Charge management of plug-in electric vehicles for distribution transformer life enhancement

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.

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.

This paper investigates optimal operation management of plug-in electric vehicles (PEVs) in distribution systems. In this regard, the high penetration of electric vehicles (EVs) especially in the form of PEVs is another source of energy that can help the grid if optimally managed. In order to make the existence of PEVs to a suitable opportunity for the grid, vehicle-to-grid (V2G) technology is employed to change the PEVs from moving loads into moving sources. We also suggest a novel smart strategy to first manage the operation of PV and PEVs for providing the operator targets and second model the uncertainties generated by both PV and PEV in the system. The proposed method uses particle swarm optimization algorithm (PSO) along with the point estimate method (PEM) to deal with these issues. The 69-bus IEEE test system is employed as the case study to examine the high performance and ability of the proposed algorithm.

The paper aims at making online forecast of electrical load at the MV-LV transformer level. Optimal management of the Plug-in Electric Vehicles (PEV) charging requires the forecast of the electrical load for future hours. The forecasting module needs to be online (i.e update and make forecast for the future hours, every hour). The inputs to the predictor are historical electrical and weather data. Various data driven machine learning algorithms are compared to derive the most suitable model. The results indicate that an online forecasting method has an error between 2–5% for the future 24-hour. The decentralized management system works well with the forecasting data.

A novel parallel-series hybrid meta-heuristic method for solving a hybrid unit commitment problem

This paper proposes an application of an evolutionary game dynamics called the escort dynamics (ED) for the decentralized load management of plug-in electric vehicles (PEV). Different from earlier contributions, in the present approach, PEVs work together in a fair scheme in order to provide several ancillary services to the grid: Load shifting, active power balancing, and partial supply of reactive power demand on each phase of the distribution transformer. Meanwhile, batteries are guaranteed to be fully charged according to the constraints imposed by the owners. In the proposed formulation, chargers can be either three phase or single phase; however, in this paper, only three-phase chargers are considered. The key concepts behind ED, especially for escort functions, are provided at the beginning of this paper. Based on these concepts, the assumptions and analogies followed for the construction of the proposed approach are explained in detail, especially for the proposed definition of escort functions. A multipopulation scenario is proposed for the interaction of several PEVs using local ED routines. This interaction among populations follows another well-known evolutionary game dynamics called the best reply dynamics. Performance is evaluated using real data measured from a distribution transformer from the SOREA utility grid company in the region of Savoie, France.

The charging management of plug-in electric vehicles (PEVs) in San Francisco considering the effect of drag force on the vehicles, the real driving routes of vehicles, the social aspects of drivers' behaviour, the type of PEVs and the PEV penetration level is presented in this study. In this study, the drivers' responsiveness probability, to provide vehicle-to-grid service at the parking lot, is modelled with respect to the value of the incentive, drivers' social class and the real driving routes in San Francisco. Herein, the Monte Carlo Markov Chain is applied to estimate the hourly probability distribution function of the state of charge (SOC) of the PEV fleet in the day. The main data set applied in this study includes the real longitude and latitude of driving routes of vehicles in San Francisco, recorded in every four-minute interval of the day. In this study, a stochastic model predictive control is applied in the optimisation problem to address the variability and uncertainty issues of PEVs' SOC and renewables' power. Herein, quantum-inspired simulated annealing algorithm is applied as the optimisation technique. It is demonstrated that the type of PEVs, the PEV penetration level and even the social class of drivers can affect the problem results.

The orderly charging and discharging management of plug-in electric vehicles (PEV) can enrich the control means of power system. However, frequent state transformation of charging and discharging may affect the battery life. This paper proposes two PEV dispatch methods taking battery life into consideration. The first is to cluster and classify PEVs based on their charging urgency, and arrange their charging and discharging behaviors accordingly. Under the premise of meeting the travel demand of each PEV user, the second method continuously charges/discharges the PEVs until the required power of PEV charging stations is satisfied. Finally, numerical studies demonstrate the effectiveness of the presented methods.

New smart load management (SLM) approach for the coordination of multiple plug-in electric vehicle (PEV) chargers in distribution feeders is proposed. PEVs are growing in popularity as a low emission and efficient mode of transport against petroleum-based vehicles. PEV chargers represent sizeable and unpredictable loads, which can detrimentally impact the performance of distribution grids. Utilities are concerned about the potential overloads, stresses, voltage deviations and power losses that may occur in distribution systems from domestic PEV charging activity as well as from newly emerging charging stations. Therefore this study proposes a new SLM control strategy for coordinating PEV charging based on peak demand shaving, improving voltage profile and minimising power losses. Furthermore, the developed SLM approach takes into consideration the PEV owner preferred charging time zones based on a priority selection scheme. The impact of PEV charging stations and typical daily residential loading patterns are also considered. Simulation results are presented to demonstrate the significant performance improvement offered by SLM for a 1200 node test system topology consisting of several low-voltage residential networks populated with PEVs.

Decreasing initial costs, the increased availability of charging infrastructure and favorable policy measures have resulted in the recent surge in plug-in electric vehicle (PEV) ownerships. PEV adoption increases electricity consumption from the grid that could either exacerbate electricity supply shortages or smooth demand curves. The optimal coordination and commitment of power generation units while ensuring wider access of PEVs to the grid are, therefore, important to reduce the cost and environmental pollution from thermal power generation systems, and to transition to a smarter grid. However, flexible demand side management (DSM) considering the stochastic charging behavior of PEVs adds new challenges to the complex power system optimization, and makes existing mathematical approaches ineffective. In this research, a novel parallel competitive swarm optimization algorithm is developed for solving large-scale unit commitment (UC) problems with mixed-integer variables and multiple constraints – typically found in PEV integrated grids. The parallel optimization framework combines binary and real-valued competitive swarm optimizers for solving the UC problem and demand side management of PEVs simultaneously. Numerical case studies have been conducted with multiple scales of unit numbers and various demand side management strategies of plug-in electric vehicles. The results show superior performance of proposed parallel competitive swarm optimization based method in successfully solving the proposed complex optimization problem. The flexible demand side management strategies of plug-in electric vehicles have shown large potentials in bringing considerable economic benefit.

Distributed scheduling for multi-energy synergy system considering renewable energy generations and plug-in electric vehicles: A level-based coupled optimization method

An adaptive approach for distribution system reconfiguration and charging management of plug-in electric vehicles (PEV) is presented in this study. A stochastic model predictive control is applied to stochastically, adaptively, and dynamically reconfigure the system, manage the incidental charging pattern of PEVs, and deal with the variable and uncertain power of renewable energy sources. The objective function of problem is minimizing daily operation cost of system. Herein, the geography of area is considered and the behavior of PEVs' drivers (based on their income level) is modeled with respect to the value of incentive and their hourly distance from each charging station. It is shown that behavioral model of drivers is able to affect the optimal results of problem. The simulation results demonstrate the competence of the proposed approach for cost reduction and making the problem outputs robust with respect to prediction errors.

The rapid growth of rooftop photovoltaic (PV) arrays installed in residential houses leads to serious voltage quality problems in low voltage distribution networks (LVDNs). In this paper, a combined method using the battery energy management of plug-in electric vehicles (PEVs) and the active power curtailment of PV arrays is proposed to regulate voltage in LVDNs with high penetration level of PV resources. A distributed control strategy composed of two consensus algorithms is used to reach an effective utilization of limited storage capacity of PEV battery considering its power/capacity and state of charge. A consensus control algorithm is also developed to fairly share the required power curtailment among PVs during overvoltage periods. The main objective is to mitigate the voltage rise due to the reverse power flow and to compensate the voltage drop resulting from the peak load. Overall, the proposed algorithm contributes to a coordinated charging/discharging control of PEVs battery which provides a maximum utilization of available storage capacity throughout the network. In addition, the coordinated operation minimizes the required active power which is going to be curtailed from PV arrays. The effectiveness of the proposed control scheme is investigated on a typical three-phase four-wire LVDN in presence of PV resources and PEVs.

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.

Plug-in electric vehicle (PHEV) is emerging as most environmental friendly and widely used transportation system in developing countries. The integration of PHEV to existing grid infrastructure requires substantial modifications to be carried out in context of load scheduling, grid reliability, peak to average demand ratio (PAR) and energy cost. Recent studies are focussed to meet such requirements using multi-objective optimization methods. The optimization technique aims to minimize demand, energy cost and increase availability of PHEVs to charge or discharge by generating suitable scheduling vector. This paper proposes separate charging and discharging scheduling vector for fulfilling the utility aim to reduce PAR and also benefit the PHEV owner by reducing overall energy cost.