MPG-OD-DDPG-Based Optimal Scheduling for Park-Level Multi-Source Synergistic Power Supply System

To address the uncertainty of renewable energy output power and load demand in the park-level multi-source synergistic power supply system and achieve economical system operation, an optimal scheduling method based on the mathematical programming guided imitation learning-oscillation-decaying deep deterministic policy gradient (MPG-OD-DDPG) algorithm is proposed. First, a scheduling model of the park-level multi-source synergistic power supply system is established with the objective of minimizing system operating cost. Second, to address the low sample utilization efficiency of the deep deterministic policy gradient (DDPG), a mathematical programming guided imitation learning (MPGIL) method is introduced to obtain demonstration experience and guide the initial training process. Then, targeting insufficient exploration capability of DDPG, an oscillation-decaying Ornstein–Uhlenbeck noise (OD-OU) is designed to enhance the exploratory capability of the algorithm through the oscillation-decaying process of noise intensity. Finally, according to the scheduling model, the reward function is designed, and the proposed algorithm is used to search for the optimal energy regulation strategy, thereby realizing the optimal scheduling of the park-level multi-source synergistic power supply system. Case study results show that compared with the DDPG, the MPG-OD-DDPG reduces the operating cost by 7.41%. It can effectively exploit the energy time-shift capability of the energy storage system (ESS), reduce the operating cost of the park-level multi-source synergistic power supply system.