Optimization Scheduling Strategies for Integrated Energy Systems Based on Improved Deep Deterministic Policy Gradient Algorithm

To address the issues of large decision space and difficulty in convergence in the optimization scheduling of integrated energy systems, in this paper we propose an optimized scheduling strategy based on the improved deep deterministic policy gradient (DDPG) algorithm. The difficulty in convergence and even failure in optimization is solved by adding a second experience pool. In order to address the optimization scheduling challenge of integrated energy systems, the algorithm is optimized by improving the network parameter update process, resulting in an increase in the efficiency of the training process. In addition, the reward function is redesigned and a non-linear reward function is adopted to further improve the stability of the algorithm. Finally, an integrated energy system composed of photovoltaic, energy storage systems, refrigeration units, electric heating units and gas boilers is simulated, and the performance of the algorithm is compared before and after the improvement. The case study indicates that the optimization scheduling strategy based on the improved deep deterministic policy gradient algorithm exhibits excellent convergence, stability and high training efficiency. Moreover, it enables flexible and efficient scheduling of the integrated energy system.