Day-Ahead Scheduling For An Electric Vehicle PV-Based Battery Swapping Station Considering The Dual Uncertainties

The facilities to realize the battery swapping mode usually occupy a large area.If large-scale photovoltaic(PV)power generation can be arranged according to local conditions,the proportion of clean energy in the primary energy of EVs can be more effectively increased,and the power supply burden of the utility grid can be reasonably reduced.However,in the problem of day-ahead scheduling for an electric vehicle(EV)PV-based battery swapping station(BSS),the complexity lies in the great uncertainties of both the PV output on the source side and the swapping demand on the charge side.Most studies have used the typical cases of uncertain factors to represent uncertainties,such as short-term forecasting,Monte Carlo simulation and multi-scene technology.One major drawback of these methods is that the timing combination of the actual values of uncertain factors inevitably deviates from the above typical cases,thus affecting the economy and applicability of day-ahead scheduling results in actual operation.In order to solve the above problems,a day-ahead economic scheduling method based on chance-constrained programming and probabilistic sequence operation is proposed in this paper for an EV BSS,considering the dual uncertainties of swapping demand and PV generation.The details are as follows:First of all,a BSS day-ahead scheduling model that can deal with the uncertainties is established by using the chance-constrained programming.The optimization objective is to minimize the cost of electricity purchased from the utility grid with the chance constraints of swapping demand satisfaction and the confidence level of the minimum cost.Then,the deterministic transformation of chance constraints is implemented based on probabilistic sequences of stochastic variables.Thereafter,the feasible solution space of the proposed model is determined based on the battery controllable load margin,and then the fast optimization method for the BSS day-ahead scheduling model is developed by combining the feasible solution space and genetic algorithm(GA).In order to evaluate the solution quality,a risk assessment method based on the probabilistic sequence for day-ahead scheduling solutions is proposed.Finally,the efficiency and applicability of the proposed method is verified through the comparative analysis on a PV-based BSS system.Results illustrate that the model can provides a more reasonable charging strategy for the BSS operators with different risk appetite.