Research on the Optimization Strategy of Virtual Power Plants in the Electricity Spot Market

Distributed energy sources, characterized by their small capacity and geographically dispersed locations, are unable to connect directly to the power grid. Virtual power plants, acting as independent control entities, can aggregate various types of distributed energy sources to participate in electricity market transactions. Under the environment of an electricity spot market, the control strategy of virtual power plants significantly affects their earnings from participating in multi-variety trading. Accordingly, this study proposes an optimized control strategy for virtual power plants participating in multi-variety trading in an electricity spot market context. Initially, the control process for a virtual power plant engaging in multi-variety trading is delineated. Subsequently, scenario planning and Conditional Value at Risk (CVaR) are utilized to address the impacts of uncertainties in wind turbine units, loads, electricity prices, and frequency regulation signals. Building on this, an optimized control model for virtual power plants in an electricity spot market environment is further proposed, using the branch and bound algorithm to solve the model and determine the optimal control strategy. Finally, the model is validated through simulation. The results demonstrate that the proposed optimized control strategy for virtual power plants not only enhances their own economic benefits and promotes the consumption of renewable energy but also alleviates the system's peak-shaving pressure, contributing to the safe and stable operation of the system.