Optimal Scheduling of PEV Charging/Discharging in Microgrids with Combined Objectives

While renewable power generation and vehicle electrification are promising solutions to reduce greenhouse gas emissions, it faces great challenges to effectively integrate them in a power grid. The weather-dependent power generation of renewable energy sources, such as Photovoltaic (PV) arrays, could introduce significant intermittency to a power grid. Meanwhile, uncontrolled PEV charging may cause load surge in a power grid. This paper studies the optimization of PEV charging/discharging scheduling to reduce customer cost and improve grid performance. Optimization algorithms are developed for three cases: 1) minimize cost, 2) minimize power deviation from a pre-defined power profile, and 3) combine objective functions in 1) and 2). A Microgrid with PV arrays, bi-directional PEV charging stations, and a commercial building is used in this study. The bi-directional power from/to PEVs provides the opportunity of using PEVs to reduce the intermittency of PV power generation and the peak load of the Microgrid. Simulation has been performed for all three cases and the simulation results show that the presented optimization algorithms can meet defined objectives.

Deep Reinforcement Learning Based Charging Scheduling for Household Electric Vehicles in Active Distribution Network

With the booming of electric vehicles(EVs) across the world, their increasing charging demands pose challenges to urban distribution networks. Particularly, due to the further implementation of time-of-use prices, the charging behaviors of household EVs are concentrated on low-cost periods, thus generating new load peaks and affecting the secure operation of the medium-and low-voltage grids. This problem is particularly acute in many old communities with relatively poor electricity infrastructure. In this paper, a novel two-stage charging scheduling scheme based on deep reinforcement learning is proposed to improve the power quality and achieve optimal charging scheduling of household EVs simultaneously in active distribution network(ADN) during valley period. In the first stage, the optimal charging profiles of charging stations are determined by solving the optimal power flow with the objective of eliminating peak-valley load differences. In the second stage, an intelligent agent based on proximal policy optimization algorithm is developed to dispatch the household EVs sequentially within the low-cost period considering their discrete nature of arrival. Through powerful approximation of neural network, the challenge of imperfect knowledge is tackled effectively during the charging scheduling process. Finally, numerical results demonstrate that the proposed scheme exhibits great improvement in relieving peak-valley differences as well as improving voltage quality in the ADN.

Multi-Agent System for Electric Vehicle Charging Scheduling in Parking Lots

As the number of electric vehicles(EVs)increases,massive numbers of EVs have started to gather in commercial parking lots to charge and discharge,which may significantly impact the operation of the grid.There may also be a deviation in the departure time of charged and discharged EVs in commercial parking lots.This deviation can lead to insufficient battery energy when the EVs leave the parking lot.This study uses the simulation software AnyLogic to build a commercial parking lot multi-agent simulation model,and the agent-based model can fully reflect the autonomy of individual EVs.Based on this simulation model,we propose an EV scheduling algorithm.The algorithm contains two main agents.The first is the power distribution center agent(PDCA),which is used to coordinate the energy output of photovoltaic(PV),energy storage system(ESS),and distribution station(DS)to solve the problem of grid overload.The second is the scheduling center agent(SCA),which is used to solve the insufficient battery energy problem due to EVs’random departures.The SCA includes two stages.In the first stage,a priority scheduling algorithm is proposed to emphasize the fairness of EV charging.In the second stage,a genetic algorithm is used to accurately determine the time interval between charging and discharging to ensure the maximum benefit of EV owner.Finally,simulation experiments are conducted in AnyLogic,and the results demonstrate the superiority of the algorithm over the classical algorithm.

Optimal temporal-spatial PEV charging scheduling in active power distribution networks

Background:The increasing penetration of a massive number of plug-in electric vehicles(PEVs)and distributed generators(DGs)into current power distribution networks imposes obvious challenges on power distribution network operation.Methods:This paper presents an optimal temporal-spatial scheduling strategy of PEV charging demand in the presence of DGs.The solution is designed to ensure the reliable and secure operation of the active power distribution networks,the randomness introduced by PEVs and DGs can be managed through the appropriate scheduling of the PEV charging demand,as the PEVs can be considered as mobile energy storage units.Results:As a result,the charging demands of PEVs are optimally scheduled temporally and spatially,which can improve the DG utilization efficiency as well as reduce the charging cost under real-time pricing(RTP).Conclusions:The proposed scheduling strategy is evaluated through a series of simulations and the numerical results demonstrate the effectiveness and the benefits of the proposed solution.

Battery electric buses charging schedule optimization considering time-of-use electricity price

Purpose–This paper aims to optimize the charging schedule for battery electric buses(BEBs)to minimize the charging cost considering the time-ofuse electricity price.Design/methodology/approach–The BEBs charging schedule optimization problem is formulated as a mixed-integer linear programming model.The objective is to minimize the total charging cost of the BEB fleet.The charge decision of each BEB at the end of each trip is to be determined.Two types of constraints are adopted to ensure that the charging schedule meets the operational requirements of the BEB fleet and that the number of charging piles can meet the demand of the charging schedule.Findings–This paper conducts numerical cases to validate the effect of the proposed model based on the actual timetable and charging data of a bus line.The results show that the total charge cost with the optimized charging schedule is 15.56%lower than the actual total charge cost under given conditions.The results also suggest that increasing the number of charging piles can reduce the charging cost to some extent,which can provide a reference for planning the number of charging piles.Originality/value–Considering time-of-use electricity price in the BEBs charging schedule will not only reduce the operation cost of electric transit but also make the best use of electricity resources.