考虑电动汽车可调度潜力的充电站两阶段市场投标策略

在电力市场环境下,充电站优化投标策略能降低电力成本,甚至通过售电获取收益.文中考虑了电动汽车成为柔性储荷资源的潜力,提出了日前电力市场和实时电力市场下充电站的投标策略.首先,基于闵可夫斯基加法提出了充电站内电动汽车集群模型的压缩方法,并建立了日前可调度潜力预测模型和实时可调度潜力评估模型.同时,考虑充电站间的非合作博弈,建立了电力零售市场下充电站的策略投标模型,并基于驻点法将其转化为一个广义Nash均衡问题.然后,提出了基于日前报价和实时报量的两阶段市场交易模式,并与合作投标模式、价格接受模式和集中调度模式进行对比.最后,基于一个38节点配电系统进行了仿真.仿真结果表明所提出的可调度潜力计算方法能够将电动汽车集群封装为广义储能设备,从而降低了模型的维度.基于可调度潜力的策略投标模型能够挖掘电动汽车的储荷潜力,实现电动汽车与电网的有序互动.

Two-stage Market Bidding Strategy of Charging Station Considering Schedulable Potential Capacity of Electric Vehicle

In the electricity market environment, charging stations (CSs) can reduce the cost of electricity by optimizing the bidding strategy, even gain profits by selling electricity. In this paper, the potential of electric vehicles (EVs) to become flexible storage and load resources is considered, and the bidding strategy of CSs in the day-ahead market and real-time market is proposed. First, the compression method of the EV clustering model in CSs is proposed based on Minkowski method. The prediction model of day-ahead schedulable potential capacity and the evaluation model of real-time schedulable potential capacity are established. Meanwhile, the strategic bidding model of CS in the electricity retail market is established considering the non-cooperative game among CSs. The model is transformed into a generalized Nash equilibrium problem based on the stationary point method. Then, the two-stage market trading mode is proposed based on day-ahead quotation and real-time declared quantity, which is compared with the cooperative bidding mode, the exogenous price mode and the centralized scheduling mode. Finally, the simulation on a 38-bus distribution system shows that the proposed calculation method of schedulable potential capacity can encapsulate the EV cluster as a generalized energy storage device, which can reduce the dimensions of the model. The strategic bidding model based on the schedulable potential can tap the storage and load potential of EVs and realize the orderly interaction between EVs and power grids.