Skip to main content
main-content
Top

About this book

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.

Table of Contents

Frontmatter

Chapter 1. Studying the Effects of Plug-In Electric Vehicles on the Real Power Markets Demand Considering the Technical and Social Aspects

Abstract
In this chapter, the impacts of plug-in electric vehicles (PEVs) on the demand profile of some of the real power markets are modelled and studied considering the technical and social aspects of problem. The power markets under study include Electric Reliability Council of Texas (ERCOT), New York Independent System Operator (NYISO), Pennsylvania-Jersey-Maryland (PJM), and Independent System Operator New England (ISO-NE). Herein, the objective function of each independent system operator (ISO) is to maximize the load factor of market demand by optimal fleet management (FM) of PEVs considering low, moderate, and high PEV penetration levels. In this study, the drivers are categorized in three different social classes based on their income level including low-income (LI), moderate-income (MI), and high-income (HI) social classes. The behavior of each social class of drivers is modelled based on the reaction of drivers with respect to the value of incentive suggested by the ISO to them to transfer their charging demand from the peak period to the off-peak one. The sensitivity analysis is performed for the load factor of market with respect to the value of incentive and social class of drivers. In addition, the value of error in the optimal value of incentive and maximum value of load factor, due to the unrealistic modelling of drivers’ social class, are investigated in each power market.
Mehdi Rahmani-Andebili

Chapter 2. Studying the Effects of Optimal Fleet Management of Plug-In Electric Vehicles on the Unit Commitment Problem Considering the Technical and Social Aspects

Abstract
In this chapter, the effects of fleet management (FM) of plug-in electric vehicles (PEVs) on the generation scheduling and unit commitment (UC) problem of a generation system are studied considering the technical and social aspects of the problem. The objective function of generation company (GENCO) is to minimize the operation cost of generation system by the optimal FM of PEVs considering low, moderate, and high PEV penetration levels. Herein, the drivers are categorized in three different social classes based on their income level including low-income, moderate-income, and high-income. In this study, the behavior of each social class of drivers is modelled based on the reaction of drivers with respect to the value of incentive, suggested by the GENCO, to transfer their charging demand from the peak period to the off-peak one. A sensitivity analysis is performed for the total cost of problem with respect to value of incentive considering different PEV penetration levels and various social classes of drivers. Moreover, the value of error (due to the unrealistic modelling of drivers’ social class) in the optimal value of incentive, minimum total cost of problem, and generation scheduling and commitment of generation units is investigated.
Mehdi Rahmani-Andebili

Chapter 3. Spinning Reserve Capacity Provision by the Optimal Fleet Management of Plug-In Electric Vehicles Considering the Technical and Social Aspects

Abstract
In this chapter, the cooperation of plug-in electric vehicles (PEVs) and generation units in providing the spinning reserve capacity of power system is studied considering the technical and social aspects of problem. The objective function of problem is to minimize the total cost of problem by optimal fleet management (FM) of PEVs considering low, moderate, and high penetration levels for them. The drivers are stratified in three different social classes based on their income level including low-income, moderate-income, and high-income. The behavior of each social class of drivers is modeled based on the drivers’ reaction with respect to the value of incentive to provide the spinning reserve capacity and vehicle-to-grid (V2G) power in normal condition and emergency, respectively. A sensitivity analysis is performed for the problem operation cost with respect to the value of incentive for each social class of drivers considering different PEV penetration levels. Additionally, the effects of unrealistic modelling of drivers’ social class on the problem results are studied.
Mehdi Rahmani-Andebili

Chapter 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

Abstract
This chapter proposes a robust approach to study the optimal operation problem of a reconfigurable electrical distribution system while optimally managing the charging/discharging patterns of plug-in electric vehicle (PEV) fleet considering their technical, social, and geographical aspects. Herein, it is assumed that the electrical system is highly penetrated by the renewable energy sources (RESs), and the total daily energy generated by the RESs is adequate for the daily electricity demand of system; however, an effective approach is necessary to reliably and economically operate it. The electrical distribution network includes the electrical loads, RESs, energy storage systems (ESSs), switches installed on the electrical feeders, and PEVs with the capabilities of vehicle-to-grid (V2G) and grid-to-vehicle (G2V). In this study, the drivers are grouped in three different social classes based on their income level, that is, low-income, moderate-income, and high-income. The behavior of each social class of drivers is modelled based on the social and geographical aspects including the drivers’ distance from a charging station (CHS) and the value of incentive to provide the V2G and G2V services at the suggested CHS and recommended period. The proposed approach includes the stochastic model predictive control (MPC) that stochastically, adaptively, and dynamically solves the problem and handles the variability and uncertainties concerned with the probabilistic power of RESs and drivers’ behavior. The simulation results demonstrate that applying the proposed approach can remarkably decrease the minimum operation cost of problem and enhance the system reliability. It is shown that the behavior of drivers can affect the optimal configuration of system, optimal status of ESSs, and even optimal scheme of PEV fleet management (FM). It is proven that the application of proposed approach guarantees the robustness of problem outputs with respect to the prediction errors.
Mehdi Rahmani-Andebili

Chapter 5. Optimal Operation of a Plug-In Electric Vehicle Parking Lot in the Energy Market Considering the Technical, Social, and Geographical Aspects

Abstract
This chapter studies the optimal operation problem of a parking lot in the energy market modelling the drivers’ behavior based on the social and geographical factors including the drivers’ income level, the distance between the PEVs and parking lot, and the driving routes. The driving routes of plug-in electric vehicles (PEVs) are modelled considering the minimum and maximum traffic speed limits and the real latitude and longitude of area around Marina City vertical parking lot in Chicago, IL 60654. Herein, the parking lot is supplied by the renewable energy sources, and it has the capability of bilateral energy transaction with the energy market through the electrical distribution system and with the PEVs using the vehicle-to-grid (V2G) and grid-to-vehicle (G2V) services. In this study, the problem is formulated as a mixed integer linear programming (MILP) problem, and it is solved for different PEV types including Tesla Model S, Citroën C-Zero, Volkswagen e-Up, and Renault Kangoo Z.E., multiple PEV penetration levels, and diverse social classes of drivers. It is proven that the driver’s social class, the PEV’s type, and even the PEV penetration level can affect the problem outcomes. In other words, the unrealistic value of these parameters may have a significant impact on the maximum profit of parking lot, the optimal operation of energy sources, and the optimal value of incentive. Therefore, the parking lot owner is recommended to specify them before solving the problem to avoid achieving any misleading result.
Mehdi Rahmani-Andebili

Chapter 6. Optimal Placement and Sizing of Parking Lots for the Plug-In Electric Vehicles Considering the Technical, Social, and Geographical Aspects

Abstract
This chapter studies the planning problem of parking lot sizing and placement in the electrical distribution network considering the security constraints of the system and modelling the technical, social, and geographical aspects of the problem. In this study, the planning problem is investigated from the local distribution company’s (DISCO) point of view to minimize the total cost of problem during the planning period considering the economic factors such as inflation and interest rates. The cost terms of planning problem include the investment cost to install the parking lots in the system and equip them with the charging/discharging stations, the present worth value of maintenance cost of parking lots and their equipment during the planning period, the present worth value of incentives paid to the drivers to motivate them to provide vehicle-to-grid (V2G) service at suggested parking lots during the planning interval, the present worth value of energy loss cost of branches during the planning time horizon, and the present worth value of energy not supplied (ENS) cost due to the power outage during the planning period. Herein, the feeder’s failure rate (FFR) and the electricity consumer’s load (residential, commercial, and industrial), as the voltage-dependent load (VDL), are modelled. Moreover, the real driving routes of vehicles in San Francisco are considered. In this study, several scenarios are defined to study the effect of different social classes of drivers, PEV penetration levels, PEV types (Citroën C-Zero and Tesla Model S), FFR model, and VDL model on the problem outputs. It is demonstrated that the abovementioned parameters can affect the security level of system (voltage profile of buses and apparent power of branches), the optimal value of problem outputs (hourly location and size of parking lots and hourly value of incentive), the problem indices (energy loss, ENS, and reliability indices of system), and the value of objective function of planning problem (minimum value of total cost of planning problem). Furthermore, it is noticed that ignoring the real models of FFR and VDL can negatively influence the optimal value of problem outputs and result in the misleading and unpractical consequences.
Mehdi Rahmani-Andebili

Chapter 7. Estimating the State of Charge of Plug-In Electric Vehicle Fleet Applying Monte Carlo Markov Chain

Abstract
Modeling the drivers’ behavior, concerning their reaction with respect to charging management programs, is a key factor for the power system operators and plug-in electric vehicle (PEV) aggregators. In this regard, the state of charge (SOC) of PEV fleet is one of the important parameters that can affect the drivers’ behavior modeling and consequently the simulation results of planning and operation problems. In this chapter, Monte Carlo Markov Chain (MCMC) is applied to estimate the hourly SOC of PEV fleet in the day. MCMC, as the specific type of stochastic process, is a powerful method to analyze the scientific dataset and determine the probability distribution function of model parameters, by repeatedly applying the dataset. The dataset used in this study includes the real longitude and latitude of driving routes of PEVs in San Francisco, recorded in every 4-minute interval of the day. The position dataset is converted to the distances travelled by the PEVs, and then the hourly SOC of PEV fleet is determined applying the technical specifications of PEVs that include the initial SOC of PEV fleet, the energy consumption index of PEVs, and the capacity of PEVs’ batteries. After estimating the best-fit line of SOC of PEV fleet and the related confidence bands, the effects of problem parameters on the MCMC simulation results are studied.
Mehdi Rahmani-Andebili

Backmatter

Additional information