Robust Optimal Scheduling Strategy for Virtual Power Plant Participation in Electric Energy and Demand Response Markets Under Multiple Uncertainties

ObjectivesTo address the uncertainties of renewable energy output and load faced by virtual power plant (VPP) when participating in electric energy and demand response markets, a robust optimal scheduling strategy considering multiple uncertainties was proposed to reduce the conservativeness of robust optimization and improve the economic benefits of VPP.MethodsA polyhedral uncertainty set based on conditional value at risk (CVaR) was constructed. On this basis, considering the uncertainties of wind power, photovoltaic output and load, a day-ahead two-stage robust optimization model of VPP participating in electric energy and demand response markets was established. Then, using a column-and-constraint generation (C&CG) algorithm and Lagrangian dual theory, the model was divided into a master problem and a sub-problem that can be solved by a solver. Finally, Monte Carlo method was used to generate a large number of wind power, photovoltaic and load data. The proposed strategy was simulated and analyzed, and compared with the optimization results of other schemes.ResultsThe proposed strategy adopting a polyhedral uncertainty set based on CVaR can make full use of historical data. Compared with the scheme using traditional uncertainty set, the total cost of VPP is reduced by about 2%.ConclusionsThe proposed strategy can significantly reduce the conservativeness of robust optimization results and enhance the economy of VPP participation in the market under multiple uncertainties.