Day-Ahead Economic Scheduling of a Virtual Power Plant with the OAPSO Algorithm

The direct integration of renewable energy into the power grid currently faces challenges related to stability and economic viability. Through the integration of a virtual power plant, the impact on the grid can be mitigated. Therefore, the objective is to minimize the overall operating cost after system integration. This involves predicting the output of new energy sources and loads for the next 24 hours, taking into account variations in electricity prices on the grid at different times. An adaptive particle swarm optimization algorithm based on opposition-based learning (OAPSO) is employed for the coordination of wind, solar, energy storage, and demand response in the dispatching plan. The calculation results show that wind, solar, storage and demand response are both involved in power supply, which can reduce the operating cost by 15.81% compared with the main network power supply.