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  • Paper 35 (Advanced Machine Learning Approach of Power Flow Optimization in Community Microgrid)

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Advanced Machine Learning Approach of Power Flow Optimization in Community Microgrid

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Published
Publication date10/10/2022
Host publicationProceedings of the 27th International Conference on Automation & Computing (ICAC2022)
PublisherIEEE
Number of pages6
<mark>Original language</mark>English

Abstract

With the increasing penetration of distributed renewable energy (DERs), the electrical grid is experiencing, on a daily basis, rapid and massive fluctuations in power and voltage profiles. Fast and precise control strategies in realtime have played an important role to ensure that the power system operates at an optimal status. Solving real-time optimal power flow (OPF) problems while satisfying the operational constraints of the community microgrid (CMG) is considered a promising technique to control the fluctuations of renewable sources and loads. This paper adopts a new deep reinforcement learning algorithm (DRL), called Twin-Delayed Deep Deterministic Policy Gradient (TD3), to solve the real-time OPF with consideration of DERs and distributed energy storages (DESs) in the CMG. Training and testing of the algorithm are conducted on an IEEE 14-bus test system. Comparative results show the effectiveness of the proposed algorithm.

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©2022 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.