Joint Optimal Allocation Of Distributed Generation And Electric Vehicle Charging Stations Considering Spatial-Temporal Characteristics

Distributed generation(DG)can reduce carbon emissions and energy transmission losses,while the popularity of electric vehicles(EV)can reduce dependence on traditional energy and improve energy efficiency.Therefore,the development of DG and EV can help topromote energy transformation and the development of a low-carbon economy.However,thelarge-scale DG and EV are connected to the distribution network,which affects the safe and stable operation of the system.Therefore,this paper studies the optimal allocation of DG and EV charging stations,which reduces system load fluctuations,improves intermittent DG consumption levels,and meets the interests of multiple stakeholders.For this study,the main work of this paper is as follows:(1)Monte Carlo random sampling is used to process probability density functions of wind speed,light intensity,and conventional load to generate multiple scenarios.Consideringthe seasonal characteristics and randomness of intermittent DG and conventional loads,thepseudo-F-statistics(FPS)index is introduced to improve the Kmeans clustering analysis algorithm for scenario reduction.This study generates typical operating scenarios of wind-photovoltaicgenerationand conventional loads and analyzesscenario characteristics to ensure the uncertainty and effectiveness of typical operating scenarios.(2)A method based on the Origin-Destination(OD)probability matrix is proposed for forecastingthe spatial-temporal distribution of Electric Vehicle(EV)charging loads,considering multiple sources of information such as traffic network and temperature.The dynamic traffic network model is analyzedtoproposethe "speed-flow" model.The OD probability matrix and Floyd algorithm are used to simulate driving paths to ensure the shortest travel time for users.EV users choose charging modes and start times through smartcharging strategies,and Monte Carlo is used to forecastthe spatio-temporal distribution of EV charging demands.(3)From the perspective of multi-stakeholder interests,this paper takes the operational revenue of the operator,the load fluctuation of the distribution grid,and user queueing time as objective functions.On this basis,the multi-objective joint optimization allocation model for intermittent DG and EV charging stations is constructed considering constraints such as DG and EV charging station installation capacity and the service range of charging station.Chaotic particle swarm algorithm is used to solve the joint planning problem to obtain the optimal location and capacity under the set parameters in the planning area.The multistakeholder interests under different scenarios are analyzed,and the effectiveness of the joint optimization allocationmodel is verified by combining with the IEEE-33 node distribution system.