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Batch-Constrained Reinforcement Learning for Dynamic Distribution Network Reconfiguration
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DOI：10.1109/TSG.2020.3005270.png)
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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.
Keyword:
Heuristic algorithms
Distribution networks
Reinforcement learning
Control systems
Voltage measurement
Stochastic processes
Optimization
Batch-constrained
data-driven control
distribution network reconfiguration
reinforcement learning
期刊
IF:
9.8
论文数:
5.7K
被引数:
4.3W
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