Robust Economic Dispatch Approach for the Multi-Energy Virtual Power Plant Considering Multiple Uncertainties

Virtual power plants (VPPs) have become a popular way of managing flexible loads and distributed energy resources in recent years. To confront multiple uncertainties caused by flexible resource prediction and response, this paper proposes a two-stage economic dispatch approach for the multi-energy virtual power plant, which includes a robust economic pre-scheduling process and an online parameter self-tuning process. The robust optimization scheduling model accounts for prediction uncertainty in renewable energy generation and load demand, using the column constraint generation algorithm to solve the scheduling problem for VPPs under varying robustness coefficients and prediction biases. Meanwhile, the online parameter calibration model addresses deviations in flexible resource response scheduling instructions, which employs a quantum genetic algorithm for parameter identification and online self-tuning. The accuracy and effectiveness of the proposed model are verified through numerical simulations. Numerical simulation results demonstrate the superiority of the proposed approach in terms of reducing the balancing costs caused by parameter bias and improving the VPP operation efficiency and economy.