考虑不确定性的高速公路光储充电站选址定容

针对高速公路并网光储充电站选址定容中存在的不确定性,提出了一种基于数据驱动的分布式鲁棒优化的两阶段选址定容方法.阶段1基于蒙特卡洛模拟方法得到充电点的位置,利用整数规划模型确定充电站的位置;阶段2兼顾充电站利益及用户满意度,建立基于数据驱动的分布式鲁棒优化定容模型,通过KL(Kullback-Leibler)散度构建以...

Siting and sizing of photovoltaic-storage charging stations on highway considering uncertainties

Aiming at the uncertainties in the siting and sizing of integrated photovoltaic-storage charging station on highway, a two-stage siting and sizing method based on data-driven DRO(Distributional Robust Optimization) is proposed. In the first stage, the location of charging point is obtained based on Monte Carlo simulation method, and the location of charging station is determined by using integer programming model. In the second stage, considering the profit of charging stations and the satisfaction of users, the sizing model based on data-driven DRO is established. A probability distribution fuzzy set centered on empirical probability distribution is constructed by KL(Kullback-Leibler) divergence, which is used to describe uncertainties. The DRO model is transformed into a mixed integer linear programming problem using risk theory, and then the number of charging piles in charging station and the capacity of photovoltaic-storage charging station are optimized. Finally, the proposed method is validated by a ring road network and its sensitivity is analyzed. The results show that the proposed method is feasible and effective, the average life cost can be reduced by configuring photovoltaic-storage equipment, and the economy and robustness of the system can be effectively balanced by controlling the KL divergence tolerance, sample number, endurance mileage, charging power, and so on.