Optimization Model for Virtual Power Plant Electricity Market Transactions

Regarding achieving coordinated control and energy management of various distributed resources, flexible control of many distributed power sources can be achieved through VPP(virtual power plants). However, the current electricity market trading models cannot adapt to the smooth development of VPP transactions. Therefore, this article proposes a VPP electricity market trading optimization model, which mainly optimizes the model from two aspects: load forecasting and model solving, This article firstly proposes a prediction technology for wind and solar power output based on BP neural network algorithm for predicting their output results. Then, based on the predicted outputs, the trading process of the electricity market is modeled to maximize wind power participation and minimize operating costs as the objective function, supplemented by constraints from various electricity markets, and solved using simulated annealing algorithm.