Distributed Energy Storage Scheduling Optimization Based on Improved Multi-agent Deep Deterministic Policy Gradient Algorithm

Abstract

The core of current energy storage scheduling optimization is to achieve multi-timescale collaborative decision-making through intelligent algorithms to improve system economy and reliability, and accelerate its evolution towards market-oriented and large-scale applications. This study is designed to develop a scenario-adaptive intelligent scheduling system. It skips the need for accurate long-term future time-series predictions, and directly leverages real-time observable information at the current decision point, such as renewable energy output, power load, electricity price and energy storage status, to complete dynamic optimal scheduling. For a single independent microgrid, an electric-hydrogen hybrid architecture is constructed, and an improved deep deterministic policy gradient algorithm with attenuated random noise is proposed. The scheduling strategy is optimized through online interaction with the target network and a soft update mechanism. For multiple interconnected microgrids, a centralized training and decentralized execution framework is adopted to achieve multi-agent collaborative optimization and autonomous decision-making. The results show that in a single microgrid scenario, the research method achieves a renewable energy utilization efficiency of 98.77% and a average operating cost of 0.381 yuan/kWh; in a multi-microgrid scenario, the average operating cost is 0.389 yuan/kWh. The research indicates that the two types of algorithms are respectively adapted to single-microgrid internal optimization and multi-microgrid collaborative scheduling, providing scenario-based solutions for distributed energy storage optimization.