基于CNN的产消群需求响应滚动优化策略

CNN-based Rolling Optimization Strategy for Prosumer Group in Demand Response

随着分布式光伏的普及,具有负荷电源双重属性的产消者大量出现。在电力现货市场的环境下,首先,基于Stackelberg模型分析产消者以及售电商在价格型需求响应中的决策机理,提出基于卷积神经网络(convolutional neural networks,CNN)的产消群决策行为预测模型,实现对产消群负荷调整及反弹量的精准预测。然后,充分考虑负荷反弹现象对于价格型需求响应的影响,提出考虑负荷反弹的滚动优化策略,提升售电商收益的同时,降低产消群负荷不平衡量,并促进分布式光伏的就地消纳。仿真实验表明,所提产消者决策行为预测模型的预测准确度在99%以上,且考虑了负荷反弹现象的滚动优化策略能够将产消群可再生能源就地消纳率提升5%以上,同时,相较于分时电价以及不考虑负荷反弹的实时电价,售电商的收益分别提升了118.8%和15.1%。

With the massive installation of distributed photovoltaics, a large number of prosumers with dual attributes of load and power supply have appeared. In the environment of electric power spot market, the decision mechanism of prosumers and electricity sellers in the price-based demand response are analyzed based on the Stackelberg model, and a convolutional neural networks(CNN)-based prosumer group decision behavior prediction model is proposed to achieve accurate prediction of load adjustment and rebound of prosumers. Furthermore, with full consideration of the impact of the load rebound phenomenon on the price-based demand response, a rolling optimization strategy considering load rebound is proposed to increase the revenue of electricity sellers, reduce the load imbalance of prosumers, and promote the local consumption of distributed photovoltaics. Simulation experiments show that the prediction accuracy of the prosumers decision behavior prediction model proposed in this paper is more than 99%, and the rolling optimization strategy considering the load rebound phenomenon can increase the prosumers’ local consumption rate of renewable energy by more than 5%. At the same time, compared to the time-of-use electricity prices and the real-time electricity prices that do not take load rebound into consideration, the revenue of electricity sellers can increase by 118.8% and 15.1%, respectively.