STGCN- and IMOPSO-PSD-Based Optimization of Unit Operation Modes of Asynchronous Networking Sending-End Power Systems with High-Penetration Renewable Energy

To address the coordinated control need for optimizing clean power transmission and ensuring stable operation of asynchronous sending-end power systems with high-penetration renewable energy, this paper proposes a fast optimization method for unit operation modes based on spatio-temporal graph convolutional network (STGCN) and Improved Multi-Objective Particle Swarm Optimization–Power System Department Software (IMOPSO-PSD) method. First, a Unit Operation Mode Optimization (UOMO) model is established, which aims to maximize the DC transmission capacity and renewable energy accommodation capacity while minimizing the voltage support imbalance degree. Second, an STGCN optimization framework integrated with system operation security constraints and loss feedback of optimization objectives is designed, transforming the solution of UOMO model into the prediction of the optimal unit operation mode. Finally, a fast optimization process for unit operation modes based on STGCN and IMOPSO-PSD is presented, where the simplified IMOPSO-PSD is used to rapidly refine and verify the prediction results of the STGCN. Simulation results based on the modified IEEE 39-bus system show that the proposed method effectively integrates fast spatiotemporal feature extraction and prediction of STGCN with precise constraint verification of IMOPSO-PSD, thus ensuring the rationality and applicability of the optimization results for unit operation modes.