Dynamic economic dispatch of integrated energy system based on generative adversarial imitation learning

Within the context of energy structure transformation, the inherent uncertainty of new energy sources presents severe challenges to the optimized operation of integrated energy system. Model -driven optimization methods are limited by the accuracy and complexity of models, resulting in lower solving efficiency. Meanwhile, deep reinforcement learning methods that have gained attention in recent years face difficulties in designing rewards and slow convergence due to dealing with high -dimensional and complex dynamic information of integrated energy system. Therefore, inspired by imitation learning, this paper proposes a real-time dispatching method for integrated energy system based on generative adversarial imitation learning. Firstly, a generator network model is designed within the deep reinforcement learning framework, and expert prior knowledge is introduced to assist the generator network in iterative learning, enhancing the model 's convergence effect. Secondly, drawing on the generative adversarial concept, a discriminator network model is developed to recognize expert and generated strategies, further designing game rewards to assist in updating the generator network parameters and avoiding the impact of subjectively defined reward functions on dispatching decisions. Finally, simulation analysis demonstrates that the proposed method can calculate the operation optimization solutions for integrated energy system more efficiently, significantly improving the accuracy of dispatching decisions and the model 's convergence efficiency.