Allocation optimization of electric vehicle charging station (EVCS) considering with charging satisfaction and distributed renewables integration

Highlights: • SD model is employed to explore the conduction mechanism of factors influential to EVCS allocation. • Using satisfaction to evaluate the k-means clustering performance of EV charging demand. • Charging demand and renewables are considered in the EVCS allocation plan comprehensively. • A multi-objective function is constructed and the best solution is calculated via GA-PSO. • Integrated methods provide useful approach for the power resources optimization in distribution network. Under the background of large-scale electric vehicle (EV) development, it is necessary to design and deploy the EVCS more scientific. Among various factors influential to the EVCS allocation, charging satisfaction and distributed renewables integration were mainly considered in this paper. First, with System Dynamics (SD) model, the key factors affecting the EVCS allocation were identified from the conduction mechanism. Then, focusing on the site selection of EVCS from the aspect of user satisfaction, k-means clustering method was used to illustrate the relationship between charging distance and satisfaction degree. On this basis, considering with renewables integration and stable operation of power system, the paper constructed a multi-objective function including voltage fluctuation, load fluctuation and connected capacity of energy storage in EVCS. Third, under the feeder framework of an IEEE 33-node, GA-PSO was employed to determine the best solution of EVCS allocation., i.e. the optimal allocation number of EVCS, the site and capacity of EVCS, and the access nodes of renewables and EVCS. Combing with the analysis results, suggestions from the aspects of technology standard, finance subsidy, land use support and energy management were proposed for accelerating the generalization of EVs and strengthening the supporting infrastructure construction.