Abstract
A day-ahead economic scheduling method based on chance-constrained programming and probabilistic sequence operation is proposed in this paper for an electric vehicle (EV) battery swapping station (BSS), considering the dual uncertainties of swapping demand and photovoltaic (PV) generation. 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.
Highlights
As an important solution to urban environmental problems, electric vehicles (EVs) have attracted extensive attention from governments, academia and industry worldwide in recent years [1], and been vigorously promoted
For the problem of solving the battery swapping station (BSS) scheduling model, the algorithm used to solve this problem is compared in [14], and the results show the superiority of genetic algorithm (GA) in solving this type of problem
According to the above literature review, the feasible solution space to the day-ahead scheduling can be constructed by using the battery load margin, and combined with GA to achieve optimal operation and high efficiency
Summary
As an important solution to urban environmental problems, electric vehicles (EVs) have attracted extensive attention from governments, academia and industry worldwide in recent years [1], and been vigorously promoted. According to the above literature review, the feasible solution space to the day-ahead scheduling can be constructed by using the battery load margin, and combined with GA to achieve optimal operation and high efficiency. (3) By using the controllable load margin of batteries in the BSS proposed in [17] to determine the feasible solution space, a fast optimization method for the BSS day-ahead scheduling model is proposed. The chance-constrained programming could well describe the uncertainty of stochastic variables so that the decision can realize the economic operation of the BSS at the given confidence level. B. OBJECTIVE FUNCTION In order to minimize the cost of electricity purchased from the utility grid, the uncertainties of swapping demand and PV power generation during the operation of the BSS are considered when the initial charging time of batteries is optimally arranged to achieve the most economic operation of the BSS. PPV (t) indicates the PV output power in the t-th period, which is a stochastic variable; p (t) indicates the grid time-of-use (TOU) price
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