Flexible Resources for Smart Cities 303082795X, 9783030827953

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Miadreza Shafie-khah M. Hadi Amini   Editors

Flexible Resources for Smart Cities

Flexible Resources for Smart Cities

Miadreza Shafie-khah • M. Hadi Amini Editors

Flexible Resources for Smart Cities

Editors Miadreza Shafie-khah School of Technology and Innovations University of Vaasa Vaasa, Finland

M. Hadi Amini Knight Foundation School of Computing and Information Sciences Florida International University Miami, FL, USA

ISBN 978-3-030-82795-3 ISBN 978-3-030-82796-0 https://doi.org/10.1007/978-3-030-82796-0

(eBook)

© Springer Nature Switzerland AG 2021 This work is subject to copyright. All rights are reserved 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

Preface

Smart city is an aggregation of modern technologies, intelligent administration, and thriving citizens enabling the city to actively develop and effectively solve the emerging problems. Scientists offer an extensive variety of advanced frameworks for the concept of smart cities to provide an optimal solution to enable the city’s intelligence, flexibility, and sustainability. The book Flexible Resources for Smart Cities paves the way for researchers from various areas of engineering and computing sciences, including electrical engineering, telecommunication, information technology, computer science, business, environment, building, industrial, and civil engineering to work at the intersections of these areas. Furthermore, it provides a comprehensive insight for the reader to get an overview of flexibility in smart cities. The first chapter introduces flexibility provided by electric vehicles and particularly communities of solar-powered electric vehicle parking lots. In the communities, parking lots can inject the energy back to the grid or other parking lots considering uncertainties of solar energy and electric vehicles. The second chapter investigates energy-awareness impacts on implementation of demand response programs. Analysis of customers’ energy consumption behaviors and their relation to energy price signals indicates that the customers with different consumption behaviors do not have the same capability for providing energy flexibility by participating in demand response programs. To address privacy and security concerns in smart cities, the third chapter presents transactive energy which provides the possibility for decentralized operational management of flexible resources. The transactive energy enables operators of energy systems in smart cities to affect the resource scheduling conducted by independent agents utilizing incentive signals. In the fourth chapter, microgrids’ utility and their potential is discussed to serve as a flexible and resilient resource for the smart city by providing capabilities such as peak shaving, demand response, and frequency regulation. Smart cities make prosumers active agents that produce, consume, and store energy. The concept of peer-to-peer energy markets makes a new framework for such agents to perform direct energy transactions with other peers in the network. v

vi

Preface

Moreover, by implementing proper mechanisms, such markets might also be employed to procure flexible services from the prosumers. In the fifth chapter, various structures for a peer-to-peer market are investigated and how these designs would be able to activate flexibility in the smart cities described. The sixth chapter presents a systematic review of the charging infrastructure planning for cities by analyzing the findings of the existing research efforts of this paradigm qualitatively and quantitatively, in order to be a guide for city designers to plan the charging infrastructure for smart cities. The last chapter investigates the significant aspects, current progress, and challenges associated with several power converters to suggest further improvements in charging systems of electric vehicles. Vaasa, Finland Miami, FL, USA

Miadreza Shafie-khah M. Hadi Amini

Contents

Community-Based Solar-Powered Electric Vehicle Parking Lots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. Muhammad Bagher Sadati, Miadreza Shafie-khah, and Abdollah Rastgou Smart Energy-Aware Cities: Customer Characterization by Energy Data Analytics to Improve Demand Response Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohsen Kojury-Naftchali and Alireza Fereidunian The Role of Transactive Energy on Management of Flexible Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sajjad Fattaheian-Dehkordi, Mohammad Gholami, Ali Abbaspour, and Matti Lehtonen

1

21

45

Microgrid’s Role in Enhancing the Security and Flexibility of City Energy Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sakshi Mishra, Ted Kwasnik, Kate Anderson, and Robert Wood

67

Peer-to-Peer Local Energy Markets: A Low-Cost Flexible Solution for Energizing Sustainable Smart Cities . . . . . . . . . . . . . . . . . . Mohammad Nasimifar and Vahid Vahidinasab

95

Planning of Sustainable Charging Infrastructure for Smart Cities . . . . . 115 Sanchari Deb A Comprehensive Topological Assessment of Power Electronics Converters for Charging of Electric Vehicles . . . . . . . . . . . . 133 Salman Habib, Farheen Ehsan, Haoming Liu, Muhammad Haroon Nadeem, Farukh Abbas, and Muhammad Numan Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185 vii

About the Editors

Miadreza Shafie-khah received his MSc and first PhD in electrical engineering from Tarbiat Modares University, Tehran, Iran. He received his second PhD in electromechanical engineering and first postdoc from the University of Beira Interior (UBI), Covilha, Portugal. He received his second postdoc from the University of Salerno, Salerno, Italy. Currently, he is an Associate Professor at the University of Vaasa, Vaasa, Finland. He is also the Guest Editor-in-Chief of IEEE Open Access Journal of Power and Energy and Associate Editor of journals IEEE Transactions on Sustainable Energy, IEEE Power Engineering Letters, IEEE Systems Journal, IEEE Access, IEEE Open Access Journal of Power and Energy, IET Renewable Power Generation, and the Guest Editor of IEEE Transactions on Cloud Computing in Demand Response Applications of Cloud Computing Technologies, and the Guest Editor of more than 14 Special Issues. He was considered one of the outstanding reviewers of the IEEE Transactions on Sustainable Energy in 2014 and 2017, one of the best reviewers of the IEEE Transactions on Smart Grid in 2016 and 2017, one of the outstanding reviewers of the IEEE Transactions on Power Systems in 2017 and 2018, and one of the outstanding reviewers of IEEE OAJPE in 2020. He is a senior member of IEEE since 2017 and has co-authored more than 440 papers that received more than 8500 citations with an h-index equal to 52. He is also the volume editor of the book Blockchain-Based Smart Grids, Elsevier, 2020. He is a top scientist in the Guide2Research Ranking in computer science and electronics, and he has won five Best Paper Awards at IEEE ix

x

About the Editors

Conferences. His research interests include electricity markets, demand response, electric vehicles, and smart grids.

M. Hadi Amini is an assistant professor at the Knight Foundation School of Computing and Information Sciences at Florida International University. He is the director of Sustainability, Optimization, and Learning for InterDependent networks laboratory (www.solidlab.net work). He received his PhD in electrical and computer engineering from Carnegie Mellon University in 2019, where he received his MSc degree in 2015. He also holds a doctoral degree in computer science and technology. Prior to that, he received an MSc degree from Tarbiat Modares University in 2013 and the BSc degree from Sharif University of Technology in 2011. His research interests include distributed optimization and learning algorithms, distributed computing and intelligence, sensor networks, interdependent networks, and cyber-physical-social resilience. Application domains include smart cities, energy systems, transportation electrification, and healthcare. Prof. Amini is a member of ACM and IEEE, and life member of IEEE-Eta Kappa Nu (IEEE-HKN), the honor society of IEEE. He served as President of Carnegie Mellon University Energy Science and Innovation Club, as technical program committee of several IEEE and ACM conferences, and as the lead editor for a book series on “Sustainable Interdependent Networks” since 2017. He also serves as associate editor of Frontiers in Communications and Networks (Data Science for Communications), SN Operations Research Forum, and International Transactions on Electrical Energy Systems. He has published more than 100 refereed journal and conference papers, and book chapters. He has edited/ authored seven books. He is the recipient of Excellence in Teaching Award from the Knight Foundation School of Computing and Information Sciences at Florida International University in 2020, the best paper award from 2019 IEEE Conference on Computational Science & Computational Intelligence, best reviewer award from four IEEE Transactions, the best journal paper award in Journal of Modern Power Systems and Clean Energy, and the dean’s honorary award from the President of Sharif University of Technology. (homepage: www. hadiamini.com; lab website: www.solidlab.network).

Community-Based Solar-Powered Electric Vehicle Parking Lots S. Muhammad Bagher Sadati, Miadreza Shafie-khah, and Abdollah Rastgou

Nomenclature Indices h PL V W

Index for time (hour) Index for PL number Index for EV number Index for scenarios

Parameters ηch ηdch Ccd Prch Prdch PrDNO PrPL Rmax SOEarv SOEdep SOEmax

Charging efficiency (%) Discharging efficiency (%) Cost of equipment depreciation ($/kWh) Charging tariff of EVs ($/kWh) Price of selling energy to DNO by the PLOs ($/kWh) Price of purchasing energy from DNO by the PLOs ($/kWh) Price of exchanging energy by the PLOs ($/kWh) Maximum charging/discharging rate (kWh) Initial SOE of EVs at the arrival time to the PLs (kWh) Desired SOE of EVs at the departure time from PLs (kWh) Maximum rate of SOE (kWh)

S. M. B. Sadati (*) National Iranian Oil Company (NIOC), Iranian Central Oil Fields Company (ICOFC), West Oil and Gas Production Company (WOGPC), Kermanshah, Iran M. Shafie-khah School of Technology and Innovations, University of Vaasa, Vaasa, Finland A. Rastgou Department of Electrical Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran © Springer Nature Switzerland AG 2021 M. Shafie-khah, M. H. Amini (eds.), Flexible Resources for Smart Cities, https://doi.org/10.1007/978-3-030-82796-0_1

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2

SOEmin tarv tdep

S. M. B. Sadati et al.

Minimum rate of SOE (kWh) Arrival time of EVs to the PLs Departure time of EVs from the PLs

Variables Pch-DNO Pch-PLOs Pch-Solar Pdch-DNO Pdch-PLOs PDNO-Solar SOE Xch

Charging power of each EV from DNO (kW) Charging power of each EV from other PLOs (kW) Charging power of each EV from solar energy’s output (kW) Discharging power of each PL to DNO (kW) Discharging power of each PL to other PLOs (kW) Solar energy’s output of each PL to DNO (kW) EVs’ state of energy (kWh) Binary variable which shows the charge status of each EV

1 Introduction Using electric vehicles (EVs) has rapidly increased in the last decade so that their number has reached more than 5 million in the world at the end of 2018. Also, under the EV30@30 scenario, the aim of EVs’ selling is 44 million vehicles per year by 2030, i.e., more than 250 million EVs in the world (https://www.iea.org/reports/ global-ev-outlook-2019). These EVs cause the installation of the public or private charging stations (CSs) or parking lots (PLs). Based on https://www.iea.org/reports/ global-ev-outlook-2019, 5.2 million charging points have been estimated in 2018, so that 90% of them are private. Usually the private parking lot owners (PLOs) maximize the profit or minimize the cost by meeting the EV owners’ (EVOs) satisfaction at departure time from PLs. Since EVs parked in the PLs for a long time, each PLO can sell the energy stored in the EVs (discharging power) to the distribution network operator (DNO) or other PLOs by arranging a proper contract with EVOs at a reasonable price. This contract should be written so that EVOs encourage to selling energy to PLOs. As a solution, a significant share of the profit that is obtained from discharging power can be paid to EVOs. Therefore, smart energy management systems (EMS) in each PL are essential to optimally charging/discharging plan for maximization of the PLOs’ profit. Also, due to the high tariff of DNO’s electricity, the PLOs can use renewable energy resources such as solar energy (SE) for achieving more profit (i.e., installing solarpowered electric vehicle parking lots (EVPLs)). In recent years, many studies have been investigating the charging/discharging schedule of EVs in EVPLs or solar-powered EVPLs that in the following some of these papers have reviewed. In [1], the capacity of EVPLs with SE considering the effect of weather uncertainties and modeling the vehicle-to-grid (V2G) capability for the enhancement of a distribution network (DN) is estimated. Considering the spinning reserve with the aim of maximization total benefit of EVPL equipped

Community-Based Solar-Powered Electric Vehicle Parking Lots

3

with a SE and distributed generators, a self-scheduling model has been studied in [2]. In [3], for obtaining optimal scheduling of EVPL in both energy and reserve market, a two-stage stochastic model is presented by minimizing the system operation cost. In [4] for optimization of EVPL scheduling, a new convexified model without considering EVs’ uncertainty and V2G ability is proposed, which minimizes the EVs’ charging cost. In [5], a mixed-integer linear programming model with the aim of minimizing daily operational cost is presented for optimization of only charging scheduling of EVs that are located in solar-powered EVPL. Using SE for EVs charging by a controlled charging program in order to be less dependent on DN’s energy, a new algorithm for maximization of the power generated by SE has been suggested in [6]. In [7], for operational scheduling of EVPL in energy and reserve market, a bi-level model is presented. The main objective of the upper level is minimizing the operation cost of the distribution system and the lower level is minimizing the cost of PL. In [8], a dynamic charging scheduling program is proposed for controlling EVs’ charging by maximization benefit of the PLO. In [9], a new four-stage algorithm by minimization operational cost considering customer satisfaction is proposed for optimal charging/discharging power of EVs in CS equipped with SE and fixed battery energy storage. In [10], by modeling of EVPL, a multi-objective optimization problem has been presented. The maximization of PLO’s daily profit and minimizing the percentage fading of battery energy storage system capacity were the main aims of the optimal power schedules. In [11], for economic operation (minimizing cost) of EVPL considering the environmental issues, a multi-objective optimized model is presented. The result showed that the suitable charge/discharge power schedule of EVs led to reduced total emission and operation costs. In [12], a model has been investigated for optimal charging/discharging power of EVs in an EVPL equipped with SE and energy storage system with the aim of minimizing electricity tariffs. In [13], with the aim of cost minimization considering EVOs’ satisfaction, a model is offered for EVs charging in EVPL integrated with an energy storage system and SE. In [14], for satisfaction of environmental and economic goals, a bi-objective model has been studied with the aim of minimizing emission and operation cost of solar-powered EVPL for determining the charging/ discharging program of EVs. In [15], by proposing an energy management strategy, the effect of solar-powered EVPL has been investigated on reducing the losses and power consumption of DN. By minimizing the expected charging costs of EV in solar-powered EVPL and increasing utilization of the solar system, a two-stage stochastic mixed-integer linear programming (MILP) has been presented in [16]. In [17], by minimizing the PLO’s total cost considering EVOs’ welfare, a stochastic optimal energy management is presented. In [18], by proposing interval multi-objective optimization with the aim of profit maximization of EV aggregator, the operational scheduling of EVs is modeled. Optimal scheduling of EV is modeled considering EVs’ uncertainties with the aim of cost minimizing for different grid purposes [19]. In [20], by adding the hydrogen storage systems, SE, and etc. to EVPL, a risk-based model for efficiency

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Table 1 Summary of literature review

Ref Year [1] 2013 [2] 2015 [3] 2016 [4] 2016 [5] 2017 [6] 2017 [7] 2017 [8] 2017 [9] 2018 [10] 2018 [11] 2018 [12] 2019 [13] 2019 [14] 2019 [15] 2019 [16] 2019 [17] 2019 [18] 2019 [19] 2020 [20] 2020 [21] 2020 Current chapter

Objective function Minimize Maximize cost profit – – – ✓ ✓ – ✓ – ✓ – ✓ – ✓ – – ✓ ✓ – ✓ – ✓ – ✓ – ✓ – ✓ – – – ✓ – ✓ – – ✓ ✓ – ✓ – – ✓ – ✓

Other ✓ – – – – – – – – – – – – – ✓ – – – – – – –

Uncertainty

Capability of PL

EVs – – ✓ – – – – – – – – – – – – ✓ ✓ – ✓ – – ✓

G2PL ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

SE ✓ ✓ – – – – – – – – – – – – – ✓ – – – – – ✓

PL2G ✓ ✓ ✓ – – – ✓ – ✓ ✓ ✓ ✓ – ✓ ✓ – – ✓ – ✓ ✓ ✓

PL2PL – – – – – – – – – – – – – – – – – – – – – ✓

investigation with the aim of minimizing cost has been probed. Also, the charging/ discharging program of EV in risk-averse and risk-neutral performance has been evaluated. In [21], by considering the EVO and PLO contract for trading energy, and charging/discharging energy trading with DNO, a model has been proposed for scheduling of EV aggregator with the aim of maximization of profit. Table 1 is presented for comparing the new community-based model with the ones presented in the literature. So, in this chapter, the concept of parking lots community, i.e., a micro-integrated system which consists of multiple parking lots that exchange energy with the DNO and each other, is introduced. To this end, a new stochastic MILP model of three solar-powered EVPLs is offered by maximizing the profit of PLOs considering EVs’ and SE’s uncertainties. Since the capability of the PLOs to trade energy with each other is not defined in the literature, the main contribution of this chapter is as follows: • Presenting the flexible community-based model of solar-powered electric vehicle parking lots • Using a simple additive weighting (SAW) method for solving the model

Community-Based Solar-Powered Electric Vehicle Parking Lots

5

The rest of the chapter is formed as follows. The concept of parking lots community and modeling of the solar-powered EVPLs is explained in Sect. 2. Section 3 is devoted to problem formulation. Numerical results are discussed in Sect. 4. Finally, conclusions are reported in Sect. 5.

2 Modeling of the Solar-Powered EVPLs As mentioned, in this chapter, the concept of parking lots community is presented where the multiple parking lots can exchange energy with each other besides trading energy with the DNO as shown in Fig. 1. It is noted that the amount of energy available for exchange, the price of this energy, and the time of exchange are reported between each PLO and the DNO at each hour. Also, with G2PL, PL2G, and PL2PL capability, an EMS unit is essential to determine the time and amount of charging/discharging power of each EV. There are many uncertainties in the operation and planning of the power system and solar-powered EVPLs. The most common way for modeling these uncertainties is a stochastic scenario-based approach. Usually, the probability distribution function (PDF) is used for the generation of scenarios. In this chapter, solar irradiation, arrival time, departure time, and the initial SOE of each EV are the main uncertainties. Mostly, for modeling of solar irradiance, the beta function is applied

Fig. 1 Interaction of PLOs with each other and a DNO in a community area

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[22]. Also, for other uncertainties, truncated Gaussian distribution, i.e., normal PDF that is limited with minimum and maximum value, is suggested [22]. The required equation for uncertainties modeling and the mean and standard deviation values for these PDFs are taken from [22]. Most optimization problems by considering all scenarios are very large. However, due to the complexity of the calculations and time limitations for modeling of uncertainty, scenarios are reduced. Therefore, it is necessary to construct a scenario reduction method to delete the scenarios with high similarity for saving computational cost. The basic concept of scenario reduction is to choose a reference scenario, compare this scenario with other scenarios, and remove the closest scenario. Here, in this chapter, the SCENRED2 tool in GAMS is used. The entire number of scenarios is 256 that is reduced to 8 scenarios. The scenario reduction model is described in [23].

3 Problem Formulation In this chapter, each PLO can trade energy with EVOs, other PLOs, and the DNO. The aim of this chapter is PLOs’ maximization profit with respect to energy exchanged with EVOs, DNO, and other PLOs. Of course, exchanging energy exists when the EVOs are encouraged. So, a part of the revenue of energy sold to DNO and other PLOs are paid to EVOs under a contract (α and β value). The EVOs also receive some money for battery depreciation due to several times discharging. PLOs also sell SE to DNO or EVOs, depending on the energy price. Therefore, the objective function of each PLO includes the revenue and cost terms as follows: 1. Revenues (a) (b) (c) (d) (e) (f)

Energy sold to EVOs by DNO’s energy (R1) Energy sold to EVOs by power generated by SE (R2) Energy sold to DNO by power generated by SE (R3) Energy sold to EVOs by other PLs’ energy (R4) Energy sold to DNO by EVs’ discharging energy (R5) Energy sold to other PLOs (R6)

2. Costs (a) (b) (c) (d)

Energy purchased from DNO for EVs charging (C1) Energy purchased from other PLOs for EVs charging (C2) Battery depreciation due to energy sold to DNO (C3) Battery depreciation due to energy sold to other PLOs (C4)

So, the objective function of each PLO is explained by Eq. (1). With the presence of three PLOs in this chapter, we face three objective functions. Since none of the PLOs are preferred by each other, the simple additive weighting (SAW) method is

Community-Based Solar-Powered Electric Vehicle Parking Lots

7

used for maximizing the sum of the objective functions by considering equal coefficient, i.e., 1/3. Therefore, the objective function is rewritten as Eq. (2): 0

ProfitPL

  ch‐Solar ch  1 ch Pch‐DNO PL,v,h,w Pr h þ PPL,v,h,w Pr h NvPL X Nw 24 B  X X   C B þ ð1  αÞPdch‐DNO Prdch þ PDNO‐Solar PrDNO CΔh ¼ ρw h h PL,v,h,w PL,h,w @ A w¼1 vPL ¼1 h¼1  ch‐DNO DNO   dch‐DNO cd   PPL,v,h,w C  PPL,v,h,w Prh þ

Nw X w¼1

ρw

N PL0 X PL0 ¼1

N PL X NvPL X 24 PL0 X X

Nv

vPL0 ¼1 PL¼1 vPL ¼1 h¼1

PL6¼PL0

0

  1 ch dch‐PLOs Pch‐PLOs PrPL þ ð1  βÞPPL,v 0 h PL0 ,vPL0 ,PL,vPL ,h,w Pr h PL ,PL ,vPL0 ,h,w B C @  AΔh    PL cd ch‐PLOs dch‐PLOs  PPL Pr C  P 0 PL,vPL ,PL0 ,v 0 ,h,w ,v 0 ,PL,vPL ,h,w h PL

PL

  1 1 1 Maximize ProfitPL1 þ ProfitPL2 þ ProfitPL3 3 3 3

ð1Þ ð2Þ

The constraints are described in Eqs. (3)–(20). The SOE in arrival, duration of presence in PL, and departure times are described in Eqs. (3)–(5). It is noted that the value of SOE at each EV is restricted with the minimum and maximum rate based on Eq. (6). According to Eqs. (7) and (8), charging/discharging of each EV are not simultaneous. EVs’ charging power by DNO is done at the mid-peak and off-peak periods. Also, EVs’ discharging power to DNO is performed at the on-peak periods. These features are explained by Eqs. (9)–(12). However, the power exchanged between PLOs and the EVs’ charging from the SE are possible every period based on Eqs. (13)–(15). Also, charging/discharging powers during each period are limited between zero and nominal charging/discharging rate. PLOs based on Eq. (16) can sell SE to DNO at 24-h periods. Eq. (17) also guarantees that the amount of EVs’ charging through SE and the amount of SE sold to the DNO are equal to the output of SE in each PLs. According to Eq. (18), for example, the amount of EVs’ charging power of PL 1 by the power of PL 2 is equal to the discharging power of PL 2 that sells to PL 1. pursuant to Eqs. (19) and (20), the total charging/discharging power of each EV in each PL is limited to four times nominal charging/discharging rate. SOEPL,v,h,w ¼

SOEarv PL,v,harv ,w

¼ harv , w



Pdch PL,v,h,w ηdch

!

  ch þ Pch PL,v,h,w η : 8 PL, v, h ð3Þ

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SOEPL,v,h,w ¼ SOEPL,v,h1,w 

Pdch PL,v,h,w ηdch

!

  ch þ Pch PL,v,h,w η : 8 PL, v, h

 harv , w

ð4Þ

SOEPL,v,h,w ¼ SOEdep : 8 PL, v, h ¼ hdep , w PL,v,hdep

ð5Þ

max SOEmin PL,v,h,w  SOEPL,v,h,w  SOEPL,v,h,w : 8 PL, v, h, w

ð6Þ

0

Pch PL,v,h,w

¼

Pch‐DNO PL,v,h,w

þ

Pch‐Solar PL,v,h,w

þ

vPL0 N PL0 N X X

PL ¼1 vPL0 ¼1 0

Pch‐PLOs PL0 ,vPL0 ,PL,vPL ,h,w

 X PL,v,h,w  Rmax : 8 PL 6¼ PL0 , v, h, w dch‐DNO 0  Pdch PL,v,h,w ¼ PPL,v,h,w þ

 ð1  X PL,v,h,w Þ  R

max

vPL0 N PL0 N X X

PL0 ¼1 vPL0 ¼1

ð7Þ Pdch‐PLOs PL,vPL ,PL0 ,vPL0 ,h,w

: 8 PL 6¼ PL0 , v, h, w

max 0  Pch‐DNO : 8 PL, v, hmid=off‐peak , w PL,v,h,w  R

ð8Þ ð9Þ

on‐peak ,w Pch‐DNO PL,v,h,w ¼ 0 : 8 PL, v, h

ð10Þ

max : 8 PL, v, hon‐peak , w 0  Pdch‐DNO PL,v,h,w  R

ð11Þ

mid=off‐peak Pdch‐DNO ,w PL,v,h,w ¼ 0 : 8 PL, v, h

ð12Þ

max : 8 PL 6¼ PL0 , v, h, w 0  Pch‐PLOs PL0 ,vPL0 ,PL,vPL ,h,w  R

ð13Þ

max 0  Pdch‐PLOs : 8 PL 6¼ PL0 , v, h, w PL,vPL ,PL0 ,vPL0 ,h,w  R

ð14Þ

max 0  Pch‐Solar : 8 PL, v, h, w PL,v,h,s  R

ð15Þ

DNO‐Solar  PSolar 0  PPL,h,w PL,h,w : 8 PL, v, h, w

ð16Þ

PDNO‐Solar þ PL,h,w

Nv N PL X X

Solar Pch‐Solar PL,v,h,s ¼ PPL,h,w : 8 PL, v, h, w

ð17Þ

ch‐PLOs 0 Pdch‐PLOs PL,vPL ,PL0 ,vPL0 ,h,w ¼ PPL,vPL ,PL0 ,vPL0 ,h,w : 8 PL 6¼ PL , h, w

ð18Þ

PL¼1 v¼1

24 X

max Pch : 8 PL, v, w PL,v,h,w  4:R

ð19Þ

max Pdch : 8 PL, v, w PL,v,h,w  4:R

ð20Þ

h¼1 24 X h¼1

Community-Based Solar-Powered Electric Vehicle Parking Lots

3.1

9

Solving Process of Problem

In this chapter, the objective function and all constraints are linear. Also, some of the decision variables are binary. Therefore, the proposed model is a mixed-integer linear programming (MILP) problem that is solved by CPLEX solver of GAMS. The simulation is carried out by a laptop with Core i7 up to 3.5 GHz CPU, 12 GB RAM (DDR4), and 4 MB Cash. After a scenario generation and reduction, the power output of SE and energy required of each EV are determined. Then considering prices i.e. energy exchanged between DNO and PLOs, energy exchanged between PLOs the time and charging/discharging energy of each EV to maximizing PLOs’ profit are computed. Figure 2 illustrates the flowchart of the stochastic communitybased model.

4 Simulation Results Three EVPLs with a maximum capacity of 500 EVs in each PL [2] are considered for investigation in the presented model. The required data for modeling of SEs’ uncertainty are taken from [22]. Also, the modified data for EVs are shown in Table 2 [23]. Other EV specification is presented in Table 3. Each EV also charges

Step 1

• Input data such as price of energy exchanged between PLOs, price of energy exchanged between DSO and PLOs, specification of each EV and SE.

• Caculation of EVs’ energy needed and SE’s output power based on SE’s uncertainty and

Step 2

Step 3

EVs’ uncertainty (SOE, t arv, t dep) considering scenario generation and reduction method.

• Computing the time and charging/discharging energy of each EV for operational scheduling of each PL and maximizing PLOs’ profit.

Fig. 2 Flowchart of the new model of solar-powered EVPLs Table 2 The modified probability distribution of EVs [22]

Initial SOE (%) Arrival time (h) Departure time (h)

Mean 50 6 18

Standard deviation 25 3 3

Minimum PL 1 PL 2 30 35 6 7 18 19

PL 3 45 6 18

Maximum PL 1 PL 2 60 55 9 10 22 23

PL 3 65 11 21

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Table 3 EVs specification Battery capacity of EVs Charging/discharging rate SOE min SOE max

32 kWh 10 kWh 4.8 kWh 28.8 kWh

Charge efficiency Discharge efficiency Desired SOE Ccd

90% 95% 28.8 kWh 20 $/MWh

Table 4 The price of energy exchanged between DNO and PLOs ($/MWh)

Price/time Energy purchased from DNO by the PLOs Energy sold to DNO by the PLOs Energy exchanged between the PLOs Energy sold to EVO by the SE Energy sold to DNO by the SE

Off-peak periods (01:00–06:00, 20:00–24:00) 65

Mid-peak periods (07:00–11:00, 18:00–19:00) 94

On-peak periods (12:00–17:00) –

–

–

134

94

94

94

108.1

108.1

108.1

65

94

134

Table 5 The number of EVs in arrival and departure times in three PLs at scenario 1

Arrival time PL 1 PL 2 PL 3 Departure time PL 1 PL 2 PL 3

6 183 0 189 18 424 0 422

7 79 268 72 19 33 448 36

8 57 57 57 20 21 25 20

9 181 49 59 21 10 19 22

10 0 126 44 22 12 6 0

11 0 0 79 23 0 2 0

or discharges up to 40 kWh, e.g., four times charging or discharging with maximum charging and discharging rates (10 kWh). The price of energy over 24 hours is shown in Table 4 (https://www.powerstream.ca/customers/rates-support-programs/ time-of-use-pricing.html). It is assumed that the energy sold to EVOs by PLOs (energy purchased from DNO and energy generated from other PLOs) is 15% higher than these values. Also, the time interval is 1 h. First of all, considering the information in Table 3 and solar irradiance and other required data of solar energy with the rated power of 1 MW [24], the number of EVs that enter the PLs and the number of EVs that depart from the PLs, SOE of all EVs in PLs, and output power of SE are calculated. Table 5 shows the Tarv and Tdep of each PL in scenario 1 and number of EVs. Also, the SOE of all EVs in PL 1 at scenario 1 is displayed in Fig. 3. As can be seen, the SOE of EVs is between 9.6 kWh and 19.2 kWh. The power generated by SE is illustrated in Fig. 4.

Community-Based Solar-Powered Electric Vehicle Parking Lots

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Fig. 3 The SOE of all EVs in PL 1 at scenario 1 900

Output Power (kW)

800 700 600 500 400 300 200 100 0

1 to 6 7

8

9

10

11

12

13

14

15

16

17

18 19 to 24

Hour (h) Fig. 4 The output power of SE in scenario 1

In the following, for evaluating the effectiveness of the presented model, firstly, PLOs’ profit is investigated without the community-based model. Then in the community-based model and with/without SE, by computing all parts of the objective function, the profit of each PLO is calculated.

4.1

PLOs’ Profit Without the Community-Based Model and SE

In this situation, each PLO exchanges energy with DNO and EVOs and no energy trade between PLOs. So, each PLO obtains revenue by selling energy for EVs’ charging, as well as selling EVs’ discharging energy to DNO. It is noted that under a suitable contract, 50% of discharging energies’ revenue to DNO and the cost of

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Table 6 The PLOs’ profit without the community-based model ($)

No. of PL R1 0.5  R5 C1 C3 Profit

PL 1 1460.265 340.775 1269.796 101.724 429.520

PL 2 1933.656 583.3946 1681.440 174.147 661.463

PL 3 1319.579 330.796 1147.460 98.745 404.170

Table 7 The charging/discharging energy without the community-based model (MWh) No. of PL Charging energy Discharging energy Table 8 PLO 1’s profit with community-based model ($)

PL 1 13.566 5.086

PL 2 17.944 8.707

No. of PL R1 R2 R3 R4 by PLO 2 R4 by PLO 3 0.5  R5 0.5  R6 to PLO 2 0.5  R6 to PLO 3 C1 C2 from PLO 2 C2 from PLO 3 C3 C4 to PLO 2 C4 to PLO 3 Profit

PL 1 without SE 1493.630 0 0 157.316 314.758 346.137 78.033 106.238 1298.808 136.796 273.703 103.324 33.205 45.207 605.067

PL 3 12.283 4.937

PL 1 with SE 1271.494 319.754 482.674 158.355 326.111 350.862 84.729 113.029 1105.647 137.700 283.575 104.734 36.055 48.097 1391.201

battery depreciation are paid to EVOs. Table 6 shows the profit of each PLO. Also, the charging/discharging energy of all EVs in each PL is presented in Table 7. According to Table 7, PLO 2 has more exchanging energy with the DNO and achieves more profit.

4.2

PLOs’ Profit with the Community-Based Model and With/Without SE

In this section, PLOs have exchanging energy together. The PLOs’ profit is shown in Tables 8, 9, and 10. Of course, for more evaluation of SE, the PLO 2 does not have SE. According to Tables 8, 9, and 10, in the community-based model, the PLOs’

Community-Based Solar-Powered Electric Vehicle Parking Lots

13

Table 9 PLO 2’s profit with community-based model ($) No. of PL R1 R2 R3 R4 by PLO 1 R4 by PLO 3 0.5  R5 0.5  R6 to PLO 1 0.5  R6 to PLO 3 C1 C2 from PLO 1 C2 from PLO 3 C3 C4 to PLO 1 C4 to PLO 3 Profit Table 10 PLO 3’s profit with community-based model ($)

PL 2 without SE 1774.806 0 0 179.476 153.417 453.294 68.398 87.564 1543.310 156.066 133.406 135.311 29.105 37.261 682.495

No. of PL R1 R2 R3 R4 by PLO 1 R4 by PLO 2 0.5  R5 0.5  R6 to PLO 1 0.5  R6 to PLO 2 C1 C2 from PLO 1 C2 from PLO 2 C3 C4 to PLO 1 C4 to PLO 2 Profit

PL 2 with other PLO SE 1773.628 0 0 194.877 142.150 463.365 68.850 81.137 1542.285 169.458 123.609 138.318 29.298 34.526 686.513

PL 3 without SE 1528.022 0 0 244.347 201.397 387.554 136.851 66.703 1328.714 212.476 175.128 115.688 58.234 28.384 646.250

PL 3 with SE 1410.426 327.795 472.604 259.968 186.615 396.150 141.787 61.804 1226.458 226.059 162.274 118.253 60.335 26.299 1437.472

profit increases. Also, with SE, the PLOs’ dependence on the DNO is somewhat reduced, and PLOs get more profit. For a better comparison between the profit of PLOs, Fig. 5 is presented. As can be seen, PLO 2 has the least increasing which in the following is also discussed. As mentioned, one of the most important constraints is Eq. (18). In this regard, Tables 11 and 12 show the energy exchanged between PLs. Also, trading energy with DNO has been added to these tables. According to Tables 11 and 12, PLO 2 has more trade energy with DNO and less energy exchange with the other PLOs.

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S. M. B. Sadati et al. 1600 Without the community-based model With the community-based model and without SE With the community-based model and SE

1400

Profit ($)

1200 1000 800 600 400 200 0

PL 1

PL 2

PL 3

Number of PL Fig. 5 Comparison between PLOs’ profit in the three models Table 11 Energy exchanged between PLOs and DNO (charging energy) (MWh) No. of PL PLO 1 PLO 2 PLO 3

From DNO Without With SE SE 13.879 11.824 16.474 16.463 14.216 13.127

From PLO 1 Without With SE SE – – 1.660 1.802 2.260 2.240

From PLO 2 Without With other SE PLOs’ SE 1.455 1.464 – – 1.863 1.726

From PLO 3 Without With SE SE 2.911 3.016 1.419 1.314 – –

Table 12 Energy exchanged between PLOs and DNO (discharging energy) (MWh) No. of PL PLO 1 PLO 2 PLO 3

To DNO Without SE 5.166 6.765 5.784

With SE 5.236 6.915 4.968

To PLO 1 Without SE – 1.455 2.911

With SE – 1.464 3.016

To PLO 2 Without SE 1.660 – 1.419

With other PLOs’ SE 1.802 – 1.314

To PLO 3 Without SE 2.260 1.863 –

With SE 2.404 1.726 –

For an accurate investigation of Tables 11 and 12, the charging/discharging time and power of each PL with SE are examined. In Figs. 6, 7, and 8, the charging power of each PL from DNO, other PL, and SE is illustrated. These figures can be evaluated in two parts, i.e., before and after on-peak periods. It is noted that based on Table 5, the time of entering EVs to PL 1 and 3 is 6:00 and PL 2 is 7:00. Before on-peak periods, the lowest energy price of the DNO is at 6:00. So at this time, no power bought from the DNO for charging of EVs. The energy exchange at 6:00 only takes place between PL 1 and 3. With the entrance of more EVs at other times, purchasing energy from the DNO and exchanging power between PL increase. At 10:00, PL 2 is filled, so PLO 2 received most of the energy at this time, but in PL 1 and 3, this

Community-Based Solar-Powered Electric Vehicle Parking Lots

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6 From DNO From PL 2 From PL 3 From SE

5

Power (MW)

4 3 2 1 0

6

7

8

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

Hour (h) Fig. 6 500 EVs’ charging power in PL 1 6 From DNO From PL 1 From PL 3

Power (MW)

5 4 3 2 1 0

6

7

8

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

Time (h) Fig. 7 500 EVs’ charging power in PL 2

amount is obtained at 11:00. Of course, some part of the energy required for charging of EVs in PL 1 and 3 is also provided by SE. At 12:00, the start of on-peak periods, no power is purchased from DNO, but trading energy between the PLOs are continued at the lower amount. Of course, this exchange is very low in PL 2. Also, for charging of EVs, SE is used in a very small amount about 0.158 MWh in PL 1 and 0.234 MWh in PL 3 at 12:00 to 17:00. EVs’ departure time from PL 1 and 3 is 18:00 and from PL 2 is 19:00. According to Figs. 6, 7 and 8, at 18:00, the receiving power is 5 MW (i.e., all EVs are charged). This amount of power in PL 2 happens at 18:00 and 19:00. Since PLO 2 has more

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S. M. B. Sadati et al. 6 From DNO From PL 1 From PL 2 From ES

Power (MW)

5 4 3 2 1 0

6

7

8

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

Hour (h) Fig. 8 500 EVs’ charging power in PL 3 2.5 To DNO To PL 2 To PL 3 TO DNO by SE

Power (MW)

2

1.5

1

0.5

0

6

7

8

9

10

11

12

13

14

15

16

17 18 to 24

Hour (h) Fig. 9 Output power of SE and discharging power of 500 EVs in PL 1

opportunity for EVs’ charging, PLO 2 has more exchanging power with DNO. According to Table 5, since the EVs depart PL 2 at 19:00, PLO 2 can charge EVs twice. This means that the EVs in the PL 2 can be discharged at least twice if properly charged at off-peak and mid-peak periods. But in PL 1 and 3, EVs can only charge at 18:00, so PLO 1 and 3 have less energy exchange with DNO than PL 2. Figures 9, 10, and 11 show the discharging power of each PL and SE’s output to DNO and other PL. According to Figs. 10 and 11, the exchanging of energy between PLOs is higher before on-peak periods. Also at 11:00, because most EVs attended the PL2G program at on-peak periods, no energy is traded between PLs. During the on-peak periods, the amount of energy sold by PLO to DNO is very high due to the energy price. Also, the output of SE at on-peak periods is sold to DNO because the

Community-Based Solar-Powered Electric Vehicle Parking Lots

17

2.5 To DNO To PL 1 To PL 3

Power (MW)

2 1.5 1 0.5 0

6

7

8

9

10

11

12

13

14

15

16

17 18 to 24

13

14

15

16

17 18 to 24

Hour (h) Fig. 10 Discharging power of 500 EVs in PL 2 2.5 To DNO To PL 1 To PL 2 TO DNO by SE

Power (MW)

2 1.5 1 0.5 0

6

7

8

9

10

11

12

Hour (h) Fig. 11 Output power of SE and discharging power of 500 EVs in PL 3

energy price is at its highest amount (i.e., 134 $/MWh). Of course, since EVs departed the PLs at 18:00, there is no exchanging of energy between PLOs, and SE is used for the charging of EVs.

5 Conclusion In this chapter, the community-based model of electric vehicle parking lots equipped with solar energy was modeled. Also, the uncertainties of solar energy and electric vehicles were considered. The aim of modeling was the maximization of profit of parking lot owners. For investigating the effectiveness of the model, this profit was

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calculated without the community-based model and also with the community-based with/without solar energy for three parking lot owners. The results were shown that by the community-based model, the parking lot owner achieved more profit than without the community-based model. Also, the main specification of electric vehicles such as arrival time, departure time, and the initial state of energy and presence of solar energy had an important effect on the increase of this profit. So, the owners of parking lots 1 and 3, due to more interaction in the communitybased model, had a 40.87% and 59.89% increase in their profit, respectively, compared to without the community-based model. This rate is only 3.17% for parking lot owner 2. Because of the characteristics of parking lot 2, their owner was less willing to exchange energy with other parking lot owners. For this reason, increasing the profit of the parking lot owner 2 was the lowest. Meanwhile, with the presence of solar energy in the parking lots, this profit was drastically increased. Despite the solar energy with a suitable capacity, the profits of parking lot owners 1 and 3 had increased 223.89% and 255.66%, respectively, compared with the community-based model. Also, the parking lot owner 2 has not changed its profit because solar energy was not used in the parking lot. In fact, by the existence of the energy management system in each parking lot, the charging/discharging schedule was properly done for parking lot-to-grid and parking lot-to-parking lot capability. Acknowledgment The authors would like to thank the Kermanshah Branch, Islamic Azad University, Kermanshah, Iran, for the support of the research project with title “Flexible co-operation of electric vehicles parking lot in a smart distribution company.”

References 1. U.C. Chukwu, S.M. Mahajan, V2G parking lot with PV rooftop for capacity enhancement of a distribution system. IEEE Trans. Sustain. Energy 5(1), 119–127 (2013) 2. M. Honarmand, A. Zakariazadeh, S. Jadid, Self-scheduling of electric vehicles in an intelligent parking lot using stochastic optimization. J. Franklin Inst. 352(2), 449–467 (2015) 3. E. Heydarian-Forushani, M.E. Golshan, M. Shafie-khah, Flexible interaction of plug-in electric vehicle parking lots for efficient wind integration. Appl. Energy 179, 338–349 (2016) 4. Y. Song, Y. Zheng, D.J. Hill, Optimal scheduling for EV charging stations in distribution networks: A convexified model. IEEE Trans. Power Syst. 32(2), 1574–1575 (2016) 5. A. Ivanova, J.A. Fernandez, C. Crawford, N. Djilali, Coordinated charging of electric vehicles connected to a net-metered PV parking lot, in 2017 IEEE PES innovative smart grid technologies conference Europe (ISGT-Europe), (IEEE, 2017), pp. 1–6 6. R. Figueiredo, P. Nunes, M.C. Brito, The feasibility of solar parking lots for electric vehicles. Energy 140, 1182–1197 (2017) 7. S. Aghajani, M. Kalantar, Operational scheduling of electric vehicles parking lot integrated with renewable generation based on bi-level programming approach. Energy 139, 422–432 (2017) 8. Y. Zhang, L. Cai, Dynamic charging scheduling for EV parking lots with photovoltaic power system. IEEE Access 6, 56995–57005 (2018)

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9. Q. Yan, B. Zhang, M. Kezunovic, Optimized operational cost reduction for an EV charging station integrated with battery energy storage and PV generation. IEEE Trans. Smart Grid 10(2), 2096–2106 (2018) 10. H.H. Eldeeb, S. Faddel, O.A. Mohammed, Multi-objective optimization technique for the operation of grid tied PV powered EV charging station. Electr. Power Syst. Res. 164, 201–211 (2018) 11. J. Jannati, D. Nazarpour, Multi-objective scheduling of electric vehicles intelligent parking lot in the presence of hydrogen storage system under peak load management. Energy 163, 338–350 (2018) 12. C.R. Chen, Y.S. Chen, T.C. Lin, Optimal charging scheduling for electric vehicle in parking lot with renewable energy system, in 2019 IEEE international conference on systems, man and cybernetics (SMC), (IEEE, 2019), pp. 1684–1688 13. W. Jiang, Y. Zhen, A real-time EV charging scheduling for parking lots with PV system and energy store system. IEEE Access 7, 86184–86193 (2019) 14. J. Jannati, D. Nazarpour, Optimal performance of electric vehicles parking lot considering environmental issue. J. Clean. Prod. 206, 1073–1088 (2019) 15. M.T. Turan, Y. Ates, O. Erdinc, E. Gokalp, J.P. Catalão, Effect of electric vehicle parking lots equipped with roof mounted photovoltaic panels on the distribution network. Int. J. Electr. Power Energy Syst. 109, 283–289 (2019) 16. K. Seddig, P. Jochem, W. Fichtner, Two-stage stochastic optimization for cost-minimal charging of electric vehicles at public charging stations with photovoltaics. Appl. Energy 242, 769–781 (2019) 17. M. Sedighizadeh, A. Mohammadpour, S.M.M. Alavi, A daytime optimal stochastic energy management for EV commercial parking lots by using approximate dynamic programming and hybrid big bang big crunch algorithm. Sustain. Cities Soc. 45, 486–498 (2019) 18. H. Ahmadi-Nezamabad, M. Zand, A. Alizadeh, M. Vosoogh, S. Nojavan, Multi-objective optimization based robust scheduling of electric vehicles aggregator. Sustain. Cities Soc. 47, 101494 (2019) 19. Z. Wang, P. Jochem, W. Fichtner, A scenario-based stochastic optimization model for charging scheduling of electric vehicles under uncertainties of vehicle availability and charging demand. J. Clean. Prod. 254, 119886 (2020) 20. Y. Cao, J. Du, X. Qian, S. Nojavan, K. Jermsittiparsert, Risk-involved stochastic performance of hydrogen storage based intelligent parking lots of electric vehicles using downside risk constraints method. Int. J. Hydrog. Energy 45(3), 2094–2104 (2020) 21. Y. Cao, L. Huang, Y. Li, K. Jermsittiparsert, H. Ahmadi-Nezamabad, S. Nojavan, Optimal scheduling of electric vehicles aggregator under market price uncertainty using robust optimization technique. Int. J. Electr. Power Energy Syst. 117, 105628 (2020) 22. S.M.B. Sadati, J. Moshtagh, M. Shafie-Khah, A. Rastgou, J.P. Catalão, Optimal charge scheduling of electric vehicles in solar energy integrated power systems considering the uncertainties, in Electric vehicles in energy systems, (Springer, Cham, 2020), pp. 73–128 23. S.M.B. Sadati, J. Moshtagh, M. Shafie-khah, J.P. Catalão, Smart distribution system operational scheduling considering electric vehicle parking lot and demand response programs. Electr. Power Syst. Res. 160, 404–418 (2018) 24. M. Shafie-Khah, P. Siano, D.Z. Fitiwi, N. Mahmoudi, J.P. Catalao, An innovative two-level model for electric vehicle parking lots in distribution systems with renewable energy. IEEE Trans. Smart Grid 9(2), 1506–1520 (2017)

Smart Energy-Aware Cities: Customer Characterization by Energy Data Analytics to Improve Demand Response Performance Mohsen Kojury-Naftchali and Alireza Fereidunian

1 Introduction Energy awareness is the essence of energy efficiency, since control and management need awareness. Energy management systems aim at environmental protection by reducing CO2 and heat emissions. In essence, the energy awareness concept can be regarded by two approaches: micro approach for the end-user and macro approach for the energy management system. In micro approach, the awareness of the end-users toward the energy consumption is considered, while in macro approach, the awareness of the energy management system regarding the end-users’ energy consumption pattern is studied. Demand response (DR) is one of the most practical programs among demand-side management programs (DSM) applied to the distribution network. Efficient implementation of demand response is one of the important issues in the case of smart cities. Besides, the efficiency of implementing demand response is affected by the data related to grid. In other words, more accurate data about the grid and customers’ consumption culminates in better insight and awareness for operators and planners of the grid to design proper demand-side management programs. In other words, both of the demand response and the energy awareness are influenced by the data of customers’ consumption and technical and economic data about the grid. The smart city energy management system needs a communication infrastructure and energy meters at the customers’ premises to qualify for its energy awareness purpose. This is achieved by advanced metering infrastructure (AMI) and its relevant smart meters which provide a two-way communication between the energy

M. Kojury-Naftchali West Mazandaran Electricity Distribution Company, Nowshahr, Iran A. Fereidunian (*) K. N. Toosi University of Technology (KNTU), Tehran, Iran e-mail: [email protected] © Springer Nature Switzerland AG 2021 M. Shafie-khah, M. H. Amini (eds.), Flexible Resources for Smart Cities, https://doi.org/10.1007/978-3-030-82796-0_2

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M. Kojury-Naftchali and A. Fereidunian

management system and the customers. The energy metered data is gathered by the AMI system and stored in databases. These databases are sources of useful raw data regarding customers’ behavior, which can successfully be used in data analytics for clustering customers to characterize their consumption behavior. The consumption behavior of each individual cluster is then analyzed to investigate their capacity for successful participation in demand response (DR) programs. Importance of the mentioned issues in the above section led to more attention of some research works especially regarding increasing trend of electricity consumption [1]. As mentioned before, demand-side management programs are associated with energy consumption, price, and their relationship. Analysis of the relation between customers’ electricity consumption and price signals are performed considering the price elasticity concept [2]. As customers do not necessarily express similar consumption behaviors against the electricity price, it is useful to consider customer characterization studies [3, 4]. Analyzing customer consumption behaviors are also investigated in [5–12]. The authors of this chapter also conducted related works and analyses in this field presented in [8–10], in which customers’ capacity for participating in demand response program is the central concept. Deployment of smart meters and AMI in the energy systems produces large amount of data to be analyzed. Actually, the extracted knowledge from these analyses promotes energy awareness in the smart cities [13, 14]. Since customers play an influencing role in DSM programs, enhancement of their awareness about their consumption patterns leads to making more efficient decision. In [15], an energy management project in Japan is presented in which the results of visualization of customers’ energy consumption on their consumption behavior and energy saving activities. Another questionnaire-based study about the relation between energy-saving attitude and energy consumption pattern visualization is performed on 3000 households in Japan [16]. Electricity consumption analysis using data mining algorithms in a smart city is performed in [14] and in an electric transport in a smart city in [17]. The role of energy management system in smart cities is investigated, regarding global approaches, challenges, and future of the smart cities [18–20]. This chapter is devoted to the investigation of energy awareness effects on implementation of demand response programs, motivated by the following: • Assessing the role of new metering technologies in the electricity network in a smart city • Studying the effect of enhancing customers’ awareness on the implementation of demand response programs • Recognizing challenges of extending new technologies for improving monitoring infrastructures in a smart city • Recognizing and defining new opportunities provided by enhancement of awareness in customers and other participants of the smart grid

Smart Energy-Aware Cities: Customer Characterization by Energy Data. . .

23

Moreover, key contributions of such study is as follows: • Customers with different consumption patterns are categorized and analyzed using pattern recognition algorithms. • The capacity of different consumption patterns for participating in demand response program is analyzed. • Suitable consumption patterns for better implementation of demand response is analyzed. This chapter is organized as follows: smart energy-aware cities are briefly introduced first, followed by an introduction to demand-side management programs and then data mining methods. Subsequently, advanced metering infrastructure as a key technology influencing energy-aware smart city is introduced. Then, relation between demand-side management programs and customer awareness enhancement in the smart grid is analyzed, and the approach of this study is described. The proposed methodology in the prior section is applied on a real data set to verify practicality of the proposed methodology. This study is concluded in the last section of this chapter by discussing on the results.

2 Smart Energy-Aware Cities The growing urbanization has led to the increase in the whole population of the cities by 75% by 2050, which necessitates integrated utilization of advanced technologies to situate sustainable development in cities to mitigate the growing urbanization challenges [21, 22]. The European Commission defines smart cities as “cities using technological solutions to improve the management and efficiency of the urban environment” [23]. A smart city is a system of systems, incorporating interoperability and emergence between the systems like smart homes and buildings systems, smart energy systems, smart transportation system, smart energy system, smart communication system, smart health system, smart waste management system, and smart education system [21]. Energy awareness can be regarded as awareness about the amount, usage, source, losses, and environmental impact of the consumed energy and the methods devised to mitigate its negative impact, by continuous measurement of energy consumption and an infrastructure to map process energy data onto relevant performance measures [24, 25]. Smart energy-aware cities are smart cities that utilize the communication infrastructure system to situate a distributed smart metering system, in order to achieve both micro- and macro-energy awareness goals. Micro-energy awareness provides citizens with the awareness on their energy usage and other useful energy data to enable distributed decision-making for energy-efficient consumption, while macro-

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energy awareness delivers energy data to the centralized energy management systems (EMS) for an efficient energy management.

3 Demand-Side Management Demand-side management programs are the results of research performed to mitigate the problem of increasing need for electricity consumption. Demand-side management (DSM) is the planning, implementation, and monitoring of those utility activities designed to influence customer use of electricity in ways that will produce desired changes in the utilities load shape [26]. DSM includes only those activities that involve a deliberate intervention by the utility in the marketplace so as to alter the customer demand. Therefore, customers’ purchases of energy-efficient appliances as a reaction to the perceived need for conservation would not be classified as DSM [27]. There are six basic load shape objectives addressed in DSM shown in Fig. 1. More detailed information about these basic load shapes is described in [26]. In DSM program literature, demand response is one of the most popular one. Demand response means changing in electric usage by end-use customers from their normal consumption pattern at times of high wholesale market prices or when system reliability is jeopardized. Demand response is implemented in two ways: 1. Price-based demand response Changes in usage by customers in response to changes in the prices which is divided into three types: real-time pricing, critical-peak pricing, and time-of-use rates. 2. Incentive-based demand response Give customers load reduction incentives that are separate from, or additional to, their retail electricity rate, fixed (based on average costs) or time-varying

Strategic conservation

Peak clipping

Valley filling

Load shifting

Fig. 1 Six basic load shape [26]

Demand Side Management

Strategic load growth

Flexible load shape

Smart Energy-Aware Cities: Customer Characterization by Energy Data. . .

25

4 Data Mining Knowledge discovery process could be illustrated in the following sequences [28]: data cleaning, data integration, data selection, data transformation, and data mining. In this study, concerning discovering priorities of transformers for preventive maintenance programs, classification and clustering are two popular practical instruments.

4.1

Classification

Classification is a supervised learning algorithm, allocating each sample to the predefined classes. The system is trained using the labeled data; thus, it develops a model which is able to dedicate new sample to one of the existing classes. The process is called supervised learning, due to the existence of labeled training data, conveying the meaning of a prior knowledge about data which are used for training set. Decision trees and neural network are two powerful classifiers.

4.2

Clustering

Clustering is an unsupervised learning algorithm, in which samples are not labeled. Each cluster is specified by a cluster center which represents the cluster. The samples allocated to each cluster are highly similar to each other, while similarity is usually measured by the samples’ Euclidean distance. The smaller the distances between the sample, the higher similarity, and vice versa. To define the Euclidean distance, regarding two samples X(x1,x2, . . ., xN), Y(y1,y2, . . ., yN), with N attributes, the Euclidean distance is as follows: vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u N uX ðxi  yi Þ2 d ðX, Y Þ ¼ t

ð1Þ

i¼1

4.3

Entropy

Entropy, a term borrowed from the thermodynamics literature by Claude Shannon in information theory, denotes the information uncertainty for a random variable or a signal. In this study, the entropy represents the significance of samples allocated to

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the clusters. The larger the entropy index, the more significant the sample allocation. The definition of the entropy is presented in Eq. (2) [29]: Entropy ¼ 

k X

pðmi Þ log pðmi Þ

ð2Þ

i¼1

where p(mi) is the membership of a sample allocated to a cluster.

4.4

Davies-Bouldin Index

Davies-Bouldin is an index representing the optimal number of clusters, for a clustering process [11]. This index is defined as sum of inside-cluster deviation to between-cluster distance proportion [9]; thus, for clusters denoted as Qi, i ¼ 1, 2, . . ., Nc, the DB index is defined as:   NC max S Q j þ SðQl Þ 1 X   DB ¼ N c J¼1 l 6¼ j d Q j , Ql

ð3Þ

where S(QK) denotes the distance among the samples inside the cluster QK and d(Qj, Ql) denotes the distance between the clusters Qj and Ql. Moreover, Nc is the optimal number of cluster corresponding to the minimum value of the Davies-Bouldin index [11].

5 Advanced Metering Infrastructure One of the key requirements of the smart grid is communication infrastructure. In a smart city, customers are able to be aware of their consumption condition using data which is transferred among different players of the electricity network. Realizing the concept of smart grid in the electricity network necessitates advances in metering and monitoring equipment and infrastructures of the grid. This requirement caused focuses of investment in this case to enhance the level of automation and data transferring in metering equipment. The result of such attention resulted in automated meter reading (AMR), provided a condition by which the utilities are able to remotely read the customers’ consumption records [30]. AMR just provides remote reading of the consumption records in a one-directional infrastructure, and other requirements of the customers and utilities are not met. In the next step, advanced metering infrastructure (AMI) is introduced providing a bidirectional communication infrastructure enabling the utilities to remotely read the consumption records and analyzing the grids condition in different times. Such

Smart Energy-Aware Cities: Customer Characterization by Energy Data. . .

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technology is able to transfer data and needed information gathered from smart meters and sensors in all of the required locations of the electricity network from generation to consumption side of the network [31]. AMI provides a suitable data set containing different types of data gathered from different sources which could be used for different goals. The goal focused in this study is about the role of this technology as a key instrument of the energy-aware smart city in better implementation of DSM programs. Optimization of electricity energy usage, load management, and assessment of different load control algorithms are some of the abilities that could result from the gathered data set by smart meter system. Enhancing efficiency of financial mechanisms, technical considerations, and decision-making are some results of extending smart metering infrastructure in the electricity network which is able to promote implementation of DSM programs. As mentioned, in the smart grid, awareness of customers about their consumption patterns facilitates customers’ involvement in the events and programs of the electricity network such as DSM programs, electricity market, and load management. Enhancement of monitoring and control accessibility for the customers about their consumption patterns and market-related signals realizes better participation of customers as an active player in the energy-aware smart city. Increase of the awareness about the grids condition and customers behaviors even could be used for operation goals in the electricity network and some subjects such as fault detection, power quality monitoring, load forecasting, and forecasting of disturbances in the grid.

6 Energy-Aware Smart City for Demand-Side Management Programs One of the best metrics for representing relationship between market price and consumption behavior is price elasticity (PE). The PE is the percent of changes in customers’ consumption in response to the percent changes in the price [28]. Indeed, PE implies the sensitivity of customers’ consumption and their reaction to the changes of the electricity price. A conventional relationship between price and demand is shown in Fig. 2. By aid of demand-price curve, formula of the elasticity is defined in Eq. (4) [4]. The detailed information about elasticity is explained in [32]. Definitely, customers’ reaction in response to the price changes originates from their benefits. The benefits are associated with the types of loads if deferrable or not. There is a concept inducing customers to select a consumption pattern in response to the price changes. This concept is utility, the key point affecting customers’ behavior in their consumption. Briefly, utility makes a compromise between benefits of cost reduction and welfare decrease in the consequence of load reduction in some time slots by customers.

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Fig. 2 Price-demand curve

Demand

(dt, Pt)

Price

e¼

p0 ∂d : d0 ∂p

ð4Þ

According to the mentioned points before, one of the most routine benefits of the energy awareness is customers’ knowledge about their consumption behavior. Awareness of customers about their consumption behavior causes modifying their consumption profiles according to the price signals. Energy awareness is about understanding the following: • • • • •

How much energy we use What we actually use it for Where the energy comes from Environmental impact and depletion of resources What we can do to reduce our energy consumption and its undesirable impact There are two approaches in energy awareness:

• Micro approach – The awareness of the end-users toward the energy consumption • Macro approach – The awareness of the energy management system regarding the end-users’ energy consumption pattern Actually, the price signals of electricity are an important point influencing consumption patterns. Customers’ relative approaches to the price signals could be divided into three types: 1. Customers which do not change their consumption patterns in the consequence of awareness of their consumption patterns and price signals 2. Customers making a little change in their consumption patterns in accordance with the price signals

Smart Energy-Aware Cities: Customer Characterization by Energy Data. . .

29

3. Customers make a considerable change in their consumption patterns according to the price signals Price elasticity of demand is a concept introduced to portray the relation between price and customers’ consumption behavior. This concept shows the customers’ sensitivity to changes of prices. Actually, some of the customers prefer to compose their consumption profile so as to reduce consumption costs. But, there is an important point that limits this changes in consumption behavior which is customer welfare. Usually, changing routine consumption behavior with the aim of reducing costs reduces customers’ utility too. Therefore, the utility is one of the key points that distinguishes customers’ approach in making a little or a considerable change in their consumption behavior. Some of the customers are interested in getting comprehensive awareness about their consumption effects on their bills and their role in realizing demand-side management programs such as demand response. Energy awareness helps this group of customers to have a better insight. In the contrary, some others do not pay any attention to such cases and prefer to keep their routine consumption pattern, neglecting economic and financial consequences. Visualization of the consumption patterns helps customers to have better insight about their total energy usage and more knowledge about their role as a smart customer for better realizing of the demand-side management programs in a smart city. The smart city is a concept widely accepted in recent years aiming at providing a condition for customers to an acceptable level of welfare and optimizing needs of resources. The ascending trend of energy consumption in the cities necessitates such approach for management of the energy usage especially by regarding the approach of preserving natural resources. Combining these points results in a problem in the future of energy environment of the cities. There are two approaches to supply the ascendant need of the energy: 1. Increasing the amount of energy production or supply side of the network 2. Decreasing and managing energy consumption in the network Financial and economic considerations resulted in more attention to the second approach and produced the concept of demand-side management programs such as demand response. Actually, customers play an embossed role in better realization of the DSM programs. Involving customers in DSM programs necessitates information sharing among different participants of the network. These participants are distributed in different sides of production, transmission, distribution, and consumption in the network. Sharing information among these participants is enabled by ICT infrastructures. Indeed, having a suitable ICT infrastructure is one of the main needs of a smart city in which the necessary information or data are transferred quickly and accessible with a proper visualization. For this reason, advanced metering infrastructure (AMI) is introduced proving a bidirectional communication infrastructure for sharing information between customers and the grid. The AMI is one of the needed technologies for realizing smart cities especially from energy consumption point of view. Actually, the more resolution of the data transferred in

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the network (especially real-time monitoring and visualization of consumption) results in more comprehensive insight for the both side of consumption and the production of the grid. Certainly, if the households are aware of their electricity consumption, they could play more effective role in the implementation of DSM programs maintaining their welfare and utility.

7 Method Figure 3 shows the proposed procedure of analyzing customers’ capacities for participating in demand response program. After gathering raw data from different data centers and smart meters in the network, some preprocessing actions should be applied on them to make them suitable to be analyzed. Preprocessing algorithms are some actions by which deficiencies and ambiguities of the data set is detected and modified. Then, customers are allocated to some clusters using clustering algorithm which is conducted according to the similarities of the customers’ consumption patterns. Then, a proper model for demand response implementation is created and evaluated. Capacities of customers with different consumption patterns for participating in demand response program is estimated using the proposed model. This model is modified by different times of implementation of demand response. Actually, as the data used for this process is more suitable and comprehensive, the results are more accurate.

8 Implementation 8.1

Case Study

A real data set is used to evaluate the proposed method for analyzing customers’ capacities for participating in demand response. The data set is recorded energy consumption of 1200 households in an Irish distribution network, in 30 min intervals, provided by ISSDA (Fig. 4).

8.2

Results

First step in clustering process is determination of the cluster number, where Silhouette and Davies-Bouldin indices may be used for. As mentioned, Davies-Bouldin (DB) validity index [11] is used in this study for determining the optimum number of clusters. According to these values, the number of 24 clusters is the optimum one shown in Fig. 5. Then, customers are clustered, and the customers with similar consumption

Smart Energy-Aware Cities: Customer Characterization by Energy Data. . . Fig. 3 Outline of the proposed method

31

start

raw data set: customers consumption profiles recorded by smart meters

Preprocessing of dataset: preprocessing actions are applied on the raw data set

clustering phase: customers load profiles are allocated to some clusters according to their similarities

Constructing model: a model is constructed to implement demand response

Implementation of demand response: demand response is implemented on different clusters

Estimation phase: capacities of different clusters for participating in demand response is estimated using the results of the prior step

Modification of the model: the estimation model is modified in the consequence of implementing demand response for several times

Evaluating demand response: the capacities of different clusters for participating in demand response is and the implemented demand espouse program is evaluated in this phase

Procedure evaluation: role of the AMI in enabling implementation of demand response program is evaluated end

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External Grid 2 3

50 49

4 47 5 48 6

9

26

23

67 66

24 22 21 20 19

28 29

18 17

10 11

12

43 44 42 41 45 46

31

33 35 53

32 34

55 57

54 65

13 14

40

30

8

52

27

37

7 51

25

36

1 38 39

61 58

56

59

62 60

63 64 65

65 15

16

Fig. 4 69 buses test case Fig. 5 Davies-Bouldin index against the number of clusters

2.2

DaviesBouldin Values

2 1.8 1.6 1.4 1.2 1 0.8 0

5

10

15

20

25

Number of Clusters

patterns are allocated to clusters. Now, there is a cluster center for each individual cluster as its representative. After that, DR is implemented, and the effect of consumption reduction corresponding to different clusters are estimated. It is expected to see customers’ consumption reduction after participating in DR programs, but these reduction and consumption profiles are different for different clusters which are shown in Figs. 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, and 17.

Smart Energy-Aware Cities: Customer Characterization by Energy Data. . .

33

Fig. 6 Customers’ consumption curves before and after DR

Fig. 7 Customers’ consumption curves before and after DR

Fig. 8 Customers’ consumption curves before and after DR

Consumption profiles before and after implementing DR are shown by blue and red curves, respectively. Moreover, approximation between the estimated and observed capacities for different load reductions (10%, 20%, 30%, 40%) is reported in Figs. 18, 19, 20, and 21. In addition, the estimated and observed capacity of each cluster for participating in DR in case of 10%, 20%, 30%, and 40% reduction in their consumption is

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Fig. 9 Customers’ consumption curves before and after DR

Fig. 10 Customers’ consumption curves before and after DR

Fig. 11 Customers’ consumption curves before and after DR

reported in form of radar charts in Figs. 22, 23, 24, and 25. Obviously, implementation of DR programs for different times shows variations in customers’ capacities in case of consumption reduction 10%, 20%, 30%, and 40% (Figs. 26, 27, 28, and 29).

Smart Energy-Aware Cities: Customer Characterization by Energy Data. . .

Fig. 12 Customers’ consumption curves before and after DR

Fig. 13 Customers’ consumption curves before and after DR

Fig. 14 Customers’ consumption curves before and after DR

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Fig. 15 Customers’ consumption curves before and after DR

Fig. 16 Customers’ consumption curves before and after DR

Fig. 17 Customers’ consumption curves before and after DR

Smart Energy-Aware Cities: Customer Characterization by Energy Data. . .

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Fig. 18 Approximation between estimated and observed capacities for demand response (10% load reduction)

Fig. 19 Approximation between estimated and observed capacities for demand response (20% load reduction)

Fig. 20 Approximation between estimated and observed capacities for demand response (30% load reduction)

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Fig. 21 Approximation between estimated and observed capacities for demand response (40% load reduction)

Fig. 22 Estimated and observed capacities for demand response (10% load reduction)

Fig. 23 Estimated and observed capacities for demand response (20% load reduction)

Smart Energy-Aware Cities: Customer Characterization by Energy Data. . .

Fig. 24 Estimated and observed capacities for demand response (30% load reduction)

Fig. 25 Estimated and observed capacities for demand response (40% load reduction)

Fig. 26 Approximation of the observed and estimated capacities for demand response (10%)

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Fig. 27 Approximation of the observed and estimated capacities for demand response (20%)

Fig. 28 Approximation of the observed and estimated capacities for demand response (30%)

Fig. 29 Approximation of the observed and estimated capacities for demand response (40%)

Smart Energy-Aware Cities: Customer Characterization by Energy Data. . .

41

9 Conclusions In this chapter, role of smart metering and monitoring technologies on implementation of demand response program is analyzed. Deployment of such technologies in the distribution network enhances awareness of the players in the electricity network market. As customers are the players of this market, enhancement of energy awareness about them causes their better participation in such programs. This better participation refers to actions and decisions by which benefits of the both customers and utilities from implementation of demand response program are met. Suitable implementation of demand-side management programs is a key step in the realization of smart grid and in the next step smart city. For this reason, customer characterization is performed to determine capacities of customers with different consumption patterns for participating in DSM programs. Customers are clustered according to their consumption patterns, and demand response is implemented using these customers to determine capacities of each one of these clusters. Customers’ capacities basically root in customers’ welfare to choose a specific consumption behavior. According to their welfare, customers reduce their consumption in different hours to meet both their welfare and economic considerations. For example, corresponding capacities of cluster number 2 for 10%, 20%, 30%, and 40% reduction in consumption are 73.42%, 77.42%, 81.83%, and 91.15%, respectively. These approximations for the cluster number 4 are 93.05%, 67.81%, 87.37%, and 83.65%, respectively. Obviously, the variation of capacities for cluster number 4 does not vary as the variation of the cluster number 2. Actually, there are some other points which are considered as future studies: 1. Clustering consumption time series using different adaptive algorithms 2. Economic considerations of implementing demand response programs in relation with smart meter deployment The role of improving monitoring mechanisms and infrastructures in realization of smart grid could be analyzed.

References 1. International Energy Agency, World Energy Outlook (IEA, Paris, 2009)., http://www. worldenergyoutlook.org/docs/weo2009/WEO2009_es_english.pdf 2. D.S. Kirschen, G. Strbac, P. Cumperayot, D.D.P. Mendes, Factoring the elasticity of demand in electricity prices. IEEE Trans. Power Syst. 15(2), 612 (2000) 3. C.L. Su, D. Kirschen, Quantifying the effect of demand response on electricity markets. IEEE Trans. Power Syst. 24(3), 1199 (2009) 4. W. Yang, R. Yu, M. Nambiar, Quantifying the benefits to consumers for demand response with a statistical elasticity model. IET Gener. Transm. Distrib. 8, 503 (2014) 5. G. Chicco, R. Napoli, F. Piglione, P. Postolache, M. Scutariu, C. Toader, Load pattern-based classification of electricity customers. IEEE Trans. Power Syst. 19(2), 1232 (2004)

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6. G. Chicco, R. Napoli, P. Postulache, M. Scutariu, C. Toader, Customer characterization options for improving the tariff offer. IEEE Trans. Power Syst. 18, 381–387 (2003) 7. J. Kwac, J. Flora, R. Rajagopal, Household energy consumption segmentation using hourly data. IEEE Trans. Smart Grid 5, 420 (2014) 8. M.K. Naftchali, A. Fereidunian, H. Lesani, Identifying susceptible consumers for demand response and energy efficiency policies by time-series analysis and supplementary approaches, in 24th Iranian Conference Electric and Electronic (ICEE 2016), Shiraz, Iran, (IEEE, Washington, DC, 2016) 9. M.K. Naftchali, A. Fereidunian, H. Lesani, AMI data analytics: customer characterization by relief algorithm and supplementary tools, in 25th Iranian Conference Electric and Electronic (ICEE 2017), Tehran, Iran, (IEEE, Washington, DC, 2017) 10. M.K. Naftchali, A. Fereidunian, H. Lesani, AMI data analytics; an investigation of the selforganizing maps capabilities in customers characterization and big data management, in Smart Grid Conference (SGC 2017), Tehran, Iran, (IEEE, Washington, DC, 2017) 11. M. Spinoza, C. Joie, R. Belmanse, B. De Moor, Short-term load forecasting, profile identification, and customer segmentation: a methodology based on time-series. IEEE Trans. Power Syst. 20(3), 1622 (2005) 12. V. Figueiredo, F.J. Duarte, F. Rodrigues, Z. Vale, J.B. Gouveia, An electric energy characterization framework based on data mining techniques. IEEE Trans. Power Syst. 18, 596–602 (2005) 13. I.A. Hashem, V. Chang, N.B. Anuar, K. Adewole, I. Yaqoob, A. Gani, E. Ahmed, H. Chiroma, The role of big data in smart city. Int. J. Inf. Manag. 36(5), 748–758 (2016) 14. N. Komninos, What makes cities intelligent? in Smart Cities: Governing, Modelling and Analysing the Transition, ed. by M. Deakin, (Taylor and Francis, Hoboken, NJ, 2013), p. 77, ISBN 978-1135124144 15. R. Tanaka, S. Doi, T. Konishi, N. Yoshinaga, S. Itaya, K. Yamada, SocInfo 2011, “EcoMAME”: ecology activity promotion system based on human psychological characteristics. Lect. Notes Comput. Sci 6984, 324–327 (2011) 16. T. Konishi, F. Murakami, R. Tanaka, S. Itaya, S. Doi, K. Yamada, Psychological research of relation between attitude for power saving and visualization effect of other’s saving behavior, in Proceedings of the 39th Annual Meeting of the Behaviormetric Society of Japan, (2011), pp. 383–386 17. R. Ruggieri, M. Ruggeri, G. Vinci, Efficient energy and electric transport in a smart city: evaluation of sustainability and competitiveness, in IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe), (IEEE, Washington, DC, 2020) 18. M. Pedro, R. Bolívar, Transforming City Governments for Successful Smart Cities (Springer International Publishing, Cham, 2015) 19. C. Etezadzadeh, Smart City – Future City?: Smart City 2.0 as a Livable City and Future Market (Springer, New York, NY, 2016) 20. R. Papa, R. Fistola, Smart Energy in the Smart City: Urban Planning for a Sustainable Future (Springer International Publishing, Cham, 2016) 21. IEEE, Smart Cities Community (IEEE, Washington, DC, n.d.)., https://smartcities.ieee.org. Accessed 19 Apr 2021 22. Thales Group, Secure, Sustainable Smart Cities and the IoT (Thales Group, Paris, n.d.)., https:// www.thalesgroup.com/en/markets/digital-identity-and-security/iot/inspired/smart-cities. Accessed 19 Apr 2021 23. European Commission, Smart Cities (EC, Brussels, n.d.)., https://ec.europa.eu/info/eu-regionaland-urban-development/topics/cities-and-urban-development/city-initiatives/smart-cities_en. Accessed 19 Apr 2021 24. EnergyLens, Energy Awareness (n.d.), https://www.energylens.com/articles/energy-awareness. Accessed 19 Apr 2021

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The Role of Transactive Energy on Management of Flexible Resources Sajjad Fattaheian-Dehkordi, Mohammad Gholami, Ali Abbaspour, and Matti Lehtonen

Nomenclature Variables PGen i,t Ch,ESS=EV , Pi,t

Dis,ESS=EV Pi,t

PRU,Gen , PRD,Gen i,t i,t RU,Ch,ESS=EV

Pi,t

RU,Dis,ESS=EV

Pi,t

RD,Ch,ESS=EV

, Pi,t

RD,Dis,ESS=EV

, Pi,t

ESS=EV

E i,t

Ch,ESS=EV

αi,t

Dis,ESS=EV

, αi,t

PDriving,EV i,t FlexAgent,ramp
up k,t,n

Power generation by generation unit i at time t Charging/discharging power of storage/electric vehicle unit i at time t Ramp-up/down power provided by generation unit i at time t Ramp-up/down power provided by charging state of storage/electric vehicle unit i at time t Ramp-up/down power provided by discharging state of storage/electric vehicle unit i at time t Stored energy in storage/electric vehicle unit i at time t Binary variables for impeding simultaneous charging/ discharging power of storage/electric vehicle unit i at time t Power consumption for driving electric vehicle unit i at time t Flexible ramp-up service provided by agent k in iteration n at time t

S. Fattaheian-Dehkordi (*) Aalto University, Espoo, Finland Present Address: Sharif University of Technology, Tehran, Iran M. Gholami · A. Abbaspour Sharif University of Technology, Tehran, Iran M. Lehtonen Aalto University, Espoo, Finland © Springer Nature Switzerland AG 2021 M. Shafie-khah, M. H. Amini (eds.), Flexible Resources for Smart Cities, https://doi.org/10.1007/978-3-030-82796-0_3

45

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BonusAgent,Ram
up k,t,n FlexAgent,Congestion
l k,t,n BonusAgent,Congestion
l k,t,n PAgent k,t,n

Received bonus by agent k for providing ramp-up service in iteration n at time t Flexible service provided by agent k for congestion alleviation in iteration n at time t Received bonus by agent k for providing congestion alleviation service in iteration n at time t Power request by agent k in iteration n at time t

Parameters CostGen i λt PMax,Gen , PMin,Gen i i PRamp,Gen i Ch,ESS=EV

ρi

Dis,ESS=EV

, ρi

Min,ESS=EV

Ei

Max,ESS=EV

, Ei

αV2G,EV i,t Ramp upPermisible t PAgent,Initial k,t TEramp
up t,n TEAgent,Congestion
l k,t,n loadingline l,t Permisible line

loadingl,t

Operational cost of generation unit i Energy price at time t Maximum/minimum possible power generation by generation unit i Permissible ramping in power production of generation unit i Charging/discharging efficiency of storage/electric vehicle unit i Minimum/maximum possible stored energy in storage/ electric vehicle unit i Determine whether electric vehicle unit i at time t is connected to the grid or not Permissible ramp-up in net load of the system at time t Preliminary determined power request by agent k Transactive signal associated with ramp-up service in iteration n at time t Transactive signal associated with congestion alleviation announced to agent k in iteration n at time t Loading of line l at time t Permissible loading of line l at time t

Sets ωk, Gen ωk,ESS=EV ωk, Demand

Set of all generation units in the system Set of all ESS/EV units in the system Set of all demand units in the system

1 Introduction Over recent years, with the introduction of restructuring and privatization concepts, energy systems have encountered a substantial shift from the structure, operation, and planning perspectives. In this regard, the increasing trend of integrating renewable energy sources (RESs) and flexible resources such as electric vehicles (EVs),

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47

energy storage systems (ESSs), and conventional distributed generations (CDGs) has significantly increased the importance of procuring flexibility services from local responsive resources. Moreover, development of smart cities requires the transition of energy systems as the backbone of our human community toward smart multiagent systems, which should be efficiently managed to ensure the supply-demand balance in the system. Note that the development of communication infrastructures as a characteristic of smart cities would facilitate the development of multi-agent structures in smart energy systems [1–3, 37]. In smart energy systems, system operators would depend on local flexible resources in order to address operational issues in the system. In other words, flexibility services provided by local flexible resources should be taken into account to resolve operational issues primarily raised by the integration of new local resources (i.e., RESs) and development of multi-agent structures in energy systems. Nevertheless, local resources in multi-agent energy systems would be scheduled by independent entities, which would limit the control of system operators over the operational scheduling of local responsive resources. In this regard, new methodologies should be developed to enable system operators to procure flexibility services from responsive resources, while taking into account the privacy concerns associated with private customers. In other words, efficient management of the procedures associated with procuring flexibility services from independent agents would be a key role in facilitating the development of energy systems in smart cities. In recent years, different research works have been conducted on activating flexibility services and managing exchanges of flexibility services in energy systems. In this respect, development of an accurate model for combined-cycle units as flexible resources is investigated in [4]. Reference [5] develops a dynamic pricing model for charging stations of electric vehicles to procure flexibility ramp-up service. This study shows the importance of optimal scheduling of local resources to reliably operate energy systems. A home energy management system is developed in [6] to optimize the day ahead operational cost while providing local flexibility services. Reference [7] has proposed a flexibility index and analyzed the operation of power systems from the flexibility point of view. Furthermore, operational management of the energy systems with significant penetration of RESs considering flexibility options is studied in [8]. In this context, efficient management of storage units, as well as demand response programs, are taken into account in [7–9] to provide flexibility services for reliable operation of energy systems. Moreover, the authors in [10] have studied advantages of considering an optimal bidding strategy for MGs as energy and ancillary services for power grids. Recently, transactive energy (TE) is taken into consideration in research and field studies in order to develop operational frameworks that facilitate cooperation of local resources in the efficient operation of smart grids [11, 12]. TE is a new concept that employs transactive control signals in order to coordinate the supply and demand in the system. In other words, this new technique provides a control framework, in which the information exchange between independent entities in the system would be limited to the economic signals (i.e., transactive control signals) and the amount of service exchanges. In this respect, several pilot projects have been conducted by

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different utilities and research groups, which shows the applicability of the TE concept in the management of operational services in energy systems. Accordingly, economic-based mechanisms provided by a TE framework could enable the system operators to incentivize independently operated flexible resources to contribute to the provision of flexible service in the system. Furthermore, TE-based flexibility management schemes would address privacy concerns in smart energy systems with multi-agent structures [13, 14]. As discussed, TE concept could be a useful asset in developing an appropriate framework to be applied to future smart energy systems in order to efficiently manage local flexible resources. Consequently, this chapter aims to provide a thorough snapshot of the role of TE in activating flexibility services in smart grids. In this regard, along with providing a general perspective of managing flexibility resources by utilizing TE concept, activating flexibility services in smart energy systems in order to address ramping and congestion issues in the network is thoroughly analyzed. In other words, applications of TE technique for procuring flexibility services from local responsive resources to alleviate ramping-up and congestion issues in smart energy systems with multi-agent structures are investigated in this chapter. RESs such as photovoltaic and wind power units would play a significant role in supplying the demand in smart energy systems. However, the intermittency and stochasticity associated with RESs could cause problems in the flexible operation of the system. Respectively, the sudden variation in power generation by RESs due to the weather changes could cause intense ramping in the net load of the system. Consequently, local flexible resources could provide flexibility services in order to address the intense ramp-up in smart energy systems, which would finally improve the reliability of the system. As a result, TE concept could be employed by responsible entities (i.e., system operators) to activate flexible ramp-up service from independently operated responsive resources in order to address the ramping issues in the system [1, 15]. Reliable operation of smart energy systems could be challenged by the congestion in grids, which is resulted from overproduction/overconsumption in the system. In this regard, the reliable operation of energy systems could be threatened due to the congestion issues raised by the over-power production/overconsumption of RESs/ demands [2]. As a result, grid operators could employ TE concept to incentivize the contribution of flexible resources to alleviate the congestion in the system. In other words, grid operators as entities responsible for reliable operation of the grids would provide independent agents of the system with incentive signals to revise their power scheduling in order to alleviate the congestion in the grid. It is noteworthy that the TE-based flexibility management of smart energy systems would finally result in minimizing the forced curtailment of local resources (e.g., RESs and demands) due to operational issues raised based on the over-power generation/overconsumption and intermittency accustomed with these resources. Based on the above discussions, primary contributions, as well as structure of this chapter, could be organized as follows:

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• Providing a general and practical conceptual structure based on the TE concept in order to enable system operators in multi-energy smart systems to procure flexibility services from local responsive resources: – The role of flexibility services for facilitating transformations of energy systems is discussed. – Decentralized/centralized management of flexible resources in energy systems. – Characteristics of smart energy systems that facilitate deployment of the TE technique for decentralized management of resources are discussed. – Features of TE technique for development of a practical management scheme are studied. – The procedure of defining TE control signals is demonstrated. – Practicality and effectiveness of the TE technique for managing operational services in energy systems are investigated. • Mathematical modeling of TE-based flexibility management schemes: – Developing a detailed TE-based scheme to manage ramp-up issues in a smart energy system by procuring flexibility services from local responsive resources – Developing a framework that facilitates decentralized flexibility service activation in multi-agent energy systems in order to alleviate grid congestion caused by over-power generation/overconsumption of RESs/demands

2 Flexibility Service Requirements for Transformation of Energy Systems Development of smart cities would lead to significant transformations in their energy systems. In this context, smart energy systems would be characterized by the high penetration of distributed energy resources (DERs) which would be operated by independent agents. Respectively, the new multi-agent structure of the system would challenge the concurrent business and physical models of operating energy systems [16]. In other words, the intermittency of DERs would increase the uncertainty and variability of operating conditions of the system, which results in increasing the flexibility requirements for reliable operation of the energy systems. The primary point in operating the energy systems is maintaining the supply-demand balance in each operating point of the system; otherwise, the energy imbalance could lead to partial blackouts in the system. That is why flexibility services would play a significant role in reliably operating the smart energy systems to avoid curtailment of resources. Conventionally, the flexibility services required for the operation of the system are provided by bulk generation units; however, the capacity of these resources to

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provide operational services could not address the increasing requirements in the flexibility level of the system. This is based upon the fact that in the business model of the energy systems, the increasing level of RESs with lower operational and investment costs would limit the investments and operational commitments of conventional bulk generation units. Consequently, system operators must rely on local responsive resources to cope with the flexibility requirements for reliable operation of the system. The increasing integration of DERs has led to the movement of energy systems from consumption-following structures to supply-following structures, which could affect the reliability and flexibility of the system. In other words, intermittency of DERs would challenge the limited flexibility capacity in energy systems. Furthermore, in the conventional structures, the operational scheduling of resources would be conducted by central entities, while the privacy concerns and the cost of collecting and analyzing the operational information in a central manner would impede the implementation of central management structures for operating smart energy systems. Respectively, the development of smart energy systems with decentralized structures could affect the reliability and flexibility of the system in a negative manner. That is why novel management concepts should be employed by system operators, in which operators would be able to activate flexibility service from independently operated responsive resources. Respectively, while independent agents strive to maximize their profits in smart distributed energy systems, these flexibility management frameworks would enable the system operators to procure flexibility service from local responsive resources. In this regard, TE concept is a practical technique that facilitates the evolvement of conventional management procedures to support flexibility management in smart energy systems with multiagent structures. In summary, the challenges with the conventional centralized operational management techniques for implementation in smart energy systems could be categorized as follows: • Customers of the system would become prosumers while they are not accustomed to participation in energy markets. Customers prefer the stable operation of the system. • The local energy markets may not have enough liquidity, and there is a risk of price spikes. • There is a limited flexibility capacity in the system due to stochasticity and variability of DERs. • The fixed cost may be increased, while customers prefer to merely pay for services that they utilize. • System operators have less control over the scheduling of local resources in the system. • Central collection and analysis of overall operational information are not expandable for smart energy systems. Moreover, there are privacy concerns with these methods.

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3 Managing Flexibility Service Based on Transactive Energy Concept The evolvement of smart energy systems with multi-agent structures would require the development of fair flexibility management mechanisms that address the objectives of each independent entity in the system. In other words, the developed mechanisms should cope with the distributed nature of smart energy systems while motivating the performances of each entity in a way that ensures the reliable and flexible operation of smart energy systems. TE systems enable the interaction of distributed entities utilizing a framework that incorporates reliability and control of the system while enabling operational service trading in the system. In this respect, flexibility management schemes could monetize the contribution of independent agents to provide flexibility services, which would significantly facilitate the participation of the agents in the reliable operation of the system. Respectively, TE technique seems to be a promising way of developing operational frameworks for activating flexibility service in smart energy systems. Furthermore, TE concept facilitates managing end-to-end flexibility service exchanges in multi-agent systems. In this regard, TE-based techniques enable the coordinated decentralize control of smart energy systems to improve the flexibility of the system. Note that the TE-based coordination of exchanging flexibility services in the system may address economic profits, reliability, and environmental concerns in the system.

3.1

Determining Transactive Control Signals

TE approaches facilitate the development of smart energy systems and the privatization of energy systems, which would extend the control of independent agents over their energy assets. In this context, TE-based business models enable the private operation of local resources while providing agents with value streams to reliably integrate into the system. In other words, TE technique offers economic incentives to independent agents in the system in order to incentivize them to operate their resources in a way that supports the system’s reliable operation. Consequently, economical values (i.e., transactive control signals) would be employed as coordination parameters to develop control mechanisms that facilitate activating flexibility service from independent agents. In this context, independent agents in a smart energy system would consider the transactive signals and the operational characteristics of their respective resources to develop their local optimization models. As a result, defining the transactive signals that efficiently link the required operational service and the optimization conducted by each agent is an indispensable characteristic of management mechanisms developed based on the TE concept. In this context, determining a transactive signal means the procedure of defining the economic or engineering value associated with the service transaction between entities. In other words, TE-based management of exchanging operational services

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in the system would be based on a process that entities involved in the service transaction come to an agreement regarding the value of the service exchange. The procedure of determining the value associated with the service transition in the system could compose of local markets, tariffs, bilateral contracts, or selfoptimization by each entity. By the way, the developed procedure should comply with the service requirement objectives and the operational goals and behaviors of independent agents. In this context, based upon the physical infrastructures in the smart energy systems and the business models of the systems, different methods could be deployed to develop value realization procedures in TE-based flexibility management schemes. Consequently, there would be a multi-stage evolvement process aligned with the expansion of the cyber-physical and business models in smart energy systems, which determines the possibility of employing TE concept for managing service exchanges in the system.

3.2

Application of TE Concept in Smart Energy Systems

Development of smart cities would be based on the implementation of novel technologies in energy systems and increasing the interaction of smart devices in energy networks. Furthermore, the expansion of DERs, microgrids, and net-zero buildings would impact the extent to which various entities in a smart energy system are supposed to interact to ensure that the objectives of entities and the system are addressed. In this regard, based on the development of the multi-layer management structure of smart energy systems, there are not any parties that would be able to operate the system in a central manner; therefore, TE concept is a practical way for coordinating independent agents in a smart energy system. TE technique along with the evolving ecosystem of smart energy systems facilitates the interaction and interoperability among various entities in the system. In this respect, TE technique could be deployed in different operational levels of energy systems in order to provide the coordinated decentralized control of service exchanges in the system. In other words, based on the distributed nature of the smart energy systems, TE concept could be utilized for the management of service transactions at the grid level or even in the customers’ facilities. In this regard, independent agents could voluntarily participate in the TE-based frameworks and contribute to the flexibility and reliability improvements of the system while maximizing their respective profits. As mentioned, TE technique is based on developing transactive control signals to incentivize the contribution of various entities in the reliable operation of the system. In this regard, intermediary entities acting as the coordinator of a group of resources in the system could directly send the transactive control signals to the respective customers and optimize the flexibility service based on the received responses from each of the resources. As a result, the risk associated with intermediary entities while contributing to providing flexibility services for system operators would be minimized. Note that the implementation of the TE concept requires progress in the market and control architectures of energy systems in order to facilitate fast

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Fig. 1 Regulatory model of TE-based management schemes

operation, secure transactions, and distributed control of the system. A simplified regulatory model of TE management schemes in smart energy systems is presented in Fig. 1. It is noteworthy that TE-based flexibility management schemes could be conducted between different entities of the system.

3.3

Characteristics of TE Technique

In smart energy systems, customers would become active parties by integration of DERs, which would lead to the transition of energy systems from traditional centralized vertical structures to horizontal multi-agent configurations. In this respect, to facilitate the distributed control of the system, the communication infrastructures along with the electricity grid, which form the primary cyber-physical networks in a smart energy system, should be developed simultaneously. Flexibility management schemes developed based on the TE concept would utilize both cyber and physical networks to enable the fast efficient operation of the system. In other words, development of TE-based management frameworks requires a digital layer for facilitating information exchanges and a physical network for exchanging operational services. In general, operational schemes developed utilizing TE concept compose of policy modeling, transactive signals realization, and cyber-physical infrastructures. The characteristics and benefits of TE-based flexibility management schemes could be summarized as follows: • Facilitating interaction and interoperability in different levels of power systems (i.e., generation units, consumers, etc.) • Modeling different operational objectives for developing TE-based flexibility management schemes

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• Facilitating horizontal and distributed multi-layer control of the system • Expandability and scalability of the developed schemes • Cope with the system markets, grid control, and optimization models at customer levels • Simplified and low-cost control techniques to enable the information exchange between independent entities in the system based on the system’s communication infrastructures • Ability to be developed in simplified and user-friendly software at customer sides • Capability of information exchange with smart devices in customer sides (i.e., thermostats, electric vehicle chargers, RESs inverters, home energy management systems, etc.) • Decreasing the risk of intermediary entities in the system. • Increasing the consumers’ profits and private investments by facilitating the incorporation of local resources in the operation of energy systems • Facilitate the transition to a fair procedure for allocation of operational/investment costs of the energy system to the system’s agents based upon their utilization of each operational service • Providing an efficient coordination framework for flexibility service activation in ecosystems where the liquidity of conventional flexibility markets is not enough and could compromise the competition resulting in price spikes • Delay the investments required in generation/grid sections by procuring flexibility services from customer sides • Minimizing the dependence of local energy systems on bulk generation units for ensuring supply-demand balance in real-time operation by improving the flexibility of the system

3.4

Practicality of Transactive Energy-Based Flexibility Management Schemes

TE concept could be broadly applied for flexibility management by development of cyber-physical infrastructures in energy systems. Nevertheless, the practicality of TE-based flexibility management schemes to be applied in real energy systems could be addressed by comparing with the current management methods in power systems and pilot projects conducted by research groups and utilities in different countries. In this respect, demand response programs that aim to provide control signals (e.g., bonus/price signals) by system operators or intermediaries (i.e., demand response aggregator) for exploiting the consumption of consumers could be considered as employed TE-based flexibility management frameworks. In a demand response program in California, control signals are sent by the “open automated demand response” standard called Open-ADR to adjust over 200 MW demand [17]. In the developed program, control signals determine the time period for adjusting the demand or define a specific action in the demand side (e.g.,

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adjusting thermostats). In this demand response program, the control signals are similar to transactive control signals, which are primarily determined based on the energy price in the system. In a pilot residential demand management program which is called PowerCentsDC and conducted in Washington, D.C., the control signals are sent to thermostats at residential homes in case that hourly prices in the PJM wholesale market exceed a predetermined criterion [17]. In this respect, the cost of customers during price spikes is minimized and the peak demand is reduced by 51% in the studied area. This study shows the practicality of the TE-based flexibility management schemes in energy systems provided that the scheduling of smart devices on the consumer side could be revised based on the received transactive control signals. Note that the application of the transactive energy-based demand response program has benefited the system and customers by improving the reliability and flexibility and minimizing the operational costs of the system. In another pilot project called GridWise Transactive Energy Framework, the possibility of extending TE schemes for control purposes in energy systems is studied. This study shows the applications and benefits of TE-based control for implementation in interconnected energy systems [11, 18, 19]. TE technique could be employed by various entities in the system to procure required flexibility services from independent agents. In this regard, TE technique is deployed in the REnnovates project in order to study the participation of flexible residential demands in congestion alleviations in a distribution grid [20]. In the developed scheme, distribution system operators utilize virtual prices as transactive control signals in order to reschedule flexible resources in residential parts. The developed TE-based congestion alleviation method is applied on a field test grid and the obtained results show the effectiveness and practicality of TE-based mechanisms to procure flexibility services from independently operated local resources. Note that, in this project, the field study composes of a smart grid that provides the communication infrastructure for connection of the distribution system operator and end-user prosumers in the TE-based management framework.

4 Mathematical Modeling of Transactive Energy-Based Flexibility Management in Smart Energy Systems The integration of DERs in smart energy systems would increase variability and stochasticity associated with the operation of these systems. These transformations could challenge the reliable operation of the system; therefore, system operators and intermediary entities would rely on flexibility services provided by independent agents in order to address the potential issues in the operation of the system. That is why various flexibility management schemes could be developed based on the TE concept to address the flexibility services required by each entity in the system to address a designated operational issue. In this respect, the formulation of TE-based flexibility management frameworks for addressing ramping issues and congestion in

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Fig. 2 Simplified model of information exchange between system/grid operator and agents

the grid are thoroughly studied in this section. Note that the developed TE-based flexibility management scheme for addressing ramping issues in the system would enable the system operators to ensure that the changes in the net load’s ramping meet the ramping capacity that could be provided by upper-level systems. In other words, the system operator of a smart energy grid would be responsible for the reliable operation of the system and the power exchange with the upper-level system; hence, this method enables the operator to ensure that ramping in the power exchange at the interface of the smart energy system and the upper-level network is in the permissible range. Furthermore, the TE-based flexibility management for congestion alleviation in the grid would enable the grid operator to ensure that over-power production/overconsumption by DERs/demands would not engender congestion in the grid. Based on the distributed nature of the smart energy systems, it is considered that system/grid operators could not directly access the scheduling of local resources; therefore, incentive transactive signals are taken into account to incentivize the contribution of independently operated local resources in the efficient operation of the network. The simplified conceptual model of information exchange between the system/grid operator and independent agents is presented in Fig. 2.

4.1

Transactive Energy-Based Ramping-Up Management

Increasing the integration of RESs due to their dependence on meteorological conditions could result in increasing the uncertainty and variability in smart energy systems. In this regard, the high penetration rate of RESs in a smart energy system could cause intense ramping in the net load of the system [7]. In other words, the

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abrupt changes in the power generation of RESs could cause an intense ramp-up in the net load of the system, which could challenge the reliable operation of the system. Traditionally, bulk generation units connected to the transmission network play a key role in providing the required flexibility capacity for addressing ramping in the net load of the system. However, as a result of high operational and investment costs of bulk generation units in comparison with RESs and the potential congestion in the transmission grid, the flexibility ramp-up capacity that could be provided by the generation units connected to the transmission network is limited [15, 21, 22, 35, 36]. Consequently, the ramping in the net load of local smart energy networks should be limited to ensure the reliable and flexible operation of the power system [23, 24]. Based upon the above discussions, operators of smart energy systems could activate flexibility ramp-up services by coordinating the resource scheduling in their respective multi-agent energy system in a way that ramping of the net load meets the announced constraints by the transmission system operator. Respectively, TE concept could be taken into account by the operator of a smart energy system to efficiently procure required flexibility service without exposing independent agents to cyber threats. Based on the TE technique, the system operator should provide transactive signals to agents operating flexible resources in order to incentivize their contribution in minimizing the net load’s ramping. Ramping in the net load of the energy system depends on the difference between energy requests at two consecutive time intervals. That is why in case of the intense ramp-up in the net load, the requested power at the time interval t should be increased, while the requested power at the time interval t + 1 should be decreased to minimize the ramp-up request in the system. The value realization in the TE technique composes of an iterative information exchange process between various entities. Consequently, the system operator in an iterative manner could update the announced incentive signal until the state that provided flexibility by independent agents meets the requested ramp-up service. A simplified flowchart of the TE-based ramp-up management in smart energy systems is presented in Fig. 3. Based on the flowchart shown in Fig. 3 and the method developed in [15], the transactive incentive signal (i.e., TEramp
up Þ at iteration n is defined by (1): t,n TEramp
up ¼ TEramp
up þρ t,n t,n





  Permisible PSys
PSys t t
1
Ramp upt

ð1Þ

is the net load of the system at time t. In this respect, each agent of the where PSys t system would optimize its scheduling at each time period, and so after a finite step of conducting the TE algorithm, the optimum value of transactive signals at each time period would be determined. In this respect, the provided ramp-up service of agent k by rescheduling its flexible resources at iteration n and the received bonus by the agent are respectively formulated in (2) and (3).

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Target Required Flexible Ramp-up Service

Agents

Running resources scheduling optimization considering received transactive signals System Operator

Calculating system’s net-load Check whether received flexibility ramp-up meets the targeted ramp-up service NO

Update transactive control signals

YES

Optimal value realization in the transactive-based flexibility management scheme Fig. 3 Simplified model of the stepwise TE-based ramp-up management

h

i Agent Agent Agent,Initial Agent,Initial FlexAgent,ramp
up ¼ P
P
P
P k,t,n k,t,n k,t
1,n k,t k,t
1 8     Agent,ramp
up < TEramp
up  FlexAgent,ramp
up