An advanced real-time dispatching strategy for a distributed energy system based on the reinforcement learning algorithm
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DOI: 10.1016/j.renene.2021.06.032
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- Jin, Jingliang & Zhou, Dequn & Zhou, Peng & Qian, Shuqu & Zhang, Mingming, 2016. "Dispatching strategies for coordinating environmental awareness and risk perception in wind power integrated system," Energy, Elsevier, vol. 106(C), pages 453-463.
- Liao, Gwo-Ching, 2011. "A novel evolutionary algorithm for dynamic economic dispatch with energy saving and emission reduction in power system integrated wind power," Energy, Elsevier, vol. 36(2), pages 1018-1029.
- Tuohy, Aidan & Meibom, Peter & Denny, Eleanor & O'Malley, Mark, 2009. "Unit commitment for systems with significant wind penetration," MPRA Paper 34849, University Library of Munich, Germany.
- Sun, Mei & Li, Juan & Gao, Cuixia & Han, Dun, 2017. "Identifying regime shifts in the US electricity market based on price fluctuations," Applied Energy, Elsevier, vol. 194(C), pages 658-666.
- Kaldellis, J.K. & Vlachos, G.Th., 2006. "Optimum sizing of an autonomous wind-diesel hybrid system for various representative wind-potential cases," Applied Energy, Elsevier, vol. 83(2), pages 113-132, February.
- Anthony Papavasiliou & Shmuel S. Oren, 2013. "Multiarea Stochastic Unit Commitment for High Wind Penetration in a Transmission Constrained Network," Operations Research, INFORMS, vol. 61(3), pages 578-592, June.
- Zhang, Ling & Zhou, Peng & Newton, Sidney & Fang, Jian-xin & Zhou, De-qun & Zhang, Lu-ping, 2015. "Evaluating clean energy alternatives for Jiangsu, China: An improved multi-criteria decision making method," Energy, Elsevier, vol. 90(P1), pages 953-964.
- Jin, Jingliang & Zhou, Peng & Li, Chenyu & Guo, Xiaojun & Zhang, Mingming, 2019. "Low-carbon power dispatch with wind power based on carbon trading mechanism," Energy, Elsevier, vol. 170(C), pages 250-260.
- Younes, Mimoun & Khodja, Fouad & Kherfane, Riad Lakhdar, 2014. "Multi-objective economic emission dispatch solution using hybrid FFA (firefly algorithm) and considering wind power penetration," Energy, Elsevier, vol. 67(C), pages 595-606.
- Yuan, Xiaohui & Su, Anjun & Yuan, Yanbin & Nie, Hao & Wang, Liang, 2009. "An improved PSO for dynamic load dispatch of generators with valve-point effects," Energy, Elsevier, vol. 34(1), pages 67-74.
- Sun, Qirun & Wu, Zhi & Gu, Wei & Zhu, Tao & Zhong, Lei & Gao, Ting, 2021. "Flexible expansion planning of distribution system integrating multiple renewable energy sources: An approximate dynamic programming approach," Energy, Elsevier, vol. 226(C).
- Laura, Castro-Santos & Vicente, Diaz-Casas, 2014. "Life-cycle cost analysis of floating offshore wind farms," Renewable Energy, Elsevier, vol. 66(C), pages 41-48.
- Zhang, Mingming & Liu, Liyun & Wang, Qunwei & Zhou, Dequn, 2020. "Valuing investment decisions of renewable energy projects considering changing volatility," Energy Economics, Elsevier, vol. 92(C).
- PAPAVASILIOU, Anthony & OREN, Schmuel S., 2013. "Multiarea stochastic unit commitment for high wind penetration in a transmission constrained network," LIDAM Reprints CORE 2500, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Jin, Jingliang & Zhou, Peng & Zhang, Mingming & Yu, Xianyu & Din, Hao, 2018. "Balancing low-carbon power dispatching strategy for wind power integrated system," Energy, Elsevier, vol. 149(C), pages 914-924.
- Panapakidis, Ioannis P. & Dagoumas, Athanasios S., 2017. "Day-ahead natural gas demand forecasting based on the combination of wavelet transform and ANFIS/genetic algorithm/neural network model," Energy, Elsevier, vol. 118(C), pages 231-245.
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Cited by:
- Ding, Yihong & Tan, Qinliang & Shan, Zijing & Han, Jian & Zhang, Yimei, 2023. "A two-stage dispatching optimization strategy for hybrid renewable energy system with low-carbon and sustainability in ancillary service market," Renewable Energy, Elsevier, vol. 207(C), pages 647-659.
- Dai, Yeming & Sun, Xilian & Qi, Yao & Leng, Mingming, 2021. "A real-time, personalized consumption-based pricing scheme for the consumptions of traditional and renewable energies," Renewable Energy, Elsevier, vol. 180(C), pages 452-466.
- Zhu, Ziqing & Hu, Ze & Chan, Ka Wing & Bu, Siqi & Zhou, Bin & Xia, Shiwei, 2023. "Reinforcement learning in deregulated energy market: A comprehensive review," Applied Energy, Elsevier, vol. 329(C).
- Jin, Jingliang & Wen, Qinglan & Cheng, Siqi & Qiu, Yaru & Zhang, Xianyue & Guo, Xiaojun, 2022. "Optimization of carbon emission reduction paths in the low-carbon power dispatching process," Renewable Energy, Elsevier, vol. 188(C), pages 425-436.
- Fang, Jianhao & Hu, Weifei & Liu, Zhenyu & Chen, Weiyi & Tan, Jianrong & Jiang, Zhiyu & Verma, Amrit Shankar, 2022. "Wind turbine rotor speed design optimization considering rain erosion based on deep reinforcement learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
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Keywords
Distribution system; Economic dispatch; Coordinated dispatching strategy; Real-time control; Markov decision process; Reinforcement learning;All these keywords.
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