光储充电站, 信息间隙决策理论, 不确定性调度, 储能系统

Aiming at the scheduling optimization problem caused by the strong randomness and intermittence of photovoltaic output in optical storage charging stations, the shortcomings of traditional methods relying on probability distribution assumptions and the limited model generalization ability, an uncertain scheduling optimization method is proposed based on information gap decision theory. The multi鄄unit collaborative architecture, the operation model of optical storage and the charging station are established. Based on the information gap decision theory, the risk aversion strategy is constructed. Taking the maximum daily net income as the objective function, the multiple constraints of power balance, energy storage capacity, power grid interaction,electric vehicle charging and discharging are integrated to construct the uncertainty schedulingoptimization model. The performance of the model is verified by example analysis. The experimental results show that the proposed method can balance the economy and robustness of the system through the adjustment of risk tolerance. When the photovoltaic output is surplus, the dynamic charging and discharging of energy storage can

be matched with the load optimization to reduce the dependence of power purchase and improve the consumption rate of renewable energy.

photovoltaic-storage-charging station,  information gap decision theory, uncertainty scheduling, energy storage system