Rolling optimization method of virtual power plant demand response based on Bayesian Stackelberg game

To optimize the interaction effect between internal units and demand response of virtual power plants and enhance their transaction profit, a study on the the rolling optimization method of demand response for virtual power plants based on Bayesian Stackelberg game is conducted. Following the construction of a virtual power plant model and analysis of its operation strategy and process content, this method employs a power demand forecasting approach based on multidimensional fusion and Bayesian probability update to forecast the demand-side power requirements within the jurisdiction of the virtual power plant. Utilizing the forecast results of dynamic electricity demand, a demand response elastic matrix for virtual power plant is constructed through a rolling optimization model based on Stackelberg game. The two optimization objective functions, maximizing the supply-side income and minimizing the demand-side electricity purchase cost of virtual power plant, are transformed into maximizing the profit of power transaction for the virtual power plant. This is iteratively solved using the whale algorithm to determine the optimal power generation distribution scheme for each unit on both the supply side and demand sides. Upon testing, this method demonstrates not only the capability for peak shaving and valley filling but also improves the operating profit of the virtual power plant and optimizes user satisfaction, resulting in a relatively high comprehensive benefit index.