Adaptive Chaotic Gray Wolf Optimizer-Based Optimization of Decentralized AGC and Power Dispatching Controllers for Integrated Energy System with Heterogeneous Power Sources

With the increasing use of renewable energy sources connected to inverters in modern power systems, traditional units’ rotary inertia and frequency regulation capacity are becoming inadequate. Therefore, exploring various types of frequency regulation resources is essential. However, these resources come with different system models, capacities, and response speeds, posing a significant challenge to automatic generation control (AGC). To address this issue and enhance the frequency regulation performance of these resources, a novel distributed coordination AGC method is proposed. The proposed method allows each frequency regulation unit to utilize a separate load frequency control (LFC) controller to participate in frequency regulation based on the area control error information calculated by the dispatching center. To ensure the coordination between the heterogeneous frequency regulation resources, an adaptive chaotic gray wolf algorithm is proposed to tune the parameters of the LFC controller. Furthermore, to release the fast frequency regulation ability of high-speed frequency regulation units and better prepare for the next round of frequency regulation service, an event-triggered power dispatching strategy is proposed. Simulation results of a single-area power system with five different frequency regulation units demonstrate the superior performance of the proposed AGC method.