Optimal Scheduling of Regional Integrated Energy System Based on Advantage Learning Soft Actor-critic Algorithm and Transfer Learning

In order to improve the consumption rate of clean energy and reduce the pollution of carbon emissions to the environment, and to achieve a more generalized, robust and efficient regional integrated energy system optimal scheduling, this paper proposes an optimal scheduling of regional integrated energy system based on advantage learning soft actor-critic (ALSAC) algorithm and transfer learning.Using environmental information to communicate and interact with agents, the regional comprehensive energy system is dispatched and optimized for the purpose of low carbon and economy. In this paper, the maximum entropy mechanism for improving the robustness of soft actor-critic (SAC) is analyzed, and the performance is compared with various deep reinforcement learning algorithms and heuristic algorithms based on policy gradients. The idea of advantage learning is introduced into the update of the Q value function of SAC, which solves the problem of overestimating the Q value of the algorithm and improves the performance of the algorithm. In order to improve the learning efficiency of the agent and the generalization ability to deal with new scenarios, the parameter transfer of transfer learning is added. Calculation examples show that the optimal scheduling strategy based on ALSAC algorithm and transfer learning has good robustness, generalization ability and efficient learning efficiency, and realizes flexible and efficient scheduling of regional integrated energy systems. © 2023 Power System Technology Press. All rights reserved.