Smart Charging Solutions for Hybrid and Electric Vehicles E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
SMART CHARGING SOLUTIONS The most comprehensive and up-to-date study of smart charging solutions for hybrid and electric vehicles for engineers, scientists, students, and other professionals. As our dependence on fossil fuels continues to wane all over the world, demand for dependable and economically feasible energy sources continues to grow. As environmental regulations become more stringent, energy production is relying more and more heavily on locally available renewable resources. Furthermore, fuel consumption and emissions are facilitating the transition to sustainable transportation. The market for electric vehicles (EVs) has been increasing steadily over the past few years throughout the world. With the increasing popularity of EVs, a competitive market between charging stations (CSS) to attract more EVs is expected. This outstanding new volume is a resource for engineers, researchers, and practitioners interested in getting acquainted with smart charging for electric vehicles technologies. It includes many chapters dealing with the state-of-the-art studies on EV smart charging along with charging infrastructure. Whether for the veteran engineer or student, this is a must-have volume for any library. Smart Charging Solutions for Hybrid and Electric Vehicles: * Presents the state of the art of smart charging for hybrid and electric vehicles, from a technological point of view * Focuses on optimization and prospective solutions for practical problems * Covers the most important recent developmental technologies related to renewable energy, to keep the engineer up to date and well informed * Includes economic considerations, such as business models and price structures * Covers standards and regulatory frameworks for smart charging solutions
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Veröffentlichungsjahr: 2022
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Table of Contents
Cover
Title Page
Copyright
Preface
1 Smart Charging: An Outlook Towards its Role and Impacts, Enablers, Markets, and the Global Energy System
1.1 Introduction to Smart Charging
1.2 Types of Charging
1.3 Impact of Smart Charging on Global Energy Systems
1.4 Types of Smart Charging
1.5 Entities of a Smart-Charging System
1.6 Enablers of Smart Charging
1.7 Control Architectures
1.8 Outlook towards Smart Charging
1.9 Conclusion
References
2 Influence of Electric Vehicles on Improvements in the Electric Distribution Grid
2.1 Introduction
2.2 Evolution of the Distribution System
2.3 Electric Mobility
2.4 Charging Infrastructure for Electric Vehicles
2.5 Conclusion
References
3 Smart Charging Strategies for the Changing Grid
3.1 Introduction
3.2 Charging Strategy based on Vehicle Type
3.3 Mapping of Charging Strategies
3.4 Evaluation of Charging Strategies
References
4 Pricing Schemes for Smart Charging
Abbreviations
Nomenclature
4.1 Introduction
4.2 Concepts and Issues in Charging Pricing
4.3 Different Models of Charging Stations’ Dynamic Pricing
4.4 Classification of Charging Pricing Models
4.5 Electricity Pricing of Vehicle Discharging to Grid
4.6 Electricity Pricing Currently Used at Charging Stations
4.7 Effect of Charging Pricing on Economic Competitiveness of Electric Vehicles
4.8 Conclusion
References
5 Management of Electric Vehicles Using Automatic Learning Algorithms: Application in Office Buildings
5.1 Introduction
5.2 Proposed Charging Strategy
5.3 Test Bed and Implementation Results
5.4 Conclusion
References
6 High-Power Charging Strategies of EV Batteries and Energy Storage
Abbreviations
6.1 Introduction
6.2 EV Battery Set Model
6.3 Case Study of Charging High Power Li-Ion Battery for Energy Storage and Electric Work Machines
6.4 Proposed Constant Current and Constant Voltage Method for EV Battery Charging
6.5 Simulation Tests of EV Battery Charging
6.6 Conclusions
References
7 Integration of Fast Charging Stations for Electric Vehicles with the Industrial Power System
Abbreviations
7.1 Introduction
7.2 Structure of Hybrid EV Fast Charging Station
7.3 Use of Drive Voltage Frequency Converter for Charging EV Batteries
7.4 Fast Charging Converter Integrated with 600V DC Microgrid
7.5 Simulation and Experimental Study of Drive Voltage Frequency Converter Used to Charge EV Batteries
7.6 Conclusions
References
8 Regulatory Framework for Smart Charging in Hybrid and Electric Vehicles: Challenges, Driving Forces, and Lessons for Future Roadmap
List of Abbreviations
8.1 Introduction
8.2 EV Charging Technology and Smart Charging
8.3 Smart Charging Standards
8.4 Regulatory Framework
8.5 Conclusions and Discussion
References
9 EV Fast Charging Station Planning with Renewable Energy Sources: A Case Study of Durgapur System
9.1 Introduction
9.2 Modeling of System
9.3 Case Study on Solar and Wind Data
9.4 Problem Description and Methodology
9.5 Results and Discussion
9.6 Conclusions
9.7 Acknowledgment
References
10 Game Theory Approach for Electric Vehicle Charge Management Considering User Behavior
Nomenclature
10.1 Introduction
10.2 Problem Formulation
10.3 Profit Maximization Game
10.4 Existence and Uniqueness of Nash Equilibrium of Profit Maximization Game
10.5 Results and Discussion
10.6 Conclusion
Appendix A
References
11 A Novel SMES Based Charging System for Electric Vehicles in Smart Grids
Nomenclature
List of Abbreviations
List of Variables
11.1 Introduction
11.2 System Modeling
11.3 Impact Analysis of SME’S on SG Performance while Accommodating EVs
11.4 Conclusion
References
12 A Novel Intelligent Route Planning Framework for Electric Vehicles with Consideration of Waiting Time in Delhi
12.1 Introduction
12.2 Problem Description
12.3 Reinforcement Learning (RL) Based EV Navigation System
12.4 Results and Discussion
12.5 Conclusion
References
13 Smart Charging Management for Autonomous Vehicles: A Smart Solution for Smart Cities & Societies: COVID 19
13.1 Introduction
13.2 Autonomous Vehicles: A Promise for Next-Generation Transportation Systems
13.3 How Autonomous Vehicle Standards Ensure Safety
13.4 Autonomous Cars and Smart Cities
13.5 Benefits of Autonomous Vehicles
13.6 Adoption Perspectives for Autonomous Vehicles: COVID 19 Situation
13.7 During the Fight of Pandemic Situation: How Autonomous Vehicles are Used
13.8 Smart Charging Management for Autonomous Vehicles
13.9 Challenges Involved in Self Driving Vehicles (V2X) Driving the Development of Autonomous Vehicles
13.10 Discussion
13.11 Conclusion
13.12 Acknowledgment
References
14 Electric Vehicle Integrated Virtual Power Plants: A Systematic Review
Abbreviations
14.1 Introduction
14.2 Overview of VPP
14.3 Global Scenario
14.4 Framework for VPP
14.5 Research Initiatives
14.6 EV Integrated VPP
14.7 Conclusions
References
15 Optimal Location of EV Charging Stations by Modified Direct Search Algorithm
Abbreviations
15.1 Introduction
15.2 Problem Formulation
15.3 Methodology
15.4 Numerical Analysis
15.5 Conclusion
References
16 Recent Trends and Technologies of Electric Vehicles and Their Wireless Charging Methods: A Review
16.1 Introduction
16.2 FAME Status
16.3 Basic Operation of WPT of EVs
16.4 Components of WPT System
16.5 Advancements in WPT and Electric Vehicle Technology
16.6 Electric Vehicle Status in India
16.7 Standards of Electrical Vehicles, Infrastructure, and WPT
16.8 Conclusion
References
17 Techno-Economic Issues of Grid Connected Large Photovoltaic Plants of Smart City Prayagraj to the EV Charging Station: A Case Study (A Case Study of 5 MW Photovoltaic Power Plant at Prayagraj)
17.1 Introduction
17.2 PV Generation Feasibility Study for Prayagraj for EV Charging Stations
17.3 Modeling and Challenges of Grid Integrated Photovoltaic System
17.4 Real-Time Challenges of 5MW Solar Plant at Naini, Prayagraj, India
17.5 Whole System Layout and Description
17.6 Cost Analysis of Complete PV System
17.7 Conclusion
References
Index
Also of Interest
End User License Agreement
List of Illustrations
Chapter 1
Figure 1.1 Flow diagram to understand and judge the level of smartness based on ...
Figure 1.2 A brief on different approaches to smart charging techniques.
Figure 1.3 A schematic to differentiate V2H/V2B and V2G.
Figure 1.4 Communication between various entities in smart charging infrastructu...
Figure 1.5 Schematic of centralized controller in smart charging architecture.
Figure 1.6 Schematic of decentralized controller in smart charging architecture.
Chapter 2
Figure 2.1 Traditional and new models of distribution grid.
Figure 2.2 Global electric vehicles registrations.
Chapter 3
Figure 3.1 Evolution of electric vehicles.
Figure 3.2 Charger power based on battery capacity.
Figure 3.3 Two- and three-wheeler charger power.
Figure 3.4 Charging power for opportunity charging of electric car fleets.
Figure 3.5 Charging power for opportunity charging of electric buses.
Figure 3.6 Applicable of strategies to control charging at peak demand.
Figure 3.7 First come, first served charging approach.
Figure 3.8 Multiple possibilities of solar EV charging.
Figure 3.9 Schematic for smart battery swapping.
Chapter 4
Figure 4.1 Energy management and dynamic pricing algorithm [1].
Figure 4.2 Charging service provider business framework [1].
Figure 4.3 Framework for management of EV charging station [6].
Figure 4.4 EV charging system framework based on price negotiation [2].
Figure 4.5 Framework of proposed charging pricing strategy [5].
Figure 4.6 Representation of multi-agent EV network model [17].
Figure 4.7 EV network’s multi-agent control system under RL-AHC algorithm [17].
Chapter 5
Figure 5.1 Charging strategy for electric vehicles.
Figure 5.2 Flowchart of demand prediction process.
Figure 5.3 Flowchart of demand shifting process.
Figure 5.4 Result of clustering algorithm.
Figure 5.5 Flowchart of surrounding vehicle selection process.
Figure 5.6 Load curve of office building.
Figure 5.7 Power demand predicted by algorithms implemented.
Figure 5.8 Power demand predicted by algorithms implemented.
Figure 5.9 Standardized input data set.
Figure 5.10 Result of data clustering algorithm.
Figure 5.11 Power demand predicted by algorithms implemented.
Chapter 6
Figure 6.1 Implementation of strategy of charging an EV battery with: (a) CC cur...
Figure 6.2 Models of Li-ion battery with EV charger [18].
Figure 6.3 Characteristics of single cell of Li-ion battery of LFP200AHA type [2...
Figure 6.4 Model of battery set for ES comprising Li-ion cells.
Figure 6.5 Model of battery set 800Ah/320V/256kWh comprising 400 Li-ion cells wi...
Figure 6.6 Linear approximation of voltages of LFP200AHA Li-ion cell between 20%...
Figure 6.7 Proposed CC strategy for EV battery charging.
Figure 6.8 Computing model of DC/DC boost converter where energy storage feeds D...
Figure 6.9 Waveforms in boost mode of DC/DC converter from Figure 6.8: (a) PWM c...
Figure 6.10 Proposed solution of double function of drive VFC for EV battery cha...
Chapter 7
Figure 7.1 Local 600V DC microgrid of industrial company to supply clean energy ...
Figure 7.2 Scheme of fast EV charging station connected to 600V DC microgrid.
Figure 7.3 Simulation model of PWM inverter with battery rectifier.
Figure 7.4 Structure of drive VFCs with diode input rectifier (9) with input inv...
Figure 7.5 Single-phase equivalent diagram for inverter illustrating rectifying ...
Figure 7.6 Stages of integrating hybrid EV charging station.
Figure 7.7 Solar energy production in 25kWp PV power plant.
Figure 7.8 Charging battery with different voltage levels to receive constant ch...
Figure 7.9 Laboratory stand with three-phase transformer connected to TN-S power...
Figure 7.10 Information indicating on LCP panel of drive VFC while stabilizing R...
Figure 7.11 Various values of DC voltage rectification to ensure constant value ...
Chapter 8
Figure 8.1 Region wise global EV growth, 2010-2019 [1].
Figure 8.2 Consumer’s concern for battery EVs [4].
Figure 8.3 (a) Public and private accessible EV slow chargers by country, 2019 [...
Figure 8.4 EVs announced models launched in past and projected growth worldwide ...
Figure 8.5 Categories of EVs based on technology used [7, 8].
Figure 8.6 Schematic representation of types of EVs [7, 8].
Figure 8.7 Progress of types of smart charging.
Figure 8.8 Major SAE Standards for EVs.
Figure 8.9 Schematic diagram of EVs’ physical infrastructure.
Figure 8.10 Adoption of EVs by country [1-4].
Figure 8.11 Smart charging providing flexible provisions to grid [8].
Figure 8.12 Energy flow and participation of market for smart EV and charging st...
Figure 8.13 Effects of smart charging from supply to demand [21].
Figure 8.14 Forms of smart charging.
Figure 8.15 Charger arrangement under Level 1 and Level 2.
Figure 8.16 Charger configuration under Level 3.
Figure 8.17 Security attack: vector from charging station to customers [8].
Figure 8.18 Electric mobility international standards.
Chapter 9
Figure 9.1 System load profile of case study (a) daily and (b) seasonal.
Figure 9.2 EVs load profile (a) daily, (b) seasonal.
Figure 9.3 NIT Durgapur (NITD) topographical location.
Figure 9.4 Availability of annual data for case study (a) solar data, (b) wind d...
Figure 9.5 Flow chart for planning of EV-FCS with renewables using HOMER.
Figure 9.6 Connection diagram of (a) base and (b) proposed system without EVFCS.
Figure 9.7 Load profile on a day (Peak load 1,488.52 kW) without EV-FCS.
Figure 9.8 Projected annual savings on utility bill by different options of prop...
Figure 9.9 Monthly electrical bills of (a) base and (b) proposed system-1 withou...
Figure 9.10 Production of power by sources (a) base and (b) proposed system-1 wi...
Figure 9.11 Monthly utility bill saving with proposed system-1 and base system w...
Figure 9.12 Cash flow for proposed system-1 without EV-FCS.
Figure 9.13 Cumulative cash flow over project lifetime of base system and propos...
Figure 9.14 Base system grid purchase, AC load, and DG power output without EVFC...
Figure 9.15 Proposed system-1 grid purchase, AC load, and DG power output withou...
Figure 9.16 Monthly performance summary of base system without EV-FCS.
Figure 9.17 Monthly performance summary of system-1 without EVFCS.
Figure 9.18 Connection diagram of (a) base and (b) proposed system without EVFCS...
Figure 9.19 Load profile on a day (Peak load 1,515.19 kW) with EV-FCS.
Figure 9.20 (a) Average daily and (b) monthly EV load served by DC fast chargers...
Figure 9.21 Annual savings on utility bill by different options of proposed syst...
Figure 9.22 Monthly electrical bills of (a) base system and (b) proposed system-...
Figure 9.23 Production of power by sources (a) base and (b) proposed system-1EV ...
Figure 9.24 Monthly utility bill saving with proposed system-1EV over base syste...
Figure 9.25 Cash flow for proposed system-1EV with EV-FCS.
Figure 9.26 Cumulative cash flow over project lifetime of base system and propos...
Figure 9.27 Base system grid purchase, AC load, and DG power output with EV-FCS.
Figure 9.28 Proposed system-1EV grid purchase, AC load and DG power output with ...
Figure 9.29 Monthly performance summary of base system with EV-FCS.
Figure 9.30 Monthly performance summary of system-1EV with EV-FCS.
Chapter 10
Figure 10.1 System model replicates the operation of CSs.
Figure 10.2 Presence of CSs on road network and electrical network.
Figure 10.3 Probability of availability of EVs on road.
Figure 10.4 Typical day selected price by CSs for case 3.
Figure 10.5 Profit of CSs in different cases.
Figure 10.6 Impact of short-term index on profit.
Figure 10.7 Profit with respect to EV density without power line limit.
Figure 10.8 Profit with respect to EV density with power line limit.
Chapter 11
Figure 11.1 Proposed SMES based charging system for electric vehicles.
Figure 11.2 Block diagram of proposed fuzzification based power controlling algo...
Figure 11.3 Profiles of membership functions (1) presenting voltage (V) and (2) ...
Figure 11.4 Location of smart charging stations where EVs are charged.
Figure 11.5 Layout of proposed smart grid topology with electric vehicles.
Figure 11.6 Complete procedural flowchart for proposed system.
Figure 11.7 Voltage salability analysis comparison with and without SMES.
Figure 11.8 Comparison of load angles under major faults.
Figure 11.9 Voltage profiles of EV chargers under different SMES capacity parame...
Figure 11.10 Voltage profiles of EV chargers when different EV fleets are chargi...
Chapter 12
Figure 12.1 Reinforcement based framework for navigating system.
Figure 12.2 Architecture of online learning algorithm.
Figure 12.3 All possible routes between Delhi to Gurgaon, distance of 45km (Goog...
Figure 12.4 Learning curve comparison between RL and GA.
Figure 12.5 Performance of RL algorithm in all three cases for different objecti...
Figure 12.6 SOC limit of all cases with covered distance.
Figure 12.7 Error plot with number of days.
Figure 12.8 Frequency distribution of error after 14 days.
Chapter 13
Figure 13.1 Autonomous vehicle standards ensure safety.
Figure 13.2 Application of autonomous vehicles.
Figure 13.3 Smart charging management for autonomous vehicles.
Chapter 14
Figure 14.1 Comparison of global conventional and renewable sources of energy pr...
Figure 14.2 Renewable energy production of major countries [1] KTOE. (Source: Da...
Figure 14.3 Components of VPP.
Figure 14.4 Status of VPP in selected nations.
Figure 14.5 Framework of FENIX project.
Figure 14.6 Framework of EDISON project.
Figure 14.7 Framework of SHD project.
Chapter 15
Figure 15.1 Historical CO
2
emissions from road transport of European Union [1].
Figure 15.2 Quantitative analysis of literature on charging station placement.
Figure 15.3 Flowchart for computing AENS [3].
Figure 15.4 Flowchart for computing voltage deviation.
Figure 15.5 Search Space.
Figure 15.6 Flowchart for computation of VSF [3].
Figure 15.7 Test system [12, 15].
Chapter 16
Figure 16.1 Status of FAME II scheme as per 2020.
Figure 16.2 Evolution of Electric Vehicles.
Figure 16.3 Comparison of two-wheeler production and sales.
Figure 16.4 Charging stations allocated statewide under FAME II scheme.
Figure 16.5 Statewide vehicle sales under FAME II scheme.
Figure 16.6 Classification of WPT.
Figure 16.7 Different types of electric vehicle charging methods.
Figure 16.8 Basic operation of WPT.
Figure 16.9 Compensation topologies: (a) series-series, (b) series-parallel, (c)...
Figure 16.10 Projected BEV sales (millions) of both two- and four-wheelers by 20...
Figure 16.11 Share of types of vehicles over indian automotive market.
Figure 16.12 Annual two-wheeler sales, production, and export patterns in Indian...
Figure 16.13 Growth pattern of vehicle sales in millions in global market.
Figure 16.14 Installed base of electric cars in India.
Figure 16.15 Availability of electric chargers countrywide around globe.
Chapter 17
Figure 17.1 Annual profile for irradiance with variation in ambient temperature.
Figure 17.2 Survey of global horizontal irradiance for year.
Figure 17.3 Equivalent circuit of two diode model of PV cell.
Figure 17.4 PV and IV characteristics of two diode PV module with various solar ...
Figure 17.5 Solar power plant site with capacity of 5MW generation at Prayagraj ...
Figure 17.6 Schematic diagram of 5 MW solar power plant.
Figure 17.7 Annual energy production per year.
Figure 17.8 Monthly PV generation in AC to PV array energy in DC.
List of Tables
Chapter 1
Table 1.1 A comparison between different types of charging techniques for EVs.
Table 1.2 Differences between V2B/V2H and V2G systems in smart charging architec...
Table 1.3 List of Differences between control architectures in a smart charging ...
Chapter 2
Table 2.1 Electric vehicle models on the market.
Chapter 3
Table 3.1 Evaluation of different charging strategies.
Chapter 4
Table 4.1 Usage-based dynamic pricing in a smart grid [12].
Chapter 5
Table 5.1 Random input data.
Chapter 6
Table 6.1 EV battery DC charging standards (adapted from [9-11]).
Chapter 7
Table 7.1 Specification of components in EV fast charging station.
Table 7.2 Laboratory stand specification of EV charger with industry drive conve...
Chapter 8
Table 8.1 Regulatory framework and standards related to EVs [18-26].
Chapter 9
Table 9.1 Hybrid renewable energy system parameter values for case study.
Table 9.2 Type of charging station and characteristics.
Table 9.3 Monthly average solar clearance index global horizontal irradiance, te...
Table 9.4 Optimized proposed system installation options without EV-FCS.
Table 9.5 Installation details of components for proposed system-1 without FV-FC...
Table 9.6 Savings for proposed system-1 over base case without EV-FCS.
Table 9.7 Electricity bill of NITD-DVC utility for base system without EV-FCS.
Table 9.8 Electricity bill of NITD-DVC utility for proposed system-1 without EV-...
Table 9.9 Net present and annual cost for base system without EV-FCS.
Table 9.10 Net present and annual cost for proposed system-1 without EV-FCS.
Table 9.11 Pollutant emissions of base system and proposed system-1 without EV-F...
Table 9.12 Operational parameters of EV fast charging station.
Table 9.13 Optimized proposed system installation options with EV-FCS.
Table 9.14 Installation details of components for proposed system-lEV with FV-FC...
Table 9.15 Savings for proposed system-lEV over base case with EV-FCS.
Table 9.16 Electricity bill of NITD-DVC utility for base system with EV-FCS.
Table 9.17 Electricity bill of NITD-DVC utility for proposed system-1EV with EV-...
Table 9.18 Net present and annual cost for base system-lEV with EV-FCS.
Table 9.19 Net present and annual cost for proposed system-1EV with EV-FCS.
Table 9.20 Pollutant emissions of base system and proposed system-1EV with EV-FC...
Table 9.21 Diesel Generator (DG Set) operational parameters for base and propose...
Table 9.22 Solar PV and wind turbine operational parameters for proposed systems...
Table 9.23 Battery storage (LI) operational parameters for proposed systems with...
Chapter 10
Table 10.1 Simulation parameters for price game.
Chapter 11
Table 11.1 Simulated fuzzy conditions.
Table 11.2 Simulated SMES parameters.
Chapter 13
Table 13.1 Use of autonomous vehicle to deal with pandemic.
Chapter 14
Table 14.1 Definitions of VPP put forward by different authors.
Table 14.2 Classification of DERs [7].
Table 14.3 Benefits of VPP [9-17].
Table 14.4 VPP service providers [18].
Table 14.5 Major VPP projects [18, 19].
Table 14.6 Major research initiatives on VPP.
Table 14.7 Research initiatives on EV integrated VPP.
Chapter 15
Table 15.1 Existing research works on charging station placement.
Table 15.2 Superimposed nodes.
Table 15.3 Input parameters [15].
Table 15.4 Optimal allocation of charging stations
Chapter 16
Table 16.1 Various standards of EVs relating to interface, infrastructure, and W...
Table 16.2 Upcoming electrical two-wheelers and their specifications in Indian M...
Table 16.3 Upcoming electrical scooters and their specifications in Indian Marke...
Table 16.4 Upcoming electric four-wheeler vehicles in Indian Market by 2020.
Chapter 17
Table 17.1 Climatic conditions of Prayagraj City.
Table 17.2 Electrical ratings of two diode TATA BP solar (TBP 3235T).
Table 17.3 Cost analysis of Prayagraj 5 MW solar power plant.
Guide
Cover
Table of Contents
Title page
Copyright
Preface
Begin Reading
Index
Also of Interest
End User License Agreement
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Scrivener Publishing
100 Cummings Center, Suite 541J Beverly, MA 01915-6106
Advances in E-Mobility
Series Editor: Sulabh Sachan, PhD, Sanjeevikumar Padmanaban, PhD, and Sanchari Deb, PhD
Scope: The world’s ever-escalating energy demand accompanied by concerns of greenhouse gas emissions from the use of Internal Combustion Engine (ICE) driven vehicles have pushed mankind to look for alternative energy options for mobility. This in turn, has paved the path for electrification of road transport. Electric Vehicles (EVs) are considered as a clean and unpolluted mode of transport as well as an environmentally friendly option to tackle the problem of poor air quality. The scope of this series is to cover all of the aspects of e-mobility, including design, concepts, practical applications, and the latest trends and important developments in the science.
Publishers at Scrivener
Martin Scrivener ([email protected]) Phillip Carmical ([email protected])
Smart Charging Solutions for Hybrid and Electric Vehicles
Edited by
Sulabh Sachan
P. Sanjeevikumar
and
Sanchari Deb
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-119-76895-1
Cover image: EV Charge Station Image: Artinun Prekmoung | Dreamstime.com
EV Battery: Svyatoslav Lypynskyy | Dreamstime.com
Cover design by Kris Hackerott
Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines
Printed in the USA
10 9 8 7 6 5 4 3 2 1
Preface
Future energy resources entirely depend on locally available renewable resources due to the fossil fuel supply’s uncertainty, growing mobility demand, and increasingly stringent regulations. Furthermore, fuel consumption and emissions are facilitating the transition to sustainable transportation. The market for Electric Vehicles (EVs) has been increasing steadily over the past few years throughout the world. With the increasing popularity of EVs, a competitive market between charging stations (CSS) to attract more EVs is expected. This edited book’s outcome is intended to serve as a resource for engineers, researchers, and practitioners interested in getting acquainted with smart charging for Electric Vehicle technologies. It includes seventeen original chapters dealing with state-of-the-art studies on EV smart charging, along with charging infrastructure.
In Chapter 1, Bikash Sah and Praveen Kumar present a comprehensive review of smart charging’s role and impacts. They also describe charging, followed by the categorization of smart charging and the smart charging system’s requirements and components.
In Chapter 2, Michela Longo, Wahiba Yaïci, and Dario Zaninelli analyze a detailed examination of the distribution grid’s evolution, a thorough analysis of electric mobility, and a survey of the state-of-the-art charging infrastructure for electric vehicles.
In Chapter 3, Chandana Sasidharan and Shweta Kalia describe charging strategies based on vehicle type. They also represent different charging strategies against four objectives: peak management, green charging, flexibility resources, and cost optimization.
In Chapter 4, Ahad Abessi, Vahid Safari, and Mohammad Shadnam Zarbil present a dynamic charging pricing model. Also, pricing models of discharging power to the grid are discussed, followed by some of the charging pricing currently used globally.
In Chapter 5, Andres Alfonso Rodriguez, Luis Perdomo, Ameena Al-sumaiti, Francisco Santamaria, and Sergio Rivera present a charge strategy for charging electric vehicles in office buildings using automatic learning algorithms offering benefits to its implementation.
In Chapter 6, Jerzy R. Szymanski and Marta Zurek-Mortkal present the different charging strategies and EV battery model. They also perform a case study of charging high-power Li-ion batteries for electric work machines.
In Chapter 7, Jerzy R. Szymanski and Marta Zurek-Mortka describe a hybrid EV fast charging station’s structure to integrate with the industrial power grid.
In Chapter 8, Rajkumar Viral and Divya Asija present a theoretical regulatory framework for smart charging EVs and HVs.
In Chapter 9, Aashish Kumar Bohre, Partha Sarathee Bhowmik, and Baseem Khan present an actual case study for efficient charging infrastructure planning with renewable sources, supporting EVs’ adoption as an efficient alternative for transportation.
In Chapter 10, Lokesh Kumar presents a real-time price-competitive market structure based on game theory for EV charging stations considering various practical parameters, including wait time and reputation function.
In Chapter 11, Ubaid Rehman presents a Super-Conducting–Magnetic Energy Storing (SME’S) System to regulate the system’s voltages during charging of an EV. This enhances battery life and increases the EVs charging efficiency in the smart grid (SG).
In Chapter 12, Lokesh Kumar presents an actual case study for a novel intelligent route planning framework for an electric vehicle with consideration to waiting time.
In Chapter 13, Nadia Adnan, Sharina Md Nordin, and Shouvik Sanyal present intelligent charging management for autonomous vehicles. They also describe a brilliant solution for smart cities and societies.
In Chapter 14, Sanchari Deb, Sulabh Sachan, Mohammad Saad Alam, and Samir M Sharif present a comprehensive review of an EV integrated virtual power plant (VPP).
In Chapter 15, Sanchari Deb and Sulabh Sachan present the single objective modeling of EV charging station placement problems regarding superimposition of roads and a distribution network.
In Chapter 16, D. R. Karthik, Mallikarjunareddy Bandi, Naveenkumar Marati, Balraj Vaithilingam, and Kathirvel Karuppazhagi present a review on recent trends and technologies of electric vehicles and their wireless charging methods.
In Chapter 17, Satendra Kumar Singh Kushwaha, Satyprakash, Akhilesh Kumar Gupta, Akbar Ahmad, Bandi Mallikarjuna Reddy, and NarendraKumar Ch discuss techno-economic issues of grid-connected large photovoltaic plants of the smart City Prayagraj to the EV Charging Station.
We hope that this edited book includes a broad collection of state-of-the-art studies on the theme. Readers are expected to find these chapters inspiring and helpful while carrying out their research in the subject domain.
Acknowledgments
The editors would like to thank all the authors who have made their valuable contributions to this edited book. We also thank all the reviewers who have generously spared their time in reviewing the chapter manuscripts. Our sincere thanks go to the Scrivener Publishing and John Wiley Publication and staff for their cooperation and continuous support throughout this edited book’s production process.
– Editors
1Smart Charging: An Outlook Towards its Role and Impacts, Enablers, Markets, and the Global Energy System
Bikash Sah* and Praveen Kumar
Department of Electronics and Electrical Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, India
Abstract
The push for transport electrification has increased worldwide due to growing concerns about carbon emissions by conventional fossil fuel based vehicles. With the push of transport electrification, the exiting power systems utility grid is also evolving. Electric vehicles (EVs) are becoming popular and gaining the market share in due course of time. The increase in EVs demands more power to charge which results in a significant impact on the utility grid. Dependency on renewable energy sources and the use of local energy storage has increased. Inculcating the incremental addition of EVs and the integration of renewables and local energy storage requires overhauling the planning, monitoring, operation, and maintenance of the power system and its components. Smart charging is an EV charging technique that focusses on reducing the impact of increased power demand and helps in the integration of renewables and local energy storage. Smart charging adds flexibility in the operation of power system components with added functionalities that give augmented monitoring and control to EV users and the power system operator. The goals of smart charging are set to unleash coherency between transport electrification, low-carbon emission generation, and utilization of electricity. This chapter will define the context of “smart” with respect to “smart charging”, present an outlook towards its role and impacts on the utility grid and connected entities, and describe the enablers of smart charging, markets, and the operation of the global energy system.
Keywords: Energy system, smart charging, role, market
1.1 Introduction to Smart Charging
Organizations worldwide are working to ensure the usage of low carbon generating entities meet day-to-day requirements such as power generation and transportation [1, 2]. The use of renewables has helped meet the target in the case of power generation. At the same time, a paradigm shift in the transportation sector with the introduction of electric vehicles (EVs) is evident. This paradigm shift rolled out challenges to the existing power systems due to an increase in the demand for electricity to charge, the use of EVs as distributed energy storage, and regulating the power quality. Smart charging techniques for EVs emerge as a solution to meet the challenges [3].
Smart charging of EVs supports the convergence of EV owners’ behavior and requirements, charging, the grid, and all participants involved in the system. Support is provided by various system enablers, which include supporting technologies, policies, and stakeholders. The benefits of smart charging extend to the efficient management of charging during peak and off-peak load hours, increased penetration of renewable energy, reduced transmission losses, economic and technical benefits to users, and much more [1, 4-6]. The smart charging system will unleash more benefits when the users’ and service providers’ requirements are a defined set of operational standards that are coherently aligned.
The literature presents a broader range of developments in the smart charging systems [5, 7]. Most of the works are on developing algorithms to either maximize, minimize, or compute an optimal parameter to define an efficient working of the smart charging system. Although it is desirable to approach the smart charging system’s design to inculcate the interests of all the stakeholders, most of the work did not consider the evolution of the market or the competitiveness of service providers and their outlooks [8].
Cars in general and EVs spend more than 90% of their lifetime parked. The parking period can be used for a variety of purposes, such as local energy storage, mobile energy storage, backup support to homes and buildings, active power support to the utility grid, ancillary services support, and much more. The services rendered by EVs generate income for the EV users as well. An EV can effectively be customized for both mobility and micro-grid connected systems. Apart from the mentioned services, EVs support renewable integration as well. The power generated from renewables is intermittent but attractive as the contribution of carbon emissions in this generation is reduced drastically. The EVs, when used as local energy storage devices, act as a bridge between the utility grid and renewables.
Smart charging also renders a fascinating opportunity to scale-up, improve reliability, automate operation monitoring and control, and overhaul the existing power systems. Although the increased penetration of EVs has a serious impact on the operation of the utility grid, the added potential of EVs with goals of smart charging make the power system flexible at the consumer end, as well as to the power system operator and connected entities. This chapter will focus on the various aspects of dealing with increased penetration of EVs using smart charging. Worldwide, the definition and context of “smart” may vary depending on the requirements of the users. The next subsection will introduce the context of “smart” and describe various approaches to develop a smart system.
1.1.1 Context of SMART
The term “smart” is the most commonly added word to every application, service, and technology in recent times. The context of “smart” varies based on the definition of the manufacturer, user, and the objectives for which the application, services, and technology are developed. Any product that implies making life simpler and better than its previous counterpart is termed as “smart”. Hence, defining the context of “smart” is utterly dependent on added functionalities in a product. The functionalities may include intelligence in operation, internet connectivity in devices such as IoT, data-driven operation and analysis, learning capabilities from the deployed environment, communication between devices or entities of a system, or a combination of any or all the mentioned functionalities.
The term “smart” originates from an acronym: Self-Monitoring, Analysis, and Reporting Technology. “Smart” technologies can be broadly categorized in the following ways:
a. Smart automation devices are devices automated by programming and learning data to operate based on an intuitive interface included in the smart automation device. A geyser that operates at a particular interval of time to heat water automatically considering the environment’s ambient temperature is an example of a smart automation device.
b. Smart software devices are application based and programmed to perform analytics, display data to the user, request data from the connected subsystems in a system, or any other functionalities for which it is programmed. Such devices mostly require internet connectivity or any communication link between the connected subsystems. An example of a smart software device is an application installed in a computing system to control and monitor a factory’s operation. Smart software devices are considered to be easily scalable and upgradeable.
c. Smart hardware devices, as the name suggests, include remotely connected, monitored, and operated devices. Such devices mostly require a software-based user interface and connectivity using any communication technology to monitor and operate. Smart appliances at homes, such as smart bulbs, are one of the examples of a smart hardware device.
d. Smart computational environment: Computational environment in recent days has upgraded diversely but converges to a common theme of “smart”. The environment here refers to all the connected devices or smart devices in a system that give a platform to the user to develop and execute an operation for which the environment is proficient. The operation’s development and execution is made possible by establishing necessary communication between each internal device and required external entities. The IBM Cloud, Microsoft Azure, and Google Cloud are examples of a smart computational environment. Users have access to a variety of applications and devices that can be configured as required.
The categorization of “smart” devices is broad and not limited to the types mentioned earlier. Enhancement in existing technologies and new developments have shown vast possibilities of making existing devices smart and accessible. The addition of smart functionalities in any system should increase product capabilities, utilization, reliability, and transcend conventional product boundaries.
The context of smart charging is an amalgamation of all the “smart” technologies. The smart charging infrastructure involves the need of automation devices, software run devices, and supporting software, hardware devices, and the computational environment. Each of the mentioned entities is built with intelligence added by various algorithms that help make relevant decisions and implement them.
Any “smart” system requires proper coordination while developing and operating. The next subsection briefly explains approaches taken by the developers to ensure the addition of functionalities, which make the system smart and reliable to the users and renders market value to the developers.
1.1.2 Approaches
The paradigm of “smart” is relatively novel and rupturing the conventional product developing organization. The conceptualization of connotation demands a systematic approach. The approaches vary based on the utility and target users. A developer takes three different approaches, considering the target, to determine which functionalities are to be added. The first approach is to add smartness to the target applications accessible to users of the device. Adding functionalities to an application so that the users can monitor, control, and execute the workings of a connected system smartly is an example of the first approach.
The second approach adds functionalities to the device instead of the application that connects the user and the device. An example of the second approach is adding sensors and programmed microcontrollers to a device to operate intelligently based on the sensor data and computed parameters. The user interface connected in the second approach can be limited to data visualizations and minimal control operations. The third approach is an amalgamation of both the first and second approaches. Both the target user application and the devices connected are upgraded to develop a smart environment.
The developers of smart charging take the third approach. The third approach ensures that the overall system is intelligent to make decisions even when it is not able to coordinate with the connected devices or software. For example, while in operation, the cable connecting the distribution transformer and the charging station of a smart charging system experience a higher current than the normal value. As per the first approach, the information of fault will be conveyed to the operator of the monitoring station and the fault will continue until the operator signals to shut down the operation. There is a possibility that the cables will be damaged by the time operator responds, the operator did not respond due to negligence, or there was a communication breakdown leading to non-receipt of information at the operator end. If the second approach is taken, although the system will shut down due to fault, the operator will have no information to detect the cause of the fault. However, if the third approach is taken, the operator will get information about the fault and the system will shut down operation on its own. The third approach ensures the safety of the system and saves time working on fault correction.
1.1.3 Contributions
This chapter has described the types of charging followed by the categorization of smart charging, the requirements and components of the smart charging system, the enablers who coherently support the development, operation, and management of the smart charging system, and control architectures developed so far for implementation and integration with the conventional grid. They commenced an outlook on commerce, evolution, and competitiveness in the smart charging system market.
This section is structured to give readers an understanding of the term “smart” and its applicability in an EV charging infrastructure. The first section defines “smart” and explains the context and approaches to adding smartness. The second section deals with different types of charging: viz., uncoordinated, coordinated, and smart. The third and fourth sections describe the impact and requirements of the smart-charging system, respectively. The fifth section defines each smart-charging system’s components, followed by a discussion on various control architectures that can be used for smart charging in the sixth section. The commerce and outlook of smart-charging are explored in the seventh section, followed by a conclusion in the eighth section.
1.2 Types of Charging
The charging of EVs needs power from a source. The power source can be the conventional utility grid, local energy storage system, renewable energy systems, or a hybrid system developed by combining any of the sources mentioned. Apart from charging EVs, the power sources also feed load connected and cater services to increase the utility grid’s reliability. Charging EVs adds an extra load to the power sources. Three types of charging consider the management and distribution of power due to the addition of load from EV charging are widely discussed in the literature: viz., uncoordinated, coordinated, and smart [9, 10].
1.2.1 Uncoordinated Charging
The utility grid connecting to the load from a power source is designed to meet a particular region’s power demand. Further, the utility grid operators perform demand response or load distribution analysis to serve consumers with reliability. If an unprecedented load is added to the utility grid, the possibility of voltage fluctuations and blackouts increases [11]. Uncoordinated charging transpires when the EV’s charge is done in the form of unprecedented loads, i.e., the time to charge EVs is not scheduled in coordination with the utility grid [12, 13].
The impact of uncoordinated charging to the utility grid can be described in two ways: increased load demand and change in the shape of load profile. Increased load demand refers to the need for more kilowatts at a particular instant, as noted previously. In contrast, the change in shape of the load profile corresponds to a change in the timing of peak load and offpeak load hours. Literature reports that even a low adoption of EVs could significantly change the load profile and affect electricity infrastructure. The impacts of uncoordinated charging are not limited to the load demand and shape; phase imbalance, power quality issues, such as an increase in total harmonic distortion, increased power loss, line loading, and equipment degradation, such as transformers and circuit breakers, also impact the utility grid [11]. However, the impact of uncoordinated charging is seen on all three segments of the utility grid, namely, generation, transmission, and distribution systems, but the distribution section of the utility grid is the worst affected [14].
1.2.2 Coordinated Charging
Coordinated charging is characterized by charging EVs in coordination with the utility grid. The coordination is required to identify the present condition (load connected) of the grid or power source that will supply the power to charge EVs. The peak load and off-peak load hours of a utility grid vary based on residential, industrial, or commercial regions. In general, for the residential area, the utility grid is in peak load at evening and night hours, while the off-peak load hours are noted during late nights when people sleep. The load demand for an industrial area will depend on the working shifts and operation of factories. For commercial areas, the peak load hours will be at consumer visiting hours, i.e., during the evening. The off-peak load hours will be during the morning [6, 15].
In the case of coordinated charging, based on the regions, the process of charging is scheduled during off-peak load hours. However, it is ensured that EV owners are not barred from the services. The literature is flooded with works done to perform coordinated charging by developing optimizing algorithms, demand response strategy, load scheduling, controllers, dynamic pricing methodology, electricity market operation strategy, and time of use (ToU) [16-22]. Although the works in the literature are diverse, each of them shares the following common goals:
a. The EV owners’ need to charge at any time of the day should not be denied, irrespective of the loading in the utility grid
b. The power system operator (PSO) constraints should be coordinated and supported in the quest to charge EVs
c. Necessary support services from the EV owner to the PSO and the PSO to the EV owners should be provided via necessary coordination
d. Increased penetration of local energy storage and renewable energy sources in the utility grid
Coordinated charging of EVs is complicated, expensive, and needs standard infrastructure support for implementations. However, the benefits are immense compared to uncoordinated charging. Coordinated charging helps solve two major issues: first, congestion management, which is defined as an increase in thermal loading in transformers and cables and, second, voltage drops, which are most commonly experienced due to the addition of any unprecedented load, such as EVs [15, 23-25].
The type of charging is also a significant factor to be considered when working with coordinated charging [8, 11]. A fast-charging requires a higher amount of power to be transferred to the EV batteries in a short duration of time. In contrast, in slow charging, the requirement of power is reduced, but time is increased. The ToU and dynamic pricing algorithms are the most commonly presented in the literature to cater to the requirements of power for different charging types. Although coordinated charging solves the basic requirements of charging EVs in consideration to the utility grid’s constraints and managing EVs as a load, it fails to be a future proof system where both the EV owner and the PSO are guaranteed an optimized charging process [10, 18].
1.2.3 Smart Charging
Uncoordinated and coordinated charging worked on two different objectives. Uncoordinated charging prioritizes the requirements of EV users. In contrast, coordinated charging tries to optimize utility grid operation considering the grid’s requirements and ensuring satisfactory service to the EV users. Although coordinated charging, to some extent, meets the requirement of both the utility grid and EV users, the algorithms and controller developed are inclined to only one segment of operation, the utility grid [9, 26].
The smart charging process, on one hand, lets the EV user decide the priority and, on the other hand, adapts the charging process to meet the requirements of the PSO. For example, suppose a user opts to charge EV during off-peak load hours. In that case, incentives are given in the form of cost reduction in electricity billing. If a user prioritizes to charge rather than considering the grid’s condition, especially during peak-load hours, the electricity billing is higher. The user is not barred from getting the desired service, but an optimal solution is met between the EV owner and the PSO [27]. The smart control ensures the charging of batteries in EVs within a given time and considers PSO constraints, such as voltage and frequency regulations. The smart charging’s prime concern is to reduce the impact of EV charging and enhance grid reliability and stability. For a better understanding, Figure 1.1 shows the list of expected functionalities to define the level of smartness in the charging system.
The platform for electro-mobility (2016) in the European Union (EU) defines smart charging as: “consist[ing] of adapting EV battery charging patterns in response to market signals, such as time-variable electricity prices or incentive payments, or response to acceptance of the consumer’s bid, alone or through aggregation, to sell demand reduction/increase (grid to vehicle) or energy injection (vehicle to grid) in organized electricity markets or for internal portfolio optimization” [26]. Smart charging demands intelligent monitoring, control, and operation [1, 3, 4]. Hence, communication and coordination between the charging infrastructure entities is a must to realize smart charging. In smart charging, the entities are not just a mere power transfer system, but rather a data-rich monitoring system that can monitor, control, coordinate, communicate, forecast, and optimize the operations [2, 7]. A brief description of the various approaches presented in the literature is shown in Figure 1.2.
Figure 1.1 Flow diagram to understand and judge the level of smartness based on functionalities.
Figure 1.2 A brief on different approaches to smart charging techniques.
The definitions and requirements to call a charging infrastructure smart vary, but all the ideas converge to the following goals:
i. Guaranteed service to the users as required by optimizing all the entities’ operations and energy management in the system
ii. Grid-friendly charging of EVs considering peak shaving; grid-friendly charging of EVs is done when the utility grid has required or surplus power (off-peak hours) after meeting the need of a precedented connected load
iii. Renewable integration: the smart charging of EVs should promote the use of renewables. The use of local energy storage systems (ESS) has shown promising results in integrating renewable energy sources to the utility grid. The energy generated from the renewables can be stored in the ESS and supplied to the utility grid when the grid is at stress. EVs act as distributed energy sources by allowing the bidirectional flow of power, hence, EVs can pivot the integration of renewables.
iv. Increase reliability and stability: smart charging monitoring and control algorithms should focus on the utility grid’s demand and supply of power. The requirements of all the stakeholders in a power system should be met optimally.
Based on the discussions in the previous paragraphs, a comparison is presented in Table 1.1. Meeting the goals of smart charging is challenging, but its implementation gives an assurance of meeting the specified goals. The impact of smart charging is discussed in the next section.
Table 1.1 A comparison between different types of charging techniques for EVs.
Types of charging
Impact on the grid
Advantages
Disadvantages
Maturity
Uncoordinated
Leads to issues such as increased load demand and change in the shape of load profile, imbalance in phases, and lower power quality
It is user friendly and the deployment does not demand any support services or establishment
The capital investment cost is the least
Increased power losses in transmission line and components
Voltage and frequency fluctuations
Phase imbalance
Power quality issues such as an increase in total harmonic distortion
Degradation of transformers and transmission lines
High (Product readily available in the market and used by consumers)
Coordinated
Reduces negative impact by providing ancillary services and frequency control
Performs peak shaving and demand response
Increased utilization options to EV users such as providing ancillary services and support to the grid by charging and discharging considering grid conditions
Load management which reduces power loss and deterioration in the transmission line and transformers
Opportunity to engage users in the electricity market
The cooperation of EV users is required, which is uncertain
The incoming and outgoing of EVs is not predictable, hence relying on EVs for ancillary services and regulation can put the power system at risk
The requirement of communication infrastructure will demand huge capital investment
The assurance of a positive impact on the electricity grid is missing
Medium (pilot project implementation)
Smart
Helps in peak shaving or valley filling, power management on the grid side and energy management on the EV side, ancillary services, voltage and frequency regulation, improvement in power quality, and renewable energy integration
Eases the integration of renewable energy sources in the grid
The use of local energy storage adds flexibility to select power source-grid or energy storage for charging
Improved grid stability and reliability
Control, operation, management, and monitoring of system at ease
Promotes usage of EVs due to increased satisfaction of EV owners and PSO
Implementation challenge due to complexity
Higher risk operation as the operation and control in the infrastructure are dependent on communication systems
Demand commitment from both EV users and PSO
Variability in market operations interferes with the workings of the infrastructure
Low
1.3 Impact of Smart Charging on Global Energy Systems
The global energy system is characterized by the interconnected electricity grid which comprises of generation, transmission, and distribution systems, as well as the utilization of renewable energy sources. The price of electricity varies for regions around the world. Each country tries to ensure energy security by planning generations within the boundary. In most cases, renewables come to the rescue because recent advancements in local energy storage systems have not increased energy security. EVs are also considered as mobile/local energy storage due to the capacity of batteries used to power the drivetrain. Hence, an increase in the number of EVs in a country has achievable implications to impact the global energy system.
The direction of the flow of power plays a significant role in determining the impact of smart charging. In the case of charging, two types of viz., unidirectional and bidirectional, are described in the literature. In the case of unidirectional, there is a controlled power flow from the utility grid to the EVs to charge, while in bidirectional the power flow is exchanged between EVs and the utility grid [3, 5, 23, 24, 28-31]. When the grid is in peak load hours, controlled power flow from EVs to the utility grid meets the surplus demand and while during off-peak hours, the EVs charge using surplus power in the grid. Note that the charging process is spread out over the day and mostly controlled using algorithms. Of the two, the bidirectional flow of power is found to be better in reducing the impact of uncoordinated charging. A study by the International Renewable Energy Agency (IRENA) states that, in the short term, bidirectional smart charging is able to reduce more curtailment when compared to unidirectional smart charging [32].
Further, CO2 emissions are also reduced more in the bidirectional case, compared to the unidirectional. The long-term analysis by IRENA is done considering renewables’ integration, which includes solar and wind-based isolated systems. For the long-term, a reduction in CO2 is noticeable in bidirectional viz. when power renewables augment power production as compared to unidirectional. Hence, smart charging promotes the integration of renewables [27, 32].
