Real-time pricing method for VPP demand response based on PER-DDPG algorithm

Through ad vanced information communication and management system, virtual power plant (VPP) can realize the aggregation and coordination optimization of distributed energy, energy storage system, controllable load and other distributed energy resources. However, when making real-time price decisions according to users' demand response (DR) characteristics, the optimization effect of VPP is still limited by the evaluation accuracy of users’ DR potential and the computational burden of continuous decisions. By combining gate recurrent unit (GRU) and attention mechanism (AM), Neural Turing Machine (NTM) can extract users' response features in different environments and improve the accuracy of evaluating DR potential. Subsequently, based on the evaluation results, a deep deterministic policy gradient (DDPG) algorithm relying on prioritized experience replay (PER) is used to formulate a real-time electricity price plan. Ultimately, VPP achieves multi-objective optimization through DR management, which helps to increase the consumption amount of renewable energy resources, smooth its power fluctuation, and reduce users' electricity cost. Case study results show that the proposed method can improve the accuracy of the DR potential evaluation, reduce the response deviation to about 3%, and enhance the real-time decision calculation efficiency by 17%, which helps to optimize the smooth consumption of renewable energy.