Coordinated Dispatch of Energy Storage Systems in the Active Distribution Network: A Complementary Reinforcement Learning and Optimization Approach

2024/10/17 by Bohan Zhang, Zhang, Bohan, Zhongkai Yi +5
Energy · Engineering · #Energy Load and Power Forecasting #FOS: Electrical engineering #Power Systems and Renewable Energy #Smart Grid Energy Management #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2410.13223

openalex publication_date 2024/10/17 · openalex created_date 2024/10/20 · openalex updated_date 2026/07/28

Abstract

The complexity and nonlinearity of active distribution network (ADN), coupled with the fast-changing renewable energy (RE), necessitate advanced real-time and safe dispatch approach. This paper proposes a complementary reinforcement learning (RL) and optimization approach, namely SA2CO, to address the coordinated dispatch of the energy storage systems (ESSs) in the ADN. The proposed approach leverages RL's capability to make fast decision and address the model inaccuracies, while optimization methods ensure the ADN security. Furthermore, a hybrid data-driven and expert-experience auxiliary neural network is formulated as a rapid security assessment component in the SA2CO algorithm, enabling dynamic switching between RL and optimization methodologies. Simulation results demonstrate the proposed method's effectiveness and scalability in achieving real-time, safe, and economical dispatch of multiple ESSs in the ADN, surpassing the performance of the state-of-the-art RL and optimization methods.

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