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E-grāmata: Planning and Operation of Plug-In Electric Vehicles: Technical, Geographical, and Social Aspects

  • Formāts: EPUB+DRM
  • Izdošanas datums: 14-May-2019
  • Izdevniecība: Springer Nature Switzerland AG
  • Valoda: eng
  • ISBN-13: 9783030180225
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Planning and Operation of Plug-In Electric Vehicles: Technical, Geographical, and Social AspectsPalielināt
  • Formāts: EPUB+DRM
  • Izdošanas datums: 14-May-2019
  • Izdevniecība: Springer Nature Switzerland AG
  • Valoda: eng
  • ISBN-13: 9783030180225
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This book highlights the latest advancements in the planning and operation of plug-in electric vehicles (PEV). In-depth, the book presents essential planning and operation techniques to manage the PEV fleet and handle the related uncertainties associated with the drivers’ behavior. Several viewpoints are presented in the book, ranging from the local distribution companies to generation companies to the aggregators. Problems such as parking lot allocation and charging management are investigated, taking into consideration the technical, geographical, and social aspects in a smart grid infrastructure.

  • Discusses the technical specifications of electrical distribution and generation systems;
  • Models drivers’ behavior from the sociology and economic points of view;
  • Considers the real geographical characteristics of area and driving routes in San Francisco, CA, US; Chicago, IL, US; and Tehran, Iran.

1 Studying the Effects of Plug-In Electric Vehicles on the Real Power Markets Demand Considering the Technical and Social Aspects
1(22)
1.1 Introduction
1(3)
1.2 Modelling the Social and Technical Aspects of Problem
4(6)
1.2.1 Social Stratification of Drivers
4(2)
1.2.2 Technical Aspect
6(4)
1.3 Simulation Results
10(4)
1.3.1 Base Case
10(1)
1.3.2 Sensitivity Analysis with Respect to Value of Incentive
11(1)
1.3.3 Sensitivity Analysis with Respect to Social Class of Drivers
12(2)
1.4 Conclusion
14(6)
References
20(3)
2 Studying the Effects of Optimal Fleet Management of Plug-In Electric Vehicles on the Unit Commitment Problem Considering the Technical and Social Aspects
23(26)
2.1 Introduction
23(3)
2.2 Modelling the Social and Technical Aspects of Problem
26(2)
2.2.1 Social Classification of Drivers
26(1)
2.2.2 Technical Aspect
27(1)
2.3 Optimization Technique
28(2)
2.4 Problem Formulation
30(3)
2.4.1 Objective Function of Problem
30(1)
2.4.2 Cost Terms of Problem
31(1)
2.4.3 Constraints of Problem
32(1)
2.5 Simulation Results
33(10)
2.5.1 Base Case
33(5)
2.5.2 Sensitivity Analysis for Operation Cost
38(4)
2.5.3 Effects of Unrealistic Modelling of Drivers' Social Class
42(1)
2.6 Conclusion
43(3)
References
46(3)
3 Spinning Reserve Capacity Provision by the Optimal Fleet Management of Plug-In Electric Vehicles Considering the Technical and Social Aspects
49(26)
3.1 Introduction
49(2)
3.2 Modelling the Social and Technical Aspects of Problem
51(3)
3.2.1 Social Classification of Drivers
51(2)
3.2.2 Technical Aspect
53(1)
3.3 Optimization Technique
54(1)
3.4 Problem Formulation
55(4)
3.4.1 Objective Function of Problem
55(1)
3.4.2 Cost Terms of Problem
56(2)
3.4.3 Constraints of Problem
58(1)
3.5 Simulation Results
59(11)
3.5.1 Base Case
59(8)
3.5.2 Sensitivity Analysis for Total Cost of Problem
67(2)
3.5.3 Effects of Unrealistic Modelling of Drivers' Social Class
69(1)
3.6 Conclusion
70(3)
References
73(2)
4 Robust Operation of a Reconfigurable Electrical Distribution System by Optimal Charging Management of Plug-In Electric Vehicles Considering the Technical, Social, and Geographical Aspects
75(30)
4.1 Introduction
75(4)
4.2 Proposed Approach
79(8)
4.2.1 Modelling the Geographical, Social, and Technical Aspects of Problem
79(3)
4.2.2 Stochastic Model Predictive Control
82(4)
4.2.3 Optimization Technique
86(1)
4.3 Problem Formulation
87(5)
4.3.1 Objective Function
87(1)
4.3.2 Cost Terms
87(3)
4.3.3 Constraints
90(2)
4.4 Problem Simulation
92(8)
4.4.1 Characteristics of System and Problem
92(1)
4.4.2 Simulating the Problem in Different Scenarios
93(7)
4.5 Conclusion
100(2)
References
102(3)
5 Optimal Operation of a Plug-In Electric Vehicle Parking Lot in the Energy Market Considering the Technical, Social, and Geographical Aspects
105(44)
5.1 Introduction
105(2)
5.2 Modelling the Geographical, Social, and Technical Aspects of Problem
107(10)
5.2.1 Drivers' Behavioral Models
107(2)
5.2.2 Driving Routes
109(2)
5.2.3 State of Charge of PEVs
111(1)
5.2.4 Arrival and Departure Time of PEVs
112(3)
5.2.5 Equipping the Parking Lot with Renewables
115(2)
5.3 Problem Formulation
117(6)
5.3.1 Objective Function
117(2)
5.3.2 Equality Constraints
119(2)
5.3.3 Inequality Constraints
121(1)
5.3.4 Lower and Upper Bounds of Variables
122(1)
5.4 Mixed Integer Linear Programing as the Optimization Technique
123(1)
5.5 Problem Simulation
124(16)
5.5.1 Primary Data of Problem
124(3)
5.5.2 Studying the Problem with Tesla Model S
127(12)
5.5.3 Studying the Other PEV Types
139(1)
5.6 Conclusion
140(6)
References
146(3)
6 Optimal Placement and Sizing of Parking Lots for the Plug-In Electric Vehicles Considering the Technical, Social, and Geographical Aspects
149(62)
6.1 Introduction
149(4)
6.2 Modelling the Geographical and Social Aspects of Problem
153(5)
6.2.1 Modelling Drivers' Behavior
153(3)
6.2.2 Driving Routes in San Francisco
156(2)
6.3 Modelling Voltage-Dependent Load
158(2)
6.4 Modelling Feeder's Failure Rate
160(5)
6.5 Problem Formulation
165(4)
6.5.1 Objective Function
165(1)
6.5.2 Cost Terms
166(3)
6.5.3 Problem Constraints
169(1)
6.6 Optimization Technique
169(3)
6.7 Problem Simulation
172(32)
6.7.1 Technical Specifications of System and Problem
172(7)
6.7.2 Studying the Primary Condition of System
179(1)
6.7.3 Studying the Effects of Social Class of Drivers
180(8)
6.7.4 Studying the Effects of PEV Penetration Level
188(5)
6.7.5 Studying the Effects of PEV Type
193(3)
6.7.6 Studying the Effects of Feeder's Failure Rate
196(5)
6.7.7 Studying the Effects of Voltage-Dependent Load
201(3)
6.8 Conclusion
204(4)
References
208(3)
7 Estimating the State of Charge of Plug-In Electric Vehicle Fleet Applying Monte Carlo Markov Chain
211(28)
7.1 Introduction
211(2)
7.2 Dataset
213(4)
7.3 Method of Analysis
217(8)
7.4 Simulation Results
225(9)
7.4.1 Base Case
225(4)
7.4.2 Studying the Effect of Parameters
229(5)
7.5 Conclusion
234(2)
References
236(3)
Index 239
Mehdi Rahmani-Andebili is an Assistant Professor in the Department of Engineering and Physics at the University of Central Oklahoma, OK, USA. Before that, he was also an Assistant Professor in the Electrical Engineering Department at Montana Technological University, MT, USA, and the Engineering Technology Department at State University of New York, Buffalo State, NY, USA, during 2019-2022. He received his first M.Sc. and Ph.D. degrees in Electrical Engineering (Power System) from Tarbiat Modares University and Clemson University in 2011 and 2016, respectively, and his second M.Sc. degree in Physics and Astronomy from the University of Alabama in Huntsville in 2019. Moreover, he was a Postdoctoral Fellow at Sharif University of Technology during 2016-2017. As a professor, he has taught many courses and labs, including Power System Analysis, DC and AC Electric Machines, Feedback Control Systems Analysis and Design, Renewable Distributed Generation and Storage, IndustrialElectronics, Analog Electronics, Electrical Circuits and Devices, AC Electrical Circuits Analysis, DC Electrical Circuits Analysis, Essentials of Electrical Engineering Technology, and Algebra and Calculus-Based Physics. Dr. Rahmani-Andebili has more than 200 single-author and first-author publications, including journal papers, conference papers, textbooks, books, and book chapters. He is an IEEE Senior Member and the permanent reviewer of many credible journals. His research areas include Smart Grid, Power System Operation and Planning, Integration of Renewables and Energy Storages into Power System, Energy Scheduling and Demand-Side Management, Plug-in Electric Vehicles, Distributed Generation, and Advanced Optimization Techniques in Power System Studies.
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