面 向 需 求 响 应 的 数 据 驱 动 零 售 电 价 定 价 策 略

Demand response aims at shifting peak load to valley load and improving the load profile, so as to enhance the safety and economy of power system operation. Reasonable electricity retail pricing enables to guide users to adjust their energy consumption behaviors, thus facilitating demand response. In this context, an energy consumption behavior learning model and a customized retail pricing strategy are presented. Firstly, characteristics of energy consumption behaviors are analyzed, and long short-term memory networks based on multiple attention mechanisms are exploited to extract feature correlations and construct the energy consumption behavior learning model. Secondly, considering the price risk, a customized retail pricing model is proposed with the goal of fully developing users’demand response potential. Case simulation results demonstrate the superiority of the proposed learning model compared with traditional deep learning models, and the proposed customized retail pricing strategy is capable of developing time-varying demand response potentials while it is effective to hedge the profit risk for the retailer and the cost risk of electricity purchase for the user.