Multi-Time Scale Optimization Strategy for Virtual Power Plant Based on Reinforcement Learning and Flexible Ramp Product

The integration of large-scale renewable energy resources introduces volatility, posing heightened demands for flexible operation of power systems. Virtual Power Plant (VPP), by aggregating distributed resources, incorporate flexible ramping product (FRP) into their optimization strategies to reduce operational costs. Therefore, this paper proposes a multitimescale optimization strategy for VPP based on FRP. Firstly, a model for VPP participation in FRP market is established, considering operational characteristics of distributed resources. Secondly, scenario generation and reduction techniques are employed to address the uncertainties associated with wind power, photovoltaic generation, and load, as same as both pricebased and incentive-based demand response (DR) strategies are proposed. Finally, considering FRP and DR, a multi-timescale rolling optimization model for the VPP is constructed, and a mixed integer programming-deep Q network (MIP-DQN) is employed to solve the model. Case study results demonstrate that the proposed VPP optimization strategy exhibits excellent economic performance and flexibility.