Sequential Reconfiguration of Unbalanced Distribution Network with Soft Open Points Based on Deep Reinforcement Learning
نویسندگان
چکیده
With the large-scale distributed generations (DGs) being connected to distribution network (DN), traditional day-ahead reconfiguration methods based on physical models are challenged maintain robustness and avoid voltage off-limits. To address these problems, this paper develops a deep re-inforcement learning method for sequential with soft open points (SOPs) real-time data. A state-based decision model is first proposed by constructing Marko process-based SOP joint optimization so that decisions can be achieved in milliseconds. Then, reinforcement framework including branching double $Q$ (BDDQN) multi-policy actor-critic (MPSAC) proposed, which has significantly improved efficiency of multi-dimensional mixed-integer action space. And influence DG load uncertainty control results been minimized using status DN make decisions. The numerical simulations IEEE 34-bus 123-bus systems demonstrate effectively reduce operation cost solve overvoltage problem caused high ratio photovoltaic (PV) integration.
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ژورنال
عنوان ژورنال: Journal of modern power systems and clean energy
سال: 2023
ISSN: ['2196-5420', '2196-5625']
DOI: https://doi.org/10.35833/mpce.2022.000271