Multi-objective Robust Optimal Dispatch of Virtual Power Plant Considering Source-Load Uncertainty

In order to solve the power redistribution problem of internal participants in Virtual Power Plant (VPP), a robust multi-objective day-ahead optimal scheduling model of virtual power plant considering source-load uncertainty is established. In the model, the adjustable set of uncertainties is used to describe the wind power, electricity load and heat load, and the operating cost and carbon emission are used as the objective functions of multi-objective optimal scheduling. Dual theory is applied to transform a multi-objective robust model into a multi-objective deterministic model. In order to better obtain the Pareto frontier solution, the augmented e-constraint method is used to reconstruct the multi-objective optimization into a single-objective optimization problem. The CRITIC-TOPSIS scheme comprehensive decision method is used to obtain the ideal solution of the Pareto solution set. The results show the correctness of the model and the effectiveness of the multi-objective problem solving method.