Research on Collaborative Optimization Method of Multi-Area Integrated Energy System with High Proportion of New Energy Access

This research introduces an algorithm for cooperative management in interconnected multi-regional energy networks, integrating rapid Q-learning with simulated annealing techniques to achieve enhanced automatic power control. The former enhances the convergence speed of the agent through an accelerated learning rate, while the latter addresses local optimization challenges using the principles of simulated annealing. Moreover, this algorithm is designed with a thorough consideration of the characteristics of renewable energy, aiming to enhance the efficiency and sustainability of energy management. Simulation results based on a multi-region load frequency model of a power grid indicate that this approach demonstrates superior performance in achieving optimal cooperative control across multiple regions, offering enhanced control capabilities and faster convergence compared to other reinforcement learning methods, thus providing robust support for large-scale integration of renewable energy sources.