Batch-Constrained Reinforcement Learning for Dynamic Distribution Network Reconfiguration

2020/06/23 by Yuanqi Gao, Gao, Yuanqi, Wang, Wei +4 · 1 citation
Engineering · #Optimal Power Flow Distribution #Microgrid Control and Optimization #Smart Grid Energy Management

paper · pdf · doi:10.48550/arxiv.2006.12749

Abstract

Dynamic distribution network reconfiguration (DNR) algorithms perform hourly status changes of remotely controllable switches to improve distribution system performance. The problem is typically solved by physical model-based control algorithms, which not only rely on accurate network parameters but also lack scalability. To address these limitations, this paper develops a data-driven batch-constrained reinforcement learning (RL) algorithm for the dynamic DNR problem. The proposed RL algorithm learns the network reconfiguration control policy from a finite historical operational dataset without interacting with the distribution network. The numerical study results on three distribution networks show that the proposed algorithm not only outperforms state-of-the-art RL algorithms but also improves the behavior control policy, which generated the historical operational data. The proposed algorithm is also very scalable and can find a desirable network reconfiguration solution in real-time.

Citations

• Distribution System Reconfiguration Under Uncertain Load and Renewable Generation
• Bridging the Gap Between Value and Policy Based Reinforcement Learning
• Addressing Function Approximation Error in Actor-Critic Methods
• Toward Optimal Multiperiod Network Reconfiguration for Increasing the Hosting Capacity of Distribution Networks

Cited by

• Dual-Head Physics-Informed Graph Decision Transformer for Distribution System Restoration

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