So far, the day-ahead scheduling research of photovoltaic (PV)-based battery swapping stations (BSSs) has not fully considered the uncertainties of swapping demand and PV output. To address this issue, a day-ahead economic scheduling method based on chance-constrained programming and probabilistic sequence operation is proposed in this paper. First of all, a BSS day-ahead scheduling model with chance constraints as swapping demand satisfaction and the confidence level of the minimum cost is established. The confidence level of chance constraints is set by BSS operators. Then, probabilistic sequences of stochastic variables are constructed, and the quantitative index to measure the day-ahead scheduling risk of BSS is proposed based on sequence operation. Thereafter, the feasible solution space 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). Finally, the validity and applicability of the proposed method is verified in the case study.
Keywords PV-based battery swapping station, day-ahead scheduling, chance-constrained programming, uncertainties, probabilistic sequence