Energy System Optimization Research Based on PEC-DDPG Algorithm

Abstract

Factors such as the stochastic nature of loads in energy systems make it difficult to optimize the operation of integrated energy systems. To address these problems, an energy system economy optimization scheme based on the PEC-DDPG is proposed. Firstly, exponential moving average (EMA) is introduced into deep deterministic policy gradient (DDPG) algorithm, and prioritized experience replay (PER) is added into the experience pool to prioritize the experience to improve the learning efficiency of algorithm, and the overestimation existing in a single Critic network is solved by using multi-Critic structure. Next, the energy system optimization model is constructed, and the appropriate observation states, decision actions and reward functions are selected. Finally, simulations using energy system data of a region show that the optimization of PEC-DDPG is better than the operational optimization of DDPG algorithm.