Optimal Operation of Virtual Power Plants with Energy Storage Based on Spot Market Profit and Evaluation
Under the background of energy transition and "dual carbon" goals in China, distributed new energy (DNE) and energy storage equipment's installed capacity has increased exponentially. As an efficient distributed resource management system, the virtual power plant has also garnered significant attention from domestic scholars and institutions. Extensive research has been conducted on a series of trading strategies for virtual power plants participating in the electricity spot market. With the expanding scale of virtual power plants involved in market-based trading, the operational evaluation of virtual power plants is set to become a key focus of attention. This paper proposes an operational optimization model aimed at enhancing the participation of generation-type virtual power plants with energy storage in the spot market and reducing prediction deviations. The model considers factors such as penalties for predicted power deviations, historical positive and negative power deviation of generation aggregation units, and energy storage charging and discharging efficiency. The paper analyzes and discusses the impacts under different evaluation intensities. When the system evaluation threshold is lowered and the unit deviation penalty coefficient is increased, the model effectively reduces assessment costs and improves the daily operational benefits in scenarios involving distributed virtual power plants with energy storage.