基于信息间隙决策理论的电动汽车聚合商日前需求响应调度策略

Day-ahead demand response scheduling strategy of an electric vehicle aggregator based on information gap decision theory

电动汽车用户对于需求侧调控的响应存在相当比例的不确定性,当电动汽车充电信息不完整时,高效处理充电行为不确定性问题的方法仍然较少。因此,采用信息间隙决策理论对用户响应不确定性进行精确建模,并量化评估了相应的机会收益和风险损失。首先,为了量化分析聚合商对电动汽车预测响应率与实际响应率之间的可接受偏差度,将聚合商确定性的成本决策问题转换为计及电动汽车响应不确定度的优化问题。其次,根据聚合商对于策略风险成本的接受程度,将调度模型分为乐观型和悲观型。基于信息间隙决策理论依据给定成本与预期成本的偏差分别生成对应的机会策略和鲁棒策略,不同风险偏好的聚合商可根据此算法结果采取相应的优化策略来保证期望收益。最后,仿真结果表明,在有限充电信息环境下,所提算法能有效应对电动汽车响应不确定性问题,降低电动汽车充电聚合商的总调度成本。

There is a lot of uncertainty in the response of electric vehicle(EV) users to demand-side regulation. When the charging information of EVs is incomplete, there are still comparatively few methods to efficiently deal with the uncertainty of charging behavior. Therefore, this paper introduces information gap decision theory(IGDT) to accurately model the user response uncertainty, and quantitatively evaluate the corresponding opportunity benefits and risk losses.First, in order to quantitatively analyze the acceptable deviation between the aggregator’s predicted response rate and the actual response rate of EVs, the determinism cost decision problem of the aggregator is transformed into an optimization problem that takes into account the uncertainty of the EV’s response. Second, according to the aggregator’s acceptance of the risk cost of the strategy, the scheduling model is divided into optimistic and pessimistic types. Based on the IGDT, the corresponding opportunity and robust strategies are respectively generated according to the deviation between the given and the expected cost. Aggregators of different risk acceptance can adopt corresponding optimization strategies based on the results of the proposed algorithm to ensure expected returns. Finally, the simulation results show that in a limited charging information environment, the proposed algorithm can effectively deal with the uncertainty of EVs’ response and reduce the total dispatch cost of EV charging aggregators.