Appraising the Optimal Power Flow and Generation Capacity in Existing Power Grid Topology with Increase in Energy Demand
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- Hua, Haochen & Qin, Yuchao & Hao, Chuantong & Cao, Junwei, 2019. "Optimal energy management strategies for energy Internet via deep reinforcement learning approach," Applied Energy, Elsevier, vol. 239(C), pages 598-609.
- Singh, Antriksh & Willi, David & Chokani, Ndaona & Abhari, Reza S., 2014. "Optimal power flow analysis of a Switzerland׳s transmission system for long-term capacity planning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 596-607.
- Carleton Coffrin & Pascal Van Hentenryck, 2014. "A Linear-Programming Approximation of AC Power Flows," INFORMS Journal on Computing, INFORMS, vol. 26(4), pages 718-734, November.
- Yang, Ting & Zhao, Liyuan & Li, Wei & Zomaya, Albert Y., 2021. "Dynamic energy dispatch strategy for integrated energy system based on improved deep reinforcement learning," Energy, Elsevier, vol. 235(C).
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Keywords
alternating current model; cost of constraint relaxation; deep reinforcement learning; direct current model; energy demand; linear programming; maximum generation capacity; maximum power flow; optimal generation capacity; optimal power flow;All these keywords.
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