Multi-Source Optimized Cooperative Control for Secondary Frequency Regulation Based on GOOSE Algorithm

Amidst the robust advancement of new energy generation technologies, wind power and photovoltaic (PV) power are progressively claiming a larger share within the power grid. Concurrently, these emerging energy sources are actively engaging in grid frequency regulation. To tackle the issue of dynamic power allocation among various types of frequency regulation sources during power fluctuations, this study devises a multi-source optimal coordinated control model. It takes into account the participation of wind, solar, and energy storage systems. Addressing this challenge, the study employs the GOOSE algorithm from artificial intelligence for problem-solving. This algorithm efficiently facilitates the participation of different types of regulation sources in power distribution within their respective regulation ranges, thereby bolstering the dynamic response regulation of the regional power grid and enhancing system stability. Finally, the study validates the constructed model using an extended IEEE standard 2-area model and assesses the algorithm's performance by juxtaposing it with traditional engineering allocation methods and intelligent optimization algorithms.