Source-storage interaction optimization and scheduling based on centralized control virtual power plant

In view of the coordinated control and scheduling of distributed energy, such as controllable units, energy storage devices and fans in distribution networks, a virtual power plant source and load storage optimization scheduling model with uncertainties, demand response and network security constraints under centralized control is proposed. Firstly, the wind turbine, gas turbine, energy storage battery and load are integrated into a virtual power plant, and price-type demand response measures are implemented on the user side to improve the load curve. Then, aiming at maximizing the operating profit of the virtual power plant, the opportunity constraint model is used to describe the uncertainty of the wind turbine, load forecasting and internal power balance, and consider the operational constraints and network security constraints of each unit. The scheduling scheme is generated based on the reasonable control and coordination of the output of each component. Finally, the quantum particle swarm optimization algorithm with inertia weight adaptive adjustment is used to solve the model. The feasibility of the model and the effectiveness of the algorithm are verified by an example analysis of the IEEE 9 node improved system.