Robust Scheduling of Virtual Power Plant Under Exogenous and Endogenous Uncertainties

Virtual power plant (VPP) provides a flexible solution to distributed energy resources integration by aggregating renewable generation units, conventional plants, storages, and demands. This paper proposes novel stochastic adaptive robust optimization (SARO) model for determining the optimal self-scheduling plan VPP’s participation in day-ahead energy-reserve market. We consider exogenous uncertainties (or called decision-independent uncertainties, DIUs) associated with market clearing prices available wind generation, as well endogenous decision-dependent DDUs) pertaining real-time reserve deployment requests. A tractable methodology based on modified Benders dual decomposition is developed effectively solve proposed SARO both DIUs DDUs. Case studies are conducted verify efficiency applicability of approach. Comparative results show that method can mitigate conservatism strategy capturing satisfactory trade-off between profitability operation feasibility.