architecture is needed that can dominate different types of heterogeneous devices located in the urban area and process these data in a coordinated manner. The processing and storage of the data here can be achieved through the central architecture of the IoT system and information processing technologies. For this reason, IoT technology is an important development for smart cities and can be easily integrated into the architecture. Another important issue in the services offered in smart cities is that the authorities and citizens can easily access and monitor the stored data. For this reason, smart city architecture must be able to meet these needs. An IoT-based smart city architecture consists of three basic parts: web approach for IoT, link layer technologies and devices in the system [2]. Although the use of different standards for IoT is discussed, the use of Internet Engineering Task Force (IETF) standards is quite common. For this reason, IETF standards play a leading role in the approach of web services for IoT. In the previous sections, IoT architectures were mentioned. IETF standards, which can work in accordance with these architectures, show the feasibility of web services for IoT. Link layer technologies can be considered constrained and unconstrained due to the high data flow and the wide coverage of the network. Technologies such as All the traditional LAN, WAN, and cellular can be given as an example of the first group, the unrestricted part. However, they are generally not preferred for IoT due to their high energy consumption and complexity. The prominent technologies for the restricted part are IEEE 802.15.4, which has low energy consumption, Bluetooth, low energy, IEEE 802.11 low power, near field communication and radio frequency identification. However, since energy saving is at the forefront in these technologies, reductions in transmission speed occur. Devices, which are the last part, are considered backend servers, gateways and IoT peripheral nodes. Any device in the architecture can belong to any of these groups. Every device that can take part in the execution of the process necessary for smart cities takes its place in this part of the architecture [2].

4.2 IoT Based Smart Cities Smart cities can be considered as inner-city development vision to bring many information and communication technology (ICT) ideas to operate a city’s assets to create a better environment, improve life quality, and increase efficiency and financial status. Thanks to abilities of cloud-based IoT technology, such as receiving, analyzing and managing real-time data, people and municipalities can be integrated into a smart

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city called a digital city. This subsection overviews several smart city services based on IoT technology. Monitoring the Health of Historical Buildings: Historical buildings need proper and regular maintenance services that require continually observing the structural conditions in every old building and determining the areas impacted by external factors. The IoT technology may create a distributed database for structural measurements of historical buildings that are gathered using appropriate sensors placed in the building. These sensors can collect data about vibrations and deformations in the building, pollution levels from air quality sensors, and environmental conditions via temperature and moisture sensors. By creating such a database, expensive structural tests may not be needed, and thus, it may reduce the cost of maintenance. This database can also bring an opportunity to observe and understand the impacts of earthquakes. On the other hand, citizens can be aware of the actual conditions of the historical buildings if this database is made accessible to the public [2]. Management of Waste Materials and Smart Environment: Picking up waste materials and storage of them in dumps are primary issues in today’s modern cities because of the service costs. So, ICT can help for providing financial and ecological improvements. Waste collection costs can be reduced by intelligent systems involved in waste containers. This system can monitor the level in the container, and the vehicles can follow a route according to information provided by intelligent containers connected to a center via IoT technology and controlled to optimise vehicle routes [2]. In addition, a map showing the noise and air pollution levels can be created by installing several sensors in the city, and the data can be stored in a database accessible to citizens. So, people who go jogging can find a route with less noise and fresh air using IoT-enabled smart devices connected to the database [9]. Smart Traffic Management: Today, traffic congestion is a major problem in many cities, especially big cities. At this point, the traffic flow may be monitored by innercity IoT technologies based on cameras, cellular routers, sensors, and automation technologies for reducing congestion. In order to make a more secure and costeffective traffic flow, the following can be applied in the cities. • Installing smart and adaptive traffic lights may provide a better traffic flow, and travel durations can be reduced alongside CO2 emissions [86]. • Location-based assistance systems can be used to find refuelling stations (gas or electric vehicle charging stations) and free parking areas [87–89]. • Based on the monitored and stored data of rush hours and events accessible by citizens, traffic flow can be optimized in order to create an optimum public transportation route for improving traffic efficiency [90]. Due to the restricted budgets, governance cannot always construct new roads and bridges. Such a smart traffic monitoring system based on IoT technology may provide easiness to regional public transportation departments to deal with traffic problems quickly and cost-effectively. Thus, getting maximum efficiency from the existing infrastructure can be possible. Smart Energy Monitoring: Smart Grid technology is an advanced power system incorporating IoT-based communication technologies for monitoring and managing

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electricity activities from generation to consumption [91]. This system aims to achieve cost minimization, environmental impact reduction, efficiency, and reliability and stability of the electrical network. IoT-enabled sensors and devices enable monitoring energy consumption and provide detailed information on energy requirements for various services such as public lighting, transportation, traffic lights, and heating and cooling of public buildings [2]. This data can be used to optimize energy sources and promote energy efficiency. Moreover, integrating smart street lights into the smart city infrastructure enables the adjustment of light intensity based on weather, time, and human presence, reducing energy consumption and facilitating the use of renewable energy sources such as solar energy. IoT-based smart applications can facilitate the integration of smart street lights into the smart city infrastructure, thereby enabling energy conservation [91]. Smart Governance: Smart governance aims to analyze current and potential problems in the city and bring quick solutions based on the collected data. Another objective of smart governance is to produce better quality services and innovations. IoT-based information and communication technologies facilitate data collection and analysis, so this feeds governance processes from the viewpoint of transparency and accountability. Smart governance gives stakeholders the right to decide on improving quality in the city. Smart governance can be considered to consist of inputs, smart governance and outputs, as shown in Fig. 7. Participation of citizens means that they can be a part of the planning and development of cities. For example, citizens can inform the government about problems by reporting through IoT-enabled applications [9, 91]. Smart Economy: The smart economy can be considered an area for every city to create appropriate opportunities in its ecosystem. It also needs a strategy to establish with the other parties in the world due to innovation and competitiveness. Thus, the life quality can be improved in the city. In addition, the smart economy aims to create financial growth by providing new partnerships and business models to

Fig. 7 Structure of smart city governance

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the city. These are based on the open data from different sources obtained via ICT technologies such as IoT-enabled data collection and sharing systems. Smart Security: Smart Security can be defined as a set of functions that assess and enhance the effectiveness of urban security. The main objective of Smart Security is to safeguard citizens and ensure crisis management through advanced communication technologies. In smart cities, security systems monitor physical infrastructure such as roads and pipes. However, with the evolution of digital systems, ensuring the security of digital networks and physical networks is equally important. There are many security technologies in smart cities which are listed as follows [91]. • Physical Security Information Management collects data from security devices such as video, access control, sensors, etc. • IP Based Video Surveillance System uses video cameras with the latest technology, integrating software and hardware into this system. • Video Systems Based On Biometric Properties (fingerprint, face and iris) automatically detect guilty or wanted people by analyzing video recordings. • Vehicle Recognition System is used to detect intruders in forbidden areas, violation in roads, speed limit violations, and suspicious vehicles in critical areas. • Long Term Evolution Mobile Communication System is a fast communication technology with safe and efficient radio systems for emergencies. • Drones fly on predetermined routes and recognize anomalies based on artificial intelligence. All these technologies are monitored and managed via a control center and used to ensure security in a smart city with the help of IoT technology which provides communication between devices and people.

5 Future Perspectives This chapter presents architectures, concepts and key services for IoT, smart homes and smart cities. Nowadays, 5G and beyond technologies provide faster internet connection, low latency, higher data rates and efficient connectivity. These improvements lead to billions of smart devices, sensors and cloud technologies to enable high quality of life. The latest version of 5G, namely 5G Advanced, is opening new eras such as advanced MIMO evolution, mobile millimeter-wave (mmWave) evolution, AI-enabled air interface, green networks and 5G positioning evolution for new applications like enabling the metaverse, wide-area IoT expansion, enhancing automotive safety, industrial 5G networks and precise industrial positioning. In addition, it is planned to announce two new Releases for 5G Advanced, and then, it is expected to emerge the first 6G radio standards around 2029 or 2030. 6G technology will use even higher frequency spectrum bands than 5G’s mmWave. In every new mobile communication generation, higher data rates and more capacity are provided for people to use their smart devices and internet services. As mentioned earlier, these will lead a great amount of increase in IoT-enabled and connected devices. In this context,

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these innovations and applications will make the IoT technology more important to connect billions of devices for smart home and smart city applications.

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Advancements in DC Microgrids: Integrating Machine Learning and Communication Technologies for a Decentralized Future Necmi Altin and Süleyman Emre Eyimaya

Abstract DC microgrids are a promising solution for integrating distributed generation into the main grid. These microgrids comprise distributed generation units, energy storage systems, loads, and control units. They can operate in grid-connected and off-grid modes (islanded mode). Compared to the traditional centralized power systems, they offer a more reliable, efficient, and decentralized organization of the power system. Additionally, they provide various other benefits and are considered an environmentally friendly solution. In this chapter, the concept and components of DC microgrids and architecture of microgrids are discussed in detail. In addition, since the control strategies of the DC microgrid has cruical role in the achievent those advantages and system stability, different control strategies used in microgrids are discussed. Furthermore, it highlights the emerging machine learning and communication technologies that make these microgrids even more efficient and reliable. Overall, this chapter provides valuable insights into the advancements in DC microgrids for a decentralized future. Keywords Microgrids · Renewable energy sources · DC microgrid · Machine learning

Acronyms ANN BAN BESS

Artificial Neural Networks Building Area Networks Battery Energy Storage System

N. Altin (B) Department of Electrical and Electronics Engineering, Faculty of Technology, Gazi University, Ankara, Turkey e-mail: [email protected] S. E. Eyimaya Department of Electronics and Automation, TUSAS-Kazan Vocational School, Gazi University, Ankara, Turkey e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 B. Appasani and N. Bizon (eds.), Smart Grid 3.0, Power Systems, https://doi.org/10.1007/978-3-031-38506-3_14

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CAN CNN DBS DCL DERs DNN DT EMS GA GAN HAN HBC IAN LBC MAS MLP NAN NB NC PCC PLC PSO RNN SCADA SFM SoC SVC SVM WAN Wi-Fi

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Controller Area Network Convolutional Neural Networks DC Bus Signaling Digital Communication Links Distributed Energy Resources Deep Neural Networks Decision Trees Energy Management System Genetic Algorithms Generative Adaptive Network Home Area Networks High Bandwidth Communication Industrial Area Networks Low Bandwidth Communication Multi-Agent System Multi-Layer Perceptron Neighborhood Area Networks Naive Bayes Nearest Centroid Point of Common Coupling Power Line Communication Particle Swarm Optimization Recurrent Neural Networks Supervisory Control and Data Acquisition Switching Frequency Modulation State-of-Charge Support Vector Classifier Support Vector Machine Wide Area Networks Wireless Networks

1 Introduction In order to meet the increasing energy demand in the world in a safe and environmentally friendly way, researchers are working on new and renewable energy sources. The increase in the use of renewable energy sources, especially in energy production, has created a new definition called “Distributed Generation”, which enables to produce energy close to consumption or on-site. Along with distributed generation, a new type of grid concept called “Microgrid” has emerged to increase the efficiency and effectiveness of renewable energy sources. Microgrids offer a convenient, reliable, clean solution for integrating distributed generation into the main grid. Microgrids have their own energy sources, generation and loads and are small-scale

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energy grids with certain limits. As the ratio of renewable energy sources in total electricity production increases, the interest in microgrids has increased with the spread of distributed energy resources (DERs). The presence of DERs in the region makes microgrid implementation possible and financially viable [1]. Distributed renewable generation systems are a viable option for upcoming smart grids due to the economic and environmental advantages of reducing carbon dioxide emissions and transmission losses. Microgrids can be considered the fundamental components of smart grids in this context. Because of this, they have generated much interest in the last ten years regarding their potential and potential effects [2–4]. New technologies are essential to further the microgrid system’s development, make microgrids fully reliable, and meet environmental requirements while minimizing cost. These new technologies, the cost of technological development, changes in regulatory organizations and government policies, and an increase in load demand will directly impact microgrid applications. Future microgrid systems are predicted to be low inertia systems that are heavily dominated by renewable energy resources and power electronic-based interface units. Power electronics reliability will significantly affect the design and planning of the microgrid. Therefore, it is anticipated that the design procedure’s current reliability process will inevitably change [5]. Microgrids come in three different configurations: AC, DC, and hybrid AC/DC [6–8]. While early research primarily concentrated on AC microgrids, an increasing number of DC systems, including DC-generating renewable energy sources, energy storage technologies, and contemporary electronic loads (computers, TVs, LED lighting, electric vehicles, communication stations), are being studied. This caused DC microgrids to be among the important research areas in recent years. In addition, AC/DC hybrid microgrids, which have both AC and DC systems and combine the advantages of both systems, are emerging as a new concept [9]. Distributed generation, DC loads, energy storage systems, the grid, and a common DC bus are the main components of DC microgrids, as depicted in Fig. 1. In these systems, DC/AC converters are used to connect AC loads, while AC/DC rectifiers are used to connect AC-generating units. DC microgrids have received more attention recently due to the expansion of DC energy sources, energy storage units, and loads in power systems. Recent advancements in semiconductor technology and power electronic converters have simplified working with DC power systems. There are also several other good reasons to reconsider DC deployment. These explanations fall under the categories of loads, sources, and storage-related explanations. While much work has been done on the operation and control of AC microgrids, attention has recently begun to turn to DC microgrids because of their potential advantages over AC microgrids, including: [7, 10–12]. • Integrated DGs can be coordinated more easily because their control is based on DC voltage without synchronization. • Due to the lack of reactive power flow control, the DC microgrid is less complicated. However, harmonic content can be detrimental to the DC link.

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Fig. 1 DC microgrid

• As most DGs today produce DC outputs, unnecessary AC/DC power conversions are avoided due to the dominance of DC electronic domestic loads. This directly affects the price and losses of the system. Additionally, most of the converters used for the DC microsource’s interface are transformer-free, reducing the system’s size and cost. • In general, DC protection is challenging because there is no zero crossing. However, because of the limited contribution of the converters of the power electronic interfaced load or DGs to faults, the DC system does not experience high fault currents. • DC loads such as electric vehicles and LED lights are fed more efficiently. • Eliminates the need for synchronization of the generators, which enables rotary production units to operate at their optimum speed. • The DC microgrid is evolving due to significant increases in computers, data and telecommunications centers, LED lighting systems and other DC loads. • Despite the increasing popularity of DC microgrids in the coming years, it offers advantageous options compared to AC microgrids. At the same time, it can be operated in grid-connected and island mode, similar to an AC microgrid. • Some of the important problems of the AC microgrid systems such as managing the reactive power and phase and frequency synchronization, are eliminated in DC microgrid. Additionally, because there is no frequency in the DC system, skin effect, harmonics, proximity effect, and inrush current issues are not present. Due to the lower electromagnetic field than AC systems, DC systems are regarded as safer. • Voltage regulation is better than AC microgrids. In the following sections, the concept of DC microgrid and its components will be explained in detail. In addition, DC microgrid topologies will be introduced, their

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advantages and disadvantages and application areas will be explained. Microgrid control strategies, which significantly affect the microgrid system’s performance and make the microgrid more stable and reliable, will be explained in detail. Application areas, advantages and disadvantages of these control strategies will be discussed. The emerging communication technologies for DC microgrids are also covered. In light of new developments, machine learning techniques in DC microgrids will be examined in detail, and their implications for the future will be discussed. Finally, the book chapter will be completed with the conclusion part.

2 Components of DC Mıcrogrids DC microgrids that can operate on-grid or off-grid (island) mode are electrical energy systems consisting of distributed generation units, loads, energy storage systems, a common connection point, controller and communication systems, as in Fig. 2.

2.1 Distributed Generation and Renewable Energy Sources The most important part of a microgrid is energy sources or generators that produce energy. These renewable energy sources (PV, wind, etc.) and generators are often installed in areas close to microgrids. Effects such as fuel consumption, running costs, efficiency and output power in the electrical grid can vary from one source to another. AC sources are typically interfaced with the DC microgrid via rectifiers, facilitating power transfer to the common DC bus. This allows the microgrid to access the necessary power required for its operation [13, 14].

Fig. 2 Components of DC microgrids

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In DC microgrids, in addition to renewable energy sources such as wind turbines and solar panels, fuel-based distributed generators (diesel generators, micro gas turbines, etc.) can also be included in the system by considering their operating costs. For the energy management system of a microgrid system to be used most effectively and efficiently, all factors such as fuel costs, heat/energy conversion requirements and demand side preferences should be well analyzed, and optimum energy planning of distributed generators should be optimum be realized.

2.2 Loads Loads play a crucial role in the operation of a DC microgrid, as they represent the demand side of the system. Microgrids can cater to various load groups of different user types, including residential, commercial, and industrial consumers, each with distinct power quality and reliability requirements. Loads can be categorized as AC or DC, and their consumption and significance can further classify them. In terms of consumption, residential and small-scale buildings constitute the residential class, while the commercial load class includes medium and high-consumption locations. The industrial load class comprises factories with high and steady power consumption. In terms of importance, loads can be classified as critical and non-critical, with the former including continuous power supply requirements, such as those needed in hospitals, while the latter encompassing low-loaded homes. The microgrid participates in energy management in loads with distributed generation resources. With this participation, its importance increases in the cases called load response and demand response. In cases where the load demand is high, the production should be increased; while in cases where the load demand is low, the production is reduced, or the batteries in the energy storage system are charged.

2.3 Energy Storage System Energy storage technologies are becoming increasingly important for maintaining the quality of energy from renewable sources in microgrids. Electrical systems frequently employ storage devices to shield delicate electronic equipment from poor power supply issues. Voltage and frequency fluctuations are frequent in power systems. The system becomes unstable and unsafe if these fluctuations are not reduced. The stability of the system demands quick action. The energy storage system is capable of providing the anticipated response and stability. For long-term stand-alone wind turbine and solar panel applications, the energy storage systems can offer reliable, long-lasting, and cost-effective power savings while providing backup and emergency power to increase reliability. The energy storage system contributes to the power system by tracking loads, increasing power

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capacity, supporting power and frequency control, enhancing power quality, and lowering power fluctuation. The battery energy storage system (BESS) technology is one of the newest technologies with great potential for microgrid applications. Recent studies focus on developing new technologies as it imposes difficult requirements on BESS performance in renewable energy applications. Many energy storage systems have been developed, including ultracapacitors/supercapacitors, superconducting magnetic, fuel cells, lithium-ion, lead acid, nickel–cadmium, nickel-metal hydride batteries and flywheel systems. In addition, hybrid energy storage systems are being developed.

2.4 Point of Common Coupling (PCC) The power distribution network, power generation, and customer interface all converge at the Point of Common Coupling (PCC). It is the hub where loads, energy storage devices, and distributed generation resources are connected in DC microgrids. After that, this point is connected to the grid through a single connection. Microgrids can function in on-grid (grid-connected) and off-grid (island) modes. Most microgrids have feeders that support the distribution system and feed the loads. The feeders are connected to the distribution system with a static switch, and this switch can realize the operation of the microgrid in island mode in case of failure or maintenance.

2.5 Communication System and Controller Communication of all generation and consumption units in a DC microgrid is very important in terms of system control. Network applications state that DC microgrid and smart grid communication systems must abide by reliability, latency, bandwidth, and security requirements. Due to the numerous factors and various component requirements that depend on the applications and service expectations, choosing the right communication network for smart grids and DC microgrids is a significant challenge. A microgrid typically interfaces with an AC grid via a direct connection or a back-to-back converter to provide bidirectional power flow. The microgrid system can be decoupled from the AC grid and run in island mode if an AC grid fault occurs. Therefore, microgrid should exchange information with the grid, such as the status of the AC grid, to determine whether it needs to be reconnected to the AC grid to exchange power, even though the AC grid in island mode does not power them. A solid communication infrastructure is needed for the electrical grid and the microgrid to exchange information.

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The most significant areas of study in both academia and business are power quality, protection, economic and safe operation, active management, communication, dynamics, and control of microgrids. Microgrid controllers strive to maintain dependable autonomous operation under all conceivable circumstances while reducing communication complexity, workload, and cost.

3 Architecture of DC Microgrids The architecture of a DC microgrid is determined by the configuration in which its distributed generation sources and loads are linked to the common DC bus. Several topologies of DC microgrids, such as the single-bus, multi-bus, ring-bus, and zonal DC microgrid structures, have been described in various studies [15, 16]. This section discusses these DC microgrid architectures, their respective application areas, and their advantages and disadvantages.

3.1 Single-Bus DC Microgrid Structure The single-bus DC microgrid structure is the basic topology for all bus systems and other DC microgrid architectures. The feeder structure or radial structure are other names for this structure. Energy sources, energy storage devices, and loads are all connected to the system’s single DC bus directly or via a converter. In a DC microgrid, the single-bus topology is frequently employed. Figure 3 depicts the single-bus architecture. Due to the presence of other sources and an energy storage system, a fault in the source interface does not result in a power outage. Uncontrollable DC mains voltage and irregular battery charging are drawbacks of this topology. Additionally, many parallel power electronics converters can result in circulating current and uneven loading.

3.2 Multi-bus DC Microgrid Structure Each microgrid in a multi-bus DC microgrid system feeds power to its neighboring microgrid, as shown in Fig. 4. This system, which is more flexible than the single-bus structure, provides different voltage levels to the consumer. In a DC microgrid, the multi-bus topology is frequently employed. Through communication links between DERs, control parameters are exchanged to improve the performance and stability of the DC microgrid, and this structure makes it simpler to isolate a DC microgrid in the event of a malfunction.

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Fig. 3 Single-bus DC microgrid structure

Fig. 4 Multi-bus DC microgrid structure

3.3 Ring-Bus DC Microgrid Structure The ring-bus DC microgrid structure increases the system’s reliability while enhancing troubleshooting flexibility. The ability of the load connected to the common DC bus to be fed in both directions creates a backup route in the event of a failure. This feature offers advantages such as high reliability, high flexibility and redundancy. The ring bus architecture is shown in Fig. 5.

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Fig. 5 Ring-Bus DC microgrid structure

3.4 Zonal DC Microgrid Structure Another highly reliable DC microgrid architecture is based on the zonal configuration, as shown in Fig. 6. Different DC microgrid units are connected in series to form the zonal structure in the zonal DC microgrid structure. This kind of connection is more dependable and flexible. The DC microgrid is divided into zones by adding redundant buses, and each zone has its own load center and protection system. Each structure, or collection of structures, can be considered a zone of balanced power. The region can supply all its energy needs by utilizing distributed energy resources and energy storage systems.

4 DC Microgrid Control Strategies The most common problems in a distributed generation are transmission line disturbances, voltage and frequency fluctuations [17–19]. However, the coordinated control of every microgrid component within a central grid is challenging. Currently, microgrid control strategies are developing that offer better control functions and an ideal solution to these issues. Thanks to these control methods, studies on micro-grid

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Fig. 6 Zonal DC microgrid structure

control strategies are increasing daily with the reliability, stability and power quality of the new electricity grid concept and eliminating economic concerns. The various control targets are [20]: • Smooth transition from the islanded mode of operation to the grid-connected mode, • Control over voltage and current distribution, • Stable performance under non-linear and constant power loads, • Optimizing the production of micro sources to compete in the energy market, • Utilizing an effective and appropriate Energy Management Scheme (EMS) to manage the flow of power between the microgrid and the rest of the grid, • Effective load power sharing and appropriate DER communication channels, • Implementing appropriate control mechanisms to protect against grid failure and potential black starts, • Optimization of generation costs and economical load dispatching, • DERs’ potential to the fullest while lowering transmission losses, • The ability to supply critical loads, such as hospitals, industries, and other essential utilities, with uninterrupted power.

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In DC microgrids, control strategies are used to enhance power quality. As a result, the communication between microgrid components and control over frequency and voltage are provided. Control strategies are crucial for managing every element of the microgrid system, including the grid, renewable and traditional energy sources, electric vehicles, energy storage technologies, and the energy market.

4.1 Hierarchical Control Strategy Power systems require higher-level control systems for a few basic purposes, such as voltage control, current control and power control. Multi-level control strategies also help to achieve important goals such as power sharing between distributed generation, power quality control, supply for ancillary services, participation in energy markets, minimization of operating costs. It is difficult to achieve these goals using a single control level. For this, Hierarchical Control Strategy, where each level has different tasks, is applied. Multi-layer control can improve load sharing while also reducing load-dependent voltage deviation and current sharing degradation [19, 21, 22]. Figure 7 illustrates the hierarchical microgrid control strategies, which significantly impact the system’s performance and increase the microgrid’s stability and dependability.

4.1.1

The Primary Level Control

The primary level control controls the system’s current and voltage, which is the top control layer in a hierarchical control structure. Additionally, it reduces oscillations brought on by constant power loads in the DC microgrid system. The decentralized load-sharing control strategies are occasionally used at this layer to ensure proper power management in the system. Droop Control and the DC Bus Signaling (DBS) control methods are common primary-level control techniques. In the primary control structure, converter output and power-sharing control are realized. Droop

Fig. 7 Hierarchical control strategy

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Based methods and Non-Drop Based methods are used in power-sharing control. Primary control is mainly responsible for controlling the bus connection voltage and maintaining proportional power sharing in microgrids [21]. Bus signal, fuzzy logic, PQ, V/f, and droop controls are among the most popular primary controls. The various operating modes of the micro-sources are controlled using a bus signaling method with the bus voltage acting as an information carrier. In abnormal or island conditions, the controller also considers the load priority level and load management. Demand side management is also made possible by bus signaling techniques by injecting power from bus to bus. The fuzzy logic controller is used to control energy demand for microgrids based on battery storage. In order to control the bus voltage and manage the power flow between the hybrid energy storage systems and the bus connections, the fuzzy logic controller manages the battery system. A droop control strategy is frequently employed to prevent current from flowing between converters without the use of digital communication links. The droop control strategy selects the output power or output current for the droop feedback. While other loads may use the output current as the feedback signal, the constant power loads should use the output power as the feedback signal.

4.1.2

The Secondary Level Control

The secondary level control, the second control layer of the hierarchical control structure, undertakes the responsibility of restorıng the DC bus voltage to its rated value. The conventional secondary level control, which the microgrid central controller carries out, relies on low bandwidth communication to transfer control information. It is less reliable because of the potential for a single point of failure. The distributed control, which uses low bandwidth communication to exchange information between microgrid units, is proposed for secondary level control in order to reduce the risk of these faults. The distributed controller reduces the disadvantage of the central controller, namely single point failure. It can be implemented by using Controller Area Network (CAN) or Power Line Communication (PLC). It plays an important role in designing and planning a microgrid system for power flow analysis and flow control. Although it requires extensive computation and information collection, it provides the necessary information for the system operators to work safely. The Newton–Raphson method is commonly used in power flow analysis for microgrids. Due to poor voltage regulation and power sharing, the primary controller cannot fully manage the microgrid. The primary controller’s performance is subpar, particularly when long feeders have high line resistances. As a result, the hierarchical control strategy employs a secondary controller in addition to the primary controller. All fundamental controllers, including centralized, decentralized, and distributed, can be implemented in the secondary controller to give the primary controller voltage and current reference signals. For reliable and cost-effective operation in grid-connected or island-mode microgrids, secondary control is required at the level at which microgrid energy management system actions take place. Due to highly variable energy sources, this is challenging to accomplish in island mode microgrids. To keep up

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with the sudden changes in load and non-shippable productions in this situation, the unit dispatch command’s update rate must be high enough. The secondary control also corrects the residual voltage and frequency deviations caused by the primary control action. The sending and unit commitment of the microgrid are chosen through real-time optimization. The secondary control structure emphasises model predictive control, communication, energy storage system considerations, bidding, non-model based approach, and optimal distribution [22].

4.1.3

The Tertiary Level Control

Tertiary-level control, which regulates power flow between microgrids and the grid, is regarded as the third and last control layer in a hierarchical control structure. The three main responsibilities of the tertiary controller are the coordination of energy storage devices, lowering operating costs, cutting down on power flow losses, and managing power and energy. The tertiary level control often selects optimal set points based on long-term and primary power system requirements. The operation of multiple microgrids interacting with each other and meeting the needs and requirements of the main grid, such as voltage support, and frequency regulation, must be coordinated by this control level [23]. The tertiary controller is an additional controller that keeps microgrids running profitably and regulates them generally. Even though microgrids are much smaller than the traditional grid in terms of size, it is essential to have an efficient and economical power flow, which the tertiary controller enables. Power management between microgrids and the conventional grid, within various microgrid clusters, and between various distributed generation units of the same microgrids is the primary responsibility of the tertiary controller. Different heuristic methods, including particle swarm optimization (PSO) and genetic algorithms (GA), are applied to the tertiary level controller in microgrids.

4.2 Centralized Control In the central control method, the parameters of the microgrid system and local loads are controlled by a central control unit. In this method, all information about distributed generation and loads in the microgrid is collected by a central unit and then decisions are made for loads and distributed generation. In this method, there is a time delay in the information due to measurement and communication in the control loop, which is an important limitation depending on its size [24]. The main component of such a system, which aids in the simpler operation of the centrally controlled system, is communication. The system operator first gathers the data from the various DC microgrid units. The processed data is then sent to the devices via a suitable communication medium and the required control commands. Strong controllability of the entire system, the desire for a single controller, the

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capacity to define broad strategies for system control, and observability are a few benefits of the centralized controller [20]. Since the data is gathered and commands are sent back via Digital Communication Links (DCL) after the data has been processed, the central control requires effective communication system. The benefit of this control is that it offers systemwide traceability and controllability. The failure from a single point, low reliability, flexibility, and scalability are just a few of the drawbacks of this control strategy. As a result, this control strategy is better suited for small-scale microgrid systems, where only a limited amount of information must be gathered, and control is possible through low bandwidth communication (LBC) structures. One type of centralized control is the master–slave control strategy. A converter acting as a Voltage Source is regarded as the master in this control strategy and is in charge of regulating the DC bus voltage. Others follow the main converter pattern and function as Current Source. High Bandwidth Communication (HBC) serves as the foundation for this control strategy. This control strategy’s drawback includes the potential for single-point failure, connection to the main converter, requirement for supervisory control, lack of scalability, and shorter battery life.

4.3 Decentralized Control The decentralized control system has no communication link; independent controllers use local variables to direct the distributed units. This control strategy is regarded as the most reliable because no communication links are necessary between the various system units, despite some performance limitations caused by incomplete information about other system units. The decentralized control provides better coordination with power systems than central control. In contrast, the response time of the decentralized control is high. There is no central control entity in the decentralized control. Different control centers receive various information, and various control centers issue various control commands. When the input signal and system signals are interrelated, all control functions are distributed in each submodule to complete the output of each module [25]. In microgrids with centralized plans and centralized control, an efficient operation may not always be achieved especially when the number of distributed generation units significantly increases. The following factors can be used to explain this situation: computational challenges brought on by a large number of controllable resources, including distributed generation units and loads; a lack of a dedicated administrative unit; communication needs caused by the geographical distribution of resources; the necessity of frequent redesign; the difficulty of data sharing; and the reliability and vulnerability of the central controller as a common point of failure.

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4.4 Distributed Control Although in some places it is used to explain decentralized control, mostly there is a slight difference between these two control strategies. The benefits of both centralized and decentralized control strategies are included in the distributed control strategy. The controller of each unit only communicates via a few available Digital Communication Links with units directly adjacent to it. This makes it possible to easily accomplish goals like proportional load power sharing, voltage restoration, current sharing, and State-of-Charge (SoC) balancing [26]. The significant rise in the number of distributed production units sometimes makes implementing a centralized control system challenging. The distributed control strategy is a wise choice in this scenario. Interactions between units are taken into account by distributed control techniques. Higher reliability, security, and situational awareness resulting from distributing the control task to various units based on their working hours. In order to improve the local power sharing and voltage regulation in a DC microgrid, a distributed control strategy is suggested. Data exchange occurs over a dispersed cyber-network, and a voltage observer estimates the average voltage across the grid. It uses an adaptive droop and PI control, with the current regulators controlling the DERs’ virtual impedance [20]. Consensus-based control strategy and Agent-Based Control Strategy are examples of distributed control strategy. The consensus-based control strategy is an interaction protocol that assesses how well a unit shares information with all of its surrounding units. It offers flexible control and a method for resolving the distributed optimization problem. The distributed order can be controlled by using a consensus-based approach. Microgrids use agent-based control to add intelligence to the microgrid control process. It is a popular distributed control approach used in microgrids. It is often called Multi-Agent System (MAS) control because each unit is considered an intermediary. MASs are intelligent systems with distributed intelligence to control the operation and offer an excellent tool for collecting and controlling distributed information [27].

5 Emerging Communication Technologies for DC Microgrids In DC microgrid system, communication with distributed generation sources, loads and grid is provided continuously. Microgrids operate in grid-connected mode when they exchange power with the electrical grid and stay in constant contact with it. Again, even though microgrids operate in island mode without grid connection, they can still use information from the grid, such as the grid’s status, to determine whether they need to reconnect to the main grid to exchange power. While this mechanism for information exchange between the electrical grid and the microgrid

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necessitates a dependable communication infrastructure, it also promotes the growth of communication technologies in microgrids. Most DC microgrid controllers use the information provided by the converters’ output current and terminal voltage. Other factors include the battery’s level of charge, the condition of the circuit breakers, the temperature of the PV modules, etc. Besides, additional data may also be exchanged in the microgrid. The elements in Table 1 should be considered when choosing a communication technology for a DC microgrid application because there are numerous communication protocols, each with its operating principles [28]. In DC microgrid systems, certain characteristics are crucial for communication systems, including dependability, bandwidth, and security. Due to the numerous factors and various component requirements that depend on the applications and service expectations, choosing the best communication network for smart grid and DC microgrids is a significant challenge. A DC microgrid’s performance in terms Table 1 Selection criteria for communication technology Factor

Effects on communication technology selection

Microgrid size

Microgrids are employed in various situations, from electrifying remote areas to powering vital loads. The sources in a microgrid are dispersed geographically based on the microgrid size. As a result, the communication technology ought to be able to offer data connectivity for the entire microgrid infrastructure

Bandwidth

The communication channel’s bandwidth determines the fastest data transfer rate that can be achieved through the channel. Low bandwidth communication-based technologies offer low-cost infrastructure but at the cost of slower speed and a longer communication lag. To avoid data congestion, which eventually causes packet drops and retransmissions in the communication channel, the right choice of bandwidth is necessary

Number of power electronic converters

Different communication technologies limit the number of devices or power electronic converters that can be supported in a system. A moderate or high value, also related to the selected data rate and bandwidth of the communication network, will ensure a highly scalable system

Latency

A communication network’s latency is the time data moves from a sending node to a receiving node. Delay in communicated values will also affect the system response because the control signals generated by distributed secondary controllers depend on the communicated values. Therefore, it is preferable to choose a low-latency communication technology

Flexibility to future expansion

The amount of data that must be sent over the communication network grows as there are more distributed generation systems. This calls for a network architecture that can support more power electronic converters in the future to ensure the microgrid will operate satisfactorily

Installation and operation cost

For various communication technologies, there are different upfront costs and ongoing expenses. A wired communication network typically costs more to set up than a wireless network

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Fig. 8 Different communication networks technologies are used in a DC microgrid system

of power-sharing, MPPT, protection, online system monitoring, stability, and reliability will be enhanced by the addition of a communication network. Different control strategies are available for DC microgrid communication systems to enhance performance. Communication systems always have the option to change their configuration in case of unusual operating circumstances. These channels of communication can be used to help safeguard information. Depending on the communication difficulties, there are various protection solutions. Depending on the size and use of the DC microgrid, the communication network may be a wide area network, neighborhood area network, building area network, home area network, or industrial area network [29]. Depending on the application requirements, communication networks in DC microgrid systems can be divided into the following categories, as shown in Fig. 8: • Consumer Premises Area Networks: Home area networks (HAN), building area networks (BAN), and industrial area networks (IAN) are examples of this network • Neighborhood Area Networks (NAN) • Wide Area Networks (WAN).

5.1 Consumer’s Premises Area Networks Numerous devices and equipment interact with the energy management system in consumer areas like homes, businesses, and factories. It is not necessary to have a very high-frequency data transmission system because these devices are close together. Therefore, for Consumers’ Premises Area Networks applications, any communication technology that can deliver a 100 kbps data rate up to 100 m coverage is typically sufficient. Home Area Networks (HAN), Building Area Networks (BAN), and Industrial Area Networks (IAN) are examples of consumer premises area networks. Many

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communication technologies are currently in use and can fulfill the needs of these network systems. Power line communication (PLC), Bluetooth, Ethernet, ZigBee, and WiFi are a few of these [30–33].

5.2 Neighborhood Area Networks (NAN) With the communication technologies supporting high data rates of 100 kbps–10 Mbps up to a 10 km coverage area, microgrid energy meters can send and receive data such as electricity consumption information via smartphones to network-connected devices at consumer locations. Current communication technologies suitable for NAN applications include ZigBee, PLC, WiFi, Cellular, Digital Subscriber Line (DSL) and WiMAX [34–36].

6 Wide Area Networks (WAN) In order to communicate with current smart grid systems and enhance power system planning, stability, and protection, future DC microgrid systems will need to be equipped with many monitoring and measuring devices, including sensors and power management controllers. Compared to traditional Supervisory Control and Data Acquisition (SCADA) systems, these wide-area monitoring and measurement applications demand higher data resolution and quicker response times. Wide Area Networks (WAN) applications require a data rate of 100 Mbps–1 Gbps, with a coverage distance of up to 100 km. Fiber optic, WiMAX, and PLC are modern communication technologies that may be appropriate for WAN applications [37].

6.1 DC Microgrid Communication Applications DC microgrids use various technologies to facilitate communication, including 2G, Satellite, LoRa, I2C, ZigBee, WiFi, Bluetooth, DSL, PLC, Ethernet, 3G, 4G, 5G and WiMax. Almost all technologies aside from 2G, satellite, and Lo-Ra have a bit rate of greater than 100 kbps. They have a higher data rate. In smaller microgrids (nanogrid or mini-grid), communication networks can be established using I2C, ZigBee, WiFi, and Bluetooth. Bluetooth, LoRa, and satellite can be used with numerous power electronic converters in DC microgrids. DSL, PLC, Ethernet, 3G, 4G, 5G and WiMax all have excellent rankings for the criteria used to choose a communication network. Due to the wide range of data rate, range, and delay, caution should be used when designing a DC microgrid with PLC as a communication network [28].

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The ZigBee communication system provides a wireless data communication system for DC microgrids, and is a low-cost and low-power gadget. ZigBee technology’s dependability is assessed in power system applications, including the proportion of successful and unsuccessful data packets sent during abruptly high power transients [38]. In addition, an encryption code has been developed for ZigBee to increase the security of the transmitted data. [39]. An enhanced power line communication (PLC) strategy utilizing the switching frequency modulation (SFM) of a power converter is being researched as a way to increase the reliability of DC microgrids. The PLC strategy increases system reliability by using the voltage fluctuation in the DC bus voltage generated as an information signal by the converter’s switching operations [40]. Due to its high communication performance and widespread use, a 4G wireless communication network is used to transfer data for a DC microgrid communication system [41]. Since there are high expectations for both observable gains in Wi-Fi network coverage and increases in Wi-Fi speed, the main issue with the effective adoption of IoT technologies used in microgrids relates to the speed and coverage of wireless networks (Wi-Fi). Globally, Wi-Fi speed is constantly increasing and this communication technology is increasing its impact day by day [42]. Future DC microgrid protection technologies will need faster operating speeds and more effective interoperability than the current SCADA system to exchange large amounts of data. As a result, it is anticipated to satisfy the demands for future high-speed communications for DC microgrids supported by Fiber Optic and WiMAX technologies [43]. 5G communication technology, with its fast transmission speed, offers a significantly better communication technology than previous generations for real-time microgrid analysis and monitoring, reducing communication latency. Active adoption of 5G in the management and control of microgrids improves data transmission and receiving, reduces latency, provides a higher information density, and contributes positively to resilience to the various changes that microgrids can experience under continuous operating conditions. It can be referred that the deployment of 5G allows electrical microgrids to be more resilient in their management and control, which impacts the sustainable development goals both directly and indirectly [5].

6.2 Challenges in DC Microgrid Communication Infrastructures The communication must take place securely in DC microgrids. Although there are many communication protocols today, in order to provide reliable communication infrastructures with the development of new-generation communication technologies in the future, the following should be fulfilled: • Many communication protocols and technologies are used to provide communication in DC microgrid systems. These protocols have their own working principles.

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The integration and interoperability of these different technologies will require common protocols and standardizations [44]. • Power grids that are currently in use have communication networks that support SCADA systems that are very old and were not built with a lot of data exchange capacity to support a lot of smart electronic devices. A key area for research will be how to adapt the protocols of current networks to handle future changes in network requirements. • It is crucial to consider the ideal data rate and bandwidth for these communication networks to plan for future demands and expansions as new communication technologies for DC microgrids and smart grids become available. • Cyber security and data privacy is an issue that is getting more and more important. It is important to provide many benefits, such as the deployment of communication infrastructure, higher reliability, energy efficiency and increasing transparency in the system, especially for DC microgrids, which are widely used in the military field. This will also raise cybersecurity and data privacy issues [45, 46]. It is predicted that in the not-too-distant future, a large number of cities will be linked via communication systems that permit the interconnection of various systems, thereby ensuring the connectivity, speed and response time of these components in an electrical system, smart grid, or microgrids with the expanding development of the Internet of Things. To enhance the management of microgrids, it is crucial to examine the integration of 5G technology.

7 Machine Learning Techniques in DC Microgrids Machine learning techniques are being applied in microgrids as a promising solution to improve further the performance of power system protection, which is an essential element in DC microgrids. Machine learning algorithms can potentially revolutionize grid protection strategies with increased access to real-time and historical data on contemporary power systems. Machine learning can significantly improve fault location, detection, and response [47–49]. The protection of DC microgrid systems is quite a challenge, unlike the conventional AC systems. Knowing the fault location in the distribution network leads to fast repair, maintenance and reduction of unnecessary power outages, machine learning techniques are used for these determinations. Due to the numerous converters and communication networks found in DC microgrids, protection strategies for the system call for the use of technologies from the IOT, blockchain, artificial intelligence, machine learning, deep learning, and converter topology. However, more work is needed to overcome the shortcomings of the current protection schemes for all scenarios and conditions because of the lack of standards and participation. In DC microgrids, fault detection is facilitated by protection schemes based on machine learning. Recent studies [50, 51] have looked at how machine learning-based protection schemes can enhance fault detection in

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AC systems, but these protection schemes have not been sufficiently studied in DC microgrids. Machine learning and deep learning-based models are promising solutions for predicting consumer demands and energy production from distributed energy sources. DC microgrid applications have limited applications due to the lack of well-defined protection standards. In these low-voltage DC distribution networks, especially arc faults, load switching faults, partial shading of photovoltaic modules, variation in solar irradiance and wind speed, and DC cable faults etc. problems arise. Deep learning applications in microgrids are studied by many researchers for accurate fault classification and distance calculation for effective monitoring and protection coordination of DC microgrids [52–55]. To cope with the intermittent nature of wind and solar power in the microgrid, power estimation is required for efficient energy management. Deep learning-based methods are used for energy management and power estimation. The most widely used deep learning-based methods are artificial neural networks (ANN), deep neural networks (DNN), convolutional neural networks (CNN) and recurrent neural networks (RNN) methods. Table 2 summarizes the applications of deep learning methods used in microgrids. Machine learning is a potential tool for intelligent error detection due to its effective pattern recognition and adaptability to systems with versatile operating conditions. Machine learning algorithms can be classified as Support Vector Classifier (SVC), Bernoulli Naive Bayes (NB), Decision Trees (DT), Nearest Centroid (NC), Multi-layer Perceptron (MLP) [61].

7.1 Support Vector Classifier (SVC) Based on estimating the most efficient function for data separation, the Support Vector Classifier (SVC) method classifies events according to a linear or non-linear function. In the SVC method, classes are divided along a single linear line. This boundary may be drawn more than once during the classification process. Maximum error tolerance is established by finding the line that connects both classes at a distance determined by the SVC. Test data are categorized based on where they are in relation to the border after training data and the border line have been identified [62]. Numerous studies [63, 64] have used Support Vector Machines (SVMs) to identify faults in DC systems. These studies presented successful and extremely accurate performance results, but the data they used to classify faults was primarily drawn from simulated models of DC systems. A separating hyperplane serves as the formal definition of the discriminating classifier known as the SVM. In other words, the algorithm produces an optimal hyperplane that classifies new examples when given labeled training data (supervised learning). This hyperplane is a line that divides a plane into two parts in a twodimensional space, with each class lying on either side. Getting the data necessary to discriminate between the two sets can be a very difficult goal to accomplish.

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Table 2 Definition and application of deep learning methods in microgrid Method

Definition

Application of deep learning

Artificial neural networks (ANN)

The design of an artificial neural networks (ANN) is based on how the human nervous system functions. This system aims to perform a task only by considering examples without being programmed according to a specific task rule. Neurons are a group of nodes or units that form the basis of ANN. Actually, they are the fundamental building blocks of a neural network. A neuron receives input and generates output based on its internal activation function [53, 54]

Artificial neural networks have certain characteristics that make them advantageous in developing controllers with different control levels required for microgrids to be economical and efficient and to meet energy, power quality and quantity requirements. This method is applied in many applications, including controlling microgrid distributed generation resources and scheduling, power sharing, supervisory control and optimization [56]

Deep Neural networks (DNN)

Deep neural networks (DNN) are a subset of ANNs. Between the input and output layers, they are made up of numerous hidden layers. Whether the data relationship is linear or nonlinear, the DNN processes the input with mathematical manipulation to produce the output. The probability of each output is calculated as a result of training the neural network with a training set [53, 54]

It is used in multiple microgrid applications. There are applications where a data-driven DNN is generated to model the multi-microgrid response to dynamic retail price signals. DNN is trained using historical data, and local microgrid operators are not required to provide user information. The training set also contains uncertain elements related to the microgrid system. Well-designed DNN can automatically generate multiple microgrid power exchanges in response to new input [57] (continued)

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Table 2 (continued) Method

Definition

Application of deep learning

Convolutional neural Convolutional neural networks (CNN) networks (CNN) are a subset of the deep learning family of neural networks. They are frequently utilized for pattern recognition, energy management in smart grids, and the processing of visual images. A CNN is an enhanced Multi-Layer Perceptron (MLP). A fully connected layered network, or MLP, is a network in which every neuron in one layer is fully connected to every neuron in the layer above it. In MLP, this fully connected feature often causes overfitting. CNN, however, employs various techniques for data regularization. It specifically takes advantage of the data’s hierarchical pattern and assembles it into several simpler patterns [53, 54]

Although integrating a microgrid with contemporary power systems can resolve problems brought on by widespread distributed generation, it can also result in severe voltage instability issues. Convolutional neural networks are used in various applications to provide an online method for analyzing voltage security in a microgrid [58, 59]

Recurrent neural networks (RNN)

Many applications use a recurrent neural networks (RNN) for optimization that allows the optimal operation of a grid-connected microgrid. Global optimization problems are solved by using RNN in many applications to determine the optimum amount of power supplied by distributed energy sources [60]

A specific category of ANNs called recurrent neural networks (RNN) was created to process sequential data. Conventional networks typically train each sample independently. However, such independent training is insufficient, especially for data that exhibit temporal relationships. RNN offers a solution to this issue by accepting inputs sequentially. Unlike other feed-forward ANNs, they contain feedback links in the units of the hidden layer. The RNN can process temporal data and learn sequentially as a result. In addition, unlike other ANNs, the RNN stores sequential data in a hidden layer that serves as a memory [53, 54]

In essence, SVM is a supervised machine learning algorithm that can be applied to a variety of problems involving classification or regression of a given data set. SVM is almost always used for classification issues so that a hyperplane can distinguish one data set from another. The plot of each data point as a point in n-dimensional space with the value of each feature fitting the value of a specific coordinate in n-space is the visual and logical representation of SVM [65]. The classification is then carried out by locating the hyperplane in Fig. 9 that separates two classes to a specified level of satisfaction. Using system datasets that can be used for cyber-attack detection and classification, SVMs typically build hyperplane clusters in high-dimensional space. SVMs are, therefore, very effective for nonlinear classification. A tagged dataset and an

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Fig. 9 General representation of classification hyperplane in SVM algorithm

unlabeled dataset are combined automatically by an intrusion detection system to provide information about unidentified attacks [66].

7.2 Bernoulli Naive Bayes (NB) The Naive Bayes (NB) methods are a class of supervised learning algorithms that apply the Bayes theorem under the “naive” assumption that every pair of features will be conditionally independent given the value of the class variable [67]. In order to classify and train on data that is distributed using multivariate Bernoulli distributions, Bernoulli NB uses the Naive Bayes algorithms. The Naive Bayes classifier ıs a probabilistic classifier that relies on independent properties and Bayes’ theorem is. Due to its maximum likelihood methodology, it is a good candidate for cyber-attack detection and classification on microgrids. Some studies use a data-driven method that combines binary feature selection algorithms and Naive Bayes classifier to select the optimal number of features [27]. The Naive Bayes classifier is used to differentiate between healthy systems and those that are being attacked. The method performs well in tests using datasets for intrusion detection [68].

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Common intrusion detection is converted into a Bayesian framework using the Hybrid Bernoulli Random Set (HBRS) method, and the results are integrated with the Kullback–Leibler HBRS mean to create a distributed approach. Simulations are performed on a large-scale power system, proving the detector’s effectiveness. Simulations performed on a large-scale power system validated this detector’s performance. In [58, 59], an ideal Bayesian strategy was developed to lower the cost of defending against attacks that misplace data. The measurement sets for the power grid are also determined using graph theory. To estimate sets of measurements, the Bayes method is used. The simulation is run on an IEEE 14-bus power system, demonstrating the approach’s validity.

7.3 Decision Trees (DT) The decision tree (DT) algorithm is one of the efficient machine learning algorithms that can be used with classification and regression methods. A sizable dataset should be analyzed because the proposed scheme’s main bus voltage and load feeder current are continuously measured. Additionally, this algorithm excels at handling complex datasets. Using this algorithm, a suitable classifier can be created [69]. DT is a non-parametric supervised learning technique for classification and regression. A tree can be considered a piecewise fixed approximation, but it estimates the value of a target variable by mastering straightforward decision-making processes derived from data features [67]. Due to the lack of experience handling DC faults and the scarcity of DC circuit breakers in the oversized range, DC microgrid protection systems are still in their infancy. Some studies solve the problem of fault detection and classification in DC microgrids with a two-layer machine learning scheme based on a DT [70].

7.4 Nearest Centroid (NC) Nearest Centroid (NC) is a simple classification algorithm. This algorithm represents each class according to the centroid of its components. Based on the assumption that the distances between samples belonging to the same class should be kept at a minimum, the NC finds the centers of each class and then assigns the unknown sample to the class with the closest center [71]. In order to reduce the mistakes in PV, load, wind energy, and price prediction, there is a NC application that compares each scenario from the given data set with the nearest centroid by using the Weibull distribution. The Euclidean distance method determines the distance between the result and the chosen scenario. Centers are continually updated, and new centers are used for clustering. The idea of trip chain information base modeling has been used to address PV’s ambiguous behavior [72].

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7.5 Multi-layer Perceptron (MLP) A supervised neural network model called Multi-Layer Perceptron (MLP) uses backpropagation to train [67]. The input layer, the hidden layer, and the output layer are its three layers. The input model’s weighted sum determines the MLP structure’s output. The weighted sum concept can benefit any layer [73]. A correct protection method for DC microgrid systems based on Multilayer Sensor (MLP) has been studied [74]. In the method, precise fault location estimation is aimed at instantaneous current monitoring of each part of the microgrid, regardless of the type and size of the fault, current and amount of power supply in microgrids. Studies show that the MLP method successfully estimates fault location [75]. The MLP can perform a nonlinear mapping with excellent accuracy by selecting the appropriate number of layers and neural neurons. The MLP neural network is used to predict the fault location, as it performs non-linear mappings with sufficient accuracy and lower computational effort and complexity.

7.6 Challenges Machine Learning Techniques in DC Microgrids Machine learning techniques are expected to play an important role in DC microgrids in the future. New and advanced deep learning techniques, such as the generative adaptive network (GAN) and augmented deep learning, have yet to be put to much practical testing. However, these new techniques will be used in important roles in microgrids thanks to their real-time response and hardware-software control features. Machine learning algorithms, especially ensemble methods have offered significantly better performance in many microgrid scenarios. Some deep learning techniques can be investigated for use in various applications in the microgrid to improve microgrid and power system designs. However, the continuous development of machine learning poses some difficulties in microgrid studies. Optimization requirements in power systems and microgrids mandate developing and incorporating machine learning algorithms to provide the highest accuracy, reliability, optimum response time, and other desirable features. Studies on machine learning in DC microgrids are few and insufficient. Therefore, this research area still needs further study. Although some of the proposed models encourage dynamic decision-making at the edge device or equipment through machine learning algorithms, an end-to-end system approach with hardware-software integration for realtime control and the response of microgrid and grid equipment is still being investigated [76]. Machine learning and deep learning techniques use historical data to make predictions about the future. However, relying heavily on big data requires massive storage devices. Large-scale processing requirements are yet another difficult problem when using deep learning-based techniques.

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8 Conclusion DC microgrids have attracted many researchers’ attention because of their numerous advantages. To fulfill the expectations, a DC microgrid should be designed based on proper architecture and employ an advanced control algorithm and communication network. Besides, to achieve superior performance in terms of stability, efficiency, and reliability, it should employ machine learning algorithms seeking optimums for the system and possible threats. In this context, in this chapter, the concept of DC microgrid and its components are explained in detail, and DC microgrid topologies are introduced. Microgrid control strategies, which have a very important effect on the performance of the microgrid system and make the microgrid more stable and reliable, are explained in detail. Emerging communication technologies for DC microgrids are explained, and machine learning techniques in DC microgrids are discussed in light of new developments. Some application examples for communication technologies and machine learning techniques have been provided to provide a better understanding.

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Advanced Communication and Computational Technologies in a Sustainable Urban Context: Smart Grids, Smart Cities and Smart Health Patrick Moriarty

Abstract A vast literature now exists on how modern communication and computational technologies (CCTs)—such as artificial intelligence and big data, and their use in smart grids, smart cities, smart health, and energy demand management—can help overcome both the environmental and socio-economic challenges cities (especially large ones) presently face. Smart grids, for example, promise to allow greater percentages of intermittent renewable energy in the grid, particularly the rapidly increasing supplies of wind and solar energy, and to help match electricity production to demand. There are many potential advantages possible with advanced CCT, but they need careful implementation because many potential problems can also occur. This review first examines what is needed to produce ecologically sustainable and more equitable cities. A critical aspect of this is the need for a global Earth Systems Science approach to include the environmental damages caused elsewhere by a given city. It then examines how the new CCT can potentially help achieve these aims. An important conclusion is that, in most cases, advanced technology availability is insufficient; strong policies are also needed. The shortcomings of actually implemented or proposed approaches are also examined. Finally, it discusses what future decades might bring and the implications for the new CCTs. Keywords Artificial intelligence · Automated vehicles · Big data · Energy efficiency · Global environmental problems · Renewable energy · Smart cities · Smart grids · Smart health · Urban sustainability

Abbreviations AGI AI

Artificial general intelligence Artificial intelligence

P. Moriarty (B) Department of Design, Monash University-Caulfield Campus, P.O. Box 197, Caulfield East, VIC 3145, Australia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 B. Appasani and N. Bizon (eds.), Smart Grid 3.0, Power Systems, https://doi.org/10.1007/978-3-031-38506-3_15

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Automated vehicle Business as usual Big data climate change Carbon capture and sequestration Closed-circuit TV Carbon dioxide removal Carbon dioxide Carbon dioxide equivalent Department of Transport Ecological Footprint Energy Information Administration (US) Exajoule (1018 J) European Union fossil fuel Gross Domestic Product Greenhouse gas Gigajoule (109 J) Gigatonne (109 tonne) Gigatonne carbon Human Development Index International Air Transport Association International Council for Local Environmental Initiatives Communication and Computational Technology International Energy Agency International Monetary Fund Internet of Things Intergovernmental Panel on Climate Change Megajoule (106 J) Megatonne (106 tonne) Megawatt (106 W) Megawatt-hour Organization for Economic Cooperation and Development Organization of the Petroleum Exporting Countries parts per million Photovoltaic Renewable energy Revenue pass-km Smart Grid Sulphur dioxide Sustainable Development Goal Solar geoengineering Trillion passenger-km Terawatt-hr (1012 W-hr) Urban heat island

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United Nations United Nations Conference on Climate Change US dollars Vehicle-to-grid World Health Organization

1 Introduction This section first examines global environmental, before focusing on urban ones. Global problems include not only climate change, but also ocean deterioration, biodiversity loss and various forms of pollution. Urban environmental challenges are many and include traffic-related issues like air pollution, and the urban space dedicated to private transport, as well as urban inequality.

1.1 Global Environmental Problems Our planet faces a variety of serious risks to environmental sustainability. Although global climate change has commanded the most attention, other challenges may be just as serious. These problems include loss of biological diversity [11, 26, 84] which Naeem et al. regard as more serious than climate change (CC), loss of ocean phytobiomass, growth of hypoxic regions, and progressive acidification of the oceans [8, 27, 35, 117], and chemical and plastic pollution on both land and sea [36]. Overcoming these global challenges will be made more difficult by the continued global population increase [22]. In their latest revision, the UN, in their medium forecast, expect the world’s population (which reached eight billion in November 2022 to continue its growth out to 2050, when the population will reach 9.7 billion, before levelling off at around 10.4 billion in the 2080s [113]. Just as important as the variety of environmental sustainability challenges is the limited time we have for action. For CC, the frequency and severity of extreme weather events—floods, wildfires, drought and heatwaves—are rising in many regions. The existence of several global threats and the limited time for finding solutions exacerbate the search for solutions, whether based on modern communication and computational technology (CCT), such as smart grids (SGs), or more conventional approaches. In 1990, when the first report of the Intergovernmental Panel on Climate Change (IPCC) was released, there was still time for solutions such as shifting to non-carbon fuels. But despite increasingly urgent warnings from successive reports [44, 45], annual carbon dioxide (CO2 ) emissions, atmospheric concentrations of CO2 and other greenhouse gases (GHGs), and global average temperature have all continued their steady rise. By 2022, atmospheric CO2 levels at Mauna Loa had reached 419 parts per million (ppm), some 18% higher than the 354 ppm value in

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1990 [33]. A Nature Editorial [3] on the November 2022 UN Conference on Climate Change (UNFCCC) reported that: ‘For warming to be limited to 1.5 °C, emissions need to fall by 45% from 2010 levels by 2030.’ But the UNFCCC expected emissions to be 10% above the 2010 levels by that year [114]. Another important global problem that will deeply affect the search for solutions is increasing global inequality. Although average GDP per capita levels are increasing even at the international level, inequality is rising at the household level [19]. This inequality is reflected in household carbon emissions: Kartha et al. [56] estimated that in 2015, the world’s top 10% of households produced 49% of emissions, whereas the bottom 50% of households only accounted for 7%. This conclusion shows that climate change (CC) mitigation is not just the responsibility of countries with high average per capita emissions but also of high-emitting households in countries with lower average emissions per capita. Various approaches have been suggested, and even partly implemented, to move Earth toward ecological sustainability. As already mentioned, the limited time now available for solving these problems complicates the search for solutions. Proposed solutions include moving rapidly toward low-carbon energy sources (the various forms of renewable energy (RE) and nuclear power), and the largely untried technologies of carbon dioxide removal (CDR), including both biological and mechanical methods and solar geoengineering [78]. Some also see greatly improved technical energy efficiency of energy-using devices (e.g. [65]) as an important means of cutting energy use and emissions. However, improving electrical energy efficiency (for appliances, for example) can prove counterproductive. This occurs not only because of the well-known energy rebound effect [97] but also because of present global inequality in the ownership of private vehicles and domestic appliances and levels of air travel [79]. Any improvement in the efficiency of private transport vehicles or domestic whitegoods is likely to be negated by rising ownership in presently low-ownership countries. The share of nuclear energy in global electricity production peaked in 1996 at 17.5%, but by 2021 its share had fallen to under 10% [9], and this share is not expected to increase much in future, if at all [75]. RE sources have gradually increased their share of global commercial primary energy, but they had only a 13.5% share in 2021 [9]. Further, fossil fuel (FFs) consumption has risen alongside the growth in RE output, viewed worldwide, non-carbon sources have not substituted for FFs. Wind and solar intermittent electricity sources are growing the most rapidly, and this trend is likely to continue, given the low remaining potential for non-intermittent RE electricity sources, hydro, geothermal and biomass [78]. Aall et al. [1] have even stressed the ‘Climate risks of the transition to a renewable energy society’. CDR and solar geoengineering technologies have been discussed for several decades. However, one favoured CDR technology, carbon capture and sequestration (CCS), presently sequesters under 100 megatonnes (Mt) annually, compared to the billions of tonnes needed for CCS to be a significant CC mitigation method. If a major solar geoengineering program was implemented, global temperature reductions could be expected in a year or so, but global precipitation patterns would

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change—a problem in an already water-stressed world. Further, the other CO2 challenge to global sustainability, ocean acidification, would continue unabated. Renewable energy (RE) will thus need to supply nearly all the world’s energy needs in future, even though it has its own sustainability and global equity problems [79, 81]. An increasing number of researchers, particularly degrowth researchers [42, 100] and ecological footprint advocates (e.g. [115]), have argued that further economic growth is now incompatible with global ecological sustainability. Even the UN is starting to feel that gross domestic product (GDP) is a poor measure of human welfare and should be complemented by other welfare indicators, such as the Human Development Index (HDI) or perhaps ones linked to the 17 Sustainable Development Goal (SDGs) of the UN, none of which—on present trends—look likely to be met by the target year 2030 [71]. It is difficult to assess what such a change would mean for the two CCT approaches discussed in this review, SGs and smart cities (SCs), although [85] has discussed what a degrowth perspective would mean for cities.

1.2 Urban Environmental Problems In 1950, under 30% of the global population resided in cities, but in 2022 the figure was over 55%, and is expected to increase to over 68% by the year 2050 [112]. About 90% of these new urban residents, expected in 2050 will live in Africa or Asia. Further, a rising share of this urban population will live in ‘megacities’—cities of over 10 million residents. In 1950, there were only two such megacities (New York and Tokyo), but by the year 2035, 43 are expected. Over 550 million, or 13% of the global urban population, live in such cities, with most of these cities being in low- or middle-income countries. Although only 55% of the global population resides in cities, such cities generate about 80% of the global Gross Domestic Product (GDP) [122] and are likely responsible for a similar share of global energy use and resulting CO2 emissions. Cities might appear to occupy only a small fraction of the Earth’s surface, but they rely on extensive non-urban areas for vital inputs such as food, materials, water, and energy. Many city governments have recognised cities’ many sustainability problems, and have formed the International Council for Local Environmental Initiatives (ICLEI). ICLEI describes the organisation as ‘a global network of more than 2500 local and regional governments committed to sustainable urban development’ [46]. In addition to the global problems mentioned in Sect. 1.1, cities, particularly large ones, face a range of more local problems. These include the following urban sustainability challenges [76]: • • • • •

Traffic injuries and fatalities Air pollution from traffic and other sources Noise from traffic and other sources Light pollution in cities Urban income inequality

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• Urban health and well-being risks • Urban heat island (UHI) effects • Land resumption for roads and car parks. These sustainability problems are interconnected since, as shown in more detail in Sect. 2.3, noise and air pollution, the UHI effect, and traffic injuries negatively affect the health and wellbeing of urban residents. However, these adverse health effects are not uniformly spread throughout the city. Low-income residents are often spatially concentrated in certain city areas, which are often disproportionally affected by noise, light, and air pollution. These multiple assaults on urban health and wellbeing mean that it is often impossible to minimise all simultaneously, and trade-offs become necessary. This section has outlined our global sustainability challenges and then looked at those confronting urban sustainability. What changes are needed to produce ecologically sustainable and more equitable cities? One important point stressed above is the need to adopt a global Earth systems science approach so that the environmental damages caused elsewhere by a given city are included in any urban sustainability assessment. In Sect. 2, the extent to which the new CCT, particularly in the form of SGs and smart cities, can help achieve these aims is examined, and it is concluded that strong policies are usually needed. Section 3 presents the shortcomings of implemented privacy, security, reliability, and cost approaches. Finally, Sect. 4 discusses what future decades might bring and the implications for the new CCT.

2 The Potential for Advanced CCT Approaches in Cities In general, the more information we have on a given subject, the better-informed should be the decisions made [68]. The new CCT enables vastly more data to be collected and analysed than is possible by traditional methods and can potentially improve urban sustainability and equity. For example, there is often a serious lack of reliable and up-to-date statistics on vehicle (and non-motorised) passenger-km at both the city and the global levels. Only air travel statistics are reliable. Interest and research on applications of advanced CCT for creating more sustainable cities have grown in recent years, as illustrated by Scopus-refereed publication counts for various IT terms, as shown in Fig. 1. Publications on Artificial Intelligence (AI) and the Internet of Things (IoT), both fundamental for implementing SGs or smart cities, are far more frequent than these two latter terms. Research on AI started far earlier than the other topics, and even in the year 2000, annual counts numbered over 3400, compared with close to zero for the other terms. The terms SGs and SCs will be examined in Sects. 2.1 and 2.2, respectively. However, the terms AI, big data (BD), and IoT need further explanation. AI and machine learning have been around for many decades, whereas the term IoT is far more recent and appears to have only come into use around 2000 [118]. AI is needed to handle BD, the vast amounts of data that come from science projects like the

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hunt for the Higgs boson, the billions of IoT sensors, from data trail of the vast volume of social media postings, mobile smartphones, and publicly available data such as weather forecasts, traffic data or geodata. Nevertheless, there is no agreedupon definition for the term BD, although [20] claimed this is ‘not necessarily a bad thing’. Section 2.1 examines how SGs can contribute to urban and global sustainability, particularly as RE replaces FF for electricity production. Section 2.2 discusses some key aspects of SCs. Although there are a large number of applications of CCT in cities, including the use of IoT for monitoring the condition of buildings and bridges, for smart street and domestic lighting, and, of course, allowing two-way information flows between urban residents and urban governments, this section will focus on two key and much-discussed aspects of smart cities: smart transport and smart health.

2.1 Smart Grids (SGs) There is no precise definition of SGs. Kovacic and Giampietro [60] gave the following list derived from the literature: ‘access to electricity as a human right, the decentralization of the energy system and the changing role of consumers, sustainability issues, energy security, and climate change.’ They have gone further and argued that

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different definitions of SGs contain contradictions. Nevertheless, more recent definitions generally assume SGs to have all or at least most of the following properties [25, 89, 119]: • • • •

More reliance than at present on renewable energy (RE) sources More efficient use of electrical energy Significant reductions in the adverse environmental impact of electric production The ability to charge parked electric vehicles (EVs), as in vehicle to grid (V2G) systems for EVs • Load demand management ability, based on smart meters and smart appliances, in order to better match electric supply, particularly as both RE supply and power demand are both intermittent. An essential feature of SGs is much more reliance on RE sources. Not only will such reliance inevitably increase—perhaps more slowly than desired—but it will largely have to come from intermittent sources, largely wind and PV solar. The other three RE sources used—hydro, bioenergy, and geothermal—can all produce dispatchable electricity, but their remaining technical potential is too small. Also, expanding hydropower will have serious environmental problems, and bioenergy faces competition for food and materials production [77, 79]. This inevitable shift to intermittent power sources will necessitate profound changes to the grid. The traditional electric grid consists of mainly large generating units which supply electricity to customers in homes, factories, or offices. These generating units, especially major hydro plants, were often located in remote locations, necessitating the building of high-voltage power lines to load centres. Power transfers were one-way and adjusted to varying consumer electricity demand by either shutting down or starting up generating units. SGs help ensure adequate power quality, keeping voltage and frequency within limits and ensuring the power factor (‘which measures how much of the electricity delivered is useable by customers’) is high. In North America, home appliances and electronics are designed to operate within an alternating current (AC) range of 106– 127 V. Voltage deviations from the prescribed range, whether too high or too low, lower device efficiency, and can even damage equipment, or cause it not to function. An important component of SGs are smart meters, which can provide both the utility and customers with detailed information about the power delivered to homes. They also include a step-down voltage transformer to enable modern digital equipment to function [103]. SGs have made possible two-way transfers of power. These transfers arise because many consumers produce electricity, particularly from photovoltaic (PV) cell arrays. In Germany, millions of residences are fitted with rooftop PV cells. All PV arrays produced 51.4 terawatt-hr (TWh = 1012 W-hr) in 2020, or 10.5% of Germany’s electricity production, with around 15 TWh coming from rooftops—far less than the rooftop technical potential of over 100 TWh [32]. Since householders can sell their excess electricity, prosumers have replaced consumers who consume and produce energy. This two-way power flow makes the grid more complex, but on the other hand, it does allow for more decentralisation of the power supply. This is important

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since, in Europe, one of the constraints on grid integration is the difficulty of building new high-voltage lines in the face of citizen opposition [14]. Due to their intermittent production profile, wind and PV solar require more transmission capacity for a given TWh of power. There are two ways in which SGs can manage demand. First, they can shed nonessential power loads; domestic freezers, refrigerators, and air-conditioning units can be temporarily switched off with minimal inconvenience to consumers. Second, with variable electricity prices, the shortfall in electricity production will be reflected in higher real-time electricity prices, which will further reduce (or time shift) demand. The problem is, of course, the variable nature of wind and PV solar output. However, wind and solar output can be forecast with improved accuracy some days ahead [98], and these forecasts, along with the corresponding electricity price forecasts, can be communicated to consumers. This would enable some household tasks, for example, clothes washing or vacuuming, to be postponed until electricity costs were reduced [116].

2.2 Smart Cities: General Considerations Like the term SGs and IoT, there is no agreed-upon definition of a smart city or what attributes are considered essential. Because of this definition vagueness, it is impossible to say how many intelligent cities there are, but it is probably over 200. Keshavarzi et al. [58] have reviewed what the term ‘smart city’ might mean. They argued that ‘Researchers conceptualize the smart city as a “digital”, “intelligent”, “real-time”, “green”, or a “sustainable” city […].’ Attributes that are commonly listed include: • In general, the use of a wide range of CCTs to gather useful information on diverse topics such as the state of infrastructure, buildings, crime, healthcare, and pollution • Increased digital contact and information flow between citizens and urban government • Increased sustainability because of reductions in energy use, air pollution and carbon emissions • Increased equity and health/wellbeing for all urban residents • Promotion of (sustainable) urban economic growth. One approach to a smart city definition is to examine the criteria used to rank the world’s top 20 sustainable smart cities. The criteria used by Disruptive Technologies were as follows [40]: • • • •

Technological provision Environmental, social, and economical sustainability Economic and social development Air quality

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Energy transition towards renewables Quality of living Waste per population Water sustainability Human infrastructure and networked markets Environmental, social and governance performance Smart city ecosystem.

Eleven of their 20 cities were in Western Europe, with the top five (ranked from fifth to first) being Stockholm, London, Zurich, Oslo, and Copenhagen. All the rest were in the OECD, except for Taipei, Hong Kong, and Singapore in Asia, and, importantly, all 20 were cities with high GDP per capita. The alternative top 20 ranking produced by the Smart City Observatory [12] was very different and included many cities not on the Disruptive Technologies list. One key criterion which is lacking from the Disruptive Technologies list (and possibly, other rankings as well) is CO2 emissions per capita; its inclusion alone would not only have altered the smart cities ranking but, more fundamentally, ruled out many smart cities from any world ranking of ‘most environmentally sustainable cities’. Yigitcanlar et al. [126] had similar misgivings, arguing that: ‘The findings provide evidence that the current smart city practice fails to incorporate an overarching sustainability goal that is progressive and genuine.’ Two already-implemented CCTs for urban transport are programs that help individual motorists find parking spots and software that help improve traffic flow; both may hinder overall urban sustainability. The problem is positive feedback effects [54]. Improving parking and traffic flow will encourage urban travellers to travel more by car, or at least not to switch to alternative, more ecologically-friendly modes. Also, Newman and Kenworthy [86] and McIntosh et al. [72] have shown that cities with more parking spaces per central business district workplaces, and higher average road speeds, have higher per capita car energy use and transport CO2 emissions. Even if, for a given trip, the individual motorist saves on fuel and produces less air pollution and emissions, from a city-wide viewpoint, it does not reduce energy/emissions because it leads to more car travel. A potentially serious problem is that some of the stated aims may be contradictory. For instance, it may not be possible to reconcile ecological sustainability—considering all the global environmental challenges Earth faces with continued global growth in GDP, as discussed in Sect. 1. Sections 2.3.1–2.3.3 examine some examples of smart transport, an important component of smart cities. According to [92], there are an estimated 1.45 billion vehicles on the world’s roads, which is forecasted to grow to 2.80 billion by 2036. Should the 2036 vehicle fleet estimate be anywhere near the actual figure, it underscores the need for smart transport to manage the ever-rising complexity of urban transport systems.

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2.3 Smart Transport Although smart transport covers topics like parking spot updates, traffic network optimisation, and personalised public transport arrival time updates, below only three important and much-discussed topics are covered: electric vehicles, automated vehicles, and travel substitution by CCTs.

2.3.1

Electric Vehicles

EV numbers are growing rapidly worldwide, and by the end of 2021 EVs (excluding 2- and 3-wheelers) numbered 16.5 million. The International Energy Agency (IEA) forecast is for the global fleet to rise to 200–350 million as early as 2030, depending on the chosen scenario [49]. EVs have been widely promoted to tackle several urban sustainability problems, including urban vehicle air, noise pollution, and even energy security fears from over-reliance on petroleum-based fuels. These benefits are far more modest if electric power is mainly derived from fossil fuels (FFs), as at present, the problems are mainly shifted elsewhere, not solved. Some vehicle air pollution remains since particulate pollution from brake linings and tyre wear is unchanged. Even asphalt is a major source of ‘secondary organic aerosol precursors’ [55]. Land needs for vehicle movement, and parking are also unchanged. Worldwide traffic collisions annually result in 1.35 million deaths and 50 million injured [92]. Such collisions have two effects, apart from the human death and injury caused, they also either shorten the useful life of affected vehicles or increase the energy and GHG costs of vehicles needing repair and new parts. Light pollution, like urban air and noise pollution, can affect the health and wellbeing of urban residents and the circadian rhythms of urban flora and fauna [70]. Again, it is not reduced by any shift to EVs. Nevertheless, many think it is likely that urban private vehicles will be nearly all EVs in a few decades. The reasons for this are the need to reduce oil consumption for energy security and CO2 reduction. In addition, strong government support, and the potential elimination of engine pollutants, will help their growth. Many cities worldwide plan to eliminate internal combustion vehicles, some as early as the mid-2030s [93].

2.3.2

Automated Vehicles

The Society of Automotive Engineers (SAE) has proposed standard levels of vehicle automation, from 0 (no automation) to 5 (full automation). The widespread introduction of fully automated vehicles (AVs), it has been argued, can save road space and reduce fuel use, pollution and vehicle collisions. AVs would remove human error, which is at least partly the cause of 93% of traffic accidents [37]. The idea is not new

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and can be traced at least back to the Intelligent Highway Vehicle Systems programs of the 1980s [80]. AVs at level 5 can also reduce vehicle weight by eliminating the need for manual steering equipment and present safety features, which will cut vehicle material requirements and thus energy use—both for manufacture and vehicle operation—and system-wide GHG emissions. Harb et al. [39] reviewed the literature on the implications of AVs—such as their acceptance by the motoring public, ownership preference and travel levels. They found that most studies consistently predict an increase in travel—an increase of anywhere from 1 to 90%—depending on the assumptions used. Motorists were also found to prefer private rather than joint ownership. Taiebata et al. [109] modelled the effect of full adoption of AVs by US households. They calculated a 2–47% increase in travel for an average household, which would translate into a roughly similar rise in energy use. Regarding the ability of widespread AV adoption to reduce traffic collisions and injuries, there are other, simpler, ways of achieving this aim. The World Health Organization (WHO) [123] have shown that a reduction in vehicle speed can be effective in that ‘every 1% increase in mean speed produces a 4% increase in the fatal crash risk and a 3% increase in the serious crash risk.’ Overall, while partial automation of driver tasks, such as automatic braking, power steering, and lane keeping, are been widely implemented [108], these innovations still require a driver, who is assumed to be responsible for traffic law infringements or vehicle involvement in accidents. Thorny legal and ethical issues arise with fully automated (SAE level 5) vehicles. It is thought that a fully automated vehicle fleet would have far fewer accidents than at present, but will non-vehicular road users have similar accident reductions? [6]. Also, should AVs be programmed in case of an unavoidable collision to protect the occupants or minimise serious injury? In 2020, Malik and Sun (2021 optimistically wrote: ‘We expect to see hundreds of thousands of smart connected cars in a matter of months […]’. In contrast, more recent reports by Gruyer et al. [37], Chafkin [18], and Powers [94] have thought that the challenges still facing AVs could mean that they will never replace human-driven vehicles. As Chafkin entitled his article: ‘Even after $100 billion, self-driving cars are going nowhere.’

2.3.3

Substitution of Travel by CCT

The substitution of passenger travel by land and air by IT has been discussed since around the mid-1970s, where the main emphasis was working from home. The decades since then have seen the rise of the internet, which has greatly improved the potential for such trip displacement. Yet, existing research is equivocal about whether CCT will result in a net drop in travel. The covid-19 pandemic and the subsequent restrictions on travel, both local and particularly air travel, greatly increased both working and studying from home, and teleconferencing, allowing a test of the technical feasibility of such substitutions. It is important to remember that governments

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constrain travel itself. Today, with the general easing of travel restrictions, the use of CCT for travel substitution is once again optional. Noussan and Tagliapietra [88] used plausible scenarios to examine the overall effects of CCT penetration in a European context. They concluded that either increases or reductions in both transport energy use and carbon emissions were possible. A comparison of travel levels on the US road system in the year 2019, before the pandemic, with levels for the years since then confirm that after the drop in 2020, road travel levels rebounded and by March 2022, were higher than for March 2019, as was the 12-month moving average for 2022 compared with 2019 [24]. No net replacement of road travel by CCT appears to have occurred. The US experience suggests that technology alone will not induce a substitution. Instead it will need strong supporting policies, as happened during the widespread pandemic-induced lockdowns, when physical travel levels were reduced by government-imposed restrictions. Compared to the small or even negative sustainability benefits from the introduction of either EVs or AVs (which are assumed to be EVs) travel substitution by CCT shows the most potential. The overall sustainability benefits of EVs will be minor, and a large-scale shift to AVs seems unlikely anytime soon—although there may be more scope for public transport and freight vehicles [37]. Even strong government backing of EVs/AVs will not greatly improve urban sustainability, but strong backing of CCT for travel substitution can significantly cut urban travel. Nevertheless, as seen during 2020, the policies will need to be drastic—matching the urgency of global and city sustainability challenges discussed in Sect. 1. If private vehicular urban travel in future is greatly cut by policy-backed CCT travel substitution, the case for smart transport will need to be examined afresh, as the present focus on traffic flows and parking will be less important. Further, proposed innovations such as vehicle-to-grid (V2G) technology for EVs/AVs [95] would prove less relevant.

2.4 Urban Equality, Health and Wellbeing In contrast to most studies on smart cities and inequality, Caragliu and Del Bo [15] undertook an empirical study of 106 European cities and found that ‘smart cities are associated with lower levels of urban income inequality.’ While this finding is encouraging, it would be useful to know whether inequality levels were already less in such cities before they became—or were labelled—‘smart’. Urban health and well-being are adversely affected to at least some extent by all the factors listed in Sect. 1.2. Urban air pollution has received the most attention, as it causes more deaths and illness than the other urban environmental problems. Sicard et al. [101] have reported that global air pollution led to over 4 million premature deaths worldwide in 2019. Average air pollution levels can vary greatly across a given city region, but they can also vary by time of day and season. Air pollution is also a causative factor in Alzheimer’s disease [91]. Hegewald et al. [41] examined the health effects of road traffic noise in Hesse, Germany, and found that such noise caused 435 disability-adjusted life-years

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(DALYs) per 100,000 persons in 2015. They further estimated that ‘a hypothetic uniform road-traffic-noise reduction of 3 dB would prevent 23% of this burden of disease.’ Münzel et al. [82] found that for Europe, ‘Outdoor light at night increases the risk of coronary heart disease.’ Although mainly a non-urban problem, it can also adversely affect urban flora and fauna by disrupting their life cycles [70]. The presence of readily accessible urban parks has been found to be important for the mental health and wellbeing of urban residents [5]. Further, evapotranspiration from urban vegetation and parks counteracts the warming from the UHI effect [102]. Because factors like traffic noise and air pollution are unevenly distributed spatially in cities, so are the adverse health effects. Nadybal et al. [83] have documented noise pollution inequities, Ma [67], the ‘clean air haves and have-nots’. How can the new CCTs help improve urban health and wellbeing? A relatively recent approach is to use ‘citizen science’ to measure air pollution at a detailed level. Lu et al. [66] have described how they provided 22 disadvantaged households in Southern California with low-cost sensors to measure outdoor particulate pollution levels. The potential for citizen science is much greater, it has been used in the Netherlands to assess air pollution levels by location and time of day [116]. These real-time readings could greatly benefit pollen allergy sufferers, for example. Many people are already fitted with various body sensors to monitor various health-relevant parameters, such as blood pressure, body temperature, or blood sugar levels. This ‘Quantified Self’ movement is becoming increasingly popular, enabling people to take more charge of their health [31]. Bhagat [7] has reported that US dollars (USD) 94 billion is now spent annually on global fitness. Wearable sensors can also serve as electronic health records for measuring simple parameters and ‘episodic and continuous monitoring of noncommunicable conditions, such as asthma, chronic obstructive pulmonary disease and cardiovascular diseases’ [7]. Bhagat even claimed that increasing healthcare’s reliance on such wearable technologies could advance human health and lead to global medical cost savings of USD 200 billion over the next two decades. Further, as reported in [118], a 2015 Goldman Sachs report indicated, healthcare IoT devices ‘can save the United States more than $300 billion in annual healthcare expenditures by increasing revenue and decreasing cost’. Another projection is that the digital health market will grow globally to over USD 500 billion by 2025 [106].

3 Potential Problems with Advanced New CCT Approaches 3.1 General Considerations Some researchers have seen serious potential risks from the widespread use of AI and BD, regardless of application. McLean et al. [73] have argued that Artificial General intelligence (AGI) can offer us both great benefits and pose great risks. They

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summarised the main risks as being the following: ‘[…] AGI removing itself from the control of human owners/managers, AGIs being given or developing unsafe goals, development of unsafe AGI, AGIs with poor ethics, morals and values, inadequate management of AGI, and existential risks.’ Tornberg and Tornberg [110] have offered an even more fundamental criticism. They argued that the abundance of digital data has ‘sparked a renewed, complex naturalism, within which social systems are increasingly approached through the formal methods of the natural sciences—seeing social structures as patterns naturally emerging from mass-interaction, which is taken to permit the leaving out of institutional, technological, and contextual aspects of social life.’ In brief, social science approaches to urban problems, with their explicit values, could disappear. As already discussed, at the core of the new CCT-based approaches for sustainable cities are a vast number of sensors—in buildings, urban infrastructure such as bridges, domestic appliances, sales items in retail stores, vehicles and transport infrastructure, among others—that can sense their environment and send information back to be processed and analysed. These sensors are the basis for the IoT [87, 118]. In 2021, the number of interconnected sensors was estimated at nearly 10 billion globally, and by 2030, this number was forecast to rise to over 29 billion [105]. Nguyen et al. have given much larger estimates: 26 billion devices in 2020, with the figure in 2030 expected to grow to as many 500 billion IoT Internet-connected devices. Farah [29] reported that for the US healthcare alone: ‘There will be up to 30 billion connected IoT and medical devices in healthcare by 2020.’ The true size of the IoT is clearly unknown [118], it is not only definitions that are subject to uncertainty. Several barriers to widespread and successful adoption need to be overcome, or at least reduced, if CCT is to develop its full potential for achieving urban sustainability. The most important concerns include privacy, security, reliability, and cost for AI and IoT, although other challenges, such as technical readiness, feasibility, and public acceptance are also present [116]. These problems are exacerbated by the vast number of interconnected sensors comprising the IoT, as just discussed. Following the approach used in Sects. 2.1–2.3, SGs, smart transport and smart health are discussed in turn for any privacy, security, reliability, and cost challenges that they face. Many challenges are similar across all three CCT applications; these are considered in this sub-section. Consider privacy: the dilemma is that we want others to have personal information about ourselves for many purposes, so that better decisions can be made. These decisions may be for welfare benefits, tax concessions, or for personal health diagnoses. Health-related data for an entire population could be analysed by AI (to assess the side effects of a new treatment or drug, for example) to yield benefits for all. Millions of Facebook and other social media users, go further, and freely post personal information about themselves in text and pictures, which might suggest that they do not consider privacy important. Nevertheless, Facebook users are concerned about privacy; over 50% of US internet users desire to improve their online security and privacy [61]. Some even argue that data mining and IoT may simply be incompatible with privacy [118].

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For, IoT, reliability problems arise because of the many sensors used. Eventually, sensors wear out and have to be replaced [62]. If many sensors are used to measure detailed urban air pollution patterns, it does not matter if some are giving faulty readings. But if few or even only one sensor is used to measure a given variable, any sensor malfunction will lead to a wrong value being assumed. Reliability is also a problem for data obtained from social media such as Twitter, which data are increasingly used in assessing public opinion, an important input for smart cities. Social media is now a widely-used BD source, which is unsurprising since 45% of the entire global population are active users [96]. While acknowledging the large amounts of data available and their timeliness, these authors identify the main problem as being poor data quality. BD and IoT are already extensively used in business, producing monetary benefits [16]. In retailing, articles for sale are tagged with Radio Frequency Identifiers [118], enabling real-time information on sales and automating stock re-ordering. Will IoT and BD produce similar economic benefits for cities in their quest for sustainability? Hittinger and Jaramillo [43] have doubted that the massive numbers of IoT sensors will lead to energy savings overall, although they add that they will lead to local energy savings. After all, the many billions of sensors presently in service need the energy to transmit information back to processing units. Again, it is necessary to look beyond local boundaries to get the complete picture of sustainability. For SGs, the monetary benefits will depend on factors such as the share of intermittent RE in the grid and the success of load shifting.

3.2 Smart Grid Challenges Zheng et al. [128] have proposed that, in general, ‘Smart grid adoption by U.S. electric utilities promises improved reliability and resilience for an ageing electric grid while enabling integration of renewable energy sources and decarbonization of the electricity sector’. However, they found that US utilities were primarily motivated by ‘internal goals such as cost reduction and improved operational performance, and less by external factors such as customer demand or pressure to accommodate renewables.’ According to Tufail and colleagues [111], the world is rapidly transitioning to SGs, which they characterise as having three levels: customers accessing the network; the network itself, including smart devices (such as customers’ smart meters) and sensors; and finally, the decision-makers who manage the network. They argue that security is a major challenge at all three levels, and cyberattacks can physically damage the grid. They also point out that as the grid increases in complexity, so does the chance of sensor faults. As the IEA [47] report: ‘The threat of cyberattacks on electricity systems is substantial and growing.’ 5G is ‘the fifth-generation technology standard for broadband cellular networks.’ It is already supplanting the 4G network and will eventually be replaced by the planned 6G network. 5G is faster (in gigabits/sec) than 4G and has a larger bandwidth,

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improving connectivity between different devices [120]. As Yue et al. [125] have pointed out, 5G networks is anticipated to ‘connect over 100 billion mobile devices and heterogeneous networks.’ Butun et al. [13] have warned that such ‘increased connection and communication also expose the control networks of the power grid to the possibility of cyber-attacks.’ Yue et al. and others, such as [28, 99], have discussed. as a countermeasure, the possible use of blockchain technology to improve security. The use of blockchain can avoid the main weaknesses of conventional systems, namely transaction forgery, censorship, and reversal of transactions [125]. Sedlmeir et al. have also stressed that its energy consumption is far less than for energy-intensive applications like Bitcoin mining. The threats can at best be minimised, they cannot be completely eliminated. In a similar vein, the IEA (2021a argue that the resilience of grids to such attacks can be improved.

3.3 Smart City Challenges Kitchin and Dodge [59], though supportive of the smart city initiatives, nevertheless have written that although ‘Smart city technologies are promoted as an effective way to counter and manage uncertainty and urban risks through the effective and efficient delivery of services’, they can also create ‘new vulnerabilities and threats, including making city infrastructure and services insecure, brittle, and open to extended forms of criminal activity.’ A general threat to urban citizens’ privacy is closed-circuit TV cameras (CCTVs). According to [10], 770 million CCTVs are in operation globally, mainly in cities, with the majority (54%) located in China. Zhao [127] has discussed the privacy implications of the new national digital health code system which is planned for China and has reported that ‘there have been instances where the system appears to have been abused to limit people’s movement for non-health-related reasons.’ However, not only China has potential privacy problems from BD and CCTV. For every 1000 Londoners, roughly 66 CCTV cameras are watching them. Bradshaw reported that the ‘average Londoner has been caught in a CCTV an average of 300 times a day’. Awad and colleagues [4] have argued that because the IoT allows us to exchange information between the connected sensors, transferring possibly sensitive data about individuals or businesses and data storage/processing sites, the data is ‘susceptible to security vulnerabilities and malware attacks.’ Malik and Sun [69] have discussed an important problem for smart transport: cyber-attacks on AVs. Such attacks could cause deaths and, in any case, will increase the costs of full AV introduction as AV companies respond to these security threats. For some smart transport applications, proponents claim cost-effectiveness. But, as discussed earlier in Sect. 2.2, the benefits from smart parking and traffic flow

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optimisation, while exceeding costs from the motorist point of view, may negatively impact on urban sustainability. CCT substitution for travel will likely be costeffective, particularly if households, businesses and urban administrations already have the necessary CCT equipment. The main challenge will be to enact policies enabling deep substitution. It is also possible that other smart city applications, such as monitoring infrastructure like bridges with sensors, will show both net environmental and monetary cost benefits—and have no privacy concerns. Ismagilova et al. [52] gave the current global spending on smart city technology as USD 81 billion, and predicted spending to reach USD158 billion by 2022. Nevertheless, we have little idea whether the benefits (which in any case are difficult to quantify) exceed the costs for the cities.

3.4 Smart Health Challenges Echoing some of the issues raised by Tornberg and Tornberg [21, 110] have argued that many see AI and BD as the answer to many healthcare problems. The new CCT, proponents claim, has the potential to reduce misdiagnosis, find new cancer treatments, and cut medical costs. The authors dispute these claims, pointing out the inherent limitations of the algorithms used, particularly their reliability and problems of interpretation and the fact that human experience can’t be fully captured by quantitative data. Sohn [104] has discussed the reliability problems that arise with using AI to diagnose cancer from computed tomography scans or covid-19 from medical scans. Pacemakers, and other devices, such as glucose level meters and blood pressure meters, can be connected to the internet, so physicians can closely monitor their patients’ health [17]. The risk is that this connection could be hacked, and the signals the pacemaker sends are altered, possibly damaging the patient [53, 116]. It appears that computerised health services (such as the United Kingdom’s National Health Service) are being targeted by ransomware attacks [74] to compromise patient privacy if the ransom is not paid. Security and privacy are thus interconnected. A novel type of reliability problem confronts those, including Quantified Self enthusiasts who wish to take a more active role in their health and rely on the internet for information about various ailments, whether they have the ailment themselves or are caring for someone who does. The internet is a valuable resource for information, but unfortunately also contains much misinformation, and medical topics on the internet are no exception. Consider the anti-vaccination movement: Farhart et al. [30] found that conspiracy beliefs and anti-intellectualism in the US had the most consistent effects on COVID-19 vaccine hesitancy. Empowerment for health can be a two-edged sword. Is smart medicine cost effective? The discussion in Sect. 2.3 suggested that it will be, at least in the near future, with potential savings of many billion USD annually. As with other application areas, the costs of the new CCT are not independent of the levels of privacy, security, and reliability attained. Regarding privacy, Wise [121]

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noted how the United Kingdom’s National Health Service launched a patient database without adequate privacy protection. Particularly for the US some researchers feel that fundamental changes are needed to how medicine is practiced. Although the US spends a much higher (and still rising) proportion of its GDP on health than other countries [124], Guzick [38] has pointed out that its citizens suffer poorer health than other OECD countries. Livne [64] refers to the problem of excess: ‘Pharmaceutical companies, medical device producers, insurers, hospitals, clinics, laboratories, and physician groups make up a profitable industry that develops, sells, and utilizes ever-more-advanced and expensive methods to diagnose and treat disease.’ Unless these practices change, the effectiveness of the latest CCT for health applications will be compromised.

4 Discussion and Conclusions This review has assessed whether application of the new CCT, particularly in the form of SGs SCs—especially smart transport—and smart health, can help achieve sustainability and equity in an urban context. Doing so requires attention to the following principles, which, as has been shown, are not always followed in the literature on these topics: • All environmental damages accounted for by cities must be included, not just those occurring within city boundaries, such as local air pollution or noise. • Feedback effects must be considered, as discussed in Sects. 2.2 and 3, as these can negate the benefits of improved energy efficiency when adopted by a global viewpoint. • The benefits and drawbacks of the new CCT approaches can only be properly assessed if compared with those of more conventional approaches. Further, even showing that SCs are more environmentally sustainable and/or equitable is not enough; it is also necessary to show that SC innovations were the cause, as many top-ranked smart cities for environmental sustainability would also have ranked highly before they were labelled ‘smart cities.’ • Technical innovations such as CCT on their own will not be enough to improve global and urban sustainability or reduce urban inequality. Profound policy changes at all government levels will be needed to improve urban sustainability. For example, policies that aim to reduce both car and air travel will be far more important than any technology innovations that improve energy efficiency or reduce carbon emissions of these modes. • Key aims for SGs or SCs should not contradict each other. As shown in Sect. 2.2, it is possible that economic growth may not be compatible with global ecological sustainability; certainly, the record since 1990, the year the first IPCC report was released, has not been encouraging. Economic growth appears to have been accompanied by a deteriorating global environment, as measured, for instance, by EF.

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• It is more likely that directly focusing on possible solutions for at least some longstanding urban problems, such as inequality or pollution, will be more fruitful than hoping solutions will arise from the analysis of vast amounts of data now available from IoT and social media. • The new CCT innovations must not only help achieve present sustainability and equity needs, they must also do so in the future, when circumstances will very likely be vastly different. • The terms SGs, SCs, BD, and IoT, all suffer from a lack of a precise and agreed-upon definition. Even worse, some attributes of each term are mutually contradictory. • Particularly for smart health, at least at present, it will be more effective to focus on the many shortcomings of health practices than to attempt to streamline these existing practices with CCT. It is also not enough that the new approaches should improve the operation of power grids and help make cities more equitable and sustainable today; they must also be forward-looking, anticipating the future challenges cities will face, which, given the immediate threats to global environmental sustainability, will likely be profound. Since the shape of these challenges may be only vaguely known today, it is important that the CCT—or even more conventional—approaches chosen—can rapidly adapt to changed circumstances. How uncertain even the near-term future is can be illustrated by examining the successive editions of the World Economic Outlook (WEO), published twice a year by the International Monetary Fund (IMF) [51], and various forecasts for energy use and air travel (see Table 1). All forecasts in 2019 were over-optimistic about 2020, as they did not foresee the Covid-related global economic downturn that occurred a few months later. Most 2020 forecasts for 2021 were similarly optimistic, but for oil and total energy, actual 2021 use exceeded expectations. The table shows that predictions for even a year ahead can be in serious error if unexpected changes occur. Table 1 Forecasting uncertainty because of the global covid-19 pandemic GDP growth (%)

Commercial energy (EJ)

Oil (EJ)

Actual 2019

+ 2.9

587.4

192.1

8.5

2019 forecast for 2020

+ 3.0

593.3a

197.0a

8.9

Actual 2020

− 3.1

564.0

174.2

2.8

2020 forecast for 2021

+ 5.8

587.4a

153.7a

5.3

Actual 2021

+ 5.9

600.0

183.0

3.5

Italics defines to distinguish forecast from actual data Sources [2, 9, 48, 50, 51, 90] a IEA [50] ‘Stated Policies Scenario’

Air RPK (trillion)

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There is little agreement on what the future will look like in the future—say the year 2050. Some researchers, particularly Kurzweil [63], feel that the new CCT will produce ‘a singularity’, transforming humankind for the better. Other researchers have argued that a pessimistic future is a real possibility and that civilisation in its present form could collapse. Gowdy [34] envisaged life returning to a huntergatherer society, but with a global population, only a fraction of that today. Steel and colleagues [107] entitled their article ‘Climate change and the threat to civilization’, while [57] have argued that we need to consider worst-case scenarios, including the possible societal collapse or even human extinction. In such extreme futures, the notion of SCs and SGs may even become irrelevant. Where does this review leave us concerning the benefits of SGs and smart cities? Are they addressing the wrong questions, providing the wrong answers, too costly compared with more traditional solutions, or, more fundamentally, do they carry existential risks? Overall, the conclusion must be that implementing SGs is more important for global and urban sustainability than implementing SGs. While new data, such as from smart public transport cards, will be useful in the short term, many important improvements can be made to cities using conventional approaches, provided the necessary policies are in place. These may also be available at considerably lower economic costs. One distinct future possibility is that the world will have to wind the global economy back to a level that is compatible with ecological sustainability, as has been stressed for decades by the late Herman Daly [23]. If so, both primary energy consumption and vehicular passenger transport will likely have to fall. Since the reduced energy will presumably be mainly FF sources, RE electricity sources will increase their share and will eventually be near 100% (with a small contribution from nuclear), necessitating SGs.

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