Research on Data-Driven Optimal Scheduling of Power System
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- White, Chelsea C. & White, Douglas J., 1989. "Markov decision processes," European Journal of Operational Research, Elsevier, vol. 39(1), pages 1-16, March.
- Perera, A.T.D. & Kamalaruban, Parameswaran, 2021. "Applications of reinforcement learning in energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
- Xiang, Yue & Lu, Yu & Liu, Junyong, 2023. "Deep reinforcement learning based topology-aware voltage regulation of distribution networks with distributed energy storage," Applied Energy, Elsevier, vol. 332(C).
- Lu, Yu & Xiang, Yue & Huang, Yuan & Yu, Bin & Weng, Liguo & Liu, Junyong, 2023. "Deep reinforcement learning based optimal scheduling of active distribution system considering distributed generation, energy storage and flexible load," Energy, Elsevier, vol. 271(C).
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Cited by:
- Gabriel Pesántez & Wilian Guamán & José Córdova & Miguel Torres & Pablo Benalcazar, 2024. "Reinforcement Learning for Efficient Power Systems Planning: A Review of Operational and Expansion Strategies," Energies, MDPI, vol. 17(9), pages 1-25, May.
- Rui Wang & Zhanqiang Zhang & Keqilao Meng & Pengbing Lei & Kuo Wang & Wenlu Yang & Yong Liu & Zhihua Lin, 2024. "Research on Energy Scheduling Optimization Strategy with Compressed Air Energy Storage," Sustainability, MDPI, vol. 16(18), pages 1-18, September.
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Keywords
grid dispatching optimization; proximal policy optimization algorithm; importance sampling; deep reinforcement learning;All these keywords.
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