Abstract: 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.

Key words: prosumer, demand response, CNN, rolling optimization, genetic algorithm