Planning and Operation of Electric Vehicles in Smart Grids (Green Energy and Technology) 3031359100, 9783031359101

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Green Energy and Technology

Mehdi Rahmani-Andebili   Editor

Planning and Operation of Electric Vehicles in Smart Grids

Green Energy and Technology

Climate change, environmental impact and the limited natural resources urge scientific research and novel technical solutions. The monograph series Green Energy and Technology serves as a publishing platform for scientific and technological approaches to “green”—i.e. environmentally friendly and sustainable—technologies. While a focus lies on energy and power supply, it also covers "green" solutions in industrial engineering and engineering design. Green Energy and Technology addresses researchers, advanced students, technical consultants as well as decision makers in industries and politics. Hence, the level of presentation spans from instructional to highly technical. **Indexed in Scopus**. **Indexed in Ei Compendex**.

Mehdi Rahmani-Andebili Editor

Planning and Operation of Electric Vehicles in Smart Grids

Editor Mehdi Rahmani-Andebili Electrical Engineering Department Arkansas Tech University Russellville, Arkansas, USA

ISSN 1865-3529 ISSN 1865-3537 (electronic) Green Energy and Technology ISBN 978-3-031-35910-1 ISBN 978-3-031-35911-8 (eBook) https://doi.org/10.1007/978-3-031-35911-8 © 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 Paper in this product is recyclable.

Preface

A major portion of energy consumption and greenhouse gas emissions in the world are related to transportation sector that have contributed to global warming and energy crisis. Since last decades, transportation electrification has been suggested as one of the solutions to mitigate the above-mentioned challenges. Electric vehicles (EVs) as the outcome of transportation electrification are being advertised by environmentalists and governments because of their economic and environmental benefits that include the possibility of their charging by renewable energy sources as the clean and free sources of energy. The governments across the world are implementing financial incentives to expedite the transition from internal combustion engine vehicles to electric ones to achieve their own energy security and climate change mitigation goals. Moreover, EVs are becoming more affordable compared to their gasoline counterparts as battery prices decrease. The research organizations have predicted that EV sales will surpass the gasoline and diesel vehicles sales in the near future. Hence, EV drivers, as the new electricity customers, will consume a significant portion of electricity in the future. This implies that the uncontrolled charging and discharging of a large number of EVs is capable of negatively affecting different parts of power systems such as causing overloading in generation systems, congestions in transmission lines and distribution feeders, and price spikes in power markets. Thus, planning and operation of EVs seem to be unavoidable and even necessary and desirable. This book covers the recent research advancements in the planning and operation of EVs in smart grid. Moreover, the book covers the security of V2G networks, the challenges in the electrification of EVs in the developing countries, and the real experiences and recent projects in the planning and operation of EVs around the world. In the first chapter, the approaches for the security enhancement of vehicle-to-grid (V2G) networks are studied and their performance is evaluated for cyberattack detection and mitigation as well as communication error elimination.

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Chapter 2 investigates the challenges in the transportation electrification and the widespread application of EVs in the developing countries from governments, customers, and electric power utilities viewpoints. Chapter 3 performs several sensitivity analyses about optimal charging management of EVs in San Francisco, CA, USA. In this chapter, the effects of parameters such as EV type, EV penetration level, and driver’s social class on the optimal charging management of EVs in San Francisco are studied and analysed. In Chap. 4, in addition to reviewing the role of EVs in the optimal operation of multicarrier energy systems, the problem is modelled and simulated as well as the results are analysed. Chapter 5 covers some of the most important projects and real experiences around the world regarding the applications of EVs. Russellville, Arkansas, USA

Mehdi Rahmani-Andebili

Contents

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Threats, Vulnerabilities, and Mitigation in V2G Networks . . . . . . . . Irum Saba, Moomal Bukhari, Mukhtar Ullah, and Muhammad Tariq

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The Challenges on the Pathway to Electromobility in Developing Countries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yasmin Emily de Souza Oliveira, Denisson Queiroz Oliveira, Osvaldo Ronald Saavedra, and Mehdi Rahmani-Andebili

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Sensitivity Analyses for Optimal Charging Management of Electric Vehicles in San Francisco . . . . . . . . . . . . . . . . . . . . . . . . . Mehdi Rahmani-Andebili Role of EVs in the Optimal Operation of Multicarrier Energy Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alireza Ghadertootoonchi, Mehdi Davoudi, Moein Moeini-Aghtaie, and Mehdi Rahmani-Andebili

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Real Experiences in the Operation of EVs Around the World . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 Hamdi Abdi and Mehdi Rahmani-Andebili

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137

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Chapter 1

Threats, Vulnerabilities, and Mitigation in V2G Networks Irum Saba, Moomal Bukhari, Mukhtar Ullah, and Muhammad Tariq

Abstract Vehicle-to-grid (V2G) networks are becoming more common in smart grid systems. This makes it imperative to implement strong security measures to guard against cyberattacks and communication errors. Researchers have suggested various methods, such as fuzzy logic-based approaches and deep reinforcement learning (DRL), to overcome these security concerns. In this chapter, we compare how well these two strategies enhance the security of V2G networks. The fuzzy logic-based approach uses a rule-based system to make decisions based on input variables, while the proposed DRL-based approach uses the deep deterministic policy gradient (DDPG) algorithm to learn the best V2G network security policies. Both methods were implemented using the software packages MATLAB and Anaconda. We analyse the effectiveness of different approaches using quantitative data and evaluate how well they work in attack detection, mitigation, and communication dependability. According to our research, both methods can successfully increase the security of V2G networks, but each has its own benefits and drawbacks. Overall, this chapter adds to the body of literature by shedding light on the potential of DRL and fuzzy logic for boosting the security of V2G networks in smart grid systems. Our results are helpful for researchers and professionals looking to build efficient security measures for V2G networks. Keywords Vehicle-to-grid · Electric vehicles · Security · Threats · Vulnerabilities · Deep deterministic policy gradient · Fuzzy logic

I. Saba (✉) · M. Bukhari · M. Ullah · M. Tariq Department of Electrical Engineering, National University of Computer and Emerging Sciences, Islamabad, Pakistan e-mail: [email protected]; [email protected]; [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Rahmani-Andebili (ed.), Planning and Operation of Electric Vehicles in Smart Grids, Green Energy and Technology, https://doi.org/10.1007/978-3-031-35911-8_1

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Abbreviations CS DDPG DoS DQN DRL EV IDS V2G

1.1

Charging station Deep deterministic policy gradient Denial of service Deep Q-network Deep reinforcement learning Electric vehicles Intrusion detection systems Vehicle to grid

Introduction

Electric vehicles (EVs) can use vehicle-to-grid (V2G) systems, a smart grid technology, to operate as distributed energy storage systems and recharge from the grid [1]. During heavy demand, EVs with bidirectional chargers can act as a grid energy source in V2G systems. This eliminates the need for fossil fuel-powered peaking plants, which are only used when there is a high electricity demand, by allowing the excess energy stored in EV batteries to help balance the power system [2]. Numerous advantages have resulted from using V2G technology in smart grids, including decreased carbon emissions, increased grid dependability, and enhanced energy management [3]. To ensure the security and reliability of the grid, V2G networks also present new cybersecurity threats and vulnerabilities that must be fixed. Cyberattacks that can interfere with power delivery, steal confidential information, or harm equipment are among the threats facing V2G networks. Weak passwords, outdated software, and a lack of secure communication methods all lead to vulnerabilities. Malicious actors can exploit these flaws to gain unauthorised access to the grid and do serious harm [4]. Figure 1.1 depicts possible assaults on V2G networks and emphasises the significance of putting security measures in place to defend V2G systems from these attacks. For its adoption and implementation to be effective, V2G network security is therefore essential. To do this, efficient security solutions must be developed to detect and counteract possible threats to the V2G network architecture. To increase the security of V2G networks, cutting-edge technologies like deep reinforcement learning (DRL) and fuzzy logic [5] have shown promise. These technologies can be used to quickly identify and address security concerns, decreasing the likelihood of successful attacks. However, ongoing efforts are needed to update the vulnerabilities and ensure the security of V2G networks. Secure communication protocols, frequent software up, and multi-factor authentication are just a few mitigation strategies to address these risks and vulnerabilities. Implementing security best practices and educating stakeholders about the dangers of V2G networks are also crucial. The different dangers and weaknesses of V2G networks in smart grids will be examined in this chapter, along with the solutions

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Fig. 1.1 Potential attacks on V2G network

that can be implemented to ensure their security and dependability. To overcome these issues and guarantee the security of V2G networks, it is crucial that stakeholders, such as manufacturers, government organisations, and utility providers, cooperate. V2G system data can guide public policy decisions and offer insightful information about energy usage. However, it’s crucial to recognise some information as critical and ensure it’s protected if this data is used to generate public information. Personal information such as user names, addresses, contact details, and billing information must be kept private to avoid identity theft and other privacy violations. Sensitive information about a user’s daily routine and energy usage patterns can be discovered from the V2G system’s energy consumption, including the amount of energy used and the time consumed. Data about the capacity, charge level, and remaining life of batteries can be exploited to infer usage patterns and cause users to worry about privacy. Finally, to prevent malicious actors from disrupting the power grid, grid status information must be kept private, including voltage, frequency, and power flow. In general, safeguarding sensitive data is necessary to preserve privacy and security in V2G systems, and access to this data must be limited to authorised individuals only.

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Related Work

Electric utilities, grid operators, renewable energy sources, and electric vehicles (EVs) are just a few of the players and technologies integrated into complex systems known as smart grids. Bidirectional energy flow between the grid and EVs is made possible by V2G networks, allowing for effective energy management and incorporating renewable energy sources. However, V2G networks are also susceptible to several online dangers, including malware, unauthorised access, and denial-ofservice attacks, which can jeopardise the security and dependability of the entire smart grid system. In recent years, numerous approaches and frameworks have been put forward to guarantee the security of V2G networks in smart grids. These solutions often address the dangers and flaws in V2G networks, such as unreliable authentication, data privacy issues, and unsafe communication [4, 5]. Denial of service (DoS), replay, and spoofing assaults are only a few of the cyberattacks the authors of [6] identified as capable of being launched against V2G networks. The authors also suggested a security architecture with intrusion detection, secure communication protocols, and access control features to lessen these dangers. The authors of [7] highlighted several weaknesses in V2G networks, such as shoddy authentication procedures, a lack of secure communication protocols, and out-ofdate software. To address these weaknesses, the authors suggested a security strategy that consists of multi-factor authentication, encryption techniques, and routine software upgrades. The authors created a secure communication protocol for V2G networks [8], including a hybrid encryption algorithm and a message authentication code. The authors tested the suggested protocol using experiments and discovered that it could successfully defend V2G networks from cyberattacks. In [9], the authors presented a security framework for V2G networks that includes access control, secure communication, and threat detection techniques. The suggested approach could successfully reduce cyberthreats to V2G networks, according to a case study the authors conducted to test it [10] outlined the weaknesses of V2G networks, such as unreliable authentication procedures, a lack of encryption, and unsecured communication protocols. To address these vulnerabilities, the authors suggested a security model consisting of access control, encryption, and authentication techniques. The authors proposed a hybrid blockchain-based security framework for V2G networks [11], including secure communication, data encryption, and trust management mechanisms. The authors proposed a V2G security framework with secure communication, access control, and attack detection features [12]. The authors tested the system through simulation and discovered it could successfully defend V2G networks from various threats. [13] outlined the dangers and difficulties related to V2G networks, including issues with interoperability, data security, and privacy. To deal with these dangers and difficulties, the authors presented a security model incorporating data protection, access management, and privacy preservation mechanisms. The authors proposed a V2G security architecture, including intrusion detection, access control, and secure communication protocols [14]. The architecture was also put through

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simulation trials by the authors, who discovered it worked well to secure V2G networks. In [15], the authors suggested a V2G intrusion detection system (IDS) that recognises and categorises cyberattacks using machine learning methods. Using actual V2G data, the authors examined the system and discovered that it could accurately identify and categorise cyberattacks. To safeguard user privacy and stop fraud, the authors of [16] presented a privacy-preserving V2G payment system that uses a blockchain-based payment protocol. To address the security issues with V2G networks, education and awareness are just as important as technical solutions. The authors of [17] emphasised the significance of informing stakeholders about the dangers posed by V2G networks and using security best practices to reduce these dangers. Regulation and policy are essential in guaranteeing the security of V2G networks in addition to technical fixes [18] outlines a regulatory framework for V2G networks that includes network interoperability, cybersecurity, and data protection requirements. According to the authors, a structure like this is required to guarantee the security and dependability of V2G networks. The authors of [19] presented a cooperative security strategy for V2G networks that entails cooperation between stakeholders, such as vehicle owners, utility companies, and governmental organisations. The authors discussed how such cooperation is required to handle the intricate and ever-changing cybersecurity concerns posed by V2G networks. Table 1.1 provides a summary of additional research contributions: These studies have certain shortcomings, such as the incomplete coverage of various aspects of V2G network security, such as user awareness and physical security difficulties, and the narrow scope of some research, which may not consider emerging risks or the scalability of suggested solutions. Additionally, some studies use qualitative or survey-based methodologies, which might not fully evaluate the security of V2G networks. Despite these drawbacks, the studies offer insightful information about the security issues and solutions for V2G networks and emphasise the need for an all-encompassing V2G network security strategy. These studies also shed light on how intricate and dynamic the cybersecurity difficulties posed by V2G networks in smart grids are. We can ensure the security and dependability of V2G networks in a sustainable energy future by putting in place comprehensive security frameworks and solutions, including secure communication, access control, and attack detection systems, and utilising machine learning and blockchain technology.

1.1.2

Types of Threats in V2G Networks

With the potential to increase energy efficiency, lower greenhouse gas emissions, and enable grid services, the implementation of EVs and V2G technologies is growing quickly. However, adding V2G networks to smart grids poses new security risks because of how connected the grid and its components are, making them more susceptible to hacker attacks. The security of V2G networks in smart grids is subject

Survey of security and privacy in V2G networks Survey of the security of V2G systems

Analysis of the security of V2G networks using game theory Review of security solutions for V2G networks

Identification of security and privacy requirements for V2G networks Analysis of the security of V2G networks using a game-theoretic approach Survey of security issues in V2G networks

[22]

[24]

[26]

[30]

[29]

[28]

[27]

[25]

[23]

Identification of security requirements for V2G networks using a Delphi study Identification of security threats and countermeasures for V2G networks using a system dynamics approach

Qualitative analysis of security threats and vulnerabilities in V2G networks Review of security challenges and solutions for V2G networks

[20]

[21]

Methodology

Study

Table 1.1 Summary of literature review Identified key security threats and vulnerabilities in V2G networks and proposed a security framework to mitigate these threats Discussed the existing security solutions and challenges in V2G networks and proposed a security framework based on cryptography and secure communication protocols Analysed the security and privacy challenges in V2G networks and proposed a taxonomy of security and privacy threats Reviewed the existing security solutions and challenges in V2G networks and proposed a taxonomy of security threats and countermeasures Analysed the security and privacy risks in V2G networks and proposed a game-theoretic approach to mitigate these risks Reviewed the existing security solutions in V2G networks and proposed a security architecture based on blockchain and smart contracts Identified security and privacy requirements for V2G networks and proposed a security and privacy model based on these requirements Analysed the security risks and countermeasures in V2G networks using a game-theoretic approach Identified security issues in V2G networks and proposed a security framework based on encryption and secure communication protocols Identified security requirements for V2G networks using a Delphi study and proposed a security model based on these requirements Identified security threats and countermeasures for V2G networks using a system dynamics approach

Contribution

Limited coverage of technical solutions and scalability issues Limited coverage of non-technical solutions and scalability issues

Limited coverage of technical solutions and scalability issues Limited coverage of non-technical solutions and scalability issues Limited coverage of physical security challenges and user awareness

Limited coverage of non-technical solutions and scalability issues Limited coverage of physical security challenges and user awareness

Limited coverage of technical solutions and emerging threats Limited coverage of emerging threats and user awareness

Lack of empirical data and limited coverage of emerging threats Limited coverage of physical security challenges and user awareness

Limitations

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to several risks that could jeopardise the system’s security, privacy, and dependability. Unauthorised access, data tampering, denial-of-service attacks, and malware infections are some dangers. For the V2G networks to be secure and stable and increase consumer confidence in the technology, several security issues must be resolved. Some of the key threats include:

1.1.2.1

Unauthorised Access

When an attacker accesses the V2G networks without authorisation, it is known as unauthorised access. This may occur if attackers get past the authentication process, obtain login information, or exploit communication protocol flaws. Once a hacker has access to the networks, they may be able to steal confidential information, tamper with the data being sent, or carry out other attacks on the system. For instance, in 2017, a group of security researchers from the University of California, San Diego, showed how they could use a Wi-Fi connection to break into a Tesla EV and take control of the numerous systems within, including the V2G networks [31]. The doors could be opened remotely, the air conditioning could be turned on, and the speedometer and other displays could be adjusted. They could take advantage of flaws in the Tesla vehicle’s software and connection methods.

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Data Manipulation

Data manipulation happens when an attacker changes or removes data sent across the V2G network. This may occur if an attacker obtains entry to the networks either by unauthorised access or by taking advantage of flaws in the communication protocols. Once the attacker gains access, they can change the transmitted data— like the rate at which EVs charge or completely delete crucial information. For instance, security researchers from the University of Michigan showed in 2017 that they could remotely hack into a Chevy Volt EV and alter the data sent by the V2G network [32]. The researchers altered the vehicle’s charging rate, possibly resulting in the battery overheating and catching fire.

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DoS Attacks

DoS attacks happen when an attacker overwhelms the V2G networks with a lot of traffic, making them unable to function correctly. This may make it difficult for authorised users to access the networks and use their services. For instance, in 2016, a group of security researchers from the University of California, Berkeley, showed how they could conduct a DoS attack on the V2G networks of a Nissan Leaf EV [33]. To achieve this, the researchers bombarded the vehicle’s V2G network with requests, making it overloaded and unresponsive.

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Malware Infections

When an attacker can introduce harmful code into the V2G network through a compromised device, a flaw in the software, or a weakness in the communication protocols, malware infections occur. Once the malware infects the network, it can steal sensitive data, manipulate the data being transmitted, or launch other attacks on the system. For example, in 2015, a team of security researchers from the University of California, San Diego, demonstrated [34] that they could remotely infect a Tesla EV with malware through a Wi-Fi connection. The researchers could do this by exploiting a vulnerability in the browser used by the Tesla vehicle. Researchers and industry practitioners have developed various security solutions to mitigate these threats, such as secure authentication and authorisation mechanisms, encryption and digital signature techniques, intrusion detection and prevention systems, and secure communication protocols. However, the effectiveness of these solutions can vary depending on the specific threat and the implementation of the system, and ongoing research is necessary to keep up with evolving threats and technologies.

1.1.3

Types of Vulnerabilities in V2G Networks

Integrating V2G networks into smart grids introduces new vulnerabilities that can expose the system to security threats. V2G networks are designed to facilitate bidirectional power flow between EVs and the grid, which requires the exchange of data and commands between the EVs and the grid infrastructure. However, this communication between the EVs and the grid creates new vulnerabilities that can compromise the security and privacy of the system. Attackers can exploit vulnerabilities in V2G networks to launch a wide range of cyberattacks, such as eavesdropping, tampering, and DoS attacks. EV users’ privacy may be compromised, the power supply may be disrupted, the EV battery may be harmed, and there may be financial losses due to these assaults. To ensure the security and stability of the smart grids, it is crucial to recognise and address the vulnerabilities in V2G networks [35]. In V2G networks, vulnerabilities can be caused by several things, including communication protocols, software and firmware, physical components, and human factors. For instance, communication protocol flaws could allow attackers to replay, change, or intercept messages sent between the EV and the grid. Attackers may be able to access a system without authorisation by taking advantage of coding faults, backdoors, or unsafe setups, thanks to flaws in software and firmware. Weak physical security measures that enable attackers to tamper with or steal EVs and charging stations can lead to physical vulnerabilities. Additionally, human factors like user ignorance and a lack of training can make V2G networks vulnerable [36]. In this section, we review a few security flaws with V2G networks in smart grids and give examples.

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Communication Protocol Vulnerabilities

V2G networks use communication protocols to transfer data between EVs and the grid. These protocols are susceptible to intrusions such as message replay, tampering, and eavesdropping. A hacker may intercept signals sent between the EV and the grid to obtain sensitive data, such as the battery’s state of charge, user-identifying information, and payment information [37].

1.1.3.2

Software and Firmware Vulnerabilities

V2G networks control EV and grid communication through software and firmware. Attackers may use these components to introduce malware, execute code remotely, or obtain unauthorised access to the system. For instance, a hacker may utilise a software flaw in the charging station to access the data of the EV and change it to harm the battery [38].

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Physical Vulnerabilities

Physical assaults like tampering with charging stations or EV theft can also affect V2G networks. An intruder could interfere with the charging station to steal confidential information or put malicious code in place that would impact the grid or the battery of the EV. Additionally, stealing an EV could give thieves access to the vehicle’s onboard computer or let them use it to launch a cyberattack [39].

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Insider Threats

Insider attacks, where a trusted employee or partner abuses access credentials to steal sensitive data, introduce malware, or harm the system, are another danger to V2G networks. For instance, a charging station operator’s employee may alter the billing system, steal user data, or introduce malware [40]. To address these vulnerabilities, researchers and industry professionals have created a variety of security measures, including user education and awareness campaigns, firmware and software updates, physical security measures, and secure communication protocols. Various approaches address the vulnerabilities; therefore, continual research and development are required to keep up with emerging threats and technology.

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Proposed Techniques

Due to the possible impact of cyberattacks on the energy supply system, the security of V2G networks in smart grids is of utmost importance. The literature has suggested several methods to deal with this problem, such as machine learning-based algorithms, encryption methods, and intrusion detection systems (IDS). A DRL algorithm is suggested for protecting V2G networks in smart grids. This strategy uses DRL’s capacity for experience-based learning and environmental adaptation to offer an efficient defence against cyberattacks. The deep deterministic policy gradient (DDPG) algorithm, a cutting-edge DRL method that has demonstrated promising results in a variety of applications, is used in the suggested DRL-based technique. An agent that can take action to safeguard V2G networks from cyberattacks is trained using the DDPG algorithm. Creating a reward function that assesses the agent’s performance in the security context of the V2G network is necessary to train the DDPG agent. The goal of the reward function is to encourage the agent to strengthen network security while punishing inactions that do so. Since DRL models can learn and adapt to new and changing environments without the need for explicit rules or programming, they are chosen to increase the security of V2G networks. DRL is appropriate for V2G networks since it can manage complicated, large-scale issues and handle many devices and data. The DRL models’ ability to learn from experience and continuously improve makes them ideal for dynamic and developing systems like V2G networks. This is another justification for using DRL. DRL models may also make predictions and decisions with greater accuracy than conventional rule-based or heuristic approaches. In addition to the DRL-based defence, a fuzzy logic-based IDS is the second method that has been suggested. The IDS is designed to identify unusual V2G network activity that could be a symptom of a cyberattack. The IDS can manage the ambiguity involved in detecting and classifying cyberattacks, thanks to its fuzzy logic-based methodology. The ability of fuzzy logic to accept erroneous or uncertain data is one benefit of employing it for V2G network security. For instance, the fuzzy inference system can produce a meaningful output based on the available data, even if a sensor reading is noisy or ambiguous. Additionally, as new information becomes available, the system’s rules can be quickly updated, enabling it to adjust to changing circumstances. The DRL-based defence and the fuzzy logic-based IDS effectively and adaptively defend cyberattacks on V2G networks in smart grids. The network can be actively protected against future cyberattacks using the DRL-based defence. In addition, the IDS can act as a second line of defence to identify and stop threats that get past the DRL-based defence. Overall, the suggested method for protecting V2G networks in smart grids provides a viable response to the security issues posed by these networks. This method can provide an adaptive and strong defence against cyberattacks and guarantee the dependable and secure operation of V2G networks in smart grids by utilising the strengths of DRL and fuzzy logic.

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Fuzzy Logic Technique for V2G Security

One simple approach for dealing with ambiguity and uncertainty is fuzzy logic. Electric grid load profiles are dynamic and ever-evolving. Fuzzy logic is a good option because the quantity of EVs and other charging station (CS) parameters, including energy use, charging, and discharging rates, are constantly changing simultaneously. Based on a specified set of rules, fuzzy logic operates. Additionally, fuzzy logic-based controllers provide flexibility in operation and are simple to modify to meet requirements [41, 42]. A potential tool for simulating complicated systems and decision-making procedures is fuzzy logic. Fuzzy systems can assess the security level in the context of V2G network security depending on pertinent parameters. Input variables, fuzzy sets, rule bases, inference engines, defuzzification, and output variables are the basic components of a fuzzy system. The variables used as inputs indicate the variables that impact V2G network security, including network activity, security incidents, security regulations, and environmental factors. Fuzzy sets, which specify linguistic terms and membership functions for each input variable, are linked to these variables. The network traffic variable, for instance, might comprise fuzzy sets like “Low”, “Medium”, and “High”, along with membership functions that specify how much each group of devices adheres to each of these sets [43]. The rule base consists of domain knowledge and experience-based rules that link input and output variables. These rules are formulated using fuzzy logic operators such as “AND”, “OR”, and “NOT”. For example, “If the network traffic is high and the security policy is weak, THEN the security score is low”. The inference engine applies the rules to the input variables and determines the output variable (security score). The output variable is a crisp value obtained through defuzzification, which converts the fuzzy output values into a crisp value. There are various defuzzification techniques, such as the centroid approach, which calculates the centre of gravity of the fuzzy output values, and the max membership method, which selects the fuzzy output value with the highest membership degree [44]. A fuzzy system for V2G network security has the following inputs: (i) Network traffic: The fuzzy system can be given information on network traffic, including data volume, transmission frequency, and types of protocols being utilised. (ii) Security events: The system can be provided with inputs about security events, such as the quantity, nature, and success rate of attempted assaults. (iii) Security policies: When assessing the security of the V2G network, information about the security policies in place, such as the potency of the access control mechanisms, the potency of the encryption, and the kinds of authentication techniques being employed, might be helpful. (iv) Environmental considerations: In addition to accounting for the network’s physical location, the types of vehicles being used, and the charging stations being utilised, the fuzzy system can also take into account environmental considerations that may have an impact on the security of the V2G network.

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Designing a Fuzzy System for V2G Network Security

Fuzzy logic is a mathematical approach to approximate reasoning and decisionmaking based on degrees of truth rather than the usual true or false binary logic. Fuzzy logic can handle incomplete, ambiguous, and uncertain information, which is common in real-world situations. One of its key features is fuzzy logic’s ability to make decisions and predictions without using a precise mathematical model. Instead, it uses the idea of fuzzy sets, making it possible to express ambiguous and imperfect information. Because of this, fuzzy systems for V2G provide a mechanism to express the complex and hazy factors that affect the security of V2G networks and to make rational decisions depending on how much of each factor the system has or doesn’t have. Fuzzy logic is a mathematical technique that helps improve the security of V2G networks. Fuzzy logic is a style of reasoning that can handle ambiguity and uncertainty, making it ideal for security systems that need to make decisions in the present. Real-time decision-making is made possible, and this is essential for security systems that react swiftly to potential security risks. To assess the potential dangers of a security issue, fuzzy logic can be used to analyse and interpret data from many sources, such as network logs and sensors. Even if the information is lacking or ambiguous, it may identify potential security issues in real time. Because of this, it is a good fit for security systems that need strong threatdetection capabilities. Fuzzy logic can also improve the response to potential security threats. By using fuzzy logic to analyse data from multiple sources, organisations can make more informed decisions about responding to potential security threats and minimising the impact of security breaches. It can also be used to make adaptive threat responses. This means that the security system can adjust its response in real time based on the changing nature of the security threat. The steps to design a fuzzy system for V2G network security are as follows: (i) Define the inputs: The inputs to the fuzzy system can comprise network traffic, security events, security policies implemented, and environmental factors. (ii) Define the output: A security score that reflects the overall security of the V2G networks may be the output of the fuzzy system. (iii) Describe fuzzy sets: The phrases employed in the fuzzy system’s rules are defined by fuzzy sets, which are linguistic variables. For instance, phrases like “low”, “medium”, and “high” could be included in the fuzzy set for network traffic. (iv) Define the rules: The rule base should have good rules that exhaust all possible sets of fuzzy inputs concerning their linguistic values. A rule might state, for instance, “If network traffic is high and security events are high, the security score is low”. (v) Describe the membership functions: Input values are mapped to the corresponding fuzzy sets using membership functions. A membership function, for instance, may translate input values between 0 and 25 for the fuzzy set “low”. In this step, membership functions are defined for each input and each linguistic value of an input.

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Fig. 1.2 Fuzzy system for V2G network security

(vi) Implementing the fuzzy system: A software programme that supports fuzzy logic, like MATLAB or Python, can be used to implement the fuzzy system. (vii) Test and evaluate the fuzzy system: To ensure the fuzzy system yields correct and helpful results, it should be tested and assessed using real-world data. Figure 1.2 shows the block diagram of fuzzy system designed for V2G network security. The specific inputs and fuzzy sets specified for each variable input influence the choice of a membership function type. Based on the above-mentioned inputs, the following are some popular forms of membership functions that might be practical for a fuzzy system of V2G network security: (i) Triangular membership function: Using the inputs “network traffic”, “security events”, “security policies”, “environmental factors”, and “security score”, one may apply the triangular membership function. For instance, the triangle membership function with parameters (0, 0, 25) might be used to construct the “network traffic” fuzzy set “low”, where 0 denotes the least input value, 25 denotes the maximum input value, and 0 denotes the peak. This shows that input values between 0 and 25 correspond to little network traffic. (ii) Trapezoidal membership function: This function can be employed for constant input for a range of values. For instance, the input variable “network traffic” has a range of values 50–100 that indicate “medium traffic”. The trapezoidal membership function with parameters (0, 50, 100, 100) might be used in this case to determine the “network traffic” fuzzy set’s “medium” value, where 50 denotes the fuzzy set’s peak and 0 and 100, the lower and upper bounds, respectively. (iii) Gaussian membership function: This function may be utilised when an input variable has a Gaussian distribution around, i.e. input values are evenly spaced around a central point. For instance, if the input “network traffic” has a normal

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distribution with a mean value of 50 and a standard deviation of 10, the fuzzy set “middle” may be represented by a Gaussian membership function with parameters (50, 10). (iv) Singleton: When the input value relates to a particular fuzzy set, and there is no uncertainty, i.e. if the membership degree of the linguistic value is one and the membership degrees for the other members are zero, the singleton membership function may be utilised. For instance, if the “security policies” input is set to “strong”, the fuzzy set “strong” might utilise a singleton membership function with a parameter of 1. Fuzzy set parameters can be assigned for a real-time V2G application. This can be substantiated through simulation studies and experimental testing to evaluate the effectiveness of the assigned fuzzy set parameters in controlling the V2G system’s inputs and outputs. By comparing the system’s performance with and without the assigned fuzzy set parameters, we can determine their impact on the V2G system’s overall performance and make necessary adjustments accordingly.

1.2.1.2

Example of a Simple Fuzzy System for V2G Network Security

The inputs to the fuzzy system for V2G network security are: (i) (ii) (iii) (iv)

Network traffic Security events Security policies Environmental factors

For each input Gaussian membership functions and for output triangular membership functions are used. The output of the fuzzy system can be security score of the V2G networks having three linguistic values: low, medium, and high. A fuzzy output of “high” indicates high V2G network security. Figures 1.3 and 1.4 show the membership functions of input and output and Figs. 1.5–1.8 show the surface representation of these membership functions. Table 1.2 lists down the membership functions, linguistic values, and fuzzy set parameters for each input and output. The fuzzy set parameters of each linguistic value, low, medium, and high, are based on the system parameters of the V2G system. These parameters are determined through analysis and experimentation to optimise the performance of the V2G system. The rule base for this fuzzy system has 54 rules consisting of all possible combinations of linguistic values for the set of inputs. Some of the rules are listed below: (i) If network traffic is “Low” AND security events are “Low” AND security policies are “Strong” AND environmental factors are “Favourable”, THEN the security score is “High”.

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Fig. 1.3 Membership functions of input

(ii) If network traffic is “Medium” AND security events are “Medium” AND security policies are “Medium” AND environmental factors are “Favourable”, THEN the security score is “Medium”. (iii) If network traffic is “High” AND security events are “High” AND security policies are “Weak” AND environmental factors are “Unfavourable”, THEN the security score is “Low”. Once the fuzzy system is implemented, it can be used to evaluate the security of the V2G networks based on the inputs provided. The output can be used to make decisions about improving the security of the V2G networks by adjusting the security policies, network topology, or other factors.

1.2.1.3

Preventive Measures

Preventive measures can be taken based on the output of the fuzzy system. If a V2G network has a low-security score, it suggests that the V2G network has vulnerabilities and that an attacker can attempt potential assaults. Some precautions that could be performed to raise the security rating and shield against potential assaults are:

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Fig. 1.4 Membership function of output

(i) Threat and security assessment: A threat assessment is a process for assessing and validating anticipated hazards, including figuring out how likely they are to occur. Security risk management should conduct a threat assessment before creating plans to lessen dangers to the organisation. Key security measures in applications are identified, assessed, and implemented during security risk assessment. Avoiding application security issues and vulnerabilities are also emphasised. The administrator should evaluate the network holistically from an attacker’s perspective by performing a risk assessment. This will help the management make informed decisions about using resources and technologies and installing security controls. As a result, finishing an assessment is an essential step in the risk management process. A thorough security assessment of the V2G networks should be carried out to identify the particular flaws and vulnerabilities influencing the security score. It will be simpler to develop a targeted mitigation plan as a result. (ii) Implement security controls: Management, operational, and technical measures intended to prevent, postpone, identify, block, or lessen vicious assaults and other threats to information systems are referred to as security controls. The discovered vulnerabilities and weaknesses in security risk assessment should be addressed by implementing the necessary security measures such as access

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Fig. 1.5 Effect of environmental factors and network traffic on security score

Fig. 1.6 Effect of environmental factors and security events on security score

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Fig. 1.7 Effect of environmental factors and security policies on security score

controls, security monitoring, and encryption. Access control can be implemented by creating separate accounts for every user, providing user accounts with the fewest rights necessary to carry out necessary tasks, only allowing administrator accounts to be used for administrative tasks, and considering putting a central authorisation system in place. Real-time or nearly real-time monitoring of events and actions on all of your organisation’s critical systems is what security monitoring entails. Security monitoring techniques include IDS and intrusion prevention systems (IPS). (iii) Educating and training staff: The personnel with access to the V2G networks must be aware of the associated risks. In regular sessions, they should be educated on the network, its risks, how to spot a problem, and potential next steps. As the type of assaults and potential hazards change constantly, this becomes more crucial. They should be trained to use security measures effectively to avoid possible risks. This could be done by providing training on safeguarding passwords, avoiding phishing scams, etc. (iv) Software and firmware updates: Updates to software correct security holes in earlier versions. It is crucial to update software to safeguard data and defend the network from threats. Updates to the equipment’s firmware and software also enhance its performance. It is crucial to regularly update software and firmware on all devices connected to the V2G networks to guarantee that known vulnerabilities are fixed and new security features are added.

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Fig. 1.8 Episodes vs average reward for different weights

Table 1.2 Input and output parameters

Inputs

Output

Network traffic

Membership functions Gaussian

Security events

Gaussian

Security policies

Gaussian

Environmental factors

Gaussian

Security score

Triangular

Linguistic values Low Medium High Low Medium High Weak Medium Strong Unfavourable Favourable Low Medium High

Fuzzy set parameters [11.94 2.674] [17.88 50.37] [1.911 97.75] [11.05 2.557] [19.15 50.35] [2.091 97.54] [9.924 2.409] [18.72 50.02] [2.1 97.53] [6.772 1.997] [2.217 97.39] [0 0 25] [0 50 100] [75 100 100]

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(v) Routine security testing: Security testing is a procedure that assesses a system’s security and identifies any potential weaknesses or security threats. Routine security testing should be performed to find any newly discovered vulnerabilities or flaws. This is crucial to ensure that no new fault or vulnerability is overlooked. It may be harmful to the network’s security if ignored. Penetration testing, vulnerability scanning, and other kinds of testing may fall into this category. (vi) Regular vulnerability assessment: It is important to conduct regular vulnerability assessments to identify potential weaknesses in the V2G network security system. This can help in taking proactive measures to mitigate the vulnerabilities and improve the network’s overall security.

1.2.2

DRL-Based Technique for V2G Security

DRL stands for “deep reinforcement learning”, a subfield of machine learning that involves training agents to perform tasks in an environment by interacting with it and receiving rewards or penalties based on their actions. DRL aims to develop agents that can learn to make optimal decisions through trial and error without explicit instructions or supervision. DRL combines ideas from reinforcement learning and deep learning. Reinforcement learning is a subfield of machine learning that focuses on how agents can learn from their environment through trial and error. In contrast, deep learning is a subfield of machine learning that employs deep neural networks to learn complex data representations. In DRL, the agent observes the environment, acts, and then gets feedback as a reward or punishment based on the action taken. The agent aims to figure out a policy a mapping between states and actions—that maximises its cumulative reward over time. Several algorithms can do this, including Q-learning, Deep Q-networks (DQNs), and actor-critic techniques [45–47]. To improve the security of V2G networks, threat and vulnerability models that can identify and stop assaults have been developed. In [40], the authors presented a DRL-based IDS for V2G networks that can recognise deviations from the network’s typical behaviour and identify known and unidentified assaults. Deep Q-network (DQN), which was trained on network traffic data to learn to detect attacks, served as the DRL algorithm in this investigation. It was discovered that the suggested DRL-based IDS had a greater detection rate and a lower false positive rate than conventional IDS techniques [47]. The authors of [46] proposed a DRL-based IPS for V2G networks that can immediately identify and stop threats. Proximal policy optimisation (PPO), the DRL algorithm employed in this study, was trained on network traffic data to learn to recognise harmful traffic and take preventative action. On a V2G network testbed, the proposed DRL-based IPS was assessed. They discovered they could successfully identify and stop various assaults, including DoS attacks and packet flooding attacks. The authors also pointed out that the IPS’s ability to learn from new attack patterns and adapt to changing network conditions thanks to DRL made it a more effective and reliable V2G network security solution.

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To identify and prevent cyberattacks on EV charging stations on V2G networks, the authors of [47] developed a DRL-based method. DDPG, a DRL algorithm trained on network traffic data to learn how to recognise and react to attacks, was employed in this investigation. It was discovered that the suggested DRL-based strategy could successfully identify and stop various assaults, including port scanning and DoS attacks. The authors also noted how DRL is a more effective and reliable V2G network security solution due to its ability to react to changing attack patterns and network conditions. The authors of [48] emphasised the significance of cybersecurity in the context of electric vehicles and renewable energy sources, which are increasingly common in the smart grid. The authors examined the impact of cyberattacks on electric car operations on the operation of the smart grid-transportation nexus using a cyberphysical modelling technique that also considered incorporating renewable energy sources. This section describes the proposed security method that is based on a DDPG. The security of V2G networks in smart grids can be improved using the DRL algorithm known as DDPG. An actor-critic algorithm uses a deterministic policy to choose actions, and gradients are used to update the policy and value functions. The DDPG algorithm can learn policies in high-dimensional, continuous action spaces and is a model-free, off-policy reinforcement learning technique. By facilitating the safe and efficient charging and discharging of EVs, it has been suggested to strengthen the security of V2G networks in smart grids. The DDPG algorithm has two components: an actor and a critic. The actor is a deep neural network that maps the system’s state to a continuous action. Another deep neural network that assesses the calibre of the actor’s action is the critic. The algorithm is based on the deterministic policy gradient theorem, which states that the gradient of the expected return for the policy parameters can be computed using the Q-function gradient. The DDPG algorithm for V2G network security is: Algorithm 1 DDPG Algorithm for V2G Network Security 1. Initialise the replay buffer B, actor network μ with weights θμ, and target actor network μ′ with weights θμ. 2. Initialise critic network Q with weights θQ and target critic network Q′ with weights θQ. 3. Set the replay buffer capacity to N. 4. Initialise a random process for action exploration and time step t = 0. 5. Repeat. 6. Receive initial observation state st. 7. Initialise exploration noise εt from random process. 8. Select action at = μ(st|θμ) + εt according to the current policy and exploration noise. 9. Execute action at and observe reward rt and new state st + 1 10. Store transition st, at, rt, st + 1 in replay buffer B.

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11. Compute the target value yi for each mini-batch transition si, ai, ri, si + 1 as: yi = r i þ γ Q0 siþ1 , μ0 siþ1 jθμ0 jθQ ′ 12. Update critic by minimising the mean-squared error loss: L = ðr þ γ  Q0 ðs0 , a0 Þ - Qðs, aÞÞ

2

13. Update actor using the sampled policy gradient: ∇a Qðs, aÞ j a = μðsÞ 14. end The actor network takes the current state of the V2G networks as input and outputs a deterministic action. The critic network inputs the current state and action and outputs a Q-value, which estimates the expected total reward starting from that state-action pair. The target networks stabilise the learning process by providing target Q-values and actions less sensitive to the current policy. The DDPG algorithm uses the Bellman equation to update the critic and actor-network parameters. The Bellman equation states that the Q-value of a state-action pair is equal to the immediate reward plus the discounted expected future reward: Qðs, aÞ = r þ γ Qðs0 , a0 Þ

ð1:1Þ

where s is the current state of the V2G network, a is the action taken by the agent, r is the immediate reward received after taking the action a, s′ is the resulting state of the V2G networks after the action is taken, a′ is the action selected by the actor network for the next state ‘s”, and γ is the discount factor that determines the importance of future rewards. The critic network’s loss function is defined as the mean squared error between the Q-value predicted by the critic network and the target Q-value, which is calculated as: L = ðr þ γ  Q0 ðs0 , a0 Þ - Qðs, aÞÞ

2

ð1:2Þ

where Q′(s′, a′) is the target Q-value, which is estimated using the target networks and Q(s, a) is the Q-value predicted by the critic network. The actor network’s parameters are updated using the gradient of the critic network with respect to the action: ∇a Qðs, aÞ j a = μðsÞ

ð1:3Þ

where μ(s) is the deterministic action selected by the actor network for the current state ‘s’.

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The overall objective of the DDPG algorithm is to maximise the expected total reward. The expected total reward is given by the sum of the immediate rewards and the discounted future rewards, i.e. the Q-value: J = E½Qðs, aÞ

ð1:4Þ

To encourage the agent to take actions that improve security and prevent threats and vulnerabilities, a reward function is designed that assigns positive and negative rewards to certain actions. The Bellman equation incorporates the reward function into the immediate reward term r. The specific form of the reward function depends on the specific requirements and priorities of the V2G network, as well as the threats and vulnerabilities that need to be addressed. A key component of the DDPG algorithm that establishes the agent’s goal and motivates it to execute the appropriate behaviours to achieve it is the reward function. The incentive function can be created for security in V2G networks to motivate the agent to take precautions against attacks and vulnerabilities. A possible reward function for DDPG in the context of security of V2G networks could be: Rðs, a, s0 Þ = w1  r 1 ðs, a, s0 Þ þ w2  r 2 ðs, a, s0 Þ þ w3  r 3 ðs, a, s0 Þ

ð1:5Þ

where s is the current state of the V2G network, a is the action taken by the agent, s′ is the resulting state of the V2G networks after the action is taken, r1(s, a, s′) is a reward for maintaining the stability of the V2G network, (s, a, s′) is a reward for preventing security threats and vulnerabilities in the V2G network, r3(s, a, s′) is a reward for improving the efficiency of the V2G network, and w1, w2, and w3 are weighting factors that determine the relative importance of each reward. The r1(s, a, s′) term can be calculated as the negative of the deviation of voltage and frequency from the nominal value, which must be maintained within a certain range to assure network stability. The r2(s, a, s′) term can be computed as the negative of the severity of the security threats and vulnerabilities in the V2G network, with a higher penalty for more severe threats and vulnerabilities. The r3(s, a, s′) term can be computed as the improvement in the overall efficiency of the V2G networks after taking the action a. This can be measured as the reduction in energy loss or the increase in the utilisation of the network. The weights w1, w2, and w3 can be adjusted based on the specific requirements and priorities of the V2G networks. The DDPG algorithm is adapted to V2G network security by using the state of the network, like traffic flow and packet headers, as inputs to the actor and critic networks and defining actions that correspond to security measures, like blocking certain types of traffic or rerouting traffic to avoid crowded nodes. The reward is defined based on the effectiveness of the security measure in preventing attacks or reducing network congestion. The use of DDPG to enhance the security of V2G networks in smart grids is important as V2G networks are complex, heterogeneous systems with many interacting components and diverse security requirements. Traditional security

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methods may need help to handle the complexity and variability of V2G networks, making it difficult to ensure the security and privacy of the network. DDPG, as a reinforcement learning algorithm, can learn optimal policies for making charging and discharging decisions in complex and dynamic environments, making it a powerful tool for enhancing the security of V2G networks. In V2G networks, DDPG can also offer granular control over the decision-making process. The DDPG agent can react to shifting network conditions and potential security threats by learning from experience and considering various action options, enhancing the overall security and resilience of the V2G network. This level of control and adaptability is essential for controlling the security of V2G networks in real-world circumstances, where security threats and network conditions might change quickly. The effectiveness of V2G networks can be improved by using DDPG to reduce the impact of security concerns. The DDPG agent can assist in ensuring that V2G networks function properly and effectively, even in the presence of security risks, by learning the best charging and discharging techniques that balance security and performance objectives.

1.2.2.1

Results

The x-axis represents the number of training episodes, whereas the y-axis represents the DDPG algorithm’s average reward. The graph demonstrates how the reward rises over time as the algorithm becomes more adept at making judgment calls to enhance the security of V2G networks in smart grids. One hundred episodes were used to train the DDPG algorithm. Figure 1.8 illustrates how the reward increases steadily as the algorithm gains more knowledge about its surroundings and how to make wiser decisions. The reward levels off around episode 60, showing that the algorithm has attained a largely constant level of performance. Nonetheless, some degree of reward variability still indicates space for further development. When compared to small weights, large weights offer a higher reward. The presence of significant weights should indicate that the performance is satisfactory. The performance of three distinct security techniques is shown in Fig. 1.9 in terms of F1 score, recall, and precision. The success of a binary classification system, which is used to decide whether an event (such as a cyberattack) has occurred or not, is frequently assessed using these indicators. The F1 score, which accounts for precision and recall, shows the classification system’s total accuracy. Recall measures the proportion of true positive predictions from all actual assaults in the system. In contrast, precision measures the proportion of true positive predictions out of all genuine positive predictions (i.e. the number of actual attacks accurately classified as attacks). As can be seen, the DDPG technique has the highest F1 score (1.01), demonstrating the highest overall accuracy in identifying assaults in the V2G network. The fuzzy model performs quite well, with an F1 score of 0.99. Both methods are capable of identifying all actual attacks in the system and have perfect recall (1); however, DDPG has higher precision (0.995) than the fuzzy model (0.985). The DQN

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V2G security models performance evaluation 1.02

Performance

1 0.98 0.96 0.94 0.92 0.9 0.88 0.86 F1 Score Fuzzy model

Recall DDPG

Precision DQN

Fig. 1.9 V2G security models performance evaluation

technique, in contrast, has a lower F1 score (0.977) than the other two alternatives. Compared to the fuzzy model and DDPG, it has a lower recall (0.95) and a poorer precision (0.92) score. Overall, these findings imply that fuzzy logic-based and DDPG approaches are effective at identifying and thwarting cyberattacks in V2G networks, with DDPG slightly outperforming fuzzy logic-based approaches in precision. However, the DQN strategy is less successful than the other two strategies. The vehicle would communicate with the charging station using the wired CAN protocol. This work also considers the CAN protocol for vehicle-to-charging station communication for threat detection. Nonetheless, this would depend on the specific characteristics and goals of the threat detection system that is being designed. Threat detection systems frequently monitor communication protocols like CAN to find potential security flaws or criminal activity. As a result, it’s feasible that this study also monitors the CAN protocol for danger detection when it comes to communication between the car and the charging station.

1.2.3

Comparative Analysis of DRL-Based and Fuzzy-Based V2G Network Security System

Several parameters, such as accuracy, efficiency, complexity, and effectiveness in addressing threats and vulnerabilities, can be used to compare DRL-based and fuzzy-based V2G network security systems. DRL algorithms are used in real-time threat detection and mitigation by DRL-based security systems. DRL-based methods are often effective for issues involving complicated decision-making in the face of ambiguity, which makes them a suitable fit for V2G network security. DRL

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algorithms have been demonstrated to be efficient in spotting and thwarting network attacks, and they can adapt to altering network conditions over time. However, they need a lot of training data, which can be difficult in particular circumstances. On the other hand, challenges involving inaccurate or ambiguous data are ideally suited to fuzzy logic-based techniques. To reduce false positives and false negatives, fuzzy logic algorithms can help increase detection accuracy and categorisation of assaults on V2G networks. Additionally, fuzzy logic algorithms are used in fuzzybased security systems to develop rules and decision-making procedures for locating and addressing risks. These systems are generally easier to use and more effective than DRL-based systems, but they might not be as accurate or flexible to shifting assault patterns. Fuzzy logic-based methods can be more difficult to create and optimise, and they could require a lot of domain knowledge to be used successfully. Overall, there are benefits and drawbacks to both fuzzy-based and DRL-based V2G network security systems. The exact demands and requirements of the smart grid network and the available resources determine which technology should be implemented. A real-time V2G application can use fuzzy logic systems and deep reinforcement learning. Intelligent control techniques for V2G systems can be developed using deep reinforcement learning, where the system learns to make decisions based on its interactions with the environment. Real-world data can be used to train the V2G system to improve performance and enable real-time adaptation to changing environmental circumstances. Fuzzy logic systems can manage erroneous and uncertain inputs and outputs, making them suitable for real-time control of V2G systems. To build effective control methods and improve the system’s performance, fuzzy logic systems can be utilised to describe the intricate interactions between a V2G system’s inputs and outputs. Overall, real-time V2G applications have effectively used both fuzzy logic and deep reinforcement learning systems to boost the effectiveness and performance of the systems. The strength and weakness of both algorithms for V2G network security is given in Table 1.3.

Table 1.3 DRL- vs fuzzy-based V2G network security system DRL

Fuzzy logic

Strength (i) Can learn from experience and adapt to changes (ii) Can handle high-dimensional state and action spaces (iii) Can find optimal solutions for complex problems (i) Can handle imprecise and uncertain data (ii) Can integrate human knowledge and experience through rule-based systems (iii) Easily interpretable outputs

Weakness (i) Require a large amount of data to train (ii) Training time can be long and computationally expensive (iii) Black box model, difficult to interpret decisions (i) Limited ability to learn from experience and adapt to changes (ii) May not handle high-dimensional state and action spaces well (iii) Not suitable for finding optimal solutions for complex problems

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Conclusions

This chapter emphasises the significance of addressing the security of V2G networks in smart grids to guarantee the reliability and safety of the power grid. It has been highlighted how V2G networks are susceptible to risks and vulnerabilities such as cyberattacks, communication breakdowns, and nefarious insiders. To strengthen the security of V2G networks and reduce the likelihood of security breaches, we have also proposed several solutions, including encryption and authentication protocols, intrusion detection systems, and machine learning-based approaches. Each strategy must be evaluated in the context of V2G networks in smart grids because each solution has benefits and drawbacks. Given the constant emergence of new threats and vulnerabilities, it is crucial to maintain and strengthen security measures. Our proposed methods for enhancing the security of V2G networks based on fuzzy logic and DRL offer promising solutions. While DRL offers a powerful way to optimise the control of EV charging and discharging processes while ensuring network security, fuzzy logic enables a more thorough and accurate assessment of the network’s security state. The DDPG algorithm we proposed has demonstrated its capability in identifying and mitigating potential security threats in real time, optimising charging and discharging schedules, and minimising energy costs while reducing the impact on the grid. The combination of fuzzy logic and DRL has shown the promise of enhancing the security of V2G networks. Our study contributes to the literature by highlighting the importance of addressing V2G network security in smart grids and proposing effective solutions based on fuzzy logic and DRL. However, there are limitations to our study, and further research is needed to evaluate the proposed solutions in larger and more complex V2G networks. Additionally, as new vulnerabilities and threats continue to emerge, the continuous upgrading of security measures in V2G networks will be necessary to ensure the safety and reliability of the power grid.

References 1. Rahmani-Andebili, M., Bonamente, M., & Miller, J. A. (2020). Charging management of plugin electric vehicles in San Francisco applying Monte Carlo Markov chain and stochastic model predictive control and considering renewables and drag force. IET Generation, Transmission & Distribution, 14(25), 6179–6188. 2. Rahmani-Andebili, M., & Fotuhi-Firuzabad, M. (2018). An adaptive approach for PEVs charging management and reconfiguration of electrical distribution system penetrated by renewables. IEEE Transactions on Industrial Informatics, 14(5), 2001–2010. 3. Rahmani-Andebili, M., Bonamente, M., & Miller, J. A. (2020). Mobility analysis of plug-in electric vehicles in San Francisco applying Monte Carlo Markov Chain. In 2020 IEEE Kansas power and energy conference (KPEC), pp. 1–6. 4. Rahmani-Andebili, M. (2019). Planning and operation of plug-in electric vehicles. Springer International Publishing.

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Chapter 2

The Challenges on the Pathway to Electromobility in Developing Countries Yasmin Emily de Souza Oliveira, Denisson Queiroz Oliveira Osvaldo Ronald Saavedra , and Mehdi Rahmani-Andebili

,

Abstract This chapter presents an investigation and a survey discussing the main challenges to disseminating electric vehicles in developing countries, considering different aspects from different points of view: customers, governments, power utilities, and other stakeholders involved in the process. Some points addressed are the advantages of electric vehicles over conventional ones, technological and financial challenges to electromobility development, and obstacles in developing countries for electric vehicle dissemination related to regulation, public policies, and environmental and financial impacts. Finally, the conclusion points to the way and possible solutions that the developing countries must deploy to achieve deeper electrification in the transportation sector. Keywords Electromobility · Electric vehicle · Sustainability · Zero-emission policies

2.1

Introduction

Sustainable development and the use of green technologies have been on the agenda of global concerns to enable the preservation of the environment and sustainable use of natural resources. Moreover, energy consumption, including the electrical one, has been facing substantial changes associated with the energy transition process, characterized mainly by the decarbonization of energy matrices in response to the issue of climate change, the decentralization of energy resources, and energy security to assure independence from geopolitical risks. The energy transition process has also gained strength due to concerns about the fluctuations of fossil

Y. E. de Souza Oliveira · D. Q. Oliveira (✉) · O. R. Saavedra Institute of Electric Energy, Federal University of Maranhão, São Luís, Brazil e-mail: [email protected]; [email protected]; [email protected] M. Rahmani-Andebili Electrical Engineering Department, Arkansas Tech University, Russellville, Arkansas, USA e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Rahmani-Andebili (ed.), Planning and Operation of Electric Vehicles in Smart Grids, Green Energy and Technology, https://doi.org/10.1007/978-3-031-35911-8_2

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fuel prices and their limited reserves since they are nonrenewable natural resources, which could soon compromise energy supply security. In this scenario, electric vehicles (EVs) have emerged as one of the most promising options for sustainable development [1–3]. One of the trends in the energy transition is related to the transportation sector with the raising of electromobility and other technologies such as hydrogen and biofuels. Global sales of electric vehicles are predicted to grow exponentially in the coming decades, and the electrification of the vehicle fleet is considered a solution to accomplish the carbon neutrality and emissions goals. Recent reports are optimistic about the electric vehicles market growth and increase in market share due to decreasing battery costs. Consequently, some challenges need to be analyzed and investigated, such as the increase in demand for raw materials, the disposal of batteries at the end of their useful life, and the significant impacts on the planning and operation of electrical power systems, among others [3–5]. In addition to these challenges, there are questions concerning the gradual substitution of the aging fleet for new models with more modern technologies and solutions, the acquisition costs, opportunity costs, development of new disruptive services regarding urban mobility, recharging infrastructure, battery performance, and regulatory concerns [6]. Industrialized countries still invest significantly in energy and transport systems based on fossil fuels due to path-dependence processes fostered by technological and institutional returns. This strong dependence represents one of the barriers to the popularization of electric vehicles [2, 7, 8]. The electric vehicle market has grown at different paces around the world. Developed countries lead this evolution with robust markets in different stages. China can be considered a mature EV market, with almost 3.4 million vehicles registered, almost half of the global stock. The Chinese EV market has 16% of the total share. Some Chinese cities have a 100% public urban EV bus fleet. Heavy-duty commercial vehicles are also a significant market in China, concentrating the existing units sold worldwide. The European Union market is the second highest, with a 1.7 million EV stock. However, the members are in different stages and face challenges. Norway, for instance, is the most developed market, with a 13% world market share in light-duty vehicles. Iceland, The Netherlands, and Sweden follow with almost 10% together. The USA is the third highest EV market, with 1.5 million EV stock [9]. There are different scenarios and stages among developing countries, but they are usually in the beginning stages compared with leading global electromobility markets. Since several countries fit into the concept of a developing country, this chapter investigates the current regional scenario in some countries in Latin America and Asia.

2.2

Electric Vehicles and Energy Transition

The great enthusiasm for disseminating electric mobility is related to the pursuit of sustainable development and greener technologies application. However, it is essential to notice that the energy transition process is slow in the power industry [1].

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The energy transition to cleaner technologies is also happening in the transportation sector, which will be responsible for 31% of total greenhouse gas emissions by 2021. Electromobility, biofuels, and hydrogen are different solutions to decrease emissions in transport and reach goals from climate agreements. Reference [10] projects greenhouse gas emissions of approximately 6 Gt CO2 by 2050 in the transportation sector in a scenario of success in the targets established in the climate agreements to reduce emissions. This value decreases to approximately 1 Gt CO2 in an optimistic net-zero scenario. An electric vehicle is powered by an electric motor (or more than one) supplied by a rechargeable battery or other portable electric energy storage device supplied by an external power source. Hybrid vehicles have both kinds of motors, an electric and a conventional one, working together to supply power to a drivetrain connected to the wheels. Some hybrid vehicles are plug-in, which means they accept an external power source to feed the batteries. Both kinds of vehicles have higher efficiency than conventional cars [11]. There are four main topologies of such vehicles, as depicted in Fig. 2.1, each one with different features [9, 12, 13]: • Hybrid vehicles (HEVs) have both conventional and electric motors. Each manufacturer develops a management system to control the engine and electric motor and manage the energy storage system. The electric motor usually works at low speeds, while the conventional engine is started at higher speeds. In all cases, the engine supplies power to the battery pack when discharged. Kinetic energy regeneration systems are also present, and the battery storage system has a low nominal capacity. • Plug-in hybrid vehicles (PHEVs) also have battery storage energy systems to supply the electric motor and a conventional engine. However, it is possible to

Fig. 2.1 Main electric vehicle configurations

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charge it by connecting to the power grid with a low-power connection for slow charging or a high-power connection for fast charging. • Battery electric vehicles (BEVs) are totally electric, and an external source must charge their batteries. Kinetic energy regeneration systems are used, but they are not enough for self-charging. • Fuel cell electric vehicles (FCEV) store energy in fuel cells, using hydrogen to produce power. Hydrogen tanks are needed in this case. According to [9], electric vehicles (EVs) have two types of charging: • Unidirectional (V1G): recharge process where the power flow is always from the grid/charging station to the vehicle • Vehicle to grid (V2G): bidirectional recharge process where energy can go from the charging station/grid to the vehicle or vice versa

2.3 2.3.1

Challenges for Developing Electromobility Capital Costs for Acquisition

Regarding capital costs for acquisition, internal combustion engine vehicles (ICEVs) are more financially accessible than any greener vehicles, which is a significant obstacle to their popularization. Although the industry has been offering new models, the acquisition cost is still a limiting factor compared to ICEV prices [9, 14]. Battery costs alone can account for up to one-third of total battery electric vehicle costs, as seen in Fig. 2.2, which compares the costs of a compact ICEV with those of a comparable BEV. The expected drop in battery prices over the years will lead to nearly equal production costs by 2030, resulting in comparable prices for both types of vehicles [15, 16]. Optimistic forecasts, as in Ref. [16], consider that ICEV and BEV costs will be similar by 2026, and by 2030, BEVs will be cheaper than ICEVs. From the point of view of maintenance, consumption, and efficiency, electric vehicles have more significant advantages than ICEVs [3]. The electric car is more economical than the conventional combustion car, as shown in Table 2.1, which presents the annual fueling expenses of an electric car and a combustion car considering the Brazilian market. The savings are about ten times lower for electric cars, not considering the costs associated with maintenance, which would increase this advantage. Electric motors do not have the same complexity as conventional engines, with thousands of moving parts and low efficiency. Conversely, EV maintenance services are more expensive due to higher complexity in systems, more skilled labor, and more expensive spare parts. Insurance costs are expected to be also higher than those for conventional vehicles. Another commercial factor that influences the cost of EVs and consequently slows down the advance of electric mobility is the high investment required by automakers in project development and deployment of assembling plants to offer

The Challenges on the Pathway to Electromobility in Developing Countries

BEV

2

2

ICEV

2030

1.9

ICEV

BEV

2030

2

2020

1.8

2020

1.3

1.2

1.6

1.5 1.3

1.6

1.8

1.2

3

1.8

2.1

2.7

1.2

1.7

0.6

2.9

0.7

1.8

35

4

2.1

0

2

16 tЄ

3

14,6 tЄ

8

2.7

2

0

20,3 tЄ

14 tЄ

3

0% 20% 40% 60% 80% 100%

Direct costs in thousand Є Others

Assembly

Chassis

Exterior

Interior

Powertrain

E-Drive

Engine/Battery

Fig. 2.2 Cost structure of current and future BEVs compared to ICEVs. (Adapted from [15]) Table 2.1 Financial savings comparing ICEVs and EVs in the Brazilian scenario (in Brazilian currency) Average distance traveled in a year Average consumption Fuel cost Total expenses

ICEV 25,000 km 11 km/L R$ 7.10/L R$ 16,136.36

EV 25,000 km 6 km/kWh R$ 0.64/kWh R$ 1612.50

these products. The high initial investment in new plant deployment, or to adapt existing ones, results in smaller-scale production, affecting final costs [9]. Higher investments in training the workforce and higher workers’ wages are also factors that contribute to higher prices. Supply chains for parts and components also face bottlenecks, mainly with the low semiconductor production, representing almost 47% of the final vehicle cost. Table 2.2 presents the prices of two versions of a compact vehicle, an electric and a conventional model, compared to conventional SUV vehicles from the same manufacturer equipped with combustion engines, considering the Brazilian market

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Table 2.2 A comparative between EVs and ICEVs (in Brazilian currency)

Manufacturer A

B

Model Compact EV Compact conventional Conventional SUV Compact EV Compact conventional Conventional SUV

Autonomy 340 km –

Price R$ 265,990.00 R$ 112,790.00

The price ratio between the conventional and electric model 2.358 –

– 265 km –

R$ 249,990.00 R$ 142,990.00 R$ 67,690.00

2.2164 2.1124 –

–

R$ 156,290.00

2.3089

[17, 18]. Names and brands are omitted in Table 2.2 and prices are compared in ratios of compact ICEV price. In addition, it is noted that for the exact initial cost, the consumer can purchase a premium conventional SUV, which is more comfortable and with more accessories, compared to a compact EV. Therefore, for a standard consumer, choosing the EV model instead of the conventional SUV makes no sense. Several manufacturers’ medium-range commercial plans concerning offering new models show a preference for developing EVs over ICEVs, while others plan to turn their portfolio 100% electric in the next decade, phasing out all ICEVs. However, if the prices do not decrease as expected, this decision can make the car acquisition a distant achievement for many customers, reaffirming the social inequalities. Subsidies are a solution to make EV prices competitive in comparison to ICEVs. Discounts or waives in taxes, subsidies in banking loans, and special credits for EV acquisition may be offered to potential customers. However, these initiatives may be questionable from a social point of view, considering the deficiencies in some fundamental areas in developing countries, such as education, health, and infrastructure, which need government investments. The social gains must be clear to society, showing measurable results from the taxes waiving. Several investigations are demonstrating the effects greenhouse gas emissions have on human health. Reference [19] shows that carbon emissions negatively impact public health and the environment, especially when the ambient temperature is above 17.75 °C. Reference [20] recognizes that mitigation strategies may benefit both health and climate protection offering the possibility to deploy socially attractive and cost-effective policies that address four domains: household energy, transportation, electricity generation, and agriculture. Reference [21] showed the relation between excess emissions and elderly mortality in Texas, USA, using data from 2002 to 2017. References [22–24] also reaffirm the benefits of reducing CO2 emissions for health and climate goals. The high acquisition costs may also contribute to creating or strengthening existing business models. A possible solution is vehicle rental or mobility services, following a trend already observed in a society where younger consumers value less the ownership of a good, valuing more the use, its purpose, and the experience it

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provides. In this context, novel services may rise to offer urban mobility changing the relationship between humans and cars. The possibilities include mobility packages, renting a car for a couple of hours, and car sharing. Since the most significant costs are concerned with the batteries, some manufacturers propose offering custom battery packs able to store different capacities to the customers, i.e., the customer may customize its EV by choosing a smaller battery pack with lower capacity, reducing the final acquisition cost of the vehicle. To accomplish such a goal, manufacturers are deploying customizable platforms for vehicle assembling.

2.3.2

Batteries, Recycling, and Second Use

In the electrical mobility scenario, batteries are a fundamental component for conserving a sustainable position in the market. Therefore, the availability of raw materials for manufacturing and other aspects such as autonomy, safety, and acquisition price permeate the discussion of promoting EVs [25]. Furthermore, the reduction in battery prices impacts the accessibility of electric vehicles since the battery cost represents up to 40% of the initial cost of a battery electric vehicle. Figure 2.3 shows that between 2010 and 2019, average battery costs dropped by 85%. They are expected to follow the downward trend in the coming years due to operational performance improvement, manufacturing volumes, and increasing tax incentives observed over the years, which positively impact the accessibility and popularization of electric vehicles [26, 27].

Fig. 2.3 Average lithium-ion battery costs between 2010 and 2019. (Adapted from [26])

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The downward trend in EV costs continued as expected through 2022. However, battery metal prices increased dramatically in early 2022, posing a significant challenge for the electric vehicle industry. The biggest obstacles to EV production and sales are rising prices for some key minerals for battery manufacturing, supply chain disruptions caused by the war in Russia and Ukraine, and the ongoing COVID19 lockdowns in some parts of China. As a result, more significant efforts are needed to diversify the manufacture of batteries and essential mineral supplies to reduce the risk of bottlenecks and price increases [4]. Battery technology can also achieve performance gains until 2030, which promotes a reduction in the price of electric vehicles and greater dissemination worldwide, increasing economic viability. However, it is important to note that while lithium-ion batteries are durable, they have a finite useful life. Therefore, regarding final disposal, end-of-life EV lithium batteries should be returned to the manufacturer for research or recycling instead of putting away inappropriately [1, 28]. The improper disposal of batteries could result in toxic chemical leaks into the environment. In addition, it could become problematic after 2025, when a substantial increase in the quantity of battery electric vehicles available is expected. According to the International Energy Agency (IEA), by 2030, 23 million electric vehicles will be sold globally, leading to 5,750,000 tons of batteries being withdrawn by 2040, assuming a battery life of 10 years and 250 kg per battery [4]. To promote vehicle battery recycling, Ref. [27] proposes regulatory interventions and incentives to change electric vehicles’ property rights concerning automotive batteries. For instance, it is proposed that electric vehicle batteries remain the manufacturer’s property; customers can buy their electric vehicles but only rent the battery to boost the circularity and recycling of this material. Furthermore, different stakeholders, such as [29], may lead successful initiatives to promote different stages in the recycling chain, gathering potential investors for partnerships and guiding campaigns to make customers aware of the recycling importance and to give information concerning current regulations. In Refs. [30–33], the main perspectives of the second-use technology of electric vehicle batteries are discussed as a solution for reusing degraded EV batteries. The second life refers to secondary applications, different from the original, where the batteries can be used after reaching 70–80% of residual capacity. These secondary applications, generally in power systems, reduce their carbon footprint, improve materials’ circularity, and reduce waste. Second-use applications can reduce the first cost impacts of EVs while extending battery life with other applications requiring less strict operational conditions than vehicular applications. Another proposal is to use EV batteries in storage systems to improve the performance of renewable generation sources in centralized and distributed facilities, reducing intermittence, decreasing dependence on fossil fuels sustainably, and providing regulation services. The battery’s second use has big potential to create novel markets and services. For example, vehicle aggregators may rent battery packs or include battery substitution after lifespan in their mobility contracts. Recycling activities also open new perspectives concerning materials investigation and chemical processes for the battery industry’s reuse of metals, plastics, and electrolytes.

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39

Charging Infrastructure

Among the obstacles to EV penetration in the mobility market, the lack of infrastructure prepared to meet this demand stands out as one of the limitations. Thus, to contribute to the integration of this technology, it is necessary to properly plan EV charging stations (EVCSs) that provide power to recharge the batteries of an electric vehicle from an electrical energy source [34, 35]. The increased availability of charging stations contributes to greater security for consumers to purchase electric vehicles and overcome barriers to autonomy and convenience. Therefore, a direct and proportional relationship exists between EV insertion in the market and the number of charging stations developed, which vary according to the demography, housing, and urban mobility [36]. With the need to install charging stations for electric vehicles, the problem of allocating strategic charging points in the city arises to satisfactorily meet customer demand considering the distance, capacity, and charging time [37]. Different methods have been used to solve the EVCS allocation problem from both the power utility and customers’ points of view, such as classical optimization techniques and metaheuristics, considering different objectives such as total cost minimization, power losses, and curtailment minimization, recharging time and driver patterns [38–42]. The heterogeneity of systems, between equipment and software systems, can generate incompatibility and become a limiting factor for disseminating EVs. Therefore, in order to avoid the problem of compatibility and enable the creation of flexible charging solutions for consumers, it is necessary to standardize equipment, such as connectors and plug-ins, as well as the definition of standard communication protocols, means of payment, in addition to a minimal legal certainty regarding the correct form of taxation of this recharge sale. The charging structures for electric vehicles can be public, semi-public, or private; they can also have fast or slow chargers. The world’s public electric charging stations reached 1.3 million units in 2020, of which 30% are fast-type chargers, and the installation of publicly accessible chargers increased by 45% in 2020, showing a slower rate than the 85% in 2019, due to the problems caused in the economy by the pandemic. Figure 2.4 shows the evolution of fast-recharging points since 2015 [4].

2.3.4

Power Grid Capability and Vehicle to Grid

Integrating smart grid technologies into conventional distribution networks provides greater reliability in the electricity supply. However, these new connected devices will require infrastructure deployment, investigations, and forecasts in order to avoid causing coordination problems in the grid. Furthermore, the growing use of electric vehicles and their recharging and connection particularities to the power system

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Fig. 2.4 Evolution in the stock of fast public EV recharging stations in 2015–2020. (Adapted from [4])

causes a sharp increase in the system load, which could cause issues to the security system, principally at peak hours [11, 43, 44]. An electric vehicle consumes an average of 4200 kWh per year, 209% more than the average residential consumption in Brazil, for instance. Therefore, this new load represents a significant impact in the coming years with the spread of electric vehicles. A controlled recharging process has been proposed in many investigations to cope with grid restrictions [11, 44–46]. References [45, 46] include stochastic approaches to describe the drivers’ commuting patterns and their willingness to provide vehicle-to-grid (V2G) services at parking lots. The vehicle-to-grid (V2G) technology can relieve the grid load and avoid overloads at peak times through the discharge capacity in the electrical grid. By adopting this strategy, the energy stored in EV batteries can be discharged at peak times and charged at off-peak intervals, thus modifying the load curve [11, 44]. The benefits and challenges to the spread of V2G technology are examined in several papers. In [44–48], the effects of using V2G technology in hybrid electric vehicles to minimize losses in distribution networks, supply reserve, and frequency regulation are investigated. In many countries, however, the V2G service is not allowed (or predicted) in power system regulations and standards. The power utilities must invest in infrastructure to reinforce the distribution network and install charging stations for electric vehicle users. Effective load forecasting is also necessary to avoid additional costs. In the forecasting process of the EV load, some information is essential, such as the probability of their connection to the grid, time of connection to the grid, duration time required for charging, and battery capacity. However, it is essential to note that the stochastic and

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uncoordinated behavior of electric vehicle owners to charge the vehicles must be taken into account in the planning and forecasting stages, as far as the different penetrations and customers’ social aspects [46–50].

2.4

Public Policies for EV Dissemination

Government, public policies, and regulations are needed to promote replacing conventional vehicles with electric vehicles and encouraging EV alternatives. Some possible initiatives are [9]: • • • • •

Bonuses and credit lines for electric vehicle acquisition Subsidies and discounts in taxes Adoption of restrictions on the use of conventional vehicles Aid to research and development and implementation of infrastructure To incentivize business models for democratizing electric mobility (car-sharing, leasing, renting, among others)

In terms of the supply and availability of energy resources, since the 1973 oil crisis, the high market power of the members of the Organisation of the Petroleum Exporting Countries (OPEC) has been evidenced, which can interfere with the nominal oil price by reducing their production. Moreover, with the start of the war between Russia and Ukraine in 2022, the world is suffering the consequences of fluctuations in the price of a barrel of oil again, which puts on the agenda the electrification of the vehicular fleet as an alternative in terms of energy security [51]. The most powerful instrument for electric vehicle dissemination is adopting public policies and financial incentives, helping to deploy a regulatory framework favorable to this dissemination. Public authorities’ establishment of regulations and targets aims to enable greater confidence in the transition to vehicle electrification and encourage stakeholders’ investment in this process. In addition, government policies may influence the future of a country’s economy by establishing appropriate market rules and signals for developing charging infrastructure, enabling new business models to emerge, and facilitating the smooth integration of EVs into grid operations. Thus, countries must coordinate with policymakers to plan electric mobility investment efforts. Public policies to encourage the diffusion of vehicle electrification are deeply embedded in the leading countries in electric mobility, covering financial and non-financial incentives [51–54]. Reference [55], for instance, demonstrates how the independent system operator (ISO) may maximize the load factor of power market demand by optimally managing the EV fleet and offering financial incentives for customers from different social classes. A circular economy must be assured to keep the sustainability of electromobility. Therefore, public regulations concerning battery recycling and reuse are also important to EV market deployment [29, 56–58]. Reference [56] brings helpful information concerning the public policies for recycling lithium-ion batteries in the USA,

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European Community, China, and Japan. Reference [58] presents a collection of policies and incentives for EV batteries and stationary systems concerning funding for research and development, tax reductions, policies, and regulations. Finally, Ref. [59] compares two kinds of public policies that encourage battery recycling: rewardpenalty and deposit-refund. The results show that both mechanisms improve collection rates and profits, but the latter presents better results. Table 2.3 summarizes some proposed goals for coordinated public policies regarding environment, energy infrastructure, and market for developing a favorable scenario for EV dissemination [29, 54, 57, 60, 61]. The transport sector decarbonization is a critical step in efforts to reduce global greenhouse gas emissions. In this regard, the governments of developed countries have adopted strict plans and measures to promote sustainability by reducing greenhouse gas emissions caused by fossil fuels. For example, in April 2021, 70 subnational and municipal governments announced 100% zero-emission vehicle targets or phasing out internal combustion engine vehicles before 2050, as shown in Fig. 2.5 [4, 5]. Table 2.3 Public policies to encourage electromobility [29, 54, 57, 60, 61] Environment Establish government goals to reduce greenhouse gas emissions Encourage new mobility services as long as they contribute directly to emissions reduction, such as car-sharing Establish goals for public transport fleet electrification, such as electric buses Long-term targets for eliminating internal combustion engine vehicle sales and short-term targets for EV inventory and sales Provide information for customers and stakeholders concerning recycling processes Develop policies and monetary incentives to encourage batteries recycling and second use Energy infrastructure Promoting the adequacy of the national grid to the intelligent management requirements of electric vehicle charging Incentive and support for the private and public charging infrastructure installation Set a standard for electric vehicle charging infrastructure equipment An incentive to research and development in the electric mobility field to advance the technology of the main EV components Market Facilitate business development of network services, which improve the electrical system and make EVs more economically competitive Create subsidies for the EV acquisition as a reduction and exemption from taxes or fees in order to mitigate the high purchase price Promote financing and credit lines for EV purchases Definition of differentiated energy tariffs, encouraging the process of recharging electric vehicles at off-peak hours Privileged circulation policies for EVs, such as exemption or discounts on tolls, preferential parking rates, and low-emission zones Tax benefits and financing for companies in the sector in order to promote industrial policy and development of the production chain for electric mobility

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Fig. 2.5 Vehicle electrification and internal combustion phaseout targets and ambitions. (Adapted from [4])

According to the International Energy Agency (IEA), public policies and incentive measures for electric vehicles’ diffusion in the energy scenario are classified into four categories: • Legislation: These are legally registered duties, such as regulations and standards. • Targets: These are announced government targets that are part of the legislation, budgetary commitments, and strategic government planning to meet national or international climate plans, such as the Paris Climate Agreement. • Ambitions: Government goals or objectives, as set out in a policy document, that characterize an implementation roadmap or strategy. • Proposals: Government goals released in public documents or embedded into legislation designed to stimulate discussion as to their feasibility. Table 2.4 highlights the main policies and measures, as well as goals and ambitions announced by region and country, which support the deployment of electric vehicles (EVs) and zero-emission vehicles (ZEVs) of different types: lightduty vehicles (LDVs), medium-duty vehicles (MDV), and heavy-duty vehicles (HDV) [4].

2.4.1

Public Policies in Developing Countries

The electrification process in some developing still faces significant challenges for a structural transformation of this segment, in which social, regulatory, political, and technical barriers interact to create inertia that favors the leadership of fossil fuels. Among the numerous challenges to be overcome for greater penetration of electromobility in the fleet, carbon lock-in stands out, which refers to the set of technologies, institutions, and standards that are inconsistent or incompatible with a low-carbon future and limit progress toward this goal [7].

Target

Legislation

Target

Proposal

Legislation

Legislation

Target

China

The Netherlands

Norway

European Union

European Union

United States

United States

Adapted from [4]

Policy type Ambition

Country/region China

Nine member states have requested the European Commission to support a date for the EU-wide phaseout of the sale of new petrol and diesel passenger LDVs (Austria, Belgium, Denmark, Greece, Ireland, Lithuania, Luxembourg, Malta, and the Netherlands) CO2 emissions standards for new heavy commercial vehicles to tighten by 15% by 2025 and 30% by 2030 (reference period: 2019/2020). ZEV mandate: 22% ZEV credit sales in passenger LDVs by 2025 in ten states (California, Connecticut, Maine, Maryland, Massachusetts, New Jersey, New York, Oregon, Rhode Island, and Vermont) State of California: Target of 1.5 million ZEV stock (LDV, MDV, HDV) by 2025 and five million by 2030

Zero-emission transport zones to be introduced in 14 cities by 2025 (number expected to increase to 30 by mid-2021) Target: 100% ZEV sales in passenger LDVs by 2025

Key policy measures and targets Ambition: 70% of passenger vehicles will be electrified (of which 40% NEVs) in 2025 and 100% in 2035 (of which 50% NEVs and 95% of those are BEVs) Target: 20% share of passenger NEV sales by 2025

Table 2.4 Main global policies and measures for implementing zero carbon emission vehicles

2016

2016

2019

2021

2016

2021

2020

Year announced 2020

All

Light-duty vehicles

Heavy-duty vehicles

Light-duty vehicles Light-duty vehicles

Light-duty vehicles All

Vehicle type Light-duty vehicles

State of California (US) (2018)

State of California (US) (2021)

European Union (2019)

Government of China (2020) Government of The Netherlands (2021) Government of Norway (2021) Government of Denmark (2021)

Source SAE China (2020)

44 Y. E. de Souza Oliveira et al.

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Brazil

In the context of electromobility and the expansion of this technology on a global scale, Brazil has significant growth potential due to having one of the leading automotive consumer markets in the world to be explored by electrical mobility. According to statistics from [9, 62], Brazil is the sixth biggest market in the world, behind China, the USA, Japan, Germany, and India. The Brazilian Association of Electric Vehicles (ABVE) announced that until September 2021, more than 66,000 electric and hybrid vehicles were registered in Brazil, as depicted in Fig. 2.6. That evidence can be associated with tax and fee reductions for the purchase of these means of transportation. In addition, Brazil’s total number of public and semi-public charging stations jumped from around 500 units in March 2021 to 754 in July, of which 735 are in operation. These numbers indicate a 50% growth in just 4 months of charging points available for electric vehicles [63]. The standardization of the vehicle interface with the electricity grid in Brazil should adapt to international standardization. Therefore, an adequate electric charging infrastructure must be used to disseminate electric vehicles in Brazil. In this scenario, the need for adequate planning of the electric sector to meet the EV demand becomes indispensable [1]. Brazil took an important step in controlling CO2 emissions when the National Congress ratified the Paris Agreement in 2016, which provides for a 43% reduction in greenhouse gases by 2030 compared to emission levels of 2005. Nevertheless, according to data from the National Energy Balance, in 2020, Brazil emitted 398.3 million tons of CO2 into the atmosphere, 45% of which came from the transport sector [64].

Fig. 2.6 Sales of EVs in Brazil from 2012 until September 2021. (Adapted from [63])

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Fiscal policies stand out as a way to increase the competitiveness of this technological innovation. In this sense, in Brazil, Resolutions N°116/2014 and N°23/2016 of the Chamber of Foreign Trade (CAMEX) exempted or reduced the EV import tax rate. Furthermore, in the adoption of government strategies to support electromobility in Brazil, in July 2018, Presidential Decree n° 9.442 changed the industrialized product tax rate; with this measure, the rate was reduced from 25% to 7% for BEVs and from 25% to 20% for HEVs [9]. Concerning investments in research, the Brazilian Electricity Regulatory Agency (ANEEL) stands out as the leading institution promoting research and development projects in Brazilian electric mobility since they represent 65% of the contributions made to projects in electric mobility related to other research support institutions in Brazil. An example of this investment was the Call 22 P&D ANEEL, which aims to generate business and market solutions for electric mobility from 2020 to 2024 [9]. Concerning programs and policies to encourage electric mobility in Brazil, Rota 2030 is the first long-term automotive industry policy implemented in Brazil, in force between 2018 and 2033, which defines a series of regulations and incentives to improve the competitiveness and logistics of the transport system in the country [9]. Among the class associations that are related to electric mobility in Brazil, the Brazilian Electric Vehicle Association (ABVE) stands out, which works with companies belonging to the industry and other actors, intending to promote debate, popularize and disseminate the EV’s themes, as well as supporting in decisionmaking on regulatory measures and articulation of actors, whether they come from the public or private sector. Furthermore, in this scenario of incentives for EV diffusion, in February 2020, the National Electric Mobility Platform (PNME) was launched, whose objective is to be an instrument of articulation between government, market, technology, and civil society, which coordinates their actions, in favor of building goals for electric mobility in Brazil [9, 63]. Concerning power system standards and regulations, The Normative Resolution N°819/2018 (REN 819/2018) was the first Brazilian regulation on electric vehicle charging, establishing the procedures and conditions for recharging activities for recharging electric vehicles in Brazil. This regulation allows any interested party to carry out charging activities for electric vehicles, including for commercial exploitation purposes, at freely negotiated prices. However, although recent regulation in Brazil has undergone significant changes concerning the topic of charging electric vehicles, there have been no significant changes, and obstacles to the implementation of V2G technology in Brazil remain, forbidding the injection of electric energy into the distribution grid [65, 66].

2.4.1.2

Latin America

Latin America has huge potential to increase its EV fleet during the current energy transition because it has a clean electricity matrix, a large share of renewable generation, and a growing transportation market expected to reach 200 million units by 2050 [67].

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Transport emissions were also high in Latin America, indicating the need to push electromobility forward to accomplish emission goals in different countries. In 2021, the EV market in Latin America was around 21 billion US dollars, and forecasts show a 14% growth yearly until 2026. However, with the COVID-19 pandemic, the price of an EV raised over 1000 US dollars, turning more expensive the prices in these markets, increasing the average EV price to 58,420 US dollars [68]. Mexico has strong PEV and BEV markets, but each state defines the financial incentives regionally. Colombia and Chile also present an increasing EV and PEV share, with European, Japanese, and Chinese brands offering several models and competing for customers [68]. The charging infrastructure is a challenge for EV development in Latin America. Statistics show a need for recharging points in most countries by 2021. Chile, for instance, only has 244 charging points registered. Brazil and Mexico show similar quantities. Such a feature strengthens the HEVs and PHEVs in the consumer preference due to the presence of a conventional engine to recharge the battery pack, giving them higher autonomy than BEVs. To overcome such problems, manufacturers invest in fast-charging infrastructure in urban areas and roads connecting large cities in these countries [67, 68]. Concerning the advances in the regulatory framework, Latin American countries approved tax reductions and tried to implement restrictions on conventional car manufacturing in the following decades. Chile, for instance, plans to electrify 40% of the private fleet by 2050. Colombia plans to have a 600,000 EV fleet by 2030 [67]. In addition, phasing out ICEVs is a target for Colombia, Chile, and Costa Rica. In the heavy-duty segment, Chile and Colombia have the largest EV bus fleet outside China, investing in clean urban transportation and assuring emissions decrease from an important source. By 2020, Chile will have more than 800 electric buses, while Colombia will have around 500 units. In other countries, such experiences are restricted to some trial units [69]. Table 2.5 summarizes the main targets for some Latin American countries.

Table 2.5 Main targets for EVs in Latin America Country Colombia Chile Mexico Panama

Ecuador Costa Rica Adapted from [68–70]

Target 100% electric bus fleet by 2035 100% light-duty EVs and urban buses by 2035 100% electric intercity buses by 2045 ICEVs phaseout by 2050. All new units must be EV 25–40% of private sales must be electric 10–20% of the private fleet must be EV 25–50% of the public fleet must be EV Electric public transportation by 2035 100% of light vehicles sold must be electric by 2050 100% buses and taxi fleet

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Table 2.6 Some initiatives for EVs in Asia Initiative Purchase and usage incentives by waiving taxes Incentives in parking fees Circulation restriction for ICEVs Investments in recharging infrastructure

2.4.1.3

Countries Uzbekistan, Pakistan, Thailand, Malaysia, Indonesia, Bhutan Indonesia Philippines Thailand, Pakistan, Nepal

Asia

The Asian EV market is under development at a different pace according to the country, but it has had the largest share of BEV sales worldwide since the last decade. The revenue in the EV market may reach 207.5 billion US dollars by 2023, and the market is expected to grow 14% annually until 2027 [68]. China, Japan, and South Korea are industry leaders in electromobility. Other countries like Thailand, the Philippines, and India present less than 1% EV share in the total market. However, when looking at two- and three-wheel vehicles, many countries have a larger zero-emission fleet. By 2020, Vietnam, the Philippines, Thailand, and Cambodia have presented a high sales volume of such vehicles [68, 70]. Table 2.6 summarizes the main initiatives in Asian countries to improve EV competitiveness [70]. Purchase and usage incentives offer consumer-based incentives, waiving or reducing duties and taxes for sales, transfers, and imports of recharging equipment. Other financial incentives regard discounts on parking fees. Non-financial ones include reserved parking lots and recharging infrastructure improvement, mainly with the high availability of fast rechargers and recharging points along the main roads.

2.5

Conclusions

The electromobility development is the face of the energy transition to a more sustainable and environmental-friendly society, offering a permanent way to reach the ambitious emission goals to slow down the climate change process. However, as described in the chapter, there exist several challenges to be faced. According to regional features, the EV market is developing at different paces worldwide. Such features regard economic, social, infrastructural, and legal aspects. Such aspects have been briefly described in the chapter, giving special attention to acquisition costs, the batteries and the challenges they bring to keep the EV sustainable, the infrastructural improvements necessary, and the regulatory background that must be developed. Unlike the more ambitious prospects, EV models are not expected to overtake the ICEVs sooner, even if the prices become equivalent soon. The more realistic scenario is that both technologies exist in a transition period, while the industry

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decides to phase out ICEVs entirely in the following decades. The challenge to the industry is not to turn EVs into a niche good but to develop electromobility democratically in society with light-duty vehicles and two-wheel and three-wheel vehicles. During this transition period, the infrastructural challenges must be overcome by installing more public and private recharging points; regulatory aspects must be improved to bring safety to new commercial relations and to rule the interactions with the power grid technically, to deploy effective chains to manage with the EV batteries assuring second-use applications and safe waste recycling, and to deploy efficient urban mobility options to avoid changing from ICEV jams to EV jams. Acknowledgments The authors thank Coordenação de Aperfeiçoamento de Pessoal do Ensino Superior (CAPES) and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) for funding this research.

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50. Dickerman, L., & Harrison, J. (2010). A new car, a new grid. IEEE Power and Energy Magazine, 8(2), 55–61. 51. Castro, B., & Ferreira, T. (2010). Veículos elétricos: aspectos básicos, perspectivas e oportunidades. (In Portuguese). BNDES Setorial, 32, 267–310. 52. Yannick, P., Marc, P., & Willett, K. (2013). A public policy strategies for electric vehicles and for vehicle to grid power. In 2013 world electric vehicle symposium and exhibition (EVS27) (pp. 1–10). https://doi.org/10.1109/EVS.2013.6914856 53. Vera, J. F., Clairand, J., & Bel, C. Á. (2017). Public policies proposals for the deployment of electric vehicles in ecuador. In 2017 IEEE PES innovative smart grid technologies conference – Latin America (ISGT Latin America) (pp. 1–6). https://doi.org/10.1109/ISGT-LA.2017. 8126718 54. van Kerkhof, M., & Boonen, A. (2013). Effective public policies for EV-dissemination. In 2013 world electric vehicle symposium and exhibition (EVS27) (pp. 1–7). https://doi.org/10.1109/ EVS.2013.6915033 55. Rahmani-Andebili, M. (2019). Studying the effects of plug-in electric vehicles on the real power markets demand considering the technical and social aspects. In M. Rahmani-Andebili (Ed.), Planning and operation of plug-in electric vehicles. Springer. https://doi.org/10.1007/ 978-3-030-18022-5_1 56. Bird, R., Baum, Z. J., Xiang, Y., & Ma, J. (2022). The regulatory environment for lithium-ion battery recycling. ACS Energy Letters, 7(2), 736–740. https://doi.org/10.1021/acsenergylett. 1c02724 57. The United States Government. United States Environmental Protection Agency (EPA). Available on: www.epa.gov [Online]. 58. The United States Government. Department of Energy. Office of Energy Efficiency & Renewable Energy. [Online]. Available on: https://www.energy.gov/eere/vehicles/battery-policiesand-incentives-search#/. 59. Hao, H., Wenxian, X., Wei, F., Chuanliang, W., & Zhaoran, X. (2022). Reward-penaltyvs. deposit-refund: Government incentive mechanisms for battery recycling. Energies, 15, 6885. https://doi.org/10.3390/en15196885 60. Zamboni, et al. (2021). Políticas Públicas e Inovações Regulatórias para Mobilidade Elétrica e a Eletrificação de Frotas Comerciais”. (In Portuguese). GESEL – Universidade Federal do Rio de Janeiro. 61. Evans, G., & Fulbrook, A. (2020). The battery-electric vehicle: Why mass adoption is inevitable, yet elusive. IHS Markit Report. [Online]. Available in: https://www.dynacast.com/-/media/ dynacast/knowledge-center/blog/the-battery-electric-vehicle-why-mass-adoption-is-inevitableyet-elusive-16052781821975007500.pdf. Accessed 18 Apr 2023. 62. OICA. (2017). Sales statistics. OICA. Disponível em: http://www.oica.net/category/salesstatistics/. Acesso em: 28/11/2021. 63. Associação Brasileira de Veículos Elétricos (ABVE). Available in: https://www.abve.org.br/ 64. Balanço Energético Nacional. Empresa de Pesquisa Energética (EPE). Available in: https://epe. gov.br. Accessed 28 Nov 2021. 65. Agência Nacional de Energia Elétrica (ANEEL). (2018). Resolução Normativa 819/2018, de 19 de junho de 2018. Regulamentação sobre recarga de veículos elétricos. (In Portuguese). 66. ANEEL, REN 1.000/2021. Regulamentação sobre Prestação do Serviço Público de Distribuição de Energia Elétrica. Agência Nacional de Energia Elétrica, Resolução Normativa 1.000, de 07 de Dezembro de 2021. 67. [online] https://www.maersk.com/insights/sustainability/electric-vehicles-in-latin-america 68. [online] https://www.statista.com/topics/9634/electric-vehicles-in-latin-america/ #topicOverview 69. Kohli, S. et al. (2022) Zero emission vehicle deployment: Latin America. The International Council on Clean Transportation (ICCT) Report. 70. Khan, T. et al. (2022). A critical review of ZEV deployment in emerging markets. The International Council on Clean Transportation (ICCT) Report.

Chapter 3

Sensitivity Analyses for Optimal Charging Management of Electric Vehicles in San Francisco Mehdi Rahmani-Andebili

Abstract In this chapter, the effects of several important parameters, such as plugin electric vehicle (EV) type, EV penetration level, and driver’s social class, on the optimal charging management of EVs in San Francisco are studied in different sensitivity analyses. The objective function of the main problem is to minimize the operation cost of electrical distribution network penetrated by renewables, where a stochastic model predictive control (SMPC) is applied in the optimization technique to address the variability and uncertainty issues of EVs’ state of charge (SOC) and renewables’ power. Herein, quantum-inspired simulated annealing (QISA) algorithm is applied as the optimization technique. In this study, the drivers’ responsiveness probability to provide vehicle-to-grid (V2G) service at the parking lot is modeled with respect to the amount of incentive, the drivers’ social class, and the real driving routes of 100 vehicles in San Francisco. Keywords Drivers’ social class · Driving routes in San Francisco · Electric vehicle (EV) · Quantum-inspired simulated annealing (QISA) algorithm · Renewables · Stochastic model predictive control (SMPC)

3.1

Introduction

The statistics show that the transportation sector is responsible for almost 30% of fossil fuel consumption and carbon emissions in the world [1]. Electric vehicles (EVs) and plug-in electric vehicles (PEV) are being advertised by the governments and environmentalists to replace the internal combustion engine (ICE) vehicles to mitigate the environmental and energy security challenges, since they can be charged by approximately free and clean renewable energy sources. The governments are implementing tax cut policies and many other incentives to speed up the transition from fossil-fueled vehicles to electric ones. Table 3.1 presents

M. Rahmani-Andebili (✉) Electrical Engineering Department, Arkansas Tech University, Russellville, Arkansas, USA e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Rahmani-Andebili (ed.), Planning and Operation of Electric Vehicles in Smart Grids, Green Energy and Technology, https://doi.org/10.1007/978-3-031-35911-8_3

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Table 3.1 The amount of saving in top 10 states of the USA because of driving on electricity instead of gas [2] State OR WA MT NV UT ND MS SD NE IA

Residential electricity cost (cents/kWh) 10.8 9.5 11.4 12.1 11.0 11.1 11.9 12.1 11.1 13.0

Retail gas cost ($/gallon) 2.44 2.59 2.30 2.42 2.27 2.24 2.11 2.26 2.22 2.25

Annual millage (VMT/driver/year) 16,262 13,529 15,630 13,750 14,090 14,227 16,452 14,619 14,097 15,135

EV driver saving ($/year) 1238 1187 1059 974 950 941 938 921 920 899

the amount of saving of a driver in the top 10 states of the USA if he/she supplies his/her car by the electricity rather than gas [2]. As can be seen, drivers in Oregon state can save about $1238/year if they utilize EVs instead of ICE cars. The research organizations have predicted that the world sales of EVs will exceed the ICE car sales in the next few decades. Nonetheless, this opportunity might be changed to a risk for any electric power system and put it under stress if the charging time of EVs is not optimally managed by the system operator. Therefore, the system operators need to optimally manage the EV fleet (by introducing a variety of incentives to the drivers) and even make profit for themselves and the drivers. Planning and operation problems of EVs have been investigated in several studies [3–11]. In [3], the optimal operation of EVs in an electrical distribution system has been studied. The planning and operation problems of EV parking lots have been investigated from the generation company’s (GENCO) and distribution company’s (DISCO) viewpoints in [4]. In [5], the optimal amount of power factor of solar parking lots has been investigated after optimally allocating and sizing them in an electrical distribution grid. In [6], the optimal amount of incentive for the EV drivers has been studied to minimize the operation cost of a generation system. In [7], the profit maximization of a charging station has been studied. In [8], the EVs’ load management problem has been solved to provide the ancillary services. The energy management problem of EVs has been investigated in [9]. In [10, 11], the charging management problem of EVs in a day-ahead energy market has been solved. However, in the abovementioned studies, the real vehicles’ mobility, driving routes, geographical features of area, and the effect of drag force on the vehicles have not been considered. In this chapter, like the references of [13, 14], the optimal charging management of EVs in San Francisco is studied from the local DISCO’s point of view to minimize the operation cost of an electrical distribution system penetrated by the renewables. Herein, stochastic model predictive control (SMPC) [13, 14] is applied in the optimization process of problem to address the variability and uncertainty issues

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55

of state of charge (SOC) of EV fleet and power of renewables. In addition, quantuminspired simulated annealing (QISA) algorithm is applied as the optimization technique [13, 14]. However, only in this chapter, the effects of EVs’ type, EV penetration level, and drivers’ social class on the optimal charging management of EVs in San Francisco are studied, and the results are analyzed in different sensitivity analyses. In this study, the real 4-min longitude and latitude of driving routes of 100 vehicles in San Francisco is used to determine the 4-min travelling distance of vehicles. Then, the 4-min SOC of EVs is calculated considering the technical specifications of each EV including the primary SOC of EV, the electricity utilization index of EV, the capacity of EV’s battery, the air density in San Francisco, the drag area of EV, the momentary velocity of EV, and the opposing wind speed. In this chapter, the drivers’ responsiveness probability (to provide vehicle to grid (V2G) at the parking lot) is modeled with respect to the amount of incentive, the drivers’ social class [low income (LI), moderate income (MI), and high income (HI)], and the driving routes in San Francisco. Herein, different EV penetration levels (low, moderate, and high) are considered and the performances of several popular EVs, namely, Tesla Model S, BMW i3, and Volkswagen e-up, are investigated in the problem. In the following, in Sect. 3.2, driving routes in San Francisco, drag force, and total energy consumption of an EV are modeled and presented. Sections 3.3–3.5 are concerned with the problem formulation, problem simulation, and conclusion of study, respectively.

3.2

Driving Routes, Drag Force, and Energy Consumption of an EV

The 4-min driving routes of 100 vehicles have been extracted from [12–14]. These routes include the temporal location (longitude and latitude) of vehicles in San Francisco. Figure 3.1 illustrates the geographical location of 100 EVs in San Francisco, CA, at 13:00 [13, 14]. The driving routes are utilized to calculate the 4-min distances (DPEV) travelled by the drivers using Pythagorean formula, as is presented in (3.1) [13, 14]. Herein, PEV , yPEV and xPEV xPEV t t t - 1 , yt - 1 are the current and previous positions of EV. DPEV = t

xPEV - xPEV t t - 1 × 85:39

2

þ

yPEV - yPEV t t - 1 × 111:03

2

ð3:1Þ

To convert the longitude-latitude-based travelling distance of a vehicle to the regular travelling distance (e.g., kilometer) in San Francisco (with the north latitude and west longitude of 37°46′ and 122°25′, respectively), one longitude degree travel and one latitude degree travel must be multiplied by 87.87 km and 111.45 km,

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Fig. 3.1 The geographical location of 100 EVs around the electrical distribution system in San Francisco, CA, at 13:00 [13, 14]

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Fig. 3.2 The distances (km) travelled by the 100 EVs during the typical day in 4-min intervals [13, 14]

respectively [15]. Figure 3.2 exhibits the 4-min distances (km) travelled by the 100 EVs during the typical day [13, 14]. The 4-min energy consumption of each EV (Ekm) can be calculated using its electricity consumption index (kWhkm) and 4-min travelling distance (DPEV), as is shown in (3.2) [13, 14]. PEV km Ekm e,t = De,t × kWhe ×

4 60

ð3:2Þ

However, to realistically model the 4-min energy consumption of each EV, the drag force (FD) must be considered in the problem. Eq. (3.3) presents the drag force that acts on the opposite direction of vehicle’s motion, where, ρAir, CD, APEV, vPEV, and vW are the air density (kg/m3), drag coefficient (a dimensionless quantity), crosssectional area of EV (m2), speed of EV (m/s), and wind speed (m/s) blowing in the opposite direction, respectively [13, 14]. The air density in San Francisco is considered about 1.225 kg/m3 [16]. Moreover, it is assumed that the wind exerts an opposite force on the vehicle, blowing with the 50% speed of vehicle. FD e,t =

1 Air D PEV PEV ve,t þ vW ρ C Ae e,t 2

2

ð3:3Þ

Figure 3.3 illustrates the drag on some specific objects and cars [17, 18]. Streamlining can reduce the drag caused by the air friction and the air pressure difference between the front and rear of the object.

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Fig. 3.3 The drag on (a) objects [17] and (b) cars [18]

The power required to overcome the drag force (PD) can be calculated using (3.4) [13, 14]. As can be seen, it is the function of cube of speed, meaning that it will be very effective at higher speeds. Moreover, the amount of energy needed to overcome the drag force is presented in (3.5) [13, 14]. Herein, Δt is the travelling interval of vehicle. D PEV W = PD e,t = F e,t ve,t þ v

1 Air D PEV PEV ve,t þ vW ρ C Ae 2

D ED e,t = Pe,t × Δt

3

ð3:4Þ

ð3:5Þ

The total 4-min energy consumption of an EV (EPEV) is sum of the energies required to supply the EV (Ekm) and overcome the drag force (ED), as is shown in (3.6) [13, 14]. km D EPEV e,t = E e,t þ E e,t

ð3:6Þ

Figure 3.4 shows the 4-min velocity (m/s) as well as the energy value (kWh) required to supply EV 3 (with the type of BMW i3) and overcome the drag force during the typical day. Figure 3.5 presents the technical specifications of some popular EV brands [19–21]. The product of drag coefficient and area is called drag area and represented by CDA. To calculate the 4-min SOC of an EV, its other technical specifications including PEV ), and the cumulative the initial amount of SOC (SOCPEV 0 ), the battery capacity (C t

amount of consumed energy ( t0 = 1

E PEV ) are considered, as is shown in (3.7) t0

[13, 14]. The hourly SOC of each EV is determined by calculating the mean value of 4-min SOC values of the EV during that 1-h period.

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Fig. 3.4 The 4-min (a) velocity (m/s) and (b) energy consumption (kWh) required to supply EV 3 and overcome the drag force during the typical day

Fig. 3.5 The technical specifications of some popular EV brands [19–21]

PEV SOCPEV e,t = SOCe,0 -

1 C PEV e

t t0 = 1

E PEV e,t 0 × 100

ð3:7Þ

Figure 3.6 shows the 4-min SOC (%) of 100 EVs during the typical day [13, 14]. As can be seen, each EV has a unique SOC pattern. Also, as is noticed,

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Fig. 3.6 The 4-min SOC (%) of 100 EVs during the typical day [13, 14]

each SOC has a descending trend, since the EVs move and consume energy. Moreover, the SOC level of some EVs is constant at some periods of the typical day, due to their stationary status.

3.3 3.3.1

Problem Formulation Objective Function

Equation (3.8) presents the objective function that minimizes the stochastic forwardlooking function at the given time step (FSFL) [13, 14]. As can be seen in (3.9), the stochastic forward-looking function is the expected value of forward-looking function (FFL) [13, 14]. For each optimization time horizon (t + 1, ⋯, t + Nτ), 100 scenarios (s 2 {SSOC, SW1, SW2, SPV1, SPV2}) are randomly chosen using roulette wheel mechanism to select the most probable and diverse scenarios [3]. Herein, φ is the occurrence probability of set of scenarios. As is shown in (3.10), the forward-looking function is sum of the hourly value of utility function (U ) during the optimization time horizon [13, 14]. Moreover, as is seen in (3.11), the utility function includes the incentives paid to the drivers to ~ ) at the parking lot (CostINC) and the motivate them to provide V2G service (PV2G LOSS ) cost of feeder’s branches (CostLOSS) [13, 14]. energy loss (E Drivers’ responsiveness probability (to provide V2G at a parking lot) has been modeled based on the amount of incentive (β), driver’s distance from the parking lot, and driver’s social class [low income (LI), moderate income (MI), and high income (HI)]. The detailed description can be found in Refs. [3, 4, 6, 13, 14].

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Sensitivity Analyses for Optimal Charging Management of Electric. . .

OFt = min F SFL t

F SFL = t

s2fS

SOC

,S ,S ,S ,S g W1

W2

PV1

61

ð3:8Þ

φt,s × FFL t,s

ð3:9Þ

PV2

Nτ

F FL t

=

U tþτ

ð3:10Þ

τ=1

U t = CostINC þ CostLOSS t t

ð3:11Þ

where = CostINC t

βt ~ × π E × PV2G e,t 100 e2PEVs

= E LOSS × πE CostLOSS t t

ð3:12Þ

ð3:13Þ

where ~ PV2G e,t =

PEV ξSC SOCPEV PPEV ηPEV e,α,β e,t - DOD × e × e × 100 1000 100 100

E LOSS = 10 - 6 t

Rb × I b,t 2

ð3:14Þ

ð3:15Þ

b2B

3.3.2

Problem Constraints

The magnitude of apparent power (|MVA|) flowing through each branch of feeder must be less than its thermal capacity (TC), as is presented in (3.16) [13, 14]. Moreover, the voltage profile of each bus of system (|V|) must be within the lower (min|V|) and upper bands (max|V|), as can be seen in (3.17) [13, 14].

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jMVAb j ≤ TCb

ð3:16Þ

minjV j ≤ V j ≤ maxjV j

ð3:17Þ

Problem Simulation

In this study, QISA algorithm has been applied to solve the optimization problem [5, 13, 14]. The problem variables include the amount of incentive at each time step (hour) of optimization time horizon (t + 1, ⋯, t + Nτ). Four q-bits have been applied to change the decimal value of incentive to the binary one, since the decimal value of incentive is changed from 0% to 100% with a 10%-step change. Herein, the inverse value of total cost of problem during the optimization time horizon has been defined as the internal energy of molten metal. The detailed description exists in Ref. [5].

3.4.1

The Primary Data of System and Problem

The electrical distribution system under study is shown in Fig. 3.1 [13, 14, 22]. All the technical specifications of the system can be found in Ref. [22]. Table 3.2 categorizes the EV penetration levels based on the driving routes in San Francisco, CA, and the number of EVs in the area [13, 14]. Table 3.3 presents the other parameters of operation problem [13, 14].

Table 3.2 Classification of EV penetration levels [13, 14] – Number of EVs

Driving route 1 ⋮ Driving route 100 Total number of EVs in the area

Table 3.3 The other parameters of problem [13, 14]

Parameter πE DODPEV ηPEV

EV penetration level Low Moderate 2 5 ⋮ ⋮ 2 5 200 500

Value 12.73 $/MWh [23] 20% 95%

Parameter TC min|V| max|V|

High 10 ⋮ 10 1000

Value 24 MVA 0.9 p.u. 1.1 p.u.

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3.4.2

Studying the Effects of Problem Parameters on the Problem Simulation Results

3.4.2.1

Studying the Effects of Social Class of Drivers

63

Figures 3.7 and 3.8 show the optimal hourly amount of incentive and the optimal hourly V2G power of responsive EVs for each social class of drivers. Herein, the type of EVs and the EV penetration level are assumed BMW i3 and moderate, respectively. As can be noticed from Fig. 3.7, lower and higher incentives must be proposed to the LI and HI drivers to provide V2G service at the parking lot, respectively.

Fig. 3.7 The optimal hourly amount of incentive (%) proposed to different drivers considering BMW i3 EVs and moderate EV penetration

Fig. 3.8 The optimal hourly V2G power (MW) of responsive EVs considering BMW i3 EVs and moderate EV penetration

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Table 3.4 The total cost of problem before and after optimal charging management (FM) of drivers with different social classes considering BMW i3 EVs and moderate EV penetration – Total cost of problem ($) Reduction (%)

Before FM 2479.0 –

After optimal FM LI drivers MI drivers 827.2 1051.0 66 57

HI drivers 1848.5 25

Fig. 3.9 The optimal hourly amount of incentive (%) proposed to the drivers for different EV penetration levels considering BMW i3 EVs and MI drivers

However, as can be seen in Fig. 3.8, even the higher values of incentive do not effectively motivate the HI drivers, while the LI drivers provide considerable V2G power for the relatively low incentive. Table 3.4 presents the total cost of problem before and after optimal charging management of EVs for each social class of drivers. As can be seen, the LI and HI drivers have the most and the least contributions to minimize the system operation cost.

3.4.2.2

Studying the Effects of EV Penetration Level

In this part, the drivers’ social class and the EVs’ type are moderate and BMW i3, respectively. As can be seen in Figs. 3.9 and 3.10, the EV penetration level positively affects the optimal amount of incentive. In other words, the more EV penetration level, the less incentive is required and the more V2G power is provided. The total cost of problem before and after optimal charging management of drivers for each EV penetration level is presented in Table 3.5. As can be seen, the charging management of high level of EV penetration results in the best result with 63% cost reduction.

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Fig. 3.10 The optimal hourly V2G power (MW) of responsive EVs at the parking lot for different EV penetration levels considering BMW i3 EVs and MI drivers Table 3.5 The total cost of problem before and after optimal charging management (FM) of drivers with different EV penetration levels considering BMW i3 EVs and MI drivers – Total cost of problem ($) Reduction (%)

3.4.2.3

Before FM 2479.0 –

After optimal FM Low pen. Moderate pen. 1706.9 1051.0 31 57

High pen. 896.7 63

Studying the Effects of EV Type

As can be seen in Figs. 3.11 and 3.12, the drivers of Tesla Model S need less incentive but provide more V2G power at the parking lot. This reality is related to the higher rated power and capacity of battery of Tesla Model S EV compared to the other brands. In addition, as has been presented in Table 3.6, the optimal charging management of drivers owning Tesla Model S and Volkswagen e-up EVs result in the most and the least cost reductions, that is, 64% and 55%, respectively.

3.5

Conclusion

It was seen that the EV type, the EV penetration level, and the social class of drivers can affect the problem outputs, and several important results were achieved that are presented in the following: • The hourly amount of incentive needs to be optimally updated during the day, since the load demand and power of renewables change.

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Fig. 3.11 The optimal hourly amount of incentive (%) proposed to the drivers owning different EV types considering moderate EV penetration and MI drivers

Fig. 3.12 The optimal hourly V2G power (MW) of responsive EVs at the parking lot with different EV types considering moderate EV penetration and MI drivers

Table 3.6 The total cost of problem before and after optimal charging management (FM) of drivers with different EV types considering moderate EV penetration and MI drivers – Total cost of problem ($) Reduction (%)

Before FM 2479.0 –

After optimal FM Tesla Model S BMW i3 884.1 1051.0 64 57

Volkswagen e-up 1116.6 55

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• Due to the mobility of EVs during the day and their variable SOC, the hourly number of responsive drivers and their corresponding V2G power change. • The HI drivers request the most amount of incentive but provide the least V2G power. • The LI drivers are the most beneficial drivers for the electric power system since they are the most responsive drivers and have the most contribution. • The more EV penetration level, the less incentive is required, and the better results are achieved. • The larger power and capacity of EVs’ battery, the less incentive is paid, the more V2G power is provided at the parking lot, and the better result is attained.

References 1. International Energy Agency (IEA). Accessed in May 2018. [Online]. Available: https://iea.org/ statistics. Accessed Mar 2019. 2. Accessed in Jun. 2018. [Online]. Available: https://www.pluglesspower.com/learn/drivingelectricity-cheaper-gas-50-states/ 3. Rahmani-Andebili, M., & Fotuhi Firuzabad, M. (2018). An adaptive approach for PEVs charging management and reconfiguration of electrical distribution system penetrated by renewables. IEEE Transactions on Industrial Informatics, 14(5), 2001–2010. 4. Rahmani-Andebili, M., Shen, H., & Fotuhi Firuzabad, M. (2018). Planning and operation of parking lots considering system, traffic, and drivers behavioral model. IEEE Transactions on Systems, Man and Cybernetics: Systems. https://doi.org/10.1109/TSMC.2018.2824122 5. Rahmani-Andebili, M. (2016). Optimal power factor for optimally located and sized solar parking lots applying quantum annealing. IET Generation, Transmission & Distribution, 10, 2538–2547. 6. Rahmani-Andebili, M., Fotuhi Firuzabad, M., & Moeini-Aghtaie, M. (2018). Chapter 11: Optimal incentive plans for plug-in electric vehicles. In Electric distribution network planning (pp. 299–320). Springer. 7. Ding, Z., Lu, Y., Zhang, L., Lee, W., & Chen, D. (2018). A stochastic resource-planning scheme for PHEV charging station considering energy portfolio optimization and priceresponsive demand. IEEE Transactions on Industry Applications, 54(6), 5590–5598. 8. Ovalle, A., Hably, A., Bacha, S., Ramos, G., & Hossain, J. M. (2017). Escort evolutionary game dynamics approach for integral load management of electric vehicle fleets. IEEE Transactions on Industrial Electronics, 64(2), 1358–1369. 9. Martinez, C. M., Hu, X., Cao, D., Velenis, E., Gao, B., & Wellers, M. (2017). Energy management in plug-in hybrid electric vehicles: Recent progress and a connected vehicles perspective. IEEE Transactions on Vehicular Technology, 66(6), 4534–4549. 10. Vandael, S., Claessens, B., & Ernst, D. (2015). Reinforcement learning of heuristic EV fleet charging in a day-ahead electricity market. IEEE Transactions on Smart Grid, 6(4), 1795–1804. 11. González Vayá, M., & Andersson, G. (2015). Optimal bidding strategy of a plug-in electric vehicle aggregator in day-ahead electricity markets under uncertainty. IEEE Transactions on Power Systems, 30(5), 2375–2385. 12. [Online]. Available: https://crawdad.org/all-byname.html. Accessed Mar 2019. 13. Rahmani-Andebili, M., Bonamente, M., & Miller, J. A. (2020). Charging management of plugin electric vehicles in San Francisco applying Monte Carlo Markov chain and stochastic model predictive control and considering renewables and drag force. IET Generation, Transmission & Distribution, 14(25), 6179–6188.

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14. Rahmani-Andebili, M., Bonamente, M., Miller, J.A. (2020). Mobility analysis of plug-in electric vehicles in San Francisco applying Monte Carlo Markov Chain. 2020 IEEE Kansas Power and Energy Conference (KPEC), 13–14 July 2020. 15. [Online]. Available: https://www.usgs.gov/faqs/how-much-distance-does-a-degree-minuteand-second-cover-your-maps. Accessed Mar 2019. 16. [Online]. Available: http://oceanbeach.org/weather/. Accessed Aug 2019. 17. [Online]. Available: http://content.whiteboxlearning.com/application/glider/g1l0304.html. Accessed Aug 2019. 18. [Online]. Available: https://www.wardsauto.com/technology/slippery-slope-production-carsmaking-jaw-dropping-aerodynamic-gains. Accessed Aug 2019. 19. [Online]. Available: https://en.wikipedia.org/wiki/Automobile_drag_coefficient. Accessed Aug 2019. 20. [Online]. Available: https://www.tesla.com/sites/default/files/blog_attachments/the-slipperiestcar-on-the-road.pdf. Accessed Aug 2019. 21. [Online]. Available: https://www.edmunds.com/electric-car/. Accessed Mar 2019. 22. Rahmani-Andebili, M. (2017). Stochastic, adaptive, and dynamic control of energy storage systems integrated with renewable energy sources for power loss minimization. Renewable Energy (Elsevier), 113, 1462–1471. 23. [Online]. Available: http://www.ferc.gov/market-oversight/mkt-electric/overview.asp. Accessed Apr 2019.

Chapter 4

Role of EVs in the Optimal Operation of Multicarrier Energy Systems Alireza Ghadertootoonchi, Mehdi Davoudi, Moein Moeini-Aghtaie, and Mehdi Rahmani-Andebili

Abstract Multicarrier energy systems have recently gained considerable attention as such a concept can address the mutual interrelation between energy carriers. Multicarrier energy systems aim to satisfy the different types of energy demands of a consumer while minimizing economic and environmental costs. Different energy conversion units are used in an energy hub to achieve this goal. One of the newly added components to energy hubs is electric vehicles (EVs). EVs could be regarded as a pure consumer, or a flexibility provider for the energy hub, as their batteries can be utilized to store the electricity and release it whenever needed. Therefore, EVs can help reduce the operational cost of the energy hub and increase its flexibility and reliability. In addition, one of the inherent characteristics of EVs is their uncertainty of behavior. The energy consumption of an electric vehicle depends on various parameters ranging from the weather condition to the drivers’ behavior. One must be aware of these stochasticities and consider their effect on the optimum scheduling of the system to optimize an energy hub. For instance, one way to reduce the degree of uncertainty is to forecast the behavior of the EV using machine learning methods and use the results in the optimization process. In this regard, numerous studies have been conducted to reveal the effects of EV integration into multicarrier energy systems. Each of these studies modeled the EVs in a specific way, either deterministic or stochastic. This chapter aims to provide a

A. Ghadertootoonchi · M. Moeini-Aghtaie (✉) Department of Energy Engineering, Sharif University of Technology, Tehran, Iran e-mail: [email protected] M. Davoudi Department of Energy Engineering, Sharif University of Technology, Tehran, Iran Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA e-mail: [email protected] M. Rahmani-Andebili Electrical Engineering Department, Arkansas Tech University, Russellville, Arkansas, USA e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Rahmani-Andebili (ed.), Planning and Operation of Electric Vehicles in Smart Grids, Green Energy and Technology, https://doi.org/10.1007/978-3-031-35911-8_4

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comprehensive review of these methods and their related assumptions. Then, at the end of this chapter, a case study will be modeled using the Pyomo optimization package, an open-source Advanced Mathematical Programming Language (AMPL) library in Python, solved using the available open-source solvers such as GLPK, CBC, and HiGHS. The described optimization codes for this chapter are available on the authors’ Gitub at Link. Keywords Energy hub · Integrated energy systems · Multi-carrier energy study · Electric vehicle · Flexibility · Distribution Systems

4.1

Introduction

The increasing demand for using EVs as a means of transportation will eventually affect transportation and power cyber-physical networks. Considering the power network, EVs can have a bidirectional connection with the system and participate in demand response programs. In this regard, various studies have investigated the possibility and potential effects of the vehicle-to-grid (V2G) and the grid-to-vehicle (G2V) models [1]. This chapter aims to analyze and investigate the role of electric vehicles (EVs) in the operation of multicarrier energy networks. To achieve such a goal, the energy hub concept is introduced (Sect. 4.2). The energy hub consists of three main parts, inputs or energy sources (Sect. 4.2.1), energy conversion units (Sect. 4.2.3), and the loads or energy demand (Sect. 4.2.2). Then, the EVs and their role in future energy networks are discussed, and their advantage and disadvantages are demonstrated (Sect. 4.3). At the end of this chapter, a case study is described to provide a comprehensive understanding for the reader. The case study is a mixed-integer linear programming (MILP) model of a residential energy hub considering the impact of EVs. In the following, the concept of multicarrier energy systems, or energy hubs, is introduced and discussed.

4.2

Multicarrier Energy Systems

Multicarrier energy systems, also known as energy hubs, have been developed to increase energy systems’ reliability and reduce their environmental effects. In these systems, the connections between the energy carriers are considered, and according to these relationships, the operational and investment costs of the system are minimized. Energy hub studies are generally divided into two categories: scheduling (i.e., operation planning) and planning (i.e., capacity determination or sizing). In the first case, the optimization models’ goal is to reveal how an existing energy system should operate. In this case, only the energy flow is optimized and operational, and environmental costs are minimized. In the second case, the goal is to determine not only the optimal energy flow but also the optimal capacity of the energy conversion equipment. Energy demand and input carriers must be specified in both cases.

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Fig. 4.1 Schematic of an energy hub (I, input; L, load) [2] Fig. 4.2 Structure of the considered energy hub

After determining the demand and the available energy carriers, the optimal structure of the energy hub is specified. Figure 4.1 shows a typical energy hub that consists of three different parts, each of which will be explained below. The energy hub shown in Fig. 4.1 can be modeled as a matrix. The energy hub matrix is also called the coupling matrix. The input vector of the energy hub turns into the output vector (energy demand) after being multiplied by the coupling matrix. In this chapter, without losing the generality of the discussion, it is assumed that the considered energy hub is a residential one. The following will explain the method of obtaining the coupling matrix of an energy hub. Suppose the energy hub consists of three energy conversion units (ECUs) that convert two inputs, I1 and I2, into three outputs, L1 – L3. The structure of this energy hub is shown in Fig. 4.2. Assuming that each ECU is modeled by its energy conversion efficiency, the governing equations of the hub are represented in Eqs. (4.1) and (4.2). 1 2 × I 1 þ ηECU × I2 Load 1 : L1 = ηECU 11 21

ð4:1Þ

3 2 Load 2 : L2 = ηECU × I 3 þ ηECU × I2 32 22

ð4:2Þ

k where ηECU is the efficiency of converting energy carrier n to load m in ECUk. The nm matrix representation of these equations is brought in Eq. (4.3).

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ECU1

η11 0

ECU

η21 2 ECU η22 2

0

ECU η32 3

2×3

×

I1 I2 I3

= 3×1

L1 L2 2 × 1

ð4:3Þ

The 2 × 3 matrix in Eq. (4.3) is the coupling matrix. A variety of studies have used multicarrier energy system modeling to find the optimal configuration and operation of an energy system. A valuable example in which the concept of coupling matrices is introduced and used is [3], and a similar method considering the impact of EVs is developed in [4]. In addition, authors in [5] modeled an energy system that consists of PV panels, wind turbines, diesel generators, and energy storage systems to cover the electricity demand of an off-grid remote region located in Rafsanjan, Iran. Another study [6] used a system of PV panels, wind turbines, diesel generators, energy storage systems, fuel cells, electrolyzers, and hydrogen tanks to satisfy the energy demand. Furthermore, to capture the effect of different uncertainties such as electricity price and demand, available solar radiation, and wind speed, some studies have utilized stochastic optimization approaches. For instance, a stochastic mixedinteger linear programming model is used in [7] to find the optimal size of the energy conversion units in an energy hub and optimize their operation. The following section will discuss the components of an energy hub, including inputs, outputs, and ECUs.

4.2.1

Energy Sources

Energy sources are the energy carriers entering the multicarrier energy system, which, after being processed by the ECUs, will turn into the final usable energy and are delivered to the consumers. In most cases, energy sources include received electricity from the electricity network and natural gas from the gas network. Figure 4.3 shows the frequency of using different energy carriers as input of the energy hub in previous studies. As is clear from Fig. 4.3, electricity is considered an input carrier in many studies. It is usually assumed that grid electricity is available with the associated price per Fig. 4.3 Input energy carriers in the energy hub optimization studies [8]

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kilowatt hour of consumption. A transformer is used to model the electricity received from the grid and adjust the voltage. It is modeled using the transformer efficiency as shown in Eq. (4.4). Pl = ηtrans × Pg

ð4:4Þ

where Pl is the power sent to the load and Pg is the power received from the grid. Similar to electricity, natural gas is available through the gas pipeline. Water could be another input for the residential energy hubs. For instance, it is used as the heating or cooling provider. The following section will introduce different types of energy demand.

4.2.2

Energy Demand

As mentioned previously, a residential energy hub is considered in this chapter. The residential sector has six main energy consumption categories: space heating and cooling, residential appliances, water heating, cooking, and lighting. Figure 4.4 shows the share of each one in the residential energy consumption of the IEA member countries [9]. As is clear from Fig. 4.4, the three primary purposes of energy consumption are space heating, residential appliances, and space cooling, which altogether stand for 88% of residential energy consumption. As a result, this section aims to familiarize the reader with these energy consumption types and provide a mathematical formulation for them.

Cooking 4%

Lighting 2%

Non-spesified 2%

Space cooling 4% Water heating 16%

Space heating 53%

Residential appliances 19% Fig. 4.4 Share of different end uses in the residential energy consumption

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Fig. 4.5 Different data-based load forecasting methods [10]

4.2.2.1

Electricity Demand

There are two primary methods to obtain the electricity demand data of a household. The first method relies on data gathering and the second on modeling the electricity consumption of residential appliances and lighting. In the first approach, the electricity consumption data of the household is obtained, and then the data is used in the optimization models. In addition, if energy consumption prediction is desired, different forecast methods and data features are tested to find the best set of features and models that predict electricity consumption with the minimum error and the highest accuracy. Figure 4.5 shows different load forecasting methods [10]. Different studies have utilized these methods for residential electricity consumption prediction. Statistical, AI, and tree-based methods are compared in [10], and the results reveal that in the univariant analysis, the tree-based approach (XGboost1) is more successful, whereas in the multivariant method, the statistical-based method (SARIMAX2) shows better performance. Considering the AI-based methods, support vector regression (SVR) is among the popular options for this purpose, and its applications are studied in [11]. In general, developing a one-fit-for-all predicting method for the energy consumption of a single household is not an easy task since it is affected by a variety of parameters such as the number of occupants, the area of the house, type, and the number of the available residential appliances and the difference in the users’ preferences. It should also be noted that the electricity consumption pattern in 1 day is different from another day. In consequence, usually, aggregation methods are considered to overcome these challenges. In this approach, the predictor is developed based on the electricity consumption data of a cluster of households or

1 2

Extreme gradient boost Seasonal Auto-Regressive Integrated Moving Average with eXogenous factors

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Fig. 4.6 Residential electricity consumption of (a) 1 household, (b) 100 households [12]

a neighborhood. Figure 4.6 shows the effect of aggregation on the fluctuations of electricity consumption data [12]. The second approach for modeling residential electricity consumption is through home appliances. These appliances can be categorized into two groups, shiftable and non-shiftable (i.e., flexible and non-flexible). The shiftable appliances can further be categorized into interruptible and non-interruptible appliances [13]. The non-shiftable appliances, as their name implies, are the ones whose operation time cannot be changed and are expected to perform their task in a predefined period. On the other hand, shiftable appliances can work in any period within a time interval and therefore are flexible. The shiftable-interruptible loads (SILs) are the ones that must work for several hours noncontinuously within a time interval, and the shiftable and non-interruptible loads (SNILs) are the ones that must work for several hours continuously within a time interval. Some examples are washing machines, cloth dryers, and dishwashers [14]. The main drawback of modeling the electricity demand of a household using the second approach is that it adds several decision variables (e.g., which appliance should work at each time) and constraints (e.g., whether they should work continuously or not) to the optimization model that increases the complexity of the model. However, the advantage of using such a mathematical formulation is that the demand response strategies can be analyzed with more accuracy in this case. Figure 4.7 shows the behavior of SILs and SNILs [15]. As Fig. 4.7 shows, in both cases, the appliance is in the operation condition within user the user-predefined time interval of [t user start , t end ]. However, SNILs operate without any interruption, while the others work in separate periods. Equations (4.5)–(4.7) show the mathematical model of SNILs [16].

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Fig. 4.7 The behavior of SILs and SNILs. (Inspired by [15])

E=

P × Ot

user Ot = f0, 1g, T = t user start , . . . , t end

ð4:5Þ

t2T

Ot = UT × St

OT = t, . . . , min t user end , t þ UT - 1

⊂ T, St = f0, 1g

t2OT

ð4:6Þ Ot - Ot - 1 ≤ St

user t 2 T = t user start , . . . , t end

ð4:7Þ

Equation (4.5) determines the energy consumption of the appliance (E) based on its power and operation time (Ot) within the specified period. Equation (4.6) shows the number of time steps at which the appliance must be in operation mode. OT is the time interval during which the appliance could work and is a subset of the userdefined period. The decision variable St represents the turn-on action for the appliance. If the model decides to turn the appliance on, the left-hand side of Eq. (4.7) turns into 1, and therefore, the right-hand side (St) must also become 1. It will force the appliance to work during the OT. The mathematical model of SILs is shown in Eqs. (4.8) and (4.9) [17]. P × Ot

E=

user Ot = f0, 1g, T = t user start , . . . , t end

ð4:8Þ

t2T

Ot = UT

ð4:9Þ

t2T

Equation (4.9) ensures that the appliance works for UT hours, yet in contrast to Eq. (4.6), there is no need to work continuously. Along with home appliances, another important residential electricity consumption is related to lighting. A simple model for calculating the lighting demand can be found in [15]. In the next section, the heating and cooling demands are described.

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4.2.2.2

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Heating and Cooling Demand

As shown in Fig. 4.4, space heating and cooling are two of the primary energy consumers in the residential sector. Therefore, it is vital to consider them as loads in the energy hub modeling. Traditionally, the heating demand is satisfied by fossil fuels such as natural gas, and a considerable amount of subsidy is paid for them [18]. Forty-two percent of the heating demand is satisfied by natural gas in 2021, while renewables account for 11%. However, according to the net-zero emission scenario of the International Energy Agency (IEA), these numbers should alter to 32% and 23% by 2030 [19]. Figure 4.8 shows the share of different fuels in providing the heating demand of the residential sector in the IEA-selected countries [20]. Similar to the previous section, the heating and cooling loads could be estimated using two approaches. The first approach is based on gathering data by direct

Norway Sweden Finland Chile Estonia New Zealand Japan Poland Greece Denmark Ireland Spain Czech Republic Austria Belarus France Belgium Germany Canada Ukraine Australia Hungary Korea Italy United States United Kingdom Netherlands Argentina 0% Gas

Oil

10%

20%

Coal

30% Heat

40% Other

50%

60%

70%

Biofuels and waste

80%

90%

100%

Electricity

Fig. 4.8 Share of different fuels in providing the heating demand of the residential sector in the IEA-selected countries [20]

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measurement or simulation software and then utilizing the data in the optimization process. The second approach is to model the heating or cooling demand of the household mathematically. The advantage of the first approach is its simplicity. However, the energy hub optimization model has no access to the thermal energy demand model and cannot control indoor temperature. The second method is more flexible as the demand model is a part of the optimization model [21]. Nonetheless, developing an integrated demand-supply model is more challenging because the demand model is usually nonlinear and complex. One simple solution to calculate the heating and cooling demand of a small building is heating degree days (HDD) and cooling degree days (CDD) [22]. The HDD and CDD are calculated using Eqs. (4.10) and (4.11), respectively. HDDm =

Dm d=1

CDDm =

Dm d=1

T heating - T mean d

þ

ð4:10Þ

T mean - T cooling d

þ

ð4:11Þ

where Theating is the heating set point temperature, and when the average daily temperature is below it, the heating system should work. Tcooling is the cooling set point temperature, and when the average daily temperature is above it, the cooling system should work. The + sign indicates that only the positive terms must be added together and Dm is the number of days in month m. After calculating HDD and CDD, the cooling or heating energy consumption during month m is calculated based on Eq. (4.12) [23]. Qm =

U × A × HDDm or CDDm × t ηheating or ηcooling

ð4:12Þ

where U is the overall heat transfer coefficient, A is the heat transfer area, t is the number of hours in 1 day at which indoor temperature is controlled, and the denominator represents the efficiency of the heating or cooling system. Figure 4.9 shows the average daily temperature, heating and cooling setpoints, and the value of the HDD and CDD. Based on Fig. 4.9, HDD is 22, and CDD is 8. Considering the value of 2 mW2 k for U, 100 m2 for A, and 0.9 for efficiencies, the heating and cooling energy demand would be: Heating : Em =

2 × 100 × 22 × 24 ffi 117:3 kWh=month 0:9

Role of EVs in the Optimal Operation of Multicarrier Energy Systems

Temperature (C)

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30 25 20 15 10 5 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Day of month HDD

CDD

T (mean)

T (Heating)

T (Cooling)

Fig. 4.9 HDD and CDD for a hypothetical building

Cooling : E m =

2 × 100 × 8 × 24 ffi 42:67 kWh=month 0:9

Therefore, the average heating and cooling demand for the considered building KWh are 1.17 KWh m2 and 0.43 m2 , respectively. Despite the simplicity of the HDD-CDD approach, in some cases, it leads to inaccurate results. A more accurate and accepted method is based on the mass and energy balance equations in buildings. The overall mathematical formulation of the thermal model, in this case, is shown in Eq. (4.13). m×c×

dT = Qinput - Qoutput dt

ð4:13Þ

where m is the indoor air mass, c is the specific heat capacity, and dT dt represents the indoor temperature inertia. Qinput accounts for all heating inputs such as solar energy, heater, and heat generated by the human body and home appliances. Qoutput represents the cooling sources like chillers and heat loss to the outdoor environment [24]. This model can be solved using discretization and having the initial temperature (T0), as shown in Eq. (4.14). m×c×

Tt - Tt - 1 = Qinput - Qoutput Δt

ð4:14Þ

In this method, all mass and energy flows in the building are identified, and a differential equation is solved typically by numerical methods to find and control the indoor air temperature, relative humidity, and CO2 concentration. Generally, these models are highly nonlinear and complex. A simple and linear version of these models is discussed in the following. Assuming that indoor air temperature is the control variable and the thermal mass of the controlled environment is lumped, a dynamic two-node thermal model can be developed using the equivalent electric circuit shown in Fig. 4.10 [25]. The governing differential equations are demonstrated in Eqs. (4.15)–(4.17) [25].

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Fig. 4.10 Equivalent electric circuit of a two-node thermal model [25]

Ci

dT a = U:A:ðT ext - T a Þ þ K i :ðT s - T a Þ þ S:W a :Qsun þ Qinternal þ Qheat - Qcold dt ð4:15Þ Cw

dT w = K w : ðT s - T w Þ dt

ð4:16Þ

K w :ðT s - T w Þ þ K i :ðT s - T a Þ þ Qsun :W w :Qsun = 0

ð4:17Þ

Equation (4.15) describes the indoor air temperature changes, while Eq. (4.16) represents the temperature change of the thermal mass. Equation (4.17) demonstrates the energy balance on the surface. This method is adopted from the European Standard EN ISO 13786. Ta is the indoor air temperature, Text is the environment temperature, Ci is the heat capacity of air, U is the overall heat transfer coefficient, A is the heat transfer area, Ki is the conductivity, S is the shadow percentage, Wa is the percentage of sun’s energy absorbed by the air, Qsun is sun’s heat, Qinternal is the heat generated from the human body and other appliances, Qheat is the heat generated by the heater, and Qcold is the heat absorbed by the chillers. The advantage of this model is that it is linear and can effectively integrate into the MILP optimization models. The next section will discuss the fresh and hot water demands.

4.2.2.3

Water Demand

Water could be another input for the residential energy hubs. The residential sector consumes fresh water for drinking and washing and hot water for showers. It is estimated that between 7% and 25% of energy consumption in the residential sector is due to hot water requirements. Freshwater consumption could be modeled as a periodic phenomenon, and the sin function can be used to estimate hourly freshwater demand inside a residential area. In [26], a double sine equation (DSE) is used to simulate the freshwater demand m3 per 15-min (Wt) intervals as shown in Eq. (4.18). W t = W b þ A1 sin

2π t þ φ1 T1

þ A2 sin

2π t þ φ2 T2

ð4:18Þ

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where T is the cycling period, A is the oscillation amplitude, φ is the beginning of oscillation in the cycle, and Wb is the offset value. This model can be tuned by having historical water consumption and can be used to generate water demand data. Domestic hot water (DHW) is another consumer of water. DHW consumption is affected by various parameters such as weather and climate condition, type of building, age, and number of inhabitants. Five different methods to estimate the heat power demand for DHW consumption are presented in [27]. The heating demand for DHW can be calculated using Eq. (4.19). QDHW =

° × cw × ðT DHW - T 0 Þ ρw × V DHW ηheater

ð4:19Þ

° is the hourly volume flow rate of the DHW in Liter where V DHW h . For a single-family dm3 , and for a multi-family building, the value building, the DHW demand is 35 Person:day dm3 is 48 Person:day [27]. To transform the daily value into the hourly values, Eq. (4.20) can be used.

° = V DHW

° V DHW,daily τDWH

ð4:20Þ

where τDWH is the number of hours that the DHW is required. Therefore, having the period within which the hot water is needed, one can calculate the average volume flow rate and heating demand (using the desired DHW temperature). Various energy conversion units (ECUs) that can be considered in an energy hub are discussed in the following section.

4.2.3

Energy Conversion Units

As mentioned in 1.2, an energy hub has three main sections, inputs or energy sources, outputs or energy demand, and a set of ECUs that connect these two sections. ECUs will transform the input energy into a form accepted by the end user to satisfy the demand. This section aims to provide the mathematical models of some of the well-known ECUs. These models will later be used in the case study of the chapter.

4.2.3.1

Boiler

Boilers are the main ECU for generating heat. They can be used for DHW generation and space heating. The input fuel of boilers can be electricity or natural gas. Eqs. (4.21) and (4.22) show the output in each case.

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Qele,b = ηele,b × E ele,b

ð4:21Þ

° × LHVng × ηng,b Qng,b = V ng

ð4:22Þ

where ηele, b is the efficiency of the electric boiler and Eele, b is the input electricity. In 3 ° is the natural gas flow rate (mh ); LHVng is the lower heating value of Eq. (4.22) V ng the natural gas, which is about 10 KWh m3 ; and ηng, b is the natural gas boiler’s efficiency. The heat generated from the boiler must be equal to the heating demand calculated based on Eqs. (4.15) and (4.19). It means that the boiler should have enough capacity to satisfy DHW and space heating demand based on Eq. (4.23). Qele,b þ Qng,b = QDHW þ Qheat

ð4:23Þ

One cleaner alternative for electric or natural gas-powered boilers is solar water heaters (SWHs), which are discussed in the next section.

4.2.3.2

Solar Water Heater

There are three main types of SWHs, SWH with a glazed collector, with an evacuated collector, and with an unglazed collector. The last one is the least efficient alternative in the market. The mathematical model of the evacuated and glazed collectors is the same and represented in Eq. (4.24) [28]. Qswh = ½F R × ðσ × αÞ × G - F R × U L × ðT fluid - T env Þ × A

ð4:24Þ

where Qswh1 is the heat collected by the glazed or the evacuated collector per unit time, FR is the heat removal factor, σ is the cover transmittance, α is the short-wave absorptivity, G is the solar radiation, UL is the overall heat loss coefficient, Tfluid is the fluid temperature inside the collector, and Tenv is the outside temperature. The typical values of FR × (σ × α) and FR × UL in glazed collectors are 0.68 and 4.9, respectively. The figures are 0.58 and 0.7 for evacuated tube collectors.

4.2.3.3

Photovoltaic Panel

Photovoltaic panels (PV) are one of the most famous ECUs widely used in energy hub studies. There are two approaches to modeling a PV system. First, attain the PV electricity generation data from online tools and software like renewables.ninja website. Second, gather the irradiation data and calculate the generated power of a panel based on the available formulations. The power generation of a solar cell depends on the panel’s efficiency and available solar irradiation. Assume G is the solar irradiation (mW2 ), ηpv is the panel

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efficiency, and A is the area of the solar system. The power output can be calculated based on Eq. (4.25). Ppv = ηpv × G × A

ð4:25Þ

where A is the total area of the PV panels. A simple approach is to consider that efficiency is a constant parameter. However, the efficiency is a function of temperature and irradiation, as shown in Eq. (4.26) [29]. ηpv = ηref × 1 - βref × T pv - T ref þ γ log 10 G

ð4:26Þ

where ηref is the PV panel reference efficiency at Tref (25 °C) and Gref (1000 mW2); βref is the temperature coefficient, which is a number between 0.004 and 0.005 °1C; γ is the radiation intensity coefficient, which is usually considered to be 0; and Tpv is the temperature of the PV panel and can be calculated based on Eq. (4.27). T pv = T env þ

T NOCT - 20 ×G 800

ð4:27Þ

where TNOCT is the nominal operating cell temperature (around 45 °C). Therefore, having the environment temperature and solar radiation, one can calculate the efficiency of a PV panel and then the power output.

4.2.3.4

Combined Heating and Power System

Combined heating and power (CHP) systems are a promising solution to increase efficiency and reduce the energy system’s environmental impact. They usually consume natural gas and generate heat and electricity. There are two main approaches to modeling the performance of a CHP system. The first method assumes that the system has a heating and electrical efficiency and generates heat and electricity based on Eqs. (4.28) and (4.29). ° × LHVng × ηCHP QCHP = V ng heat

ð4:28Þ

° × LHVng × ηCHP ECHP = V ng ele

ð4:29Þ

Therefore, the electrical and heating efficiencies are sufficient to model the CHP performance. The second and more sophisticated method is based on defining an operational or feasible region for the CHP [30]. In this case, a feasible region is defined for the system, as shown in Fig. 4.11. Based on Fig. 4.11, Eqs. (4.30)–(4.32) can be considered for the operation of the CHP system.

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Fig. 4.11 CHP system feasible operation region

Line A :

4 × ECHP þ QCHP ≥ 120

ð4:30Þ

Line B : - 4 × E CHP þ 5 × QCHP ≤ 120

ð4:31Þ

Line C : 8 × E CHP þ 3 × QCHP ≤ 800

ð4:32Þ

It should be noted that based on Fig. 4.11, the feasible region is convex. In the following section, the model of a chiller is presented.

4.2.3.5

Chillers

Similar to boilers, there are two main types of chillers. Compression chillers consume electricity and turn it into cooling, and absorption chillers consume heat (generally waste heat from other sources) and turn it into cooling. The chillers can be modeled using the coefficient of performance as shown in Eq. (4.33). Qchiller =

Ecomp or Qabs COP

ð4:33Þ

where Ecomp is the electricity consumption of the compression chiller and Qabs is the heating requirement of the absorption chiller. The coefficient of performance for absorption chillers is between 0.7 and 1.5, while that of compression chillers is more than 4 [31].

4.2.3.6

Energy Storage Systems

There are different types of energy storage systems, electricity storage, heat storage, and cooling storage. By using energy storage systems, consumers can reduce their peak power consumption costs. This is due to the fact that these devices help the

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consumers to shift the energy import from the grid from the peak time to the off- or mid-peak times. The linear model of the electricity storage system (ESS) is brought in Eqs. (4.34)–(4.38) [32]. E t = E t - 1 þ Pch t × ηch -

Pdis t × Δt ηdis

ð4:34Þ

bat ch Pch t ≤ Capch × δt

ð4:35Þ

bat dis Pdis t ≤ Capdis × δt

ð4:36Þ

E t ≤ Capbat

ð4:37Þ

ch δdis t þ δt ≤ 1

ð4:38Þ

Equation (4.34) shows the time dependency between the amount of energy stored in the ESS. Eqs. (4.35) and (4.36) show the upper bound of the input and output energy. Equation (4.37) states that the energy stored in the battery should be less than its capacity. Finally, Eq. (4.38) represents that the charging and discharging cannot happen simultaneously. It is worth mentioning that to extend the ESS life, it should not be fully charged or discharged. To prevent such a condition, Eq. (4.37) should be modified to Eq. (4.39). ð1 - ψ Þ × Capbat ≤ E t ≤ Capbat × ψ

ð4:39Þ

For instance, if ψ = 0.9, then the battery will be charged to 90% of its capacity and discharged to 10% of its capacity. In addition, the effect of self-discharge can be taken into account by adding a constant parameter to the right-hand side of Eq. (4.34) or multiplying the Et - 1 by a constant factor as shown in Eq. (4.40). Et = α1 × E t - 1 þ Pch t × ηch -

Pdis t × Δt - α2 ηdis

ð4:40Þ

where α1 is a positive number less than 1 and α2 is a positive number. The general formulation of the heat and cooling storage systems are also the same. Eqs. (4.41) and (4.42) show the mathematical model of a heat storage system [33]. mstorage water × cw ×

- T storage T storage t ° t-1 - U storage × Astorage × T storage - T env = Qin° - Qout t Δt ð4:41Þ storage T storage ≤ T storage man ≤ T t max

ð4:42Þ

° is the output heat from the storage, Ustorage is the where Qin° is the input heat, Qout overall heat transfer coefficient, and Astorage is the heat transfer area between the storage unit and the environment. The cooling storage (known as the ice storage or

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ice thermal storage) is modeled similarly except that for the ice storage, the losses are input and Qin° reduces the system temperature. Therefore Eq. (4.41) will change to Eq. (4.43) [34]. In addition, if there is ice in the system, the latent heat capacity should be considered. - T storage T storage t ° t-1 - Qin° þ U storage × Astorage = Qout Δt × T storage - T env t

mstorage water × cw ×

ð4:43Þ

In the following section, a residential case study is modeled using the MILP method in the Pyomo environment considering the effect of various EV parameters and the introduced ECUs.

4.2.3.7

Electric Vehicles

The mathematical formulation of EVs is similar to an ESS, as shown in Eqs. (4.44)– (4.50) [35]. The difference is that the EV is accessible only between the arrival time (tarr) and the departure time (tdep). PE2G þ PE2H = Pdis t t t

ð4:44Þ

1 dis dis 1 Mindis EV × λt ≤ Pt ≤ MaxEV × λt

ð4:45Þ

2 ch ch 2 Minch EV × λt ≤ Pt ≤ MaxEV × λt

ð4:46Þ

EV ch ch EEV t = E t - 1 þ ηEV × Pt -

Pdis t × Δt ηdis EV

EV, max E EV, min ≤ E EV t ≤E EV,dep EEV t ≥E

t = t dep

λ1t þ λ2t ≤ 1

ð4:47Þ ð4:48Þ ð4:49Þ ð4:50Þ

Equation (4.44) states that the discharged power from the EV can be sent either to the grid or the house. Equation (4.45) defines the upper and lower bounds of the EV discharge rate; the binary variable is to prevent charging and discharging simultaneously. The charging limitation constraint is brought in Eq. (4.46). The state of energy of the EV battery is represented in Eq. (4.47). The battery state of the charge must be in a particular range based on Eq. (4.48). Equation (4.49) shows the amount of energy that must be stored in the EV battery at the departure time. Finally, Eq. (4.50) states that the EV should be in charging, discharging, or neither mode at each time interval. It should be mentioned that in all the above equations, except Eq. (4.50), t 2 [tarr, tdep].

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4.3

87

EVs as a New Type of Electricity Consumer

Fossil fuel consumption is highest in the transport sector compared to the other economic sectors like industry, residential, agriculture, etc. As a result, the CO2, and in general, greenhouse gas emission of the transportation sector, is more than one-third of the global greenhouse gas emission per year. The annual CO2 emission from the transportation sector is nearly 8 GT y [36]. One promising solution to tackle this challenge is the utilization of EVs since they reduce emissions regardless of their energy source. To compare the emissions and cost of EVs and traditional internal combustion engine vehicles (ICEVs), the following formulation can be used. The emission of the ICEVs and EVs can be calculated based on Eqs. (4.51) and (4.52). Furthermore, the fuel or energy cost of these vehicles is computed based on Eqs. (4.53) and (4.54) [37]. EmissionICEV = Fuel consumptionICEV × Emissionfuel

ð4:51Þ

Electricity consumptionEV × Emissionelectricity ηdischarge

ð4:52Þ

EmissionEV =

CostICEV = Fuel consumptionICEV × Costfuel CostEV =

Electricity consumptionEV × Costelectricity ηcharge

ð4:53Þ ð4:54Þ

Some typical values for the parameters used in Eqs. (4.51)–(4.54) are introduced in Table 4.1. Currently, a global effort is in progress to accelerate the adaptation of these vehicles. Based on the reports of the International Energy Agency (IEA) in 2021, nearly 10% of global car sale was electric, which is four times greater than in 2019 [38]. It indicates the rapid growth of the EV market, which without a doubt will affect the energy system and household behavior. Figure 4.12 shows the EV unit sold each year and the projections for 2030 across the world [39]. As can be seen, the popularity of battery electric vehicles (BEV) is more than that of plug-in hybrid electric vehicles (PHEV). Also, it is expected that more than 16 million EVs be on the road in 2030. Currently, Tesla has the highest market share and revenue in the EV market. The share of each company in the revenue of EV manufacturing in 2021 is shown in Fig. 4.13 [39]. The most popular EV in the market is the Tesla Model 3, followed by Wuling HongGuang Mini EV and the Tesla Model Y. These three cars make up more than 65% of the top ten cars, sold in 2021. Table 4.2 shows the energy efficiency and other characteristics of Tesla model 3 and model Y [40]. All the cars introduced in Table 4.2 are more efficient than an average and typical EV as their electricity consumption is less than 200 Wh km . The most efficient electric vehicle is the Hyundai IONIQ 6 Standard Range 2WD with 150 Wh km and the least

88 Table 4.1 The value of Eqs. (4.52)–(4.55) parameters [37]

A. Ghadertootoonchi et al. Parameter Fuel consumptionICEV

Value 0.111

Electricity consumptionEV

0.193

ηcharge Emissionfuel

0.875 2.348

Emissionelectricity

1.08

Unit L km kWh km

% kg CO2 L kg CO2 kWh

Fig. 4.12 Number of EVs sold annually [39]

Fig. 4.13 Share of each company in the revenue of making EVs in 2021 [39]

efficient one is the Mercedes EQV 300 Long with 295 Wh km [41]. However, one should be aware that the large-scale adaptation of EVs will cause challenges such as: • Increase the electricity demand. • Increase the uncertainty on the demand side. • Increase the complexity of scheduling the charging, discharging, and demand management.

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Table 4.2 Important characteristics of Tesla model 3 and model Y [40] Car name Tesla Model 3 Tesla Model 3 Long Range Dual Motor Tesla Model Y Tesla Model Y Long Range Dual Motor

Usable battery (KWh) 57.5 75

Real range (km) 380 485

Efficiency (Wh/km) 151 155

57.5 75

345 435

167 172

• User preferences and battery health are new parameters that must be considered. • Consumers will face range anxiety when using EVs. One possible solution to the problem of electricity demand growth, which will increase the peak-to-average ratio, is to encourage consumers and EV owners to participate in the demand response programs and shift their load based on the suggested incentives [42]. The other conceivable solution in this regard is to specify the electricity price dynamically to encourage temporal load shifting. Such an approach will smoothen the power curve and diminish the possibility of having intense fluctuations as shown in Fig. 4.14 [43]. Considering the above, a variety of studies have been conducted to model the impact of EVs on the optimal operation of multicarrier energy systems. For instance, the impact of integrating plug-in hybrid electric vehicles on an energy hub is studied in [44]. Moreover, the impact of different EV charging patterns on the energy hub management strategies is studied in [45]. The study considered three charging patterns for EVs and analyzed their impact on the daily operational cost of the multicarrier energy system; these are rapid charging pattern, uncontrolled charging pattern, and smart charging pattern. The results show that the uncontrolled charging pattern increases the operational cost during the peak hours by about 9%, while the smart charging pattern does not affect the peak electricity consumption. In addition, the impact of EV integration on the size of energy storage units in an energy hub is studied in [46]. Based on the mentioned data, EVs are going to be one of the most important parts of the future electricity network. Therefore, their characteristics must be thoroughly investigated and considered in the energy management models. In this regard, the next section will discuss the stochasticity of EVs.

4.3.1

Uncertainties of EVs’ Energy Demand

There are some fundamental differences between EVs and other electric appliances considered in a residential energy hub. The main difference is the significant degree of uncertainties associated with these new electricity consumers. These stochasticities are related to a variety of parameters such as the distance traveled,

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Fig. 4.14 The scheduled charging considering price incentives reduces the fluctuations and smoothens the power [43]

driving habits, weather, battery life (i.e., age), initial level of energy [47], and arrival and departure times [48]. As a result, when the goal is to optimize an energy hub considering the effect of EVs, it is advisable to investigate the effect of these uncertainties. In the following sections, each of the stated parameters will be discussed.

4.3.1.1

Travel Distance

Each time users use their EVs, the distance traveled is different. This will affect the energy demand of the EV when it is returned to the house and plugged into the charge. This uncertainty can be identified by gathering data and finding the lower bound and upper bound of the distance traveled. Then, a stochastic optimization model can be used to consider the effect of the parameter on the optimal performance of the residential energy hub [49]. The daily distance traveled depends on different parameters such as the living place and whether the day is a weekday or a weekend. Studies show that the average daily distance traveled in the USA is 47 Km, while that of Denmark and the UK are

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45 and 34, respectively [50]. Authors in [51] used the Birnbaum-Saunders distribution to model the travel distance. In addition, the gamma distribution is used to model the frequency and the duration of travel (minutes). The gamma distribution is shown in Eq. (4.55). Pðxja, bÞ =

1 x xa - 1 exp b b Γ ð aÞ a

ð4:55Þ

For the daily travel frequency, a = 3.71 and b = 0.64, whereas for travel duration, a = 1.87 and b = 18.35 [51]. In the next section, the impact of the driving pattern on the energy consumption of EVs is discussed.

4.3.1.2

Driving Pattern

The driving pattern of a driver is affected by a variety of parameters such as the weather, the traffic condition, the vehicle, the driver’s habits, the road and street, and the travel itself [52]. The term “driving pattern” is usually a representation of the speed profile of the vehicle. Some of the well-known driving patterns are introduced in [53]. For instance, Fig. 4.15 shows the velocity profiles of the Extra Urban Driving Cycle (EUDC) and Highway Fuel Economy Test (HWFET). It is shown that fuel consumption and emission are related to the driving pattern for traditional internal combustion engine vehicles. Considering the EVs, the energy consumption of the vehicle will increase by 7 to 10.5% when the traffic is heavy [54].

4.3.1.3

Weather Effect

Weather-related parameters such as wind speed and direction, air temperature, and precipitation affect the energy consumption of electric vehicles. These impacts will be discussed in the following:

Fig. 4.15 The velocity profiles of the EUDC and HWFET [53]

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• Temperature: The air temperature affects energy consumption by changing the battery’s internal resistance, which affects the ability of the battery to provide energy for the EV. The less the temperature, the more the battery resistance. Additionally, it will affect energy demand for adjusting the in-car temperature (heating or cooling system). It is shown that the energy consumption of the EV could be 40% higher in the winter compared to spring and summer [55]. Figure 4.16 shows the seasonal change of the energy consumption per 100 km for a personal EV and a taxi EV [55].

Fig. 4.16 The seasonal change of the EV electricity consumption per 100 km for (a) personal use and (b) a taxi [55]

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As can be seen from Fig. 4.16, in winter the electricity consumption is higher. Hence, the energy stored in the battery when entering the charging station is lower. Such an effect should be taken into account in the optimization model of an energy hub. In addition, the effect of driving patterns and the difference between taxis and personal EVs on electricity consumption is clear in Fig. 4.16. The primary reason behind the high electricity demand in winter is the higher rate of utilizing the heating system. The power demand of the heating system (and other auxiliary systems) can be calculated using Eq. (4.56) [53]. Pfrom battery =

Pheating ηdischarge ηc

ð4:56Þ

where Pfrom battery is the power extracted from the battery for heating (or other auxiliary units) in W, Pheating is the required power for maintaining the temperature inside the car, ηdischarge is the discharge efficiency of the battery, and ηc is the DC/DC inverter efficiency. • Wind speed and direction: One of the main forces that the EV should overcome to move is the drag force, which is directly affected by the direction and speed of the wind. If the wind blows from the front, the energy consumption will be more than when the wind blows from behind the EV. The effect of wind intensity on the energy consumption of the vehicle is shown in Fig. 4.17. The energy consumption is calculated considering the HWFET driving pattern [56]. • Precipitation: Along with the drag force, the rolling resistance is another force that should be overcome so that the vehicle can move. When the weather is rainy, there is a layer of water on the road, and it is shown that the thicker the water layer collected on the road surface, the higher the RR coefficient [57]. Furthermore, on rainy days, the screen wiper should be on, which leads to higher energy consumption [58].

Fig. 4.17 The effect of wind intensity on the energy consumption of the EV (red, calm; green, light breeze; blue, moderated breeze; and black, strong breeze) [56]

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4.3.1.4

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Departure and Arrival Time

Arrival and departure times are another important and uncertain parameter that have a direct impact on the optimal operation of the energy system. It is because the optimization model should have sufficient information about the time EV is available and the time it will leave the hub so that it can manage the energy flow effectively and optimally. EV arrival time can be considered a random process. In this chapter, the methodology utilized in [59] is adopted and described. This approach relies on two probability density functions (PDF): Firstly, the lognormal PDF is used to model the total number of EVs in need of charging, and secondly, the normal PDF is utilized to model the number of EVs entering the charging station at each time slot. To specify whether an EV needs of electricity, its state of the charge (SOC) must be compared with a lower bound threshold, which is considered to be 20%. If the SOC is less than 20%, the EV needs to be charged. The SOC can be calculated using Eq. (4.57). SOCc = SOCf -

d D

ð4:57Þ

where d is the total cumulative distance traveled between the last charge and the current time slot, D is the total distance the EV can travel, SOCc is the current SOC, and SOCf is the final SOC, which depends on the EV type and model. Hence, the SOC of the EV is a direct function of the distance traveled, and if the aim is to predict the arrival time of the EVs to the charging units, the SOC should be known and estimated as accurately as possible. To estimate the distance traveled for each EV, the lognormal PDF is fitted on the relative data as shown in Fig. 4.18. The lognormal PDF is shown in Eq. (4.58).

Fig. 4.18 Daily traveled distance (blue bar) and the fitted PDF (red line) [59]

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Fig. 4.19 The process of calculating the number of EVs in need of charge

Pðdjμ, σ Þ =

ðln d - μÞ2 1 p exp 2σ 2 dσ 2π

ð4:58Þ

where d is the distance traveled in a day, μ is the mean value equal to 1.9 and σ is the standard deviation equal to 1.1, and P(d| μ, σ) is the probability that the distance d is traveled in a day. Therefore, the PDF shown in Eq. (4.58) is used to attain the daily traveled distance. Then, the distance is added to the cumulative distance traveled (d) and SOCc is calculated; if it is less than 20%, the EV needs to be charged within the next 24 h. The process is shown in Fig. 4.19. After obtaining the total number of EVs in need of charging (Nev), the second PDF is used to determine the time slots (48 half-hour slots are considered) the EVs arrive at the charging station. The normal PDF is used for this purpose, as shown in Eq. (4.59). ð t - μ Þ2 1 Pðtjμ, σ Þ = p exp 2σ 2 σ 2π

ð4:59Þ

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Table 4.3 Seasonal value of mean and standard deviation Parameter μ σ

Winter 14:00 3 h 42 min

Spring 13:12 4 h 46 min

Summer 13:24 4 h 2 min

Autumn 13:42 4 h 12 min

Fig. 4.20 Estimation of the EVs’ arrival time

where μ is the mean of the arrival time, σ is the standard deviation, and P′(t| μ, σ) shows the probability that an EV enter the charging station at time t. μ and σ are season dependent, and their seasonal value for the UK is shown in Table 4.3. To obtain the number of EVs that arrive at time slot t, algorithm 1 is used. Algorithm 1 For t = 1:48 U t = N ev -

t-1 1

EVtarrived

N = integer random between 0 and Ut R= Generate N random number between 0 and 1. Threshold = P(t| μ, σ) Eq. (53) EVtarrived = 0 For n = 1 : N If Rn ≤ Threshold EVtarrived = EVtarrived þ 1 End if End for Save EVtarrived End for The result of running these two algorithms is shown in Fig. 4.20. As can be seen from Fig. 4.20, the peak hours of EVs’ arrival are between 11 and 13. Furthermore, the location-scale distribution is used to model the departure time, as shown in Eq. (4.60) [51].

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Pðtjμ, σ, υÞ =

Γ Γ

υ 2

υþ1 2

p σ υπ

υþ

t-μ 2 σ

υ

97

- υþ1 2

ð4:60Þ

where t is the departure time and P(t| μ, σ, υ) is the probability of departure at time t. Based on the dataset utilized in [51], the values of μ, σ, and υ are 8.36, 1.08, and 2.16, respectively. Another index that affects the EVs’ electricity demand is the age of the battery, which is discussed in the next section.

4.3.1.5

The Age of the Battery

One must be aware that the battery capacity decreases over time. As a result, charging times, electricity costs, and network demand will all rise. A Li-ion battery can lose up to 20% of its capacity in the first year of operation [60]. The depth of discharge (DOD) of the battery will is a vital parameter. Studies have shown that the more the DOD, the sooner the battery capacity drops [61]. In addition, the SOC of the battery affects the energy demand of the EV. The less the current SOC of the battery, the less energy is provided by the battery. Such a relationship is clearly shown in Fig. 4.21 [56].

Fig. 4.21 The relationship between the SOC, temperature, and the energy that can be provided by the battery [56]

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Figure 4.21 shows that when the environmental temperature and the SOC of the battery are low, the ability of the battery to provide sufficient energy is diminished and vice versa. One of the available options to model mentioned uncertainties and consider their effect on the energy management models is through machine learning methods. These methods can predict some of the uncertain parameters, like the arrival and departure time, and their predictions can later be used in the optimization model.

4.3.2

Role of Machine Learning Models

Machine learning (ML) models are effective tools to model the performance of EVs and reduce the degree of uncertainty in the optimization models. It is because these models can be used to predict the energy demand, range, and arrival and departure time and even learn to predict the weather. Generally, the ML models can be categorized into three groups, supervised learning, unsupervised learning, and reinforcement learning, each of which has some applications with regard to the EVs. Figure 4.22 shows the classification of ML algorithms. The supervised learning models aim to predict the performance of the system given sufficient data about its historical behavior. These models are divided into two groups the regression models and the classification models as shown in Fig. 4.22. The regression models can be used for learning a variety of EV-related parameters such as charging energy consumption estimation [62], arrival and departure times [63], travel energy consumption estimation [64], and range estimation [65]. For instance, in [66] a support vector machine (SVM) is used to estimate the electricity

Fig. 4.22 Classification of machine learning algorithms

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99

demand of a charging station. The methodology of the study is described in the following. To apply the ML models effectively, the modeling features should be specified accurately. In [66], after gathering the historical data for a year, a set of nine attributes is used for the short-term forecast of the charging electricity demand; these features are: 1. 2. 3. 4. 5. 6. 7. 8.

Week: is a set of integers from 1 to 52. Day: represents number of days in a week starting from Monday. Time slot: shows the time t (in a half hour) in a day. Number of EVs arrived at time t: this attribute can be calculated based on the formulation introduced in Sect. 4.3.1.4. Load: the electricity demand at time t. Normalized load: the normalized value of the load at time t. Previous week load: the normalized electricity demand of the previous day in the previous week at time slot t. Average of the 4 previous weeks: the average normalized demand of the 4 previous days for the past 4 weeks at time t.

These features are used for training the SVM model and results show that the model is capable of predicting the charging demand with higher accuracy in comparison to the Monte Carlo statistical method. Figure 4.23 shows the output of the SVM model and the real data. In addition, the classification models can be used to classify the drivers based on their driving patterns and then predict the energy consumption and demand of each

Fig. 4.23 The forecast of the SVM model and the actual data [66]

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class. The model then can be used to predict the behavior of the new drivers. In the following, the unsupervised and reinforcement learning algorithms are brought: • Unsupervised learning: Unsupervised learning algorithms have no access to the label of data; in other words, they do not know which data belongs to which class. These algorithms can be used to cluster EVs based on their arrival and departure time or based on their charging behavior [67]. • Reinforcement learning: Reinforcement learning (RL) is another class of ML algorithms that is an optimal control algorithm. It can be used to learn the daily plan of the EV owner and manage the residential energy hub based on that. It can also be used to find the best and optimal route for the EV driver so that its time and distance are minimized [68].

4.4

A Case Study of Mathematical Modeling of EVs in an Energy Hub

The concept of the energy hub and the characteristics of EVs are presented and discussed in the previous sections along with their mathematical formulation. This section aims to introduce a case study considering different energy demands, sources, and energy conversion units demonstrated before. The case study starts with a simple energy hub structure and moves forward toward a more complex system. To investigate and analyze the effect of EV integration into the energy hub, different sensitivity analysis is performed. Two main variations of the optimization model of the case study are deterministic models and stochastic models. While the former is used widely for scheduling and planning of the energy hubs, the latter is more accurate since it takes into account a variety of uncertainty sources such as renewables’ energy generation and the behavior of the EV. Before introducing the case study, it is worth introducing the optimization algorithms that have been used to solve the optimal energy flow and sizing problem. The applications of some of the famous algorithms are briefly discussed in the next section.

4.4.1

Applicable Optimization Algorithms

Energy hub optimization models are usually solved using MILP optimization models. However, other models such as heuristic and metaheuristic algorithms are also applicable. The genetic and particle swarm optimization (PSO) models are among the well-known algorithms in this realm. Figure 4.24 shows the classification of heuristic optimization algorithms.

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Fig. 4.24 Different categories of heuristic optimization algorithms (inspired by [69, 70])

The PSO algorithm is utilized to optimize an energy hub consisting of a combined heating and power (CHP) system, a gas furnace, and a transformer to minimize total capital and operational cost and maximize total energy costs and emissions [71]. Another study used the PSO algorithm to find the optimal operation of an energy hub with combined heating, power, and cooling system (CCHP), electric boiler, gas boiler, electric chiller, and renewables to minimize total capital and operational cost and maximize the utilization of renewable energies [72]. The genetic algorithm (GA) is another optimization model that is used in the literature to optimize an energy hub. For instance, an energy hub consisting of a gas boiler, transformer, and CHP system is optimized using GA in [73]. With regard to the operation of EVs, the problem of optimal routing can be solved using these algorithms. As an example, authors in [74] utilized a genetic algorithm to find the best path with the minimum cost and time for an EV. In the following section, the deterministic version of the case study is presented.

4.4.2

Deterministic Approach

To begin, consider a residential energy hub with electricity, cooling, and heating demand. The energy hub has access to the electricity and natural gas grid. In addition, PV and SWH are available to satisfy a part of the heating and electricity demand, and the goal is to find the optimum energy flow and size of the ECUs in the hub. To do so, the heating, cooling, and electricity demand data of the hub for a sample day in winter and summer is assumed to be as shown in Fig. 4.25. Figure 4.25 shows the energy demand in (a) winter and (b) summer. As can be seen from Fig. 4.25, there is no heating demand in summer, which means the hot water demand is not considered in the data and the heating and cooling are only related to the space temperature control. The considered energy conversion units are a gas boiler, CHP, transformer, electric chiller, and water chiller. The structure of the hub is shown in Fig. 4.26.

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kW

a) Winter (Cold day) 3 2.5 2 1.5 1 0.5 0 1

2

3

4

5

6

7

8

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Hour

electricity

heat

cooling

kW

b) Summer (Warm day) 5 4 3 2 1 0 1

2

3

4

5

6

7

8

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Hour

electricity

heat

cooling

Fig. 4.25 The energy demand in (a) winter and (b) summer

Fig. 4.26 The structure of the energy hub

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PV and SWH energy output in Winter and Summer 2

kW

1.5 1 0.5 0 1

2

3

4

5

6

7

PV (Winter)

8

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Hour

SWH (Winter)

PV (Summer)

SWH (Summer)

Fig. 4.27 The energy generation of PV (electricity) and SWH (heating) in summer and winter

electricity price 0.3

$/kWh

0.25 0.2 0.15 0.1 0.05 1

2

3

4

5

6

7

8

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Hour

Fig. 4.28 Hourly electricity price

The energy generation of the PV and SWH is shown in Fig. 4.27. Figure 4.27 shows that the electricity output of the PV panels is higher in winter compared with summer. It is a result of the fact that the environmental temperature is higher in summer, which adversely affects the efficiency of the PV panels as described by Eq. (4.26). Finally, the hourly electricity price is shown in Fig. 4.28, and the natural gas price is constant at 0.03 $/m3. In the first deterministic case study, it is considered that the hub cannot sell energy to the grid and energy storage system is not available. The optimization model assumptions are brought in Table 4.4. The objective function of the optimization model is to minimize investment and operational costs. One should be noted that although the design cost is considerably higher, they should be paid only once, whereas the operation cost is for 1 day. To maintain the order of magnitude between these two cost components, the capital recovery factor (CRF) is used. The CRF will turn the investment costs from oncepaid to equivalent yearly payments. It is calculated based on Eq. (4.61).

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Table 4.4 The ECUs’ parameters ECU Transformer Boiler CHP

Investment cost 20 $/kW 90 $/kW 800 $/kW

Maximum size 5 kW 10 kW 4 kW

Electric chiller Water chiller PV SWH

220 $/kW 170 $/kW 100 $ 300 $

4 kW 4 kW – –

CRF =

Parameters ηT = 0.98 ηB = 0.85 ηECHP = 0:30, ηH CHP = 0:45 COPEC = 4 COPWC = 2 – –

i ð1 þ i Þn ð1 þ i Þn - 1

ð4:61Þ

where i is the discount rate and n is the life span of the energy hub; these parameters are considered to be 10% and 20 years in this chapter. In addition, as the energy demand is sampled for a cold and a warm day, to extend to model to 1 year, the weight of cold and warm days must be taken into account. The weight is different from one region to another, and in this case study, it is considered that the warm days represent 60% of the year and the cold days 40%. Considering the stated assumptions, the objective function is shown in Eq. (4.62). OF = min IC × CRF þ 365ðW warm OCwarm þ W cold OCcold Þ

ð4:62Þ

where IC is the initial cost, Wwarm is the weight of warm days, OCwarm is the energy hub operational cost on a warm day, Wcold is the weight of cold days, and OCcold is the operational cost on a cold day. The IC and OC are calculated from Eqs. (4.63) and (4.64). IC = PT CapT þ PB CapB þ PCHP CapCHP þ PEC CapEC þ PWC CapWC þ PPV BPV þ PSWH BSWH

ð4:63Þ

24

OC = t=1

PEt E gt,d þ PNG NGgt,d

d = fwarm, coldg

ð4:64Þ

where the price of the technologies (PECU) is attained from Table 4.4 and the capacity of the ECUs is shown by Capecu. For the PV panels and SWH collectors, a binary variable is used as their energy production profile is specified in Fig. 4.27. are the price of electricity and natural gas, respectively. The price of NG PEt and PNG t is constant for every hour so there is no subscript t for it. E gt,d and NGgt,d represent the electricity and natural gas imported from the grid. The electricity, heating, and cooling balances are shown in Eqs. (4.65) and (4.66).

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105

PV D EC WC ETt,d þ E CHP t,d þ E t,d = E t,d þ E t,d þ E t,d

ð4:65Þ

SWH D H Bt,d þ H CHP t,d þ H t,d = H t,d

ð4:66Þ

EC D C WC t,d þ C t,d = C t,d

ð4:67Þ

Equation (4.65) states that the electricity output of the transformer, CHP, and PV should be sufficient to satisfy the electricity demand and the electricity requirement of the electric and water chillers. Similarly, Eq. (4.66) represents the heating balance; the heating generated by the gas boiler, CHP, and the SWH must cover the heating demand. In addition, the cooling production of the water and electric chiller should satisfy the cooling demand. Equations (4.68)–(4.73) represent the mathematical relationships for calculating the electricity, heating, and cooling generation of the considered ECUs. E Tt,d = ηT E gt,d

ð4:68Þ

E CHP ECHP t,d = ηCHP NGt,d

ð4:69Þ

E EC t,d =

C EC t,d COPEC

ð4:70Þ

E WC t,d =

C WC t,d COPWC

ð4:71Þ

H Bt,d = ηB NGBt,d

ð4:72Þ

H CHP H CHP t,d = ηCHP NGt,d

ð4:73Þ

The value of the efficiencies and COPs are brought in Table 4.4. The total NG import from the grid can be calculated using Eq. (4.74). B NGgt,d = NGCHP t,d þ NGt,d

ð4:74Þ

Moreover, the ECUs’ energy generation upper bounds are presented in Eqs. (4.75)–(4.81). E Tt,d ≤ CapT

ð4:75Þ

E CHP t,d ≤ CapCHP

ð4:76Þ

C EC t,d ≤ CapEC

ð4:77Þ

C WC t,d ≤ CapWC

ð4:78Þ

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H Bt,d ≤ CapB

ð4:79Þ

E PV t,d ≤ M × BPV

ð4:80Þ

H SWH t,d ≤ M × BSWH

ð4:81Þ

where M is a big number (commonly known as the big M ). As can be seen from Eq. (4.76), the CHP upper bound affects electricity generation. Finally, the maximum allowable capacity for each technology, shown in Table 4.4, is considered in Eq. (4.82). CapECU ≤ Capmax ECU

ECU = fT, B, CHP, EC, WCg

ð4:82Þ

The results of this case study are shown in Fig. 4.29. The model is solved using the open-source GLPK solver, and the python code is accessible through this Link. Furthermore, the optimal capacity of the ECUs is brought in Table 4.5. So far, the model could not sell the excess electricity generated from the CHP unit and the PV panels to the grid. In addition, there was no electricity storage unit in the energy hub. In the second case study, these two assumptions are added to the optimization model. In the second deterministic case study, it is assumed that the model can sell the excess available electricity to the grid at a price 25% lower than the grid price. Hence, if the price of importing electricity is 1 USD, the price of selling would be 0.75 USD. In addition, the price of the electricity storage unit is considered to be

Fig. 4.29 Results of the first deterministic case study

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Table 4.5 The results of the first deterministic case study

Parameter Objective value Operation cost Initial cost Transformer capacity Boiler capacity CHP capacity Electric chiller capacity Water chiller capacity PV existence SWH existence

Unit $/year $/year $ kW kW kW (elec) kW kW – –

107 Optimal value 1064.66 872.1 1639.5 1.775 1.6 0.6 4 0 1 0

100 $/kWh, its maximum allowable capacity is 5 kWh, the maximum allowable charge and discharge is 1 kW, and the charging and discharging efficiencies are equal to 0.95. In this case, the operation cost or Eq. (4.64) will turn into Eq. (4.83). 24

OC = t=1

PEt E gt,d þ PNG NGgt,d - Ptexp,E E exp t,d

ð4:83Þ

is the amount of where Ptexp,E is the price of exporting electricity to the grid and Eexp t electricity delivered to the grid. The storage system equations are described in Sect. 4.2.3.6. Considering the electricity export and charge and discharge of the storage unit, the electricity balance will turn into Eq. (4.84) from Eq. (4.65). exp PV Ch D EC WC Dis ETt,d þ E CHP t,d þ E t,d þ E t,d = E t,d þ E t,d þ E t,d þ E t,d þ E t,d

ð4:84Þ

The results of the second deterministic case study are brought in Fig. 4.30. As can be seen from Fig. 4.30, in this case, the electricity import from the grid is reduced, and the CHP and PV systems can cover the electricity demand in winter. In addition, the operational costs are reduced in this case, which is a result of the fact that the model can sell electricity to the grid. Moreover, although the initial cost has experienced growth, the overall yearly costs diminished. Table 4.6 shows the results of the case. The python code for the second deterministic case study is available through this Link. Finally, the last case investigates the condition in which EV is considered. The parameters of the considered EV are shown in Table 4.7. In this case, the only change is related to the electricity balance constraint. In this regard, Eq. (4.84) turns into Eq. (4.85). exp EV,Ch EV,Dis PV Ch EC WC Dis = ED ETt,d þ E CHP t,d þ E t,d þ E t,d þ E t 0 ,d t,d þ E t,d þ E t,d þ E t,d þ E t,d þ E t 0 ,d

ð4:85Þ

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Fig. 4.30 Results of the second deterministic case study

Table 4.6 The results of the second deterministic case study Parameter Objective value Operation cost Initial cost Transformer capacity Boiler capacity CHP capacity Electric chiller capacity Water chiller capacity PV existence SWH existence Battery capacity Total electricity export in winter Total electricity export in summer

Unit $/year $/year $ kW kW kW (elec) kW kW – – kWh kWh/day kWh/day

Optimal value 600.78 317.84 2408.88 2.75 0.7 1.2 4 0 1 0 3.5 1.72 19.56

where E tEV,Ch is the amount of electricity received by the EV and E EV,Dis is the 0 t0 electricity sent from EV to the energy hub. t′ is the time interval within which the EV is available in the energy hub. Other equations are similar to those introduced in Sect. 4.2.3.7. The results of this case, along with the two previous cases, are presented in Table 4.8, and its python code is available through this Link. As can be seen from Table 4.8, the objective value of the optimization model is less than in the previous case. In addition, the operational costs are reduced, whereas

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Table 4.7 The parameters of the EV

Parameter Arrival time Arrival energy Departure time Departure energy Battery capacity Charge upper bound Discharge upper bound Energy upper bound Energy lower bound Charge efficiency Discharge efficiency

109

Unit hour kWh hour kWh kWh kW kW kWh kWh % %

Value 16 30 7 45 50 5 5 45 5 0.96 0.96

Table 4.8 The results of the third deterministic case study Parameter Objective value Operation cost Initial cost Transformer capacity Boiler capacity CHP capacity Electric chiller capacity Water chiller capacity PV existence SWH existence Battery capacity Total electricity export in winter Total electricity export in summer

Unit $/year $/year $ kW kW kW (elec) kW kW – – kWh kWh/ day kWh/ day

Optimal value (third case) 560.08 271.84 2453.9 5 0.7 1.2

Optimal value (second case) 600.78 317.84 2408.88 2.75 0.7 1.2

Optimal value (first case) 1064.66 872.1 1639.5 1.775 1.6 0.6

4 0 1 0 3.5 15.86

4 0 1 0 3.5 1.72

4 0 1 0

36.25

19.56

the initial cost has faced a slight growth. Overall, since the EV exports electricity to the energy hub during the second peak and import energy during the off-peak for charging, the cost has diminished. In the next section, the optimal values of Table 4.8 are reported considering the stochasticities.

4.4.3

Stochastic Approach

There are a variety of stochasticities that can be considered in the energy hub optimization problems. The most famous sources of uncertainties are variable

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Table 4.9 Stochastic scenarios and their probability

Stochasticity source Arrival time

Departure time

PV PVdeterministic

Scenario 15 16 17 6 7 8 0.75 1 1.25

Probability 0.25 0.5 0.25 0.25 0.5 0.25 0.25 0.5 0.25

0.14 0.12 Probability

0.1 0.08 0.06 0.04 0.02 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 Scneario number

Fig. 4.31 The probability of each scenario

renewable energies such as wind and solar. Moreover, when there is an EV in the energy hub, other uncertainties as described in Sect. 4.2.1 will be introduced to the hub. In this section, the model presented in the third deterministic case study is extended to consider the stochasticities of the PV and EV’s arrival and departure time, and the results are compared with the deterministic approach. The scenario-based approach is taken into account to consider the effect of the uncertainties, and the considered scenarios are described in Table 4.9. There are three sources of uncertainty each with three different scenarios. Therefore, there are 27 scenarios with probabilities ranging from 0.015625 to 0.125. Figure 4.31 shows the probability of each scenario. There are two sets of variables in stochastic optimization; the first is known as the first-stage or here-and-now variables whose value is independent of the scenarios, and the other is the second-stage variable of wait-and-see variables. In this case study, the capacities are the first-stage variables, and all the others are the secondstage variables. For more information on stochastic optimization methods, see [75].

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Table 4.10 The results of the stochastic case study Parameter Objective value Operation cost (expected) Initial cost Transformer capacity Boiler capacity CHP capacity Electric chiller capacity Water chiller capacity PV existence SWH existence Battery capacity

Unit $/year $/year $ kW kW kW(elec) kW kW – – kWh

Optimal value (stochastic case) 571.7 283.5 2453.9 5 0.7 1.2 4 0 1 0 3.5

Optimal value (third deterministic case) 560.08 271.84 2453.9 5 0.7 1.2 4 0 1 0 3.5

The only change in the mathematical formulation is that all the second-stage equations must be true for each scenario as well as each time interval and day. For instance, Eq. (4.85) will turn into Eq. (4.86). exp EV,Ch PV Ch D EC WC E Tt,d,s þ E CHP t,d,s þ E t,d,s þ E t,d,s þ E t 0 ,d,s = E t,d,s þ E t,d,s þ E t,d,s þ E t,d,s EV,Dis þ EDis t,d,s þ E t 0 ,d,s

ð4:86Þ

where s is the set of scenarios. The results of the stochastic case, along with the third deterministic case, are brought in Table 4.10, and the python code can be found in the Link. As can be seen from Table 4.10, the operation cost value has changed, but the initial cost is the same as that of the previous case study. It is because the energy demand profile has not changed, and therefore, there is no need to increase or decrease the optimal capacity of the ECUs. Nevertheless, due to the stochasticity of the PV generation and EV departure and arrival time, the electricity balance constraint is affected. It will directly affect the amount of electricity exported to the grid in summer and winter as shown in Fig. 4.32. The other parameter affected by the uncertainties is the amount of energy stored in the battery as shown in Fig. 4.33. In the next section, the conclusion of the chapter is demonstrated.

4.5

Conclusion

This chapter aimed to familiarize the reader with the concept of multicarrier energy systems, also known as energy hubs. To achieve this goal, first, the basic structure of the energy hub is introduced, and the energy inputs (i.e., sources), outputs (i.e.,

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Fig. 4.32 Electricity exported to the grid in winter and summer in the stochastic case study

Fig. 4.33 Battery state of the charge in winter and summer in the stochastic case study

demands), and energy conversion units are discussed. After the introduction of the mathematical formulation of the considered ECUs along with the formulation of EVs, the characteristics of the electric vehicles were taken into account. The second section of the chapter was devoted to the energy performance and critical characteristics of the EVs, which will affect the optimal energy flow in the energy hub. The most fundamental feature in this regard is the inherent uncertainties of the EV operation. Unlike conventional electric home appliances, EVs are not available for 24 h in the residential energy hub (considering the weekdays). Hence, their departure and arrival times must be considered when they are integrated into the energy hub. In addition, the available energy in the EV’s battery is different each time it enters the residential energy hub. The available energy depends on several parameters like the driving pattern and the environmental variables, which are completely uncertain. After analyzing the characteristics of EVs, a case study is presented in the last section of the chapter. The case has been solved using MILP in the python

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programming language in two main variations. First, the deterministic variation is developed and solved in three versions, namely, without the ability to sell electricity to the grid and without the energy storage (the first deterministic case study), with mentioned abilities (the second deterministic case study), and with the EV (the third deterministic case study). It is shown that in the last case, the overall value of the objective function is less than the others. This means that when the energy hub can transact electricity with the grid and has energy storage, the daily operational cost is reduced by 43.6% (the first and the second scenarios), whereas when the EV is integrated into the hub, the cost reduction is 47.5% (the first and the third scenarios). The second variation is solved considering the stochasticities. The results of this case showed that the uncertainties increase the operational cost of the EV by 4.3%, and as a result, solving the deterministic case will underestimate the costs of the system.

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Chapter 5

Real Experiences in the Operation of EVs Around the World Hamdi Abdi and Mehdi Rahmani-Andebili

Abstract The transportation sector that is one of the major bottlenecks in fossil energy consumption produces a high share of global environmental pollution and causes many operational costs and challenges. Therefore, this sector needs to be optimally managed, and the related challenges must be appropriately addressed. Recently, transportation electrification and electric vehicles (EVs) have been introduced as the solution. On the other hand, the development of microgrids and smart grids has made the electric transportation more attractive for researchers and operator of modern power systems. In this chapter, some of the most important and real projects and experiences in the application of EVs around the world are presented and described. Keywords Electric vehicles · Photovoltaic arrays · Wind turbines · Full cell · Battery · Charger

5.1

Introduction

In the current era, electric vehicles (EVs) have attracted a lot of attention and governments are also encouraging people to use such products. An EV is a car that has one or more electric motors and provides its energy from rechargeable batteries, which are usually placed under the floor of the car. EVs are different from hybrid EVs (HEVs) and plug-in HEVs (PHEVs) because they do not have a gasoline or diesel internal combustion engine. To recharge the EV battery, the plug must be connected to the household socket or the public electricity network. EVs are becoming more popular day by day because they do not emit polluting gases such

H. Abdi (✉) Electrical Engineering Department, Engineering Faculty, Razi University, Kermanshah, Iran e-mail: [email protected] M. Rahmani-Andebili (✉) Electrical Engineering Department, Arkansas Tech University, Russellville, Arkansas, USA e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Rahmani-Andebili (ed.), Planning and Operation of Electric Vehicles in Smart Grids, Green Energy and Technology, https://doi.org/10.1007/978-3-031-35911-8_5

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as carbon dioxide, which cause global warming and harm air quality, especially in urban environments. Although the use of EVs has caused new challenges in power systems [1, 2], this issue has brought its attractions in the fields of power system operation (i.e., unit commitment [3], energy management [4, 5], dynamic environmental economic dispatch [6], energy market [7]), and planning (i.e., transmission expansion planning [8, 9]), for researchers in this field. Also, different concepts of EVs in planning and operation [10], such as charging management [11, 12] and parking lots [13] have been focused on by researchers. Also, simulation modeling of the EVs in energy analysis [14] and smoothing the power fluctuations produced by EVs are very important [15]. In this regard, familiarity with the practical projects of EVs is very essential. In recent years, there have been major advances and research in the use of EVs in various fields, among which the following can be mentioned. • Optimal allocation of electric vehicle charging station (EVCS) infrastructure [16– 18] • Analyzing the social aspects of EVs [19, 20] • Development trends and goals of the industry in the field of EVs and charging [21–23] Some references reviewed the implemented projects regarding the EV’s different applications in various parts of the world. Here, some of them are mentioned in this section. Schauer and Garcia-Valle [24] have addressed EV activities around the world, till 2013. They divided the development of EVs into four phases: the early beginnings of development (from the 1880s to around 1930), development up to the 1990s, the Renaissance of the EV, beginning and preparing with EV (around 2010), and significant market penetration to 2020. Also, some examples of EVs and EV races are presented. Furthermore, they addressed different EV manufacturers around the world, in terms of different regions of Africa, Asia, Europe, and North America. They also mentioned the overview of EV activities and highlighted activities of the International Energy Agency (IEA) and the World Electric Vehicle Association (WEVA), which launched in 1990 in this field. The authors highlighted some European activities, including the competitive automotive regulatory system for the twenty-first century (CARS21) and Green e-Motion (in the field of research and development for EVs and erection of the charging infrastructure, with 43 partners, including some partners from major industry, utilities, EV manufacturers, municipalities, universities and research institutions and EV technology institutions), the grid for vehicles (G4V), and mobile energy resources in grids of electricity (MERGE). Finally, they addressed the EV activities in different countries, including Austria, Denmark, Finland, France, Germany, Ireland, Portugal, the Netherlands, the USA, China, India, and Japan. Marinelli et al. [25] addressed different projects in almost all European countries, including various research centers and universities. They highlighted 12 hyperprojects in Europe, including across continents EV services (ACES), autonomously

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controlled distributed chargers (ACDC), bidirectional charging management (BDL), wide-scale demonstration of integrated solutions and business models for European smart grid (WiseGRID), charging infrastructure 2.0, grid and charging infrastructure of the future (FuChar), modeling flexible resources in smart distribution grid (ModFlex), Pan-European system with efficient coordinated use of flexibilities for the integration of a large share of RES (EU-Sysflex), Struttura Urbana Multifunzionale Attiva (SUMA), creating automotive renewal (CAR), electrifying buildings for EVs (ELBE), and feasibility analysis and development of on-road charging solutions for future EVs (INSULAE). The main purpose of this section is to review some of the practical projects related to the use of EVs to know the latest developments as well as the challenges reported so that by solving the challenges, the future path can be paved for more effective use of EVs. Due to limited oil reserves, persistent noise, and air pollution, the automobile industry may be on the brink of a new arena with the advent of EVs [26]. The brief history of EVs can be broken up into the following distinct periods [27]: • • • • • •

1830–1880: the early pioneers of electric mobility 1880–1914: the transition to motorized transport 1914–1970: the rise of the internal combustion engine 1970–2003: the return of EVs 2003–2020: the electric revolution 2021 and beyond: the tipping point

Also, the brief historical timeline of the development of EV/HEV can be summarized as follows [28]: • • • • • • • • • • • •

1831: invention of electric motor. 1832–1839: first pure EV invented. 1881: first rechargeable batteries. 1915: Internal combustion engine (ICE) prevails. 1935: EV virtually disappears. 1973: Interest in EVs rises due to the oil crisis. 1990–2000: agreement and standards on CO2 emissions. 1992: first EV by Toyota. 1997: first EV by Prius hybrid. 2011: three MW hybrids sold worldwide. 2012: plug-in hybrid launched. 2020: Tesla produces 1 MW electric cars.

The statistics show that there is an increasing demand for EVs in the world; more than 6.5 million EVs were sold globally in 2021. Furthermore, by 2030, 55% of all new automobile sales in Europe will be entirely from EV type [29]. Different reasons lead to the use of EVs in different countries. For example, environmental concerns are an important determinant of EV purchases in China, while minimizing operating costs is very important in Korea for EV users [30].

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The first attempts to build an electric car date back to the 1830s, when a major revolution in this field took place in France. The first rechargeable lead-acid battery was introduced in 1859. At the end of the nineteenth century and the first half of the twentieth century, more than 30 companies were actively working on the development of electric car. Baker Motor Company was active in the late nineteenth and early twentieth centuries. It was the first company that could offer an electric car with a speed of more than 100 miles per hour. Although there were many electric cars available at that time, this was the only car that could successfully pass the safety tests. At the end of the twentieth century, General Motors built more than 1000 two-door electric cars, called the EV 1, which could reach 60 miles per hour in 8 s. Due to the lack of public favor, the company destroyed a large number of these cars. These EVs were later used by the National Aeronautics and Space Administration (NASA) for work on the moon, which included satellites and could carry two astronauts with a driving range of 57 miles [31]. Bob Beaumont, head of Sebring-Vanguard, was the founder of the so-called Citicar, an electric car with a top speed of 38 miles per hour, to reduce air pollution [31]. As the leader in electric car production in Europe, the UK had more than 150,000 electric cars with a top speed of around 25 mph in the 1980s [31]. Sales of EVs doubled in 2021 (6.6 million, nearly 10% of global cars), compared to the previous year. Back in 2012, just 120,000 EVs were sold worldwide. In 2021, more than that many are sold each week, four times the market share in 2019. This brought the total number of EVs in the world to about 16.5 million, triple the amount in 2018. Global sales of EVs have kept rising strongly in 2022 [32]. Five important points to accelerate the adoption of EVs around the world are as follows [32]: 1. 2. 3. 4. 5.

Permanent support and services of EVs. Kick-start the heavy-duty market. Promote adoption in emerging and developing economies. Expand EV infrastructure and smart grids. Ensure secure, resilient, and sustainable EV supply chains.

Generally, the available EV models by vehicle segments and power train can be described in Table 5.1 [32]. In 2010, the Clean Energy Ministerial (CEM) and the International Energy Agency (IEA) launched a policy platform known as “the Electric Vehicles Initiative (EVI)”—with 13 country members—to accelerate the electrification of the Table 5.1 The available EV models by vehicle segments and power train

Small Medium Crossover Large SUV

China (%) 13 23.5 6.4 13.1 44.3

European Union (%) 9.2 22.3 4.9 19 44.6

USA (%) 1.6 22.2 1.6 17.5 57.1

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transportation sector [33]. The number of EVs has increased from 17,000 to 7.2 million in 2019, 3.4 million (47%) EVs are in China, about 1.7 million (25%) are in Europe, and 1.5 million (20%) are in the USA [33, 34]. Apart from all the advantages of using EVs, there are emerging cases affected by them that need to be carefully studied and fixed. One of these points is charge point trauma (CPT). The CPT is defined as the physiological, psychological, and behavioral condition where EV users experience and develop anxiety or trauma in response to the availability of sufficient charge points, payment process locations, and operability [35].

5.2

Relevant Definitions

In this subsection, some basic definitions regarding EVS are presented. • HEV: These types of cars are equipped with an internal combustion engine (as the main source for longer distances) and an electric motor. The main weakness of these types is that they are not connected to the power grid, and in them, the battery is charged during braking. This type of car can move only with an internal combustion engine or a combination of both engines. Another alternative is for the internal combustion engine to power the battery and drive the wheels with electricity [36]. • Plug-in hybrid electric vehicle: This type is very similar to the first type in which the battery is charged at power stations and during braking. The electric motor can be used as the main motor because the battery is bigger, and therefore, the vehicle can travel longer distances. • Battery electric vehicle: Battery electric vehicles are fully electric vehicles or pure electric vehicles equipped with batteries. In this type, there is no combustion engine and to charge the battery, the cars are connected to the electricity grid. Among this type are the most well-known vehicles as Tesla or Nissan Leaf [36]. • Range extender electric vehicle: In this type, the electric motor is used as the main source of energy. Of course, the combustion engine is also included in the car, to charge the battery while driving to extend the distance the car can travel. This type is another version of the battery electric car [36]. • Fuel cell vehicle: Fuel cell vehicles are still under development. These cars use fuel cells that are powered by hydrogen. The hydrogen is loaded into the EV and the fuel cell electrifies the hydrogen. In battery electric vehicles, the average distance can vary from 100 to 500 km. But in hybrid cars that mainly use a combustion engine, the driving range of the electric motor is about 100 km at most [36]. Different architectures of various types of EVs are depicted in Fig. 5.1 [36]. Also, the different levels of truck and bus electrification are categorized into short-range PHEV (battery size: 10 kWh), work truck PHEV (battery size: 14/28 kWh), long-range PHEV (battery size: 40 kWh), short-range battery electric vehicles

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Fig. 5.1 The architectures of various types of electric vehicles [36]

(BEV) (battery size: 53 kWh, 131 kWh), mid-range BEV (battery size: 215 kWh), and long-range BEV (battery size: 324 kWh) [37].

5.3

EV Projects Around the World

In this section, some of the most important projects related to EV applications around the world will be addressed, and some of their details will be mentioned according to relevant references. Table 5.2 summarizes some characteristics of the studied projects. In the following, some details of these projects are reviewed. LISELEC project, electric vehicle car sharing (EVCS), located at Rochelle, a small town in France [38]: This project is dated back to the 1990s, and it is still working under the name Yelómobile. This system is offered a new form of personal transport, based on ten electrical private cars, available on a self-service basis to subscribers at stations installed across the area. Tama New Town District, Inagi City, Tokyo, Japan [39]: This project is started in September 1999, by the Association of Electronic Technology for Automobile Traffic and Driving (JSK), to ascertain the potential issues that might arise in the course of commercializing such a system and to examine the practicality of shareduse vehicles accompanied with intelligent transportation system technologies. At eight locations, the 30 EVs were used to serve 242 participants in the residential area.

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Table 5.2 A summary of some characteristics of the projects Reference [38] [39] [40] [41] [25] [42] [43]

Project title LISELEC project (Yelómobile) Tama New Town District UCR INTELLISHARE EV car-sharing system WiseGRID My Electric Avenue Smart EV charging, a solar-tovehicle (S2V)

[44]: [45]

University of Novi Sad Nanyang Technological University (NTU) University of Central Missouri Yuan Ze University (YZU) North China Electric Power University The smart campus of the Hellenic Mediterranean University (HMU) Management and monitoring of EVCSs in using the Internet of Things (IOT) platform Smart microgrid project The net-zero-energy residential building The Danish EDISON Project The EV Project by ECOtality Public–private partnership (PPP) model Samsø island Ecogrid Project

[46] [47] [48] [49] [50]

[51, 52] [53] [54] [55] [56] [57] [58] [59] [60] [61] [62] [63] [63] [63] [63] [27] [27]

Smart City Kalundborg The e-harbours project Smartcity Malaga The Jeju smart grid test bed Volt-Air Large-scale demonstration of charging of EVs EVCOM DREAM Consumer acceptance of intelligent charging Project ‘BeMobility 2.0’, Subproject ‘Micro Smart Grid’

Location France Inagi City, Tokyo, Japan University of California Rome, Italy European smartGRID UK The campus of the University of California, Los Angeles (UCLA) Serbia Singapore

Years 1990s 1999 1999

2013–2015

USA Taiwan Beijing, China Heraklion, Crete, Greece The Amazon region in Brazil Genoa University, Italy Green Village in the Netherlands Island of Bornholm

2014

China Kattegat, Denmark Bornholm Island, Denmark North Sea Region Spain Korea Belgium Denmark

2011–2014 2011–2013

Denmark Denmark Denmark

2008–2010 2012–2013 2012–2015

region of Berlin-Potsdam, Germany

2011–2015 (continued)

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Table 5.2 (continued) Reference [63] [27]

[63] [63] [63] [63] [63] [63] [63] [63]

Project title EV Network integration Context-Aware Electric Vehicle Charging Based on Real-Time Energy Prices E-mobility PRIME Demonstration project Smart Charging PowerMatching City II The Smart Peninsula Inovgrid Smartcity Malaga PRICE Ashton Hayes Smart Village Swiss2G G(E)OGREEN

[32]: [64]

The DAMRI E-Bus Project EV1

[64]

Better Place

[64]

Tesla

[65]

EV trial projects

[63] [63] [63]

Location Ireland Ireland

Years 2009–2013 2011–2012

Italy Italy Netherlands

2008–2013 2011–2014 2010–2011

Netherlands Poland Portugal Spain Spain UK SWITZER LAND between Austria, Belgium, Spain, and Switzerland Indonesia the California Air Resource Board (CARB) Australia, Hawaii, Israel, and Denmark North America, Europe, and China Canada

2011–2014 2011–2012 2008–2013 2009–2013 2011–2014 2011–2013 2010–2013 2010–2012

1996–1999 2007–2013 2003–present Started from 2011

The second project is provided to residents, who have no access to their first EVs. Initially, the second project was free of charge. During the last phase, fees were mentioned. During the first phase, the project received an overwhelming public response with 242 residents. In the second phase, the number of users dropped drastically to 20. The experiment ended in February 2002, with only 35 users remaining. UCR INTELLISHARE, an intelligent shared electric vehicle test bed at the University of California, Riverside [40]: This system started operation in 1999, and users were gradually brought into the system. At the start of 2002, approximately 350 UCR employees are using the system. On average, approximately 110 trips are made per day, with over 47,000 trips made between 1999 and 2002. After 10 years, it ended in 2010 because of logistic problems with the recharge of EVs [41]. EV car-sharing system, Rome, Italy [41]: It is reserved for students and employees, composed of 30 EVs and 56 recharging points in several departments of the University.

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WiseGRID (wide-scale demonstration of integrated solutions and business models for European smartGRID) [25]: It includes storage technologies, RES, and charging infrastructure to be used for large-scale EV deployment. The main objective is to provide a set of solutions and technologies to increase the security, stability, and smartness of the European energy grid open, consumer-centric. It includes three pilot sites Flanders, Crevillente, and Terni. Consisting of 11 charging stations (3 SPOTLINK – EVO, 4 EVLink2 Wallbox, 1 EVLink2 Parking, 2 RVE-WBSSmart, 1 V2G fast charging station (developed under project)), and 53 EVs: 44 Renault Zoe R240 (22 kWh), 6 Nissan e-NV200 (24 kWh), 2 Nissan Leaf (40 kWh), 1 Hyundai Ioniq (28 kWh). My Electric Avenue in the UK [42]: conducted from 2013 to 2015, led by EA technology with partners from academia, distribution network operators (DNOs), and industry, funded by the Low Carbon Network Fund. This project deployed more than 200 Nissan LEAFs with a battery size of 24 kWh across GB, as one of the largest EV trials in the world. The main goal is to investigate the impacts of EVs on European-style LV networks. Some of the data of this project are as follows: total cost: US $13 million, 3 years, 219 EV users, Nissan (101 technical and 118 social), 10 LV networks (9 residential and 1 commercial), and the universities of Manchester (technical analysis) and De Montfort University (socioeconomic analysis). The details can be found on myelectricavenue.info. Smart EV charging, a solar-to-vehicle (S2V) [43]: campus of the University of California, Los Angeles (UCLA): University of Novi Sad, Serbia [44]: The Faculty of Technical Sciences (FTN) microgrid is developed as a preliminary design of faculty microgrid based on different types of RES. It consists of 2 PV power plants 9.6 kW and 16.3 kW, a small WT of 2 kW, a micro CHP of 5 kWe+9.9 kWt, 2 EVs with a power of 5.88 kWh and 2.64 kWh bidirectional slow charger, a BESS of 20 kWh, and some electrical consumers. Nanyang Technological University (NTU), Singapore [45]: It includes PV, BESS, and EVs. University of Central Missouri, USA [46]: It includes PV, BESS, Wind, EV, and SC. The university campus microgrid (UCM) is proposed to facilitate research needs regarding the control and operation of microgrids. The main objectives are charging employees EVs, satisfying some parts of the residential loads, and peak load shaving in periods of high demand. The UCM consists of a 208 V AC bus, a 300 kW rooftop array PV, a 100 kWh Li-ion-based BSS, a 130F super-capacitor storage system (SSS), an electrolyzer for hydrogen production, and a fuel cell of 15 kW with 50% efficiency, 2 different load types of EVs, and part of the residential loads (about 34 student apartments). Yuan Ze University (YZU), Taiwan [47]: Electric scooter charging station. The impacts of charging electric scooters on the power quality in distribution microgrid systems including voltage variations and voltage unbalance are investigated. The technical data for the electric scooter charging station are 110 V, 6 A, 700 W, 210 Var, and 60 Hz.

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North China Electric Power University, Beijing, China [48]: It consists of PV, BESS, Wind, EV, and CHP. The smart campus of the Hellenic Mediterranean University (HMU), located at Heraklion, Crete, Greece [49]: uses a PV array, wind turbines (60 kW), battery energy storage systems (lead–carbon battery, lithium battery), and EV charger. It operates in two modes: acts as an auto-producer and increases the power transfer capability by installing an independent MV station between the HMU load and the national grid (20 kV). Management and monitoring of EVCSs in the Amazon region in Brazil using the Internet of Things (IOT) platform [50]: A complete prototype is formed with a real test bed to investigate the scalability of the real system, including 160 EVCSs. The prototype of a real project from charging to monitoring EVs, called SIMA, is introduced. It consists of electric buses, and charging stations, an IoT platform, an OCPP back-end system, and a web application (front end). During SIMA operation, the charging station sends data via OCPP to a central system, where the IoT platform is implemented in a scalable manner to receive and store the needed data. Also, the SIMA system is implemented using the physical infrastructure for electric mobility located at the Federal University of Pará (UFPA). The storage (GB), memory (GB), and CPU (Cores), for different machines, are OCPP back end and application front end: 40, 16, 4; worker 1: 700, 8, 4; worker 2: 700, 8, 4; master: 64, 4, 2; Nginx: 32, 2, 1; and Docker-composer: 40, 16, 4. During the charging process, the following information is collected by the EVCS: the instantaneous flow of electric current to the EV, numerical value (in Wh or kWh) of the imported energy; instantaneous active power imported by the EV; vehicle charging status (in %); instantaneous voltage; maximum current offered to the EV based on the vehicle’s maximum capacity; maximum power offered to the EV based on the maximum capacity of the EV; and temperature. Smart microgrid project at Genoa University, Italy [51, 52]: It includes RES, EVs, and storage systems. The Smart Polygeneration Microgrid (SPM) in the Savona University Campus, Italy, is owned by the University of Genoa and is operated since 2014, in the “Energia 2020” project framework. The main components of the system are 2 roof-photovoltaic arrays (80 + 15 kW of peak power), 3 CHP (1 kW electrical and 3 kW thermal) CSPS equipped with Stirling engines, 3 high-efficiency cogeneration micro-turbines fed by natural gas, 1 lithium-ion electrical storage system (15 batteries, in series, 25 kWh), 1 sodium-nickel chloride electrical storage system (6 batteries, parallel, 141 kWh), and 2 EV charging stations. The objective is to investigate the role of EVs in improving the fixed storage batteries of the energy management optimization process. The net-zero-energy residential building, located at the Green Village in the Netherlands [53]: includes building-integrated photovoltaic (BIPV) solar panels, a hydrogen fuel cell electric vehicle (FCEV), a residential building for combined power generation, and mobility. The project aims to investigate the end-user’s potential of implementing FCEVs in vehicle-to-grid operation (FCEV2G) to act as a local energy source. The EV is adapted using a power output socket, 10 kW DC to

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AC. The microgrid consists of ten all-electric dwellings and five cars with the different FCEV2G modes of operation. The Danish EDISON Project, located on the Island of Bornholm [54]: The main objective is to analyze the impacts of a large fleet of EVs, as the VPP, on supporting the electric grid to reduce CO2 emissions, by focusing on the ICT-based distributed software integration and optimal integration of EVs. The consortium partners include technology suppliers, energy companies, and research institutes and laboratories. The large-scale EV charging infrastructure demonstration, called The EV Project by ECOtality [55]: ECOtality has partnered with Nissan North America, the Idaho National Laboratory, General Motors, and others for data collecting from over 5000 Chevrolet Volts and Nissan LEAFsTM and over 10,000 charging systems in 18 regions across the USA, including Phoenix, AZ; Tucson, AZ; Los Angeles, CA; San Diego, CA; San Francisco, CA; Washington, D.C.; Oregon; Chattanooga, TN; Knoxville, TN; Memphis, TN; Nashville, TN; Dallas/Ft. Worth, TX; Houston, TX; and Washington State. Public–private partnership (PPP) model to the electric vehicle charging infrastructure in China [56]: The infrastructure is important for supplying energy to EVs, because of large-scale investment. The PPP model is an effective manner for the infrastructure, to leverage social capital, decrease the burden on local finance, and improve the project profitability and management. This project is an attempt to increase the number of charging piles and stations to 4.8 million and 12,000 by 2020, respectively, which are 154 times and 15 times the existing levels. Indian experience [66]: In 2000, the Mashelkar Committee set up by the Ministry of Non-conventional Energy Sources (MNES), Government of India, on high energy density batteries for EVs, recommended the development of appropriate types of batteries for EVs, and using quick charging methods and devices for charging the batteries, to accelerate the commercialization of EVs. For this purpose, electric three wheelers, passenger EVs, and a battery-operated passenger car, known as Reva, are used in New Delhi. Samsø island, located at Kattegat, Denmark [57]: This project completely relies on electricity from wind and biomass. In this project, even the transportation system, based on using EVs, should rely on RESs. Ecogrid Project, located at Bornholm Island, Denmark [58]: The system is supplied by a 36 MW wind power plant and a 16 MW biomass plant. The transportation system is based on using the fleet of EVs. The EVs are used as the electric vehicle virtual power plant (EVPP), to balance the energy supply provided by variable wind energy resources [54, 67]. Smart City Kalundborg [59]: This project was established to demonstrate a city with an energy hub to operate energy resources including control electricity, water, heat, transportation, and buildings. The project aims to provide user-friendly energy services to businesses and people. This project utilizes EVs and solar panels. The partners of this project are local energy utility SEAS-NVE, the municipality of Kalundborg, Danish Energy and Spirae, CleanCharge, ABB, Danfoss, Clever, Gaia Solar, DONG Energy, Gridmanager, and Schneider Electric.

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The e-harbours project, North Sea Region [60]: This project is established based on using the RESs. The main focus is on harbors and their wider industrial area, to maximize the integration of RESs and exploit EVs. Smartcity Malaga [61]: This project addresses some issues like smart grid network management, smart metering, and integration of RES is the “Smartcity Malaga,” which has been realized in Spain by the utility Endesa. The smart grid includes all low-voltage and medium-voltage levels. The MV network of 40 km and 72 low-/medium-voltage centers have been monitored. The used communication technology for the MV network is broadband PLC, while WiMax and 3G are used as a complementary technology. All control centers and central offices have been interconnected with the MPLS protocol. On the LV side, smart metering solutions are used with the implementation of the Endesa smart meters, meaning that the NB-PLC is used to communicating the smart meters and, in particular, the “Meters and More” technology. The project includes PV panels, WTs, and EVs. The Jeju smart grid test bed, Korea [62]: includes five areas of the SG implementation, a smart power grid, smart homes and buildings, smart transportation (the foundation for expanded distribution of EVs), smart renewable energy, and smart electricity service. In this project, Korea electric power corporation (KEPCO) and 21 participating companies invested. KEPCO, SK Energy, GS Caltex, and 39 other companies participated in smart transportation to develop EVs and charging stations, vehicle-to-grid and ICT systems, and other service models. Volt-Air: where energy meets mobility, Belgium [63]: It is established from 2011 to 2014, by Siemens company. It mainly focused on the integration of EVs in vehicle fleets and their integration in the microgrid companies and consists of three sub-labs that are connected via a common data platform for data exchange. Large-scale demonstration of charging of EVs, Denmark [63]: It was organized by ChoosEV A/S, from 2011 to 2013, for intelligent charging and communication with EVs and tested by 2400 families in 300 EVs. Development of a Secure, Economic and Environmentally Friendly Modern Power System, Denmark [63]: from 2007 to 2009, by Department of Energy Technology- Aalborg University developed several new concepts for a modern power system including RESs, ESS, DGs, and PEHEVs. EVCOM, Denmark [63]: from 2008 to 2010, by Energinet.dk to establish a concept for EVs and their communication with the power system. DREAM—Danish Renewable Energy Aligned Markets-phase 1, Denmark [63]: from 2012 to 2013, by Danish Technological Institute. It provides the necessary design and analysis of end-user solutions to make a financial and reliable accountable full-scale demonstration in succeeding projects with a large amount of EVs, heat pumps, and smart grid technology. Consumer acceptance of intelligent charging, Denmark [63]: by DTU Transport, from 2012 to 2015. It investigates the willingness of the EVs’ users to participate in intelligent charging and the consequences of actual driving patterns for the recharging infrastructure. Electric mobility pilot region of Berlin-Potsdam, Project ‘BeMobility 2.0,’ Subproject ‘Micro Smart Grid,’ Germany [63]: by InnoZ GmbH, from 2011 to

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2015, to integrate the electric mobility with grid and Smart Charging among the other objective. EV Network integration, Ireland [63]: by ESB Networks, from 2009 to 2013, to investigate the impact of EVs on the network. Context-Aware Electric Vehicle Charging Based on Real Time Energy Prices, Ireland [63]: by Intel Corp, UK, from 2011 to 2012, to manage EV charge scheduling based on predicted user needs. E-mobility, Italy [63]: by Enel, from 2008 to 2013, to enable the use of EVs with state-of-the-art recharging technologies, including 140 customers and 400 recharging stations in Rome, Milan, and Pisa. PRIME—Progetto di Ricarica Intelligente per la Mobilità Elettrica, Italy [63]: by Enel Ingegneria Innovazione S.p.A., from 2011 to 2014, to analyze the tools required for the growth of electric mobility, identifying the most appropriate incentives to promote the diffusion of EVs in Italy. Demonstration project Smart Charging, Netherlands [63]: from 2010 to 2011, by Enexis, to test an environment in which commercial market companies are enabled to provide charge services to EV customers in cooperation with EV infrastructure parties and grid operators. It initially involves 15 charge spots and 15–20 drivers. PowerMatching City II, Netherlands [63]: from 2011 to 2014, by RWE/Essent, to optimize the energy use of consumers by automatically shifting local energy production of micro CHPs, energy demand of various devices like EVs, heat pumps, and washing machines. The Smart Peninsula – pilot project of smart grid deployment at ENERGAOPERATOR SA, Poland [63]: by ENERGA-OPERATOR SA Poland, from 2011 to 2012, to test the installation of PV cells, WTs, and EV charging stations in LV grid of distributed generation. Inovgrid, Portugal [63]: by EDP Distribucao SA, from 2008 to 2013. The aim is to realistically demonstrate smart grid concepts for a significant number of users, by integrating the EV charging station. Smartcity Malaga, Spain [63]: by Endesa, from 2009 to 2013. The objectives are deployment and testing of the new energy management model and integration of DERs, EV charging, and public lighting devices. PRICE, Spain [63]: by Endesa, from 2011 to 2014, to improve the maintenance and operation of the power grid, optimize the progressive integration of RESs, and facilitate the mainstreaming of EVs. Ashton Hayes Smart Village, UK [63]: by Scottish Power Energy Networks, from 2011 to 2013, to facilitate the connection of different microgeneration technologies, such as PV, wind, and CHP, and potentially EV charging point(s) on the LV network. Swiss2G, SWITZER LAND [63]: from 2010 to 2013, by Kraftwerke Oberhasli (KWO). In the second phase of the project, the bidirectional battery charger will be developed for EVs of the future. G(E)OGREEN [63]: It is a multinational project, between Austria, Belgium, Spain, and Switzerland, started from 2010 to 2012, by VITO as the led organization.

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In this project, the EVs and data centers processing tasks as typical cases of mobile consumers and their impact on the power grid will be considered. The DAMRI E-Bus Project Across, Indonesia [32]: The Ministry of Transport is exploring ways to adopt battery electric buses, which aims to replace diesel buses with electric buses, and is in the first phase of the scale-up of electric transport in and around Jakarta. EV1 [64]: from 1996 to 1999, the entrepreneur is Roger Smith. The motivation for the project is the California Air Resource Board (CARB) mandate to diminish air pollution by zero-emission vehicles. The project is manufactured by General Motors. The type of vehicle is EV1 (two-seat car), and Magne Charge Inductive (3 h) technology is used for charging infrastructure. The full-charging time of the battery is 8 h, and the number of sold EVs to customers is 1117. The traveling range is 60–80 miles, and the state of deployment is California, Arizona. Better Place [64]: from 2007 to 2013, the entrepreneur is Shai Agassi. The motivation for the project is to make the world a better place by cutting its dependency on oil. The project is manufactured by Renault. The type of vehicle is Renault Fluence (Sedan), and a network of battery switching stations (5 min) technology is used for charging infrastructure. The full-charging time of the battery is 6–8 h, and the number of sold EVs to customers is 1200. The traveling range is 100–120 miles, and the countries of deployment are Australia, Hawaii, Israel, and Denmark. Tesla [64]: from 2003 to present, the entrepreneur is Elon Musk. The motivation for the project is the sustainability movement to the usage of green energy sources. The project is manufactured by Tesla. The types of vehicles are models 3, X, S, Y, and Cybertruck (Sedan and SUV), and supercharger V3 stations (45 min) technology is used for charging infrastructure. The full-charging time of the battery is 1–12 h, and the number of sold EVs to customers is 500,000 by 2019 and 1,000,000 by 2021. The traveling range for models 3, X, S, and Y is 300–400 miles, and for Roadster is 650 miles, and the project is developed in North America, Europe, and China. EV trial projects: there are some trial projects in this field [65], such as the EV project17 EV (15 Mitsubishi i-MiEVs and 2 Nissan LEAFs), which started from 2009, in the USA; the REV, (11 Ford), which started in 2010, in Australia; the ColognE-Mobile project (25 Ford), which started in 2010, in Germany; the SwichEV (44EVs), which started in 2010 in UK; the ActiveE (20 EVs), which started in 2011, in Ireland; the Hydro—Quebec, (30I-MiEVs), which started in 2011, in Canada; and the SAVE,65Renault—Nissan, which started in 2012, in France.

5.4

Challenges and Suggestions

Based on the study of available references, some of the most important challenges related to EV application are as follows:

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• Need for smartening the network • Need for adding required equipment to the EVs and the network, whether hardware or software • Smartening the sockets and plugs • Need for using communication protocols between the EVs and the control centers of the distribution network • Need for suitable allocation of EV charging stations • Need for government incentives • Protection against the theft of personal information • The dangers of connecting cybercriminals • Manipulation of critical immune systems • Security vulnerabilities of mobile applications • Lack of “designed-in” security • Security vulnerabilities in the supply chain • Failure to keep up with the latest security infrastructure and updates • Inadequate key management processes • Program-based malware attack • High purchase price and low speed of movement • Electric vehicle battery recycling

5.5

Conclusion

In this chapter, some of the real experiences as well as the completed and ongoing projects in the application of EVs around the world were presented and analyzed. It is generally concluded that the challenges in front of application of EVs are mainly related to the smartening networks, lack of required equipment, different communication protocols, allocation of charging stations, governments’ policies, and protection and security issues.

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Index

B Battery, 2, 3, 7–9, 32–34, 37–42, 47–49, 55, 58, 65, 67, 85–87, 89, 90, 92, 93, 97–98, 108, 109, 111, 112, 119, 121–124, 127–129, 131–133

C Chargers, 2, 39, 121, 127, 128, 131

D Deep deterministic policy gradient (DDPG), 10, 21–25, 27 Distribution systems, 54, 56, 62 Drivers’ social class, 55, 64 Driving routes in San Francisco, 55, 62

E Electric vehicles (EVs), 2, 4, 7–9, 21, 27, 32–43, 45–49, 53–67, 70, 86, 87, 89–97, 100, 101, 107–113, 119–133 Electromobility, 31–49 Energy hubs, 70–73, 77, 78, 80–82, 89, 90, 93, 100–113, 129

F Flexibilities, 11, 121

Full cell, 132 Fuzzy logic, 2, 10–20, 26, 27

M Multi-carrier energy study, 70

P Photovoltaic arrays, 128

Q Quantum-inspired simulated annealing (QISA) algorithm, 55

R Renewables, 4, 21, 38, 46, 53–55, 65, 77, 82, 100, 101, 109, 130

S Security, 2–27, 31, 32, 39–41, 53, 127, 133 Stochastic model predictive control (SMPC), 54 Sustainability, 41, 42, 132

T Threats, 2–27

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. Rahmani-Andebili (ed.), Planning and Operation of Electric Vehicles in Smart Grids, Green Energy and Technology, https://doi.org/10.1007/978-3-031-35911-8

137

138 V Vehicle-to-grid (V2G), 2, 40, 70, 128, 130 Vulnerabilities, 2, 4, 6, 8–9, 15–18, 20, 23, 25, 27, 133

W Wind turbines, 72, 128

Index Z Zero-emission policies, 42, 43