Optimization of Primary Frequency Regulation for Virtual Power Plants Based on Data-Driven and Grey Wolf Optimizer

Renewable energy generation units, compared to traditional units, have the characteristics of smaller individual capacities, a large number of cluster units, and a wide geographical distribution. The research first utilizes a data-driven methodology to analyze the operational data of the Virtual Power Plant (VPP), extracting critical features. Next, with a focus on maintaining frequency stability, the study incorporates constraints such as the rate of frequency change and the maximum allowable frequency deviation. The objective of the primary frequency regulation optimization model is to minimize the frequency security index, and the Grey Wolf Optimizer (GWO) is utilized for the solution. The Virtual Power Plant (VPP) can aggregate diverse entities across different spaces into a pseudo-power plant scheduling and control unit through modern communication and control technologies, which is an effective method to solve the challenges of large scale new energy integration into the power grid's regulation and operation. Distributed nergy esources, through participation in the auxiliary services market of the, Maximum shaving Frequency control, providing additional services to support the power grid in exchange for financial gain, achieving a win-win situation for the power grid and distributed energy resource users. Finally, the WSCC-9 system is utilized as a case study to validate the simulations, ensuring frequency stability is a key consideration, further supported by the validation results obtained. the primary frequency control of the is realized, resulting in enhancements to its grid-friendly characteristics and providing increased frequency support to the power grid.