Distributionally Robust Chance-Constrained Hydropower-Based Virtual Power Plants

Virtual power plants (VPPs) offer a versatile framework for integrating distributed energy resources by coordinating renewable generation, conventional units, energy storage systems, and flexible demand. In this study, we develop a distributionally robust optimization model to determine the optimal self-scheduling strategy for VPPs in the day-ahead energy-reserve market. To better manage the uncertainty of renewable generation, dispatchable hydropower is incorporated into the scheduling framework, and model robustness is further reinforced through a distributionally robust chance-constrained optimization approach. Numerical case studies are carried out to evaluate the performance, which demonstrates that the proposed method effectively mitigates the impact of wind uncertainty on optimal scheduling cost and enhances the robustness of the model.