A comprehensive and modular set of appliance operation MILP models for demand response optimization
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DOI: 10.1016/j.apenergy.2022.119142
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Cited by:
- Kanakadhurga, Dharmaraj & Prabaharan, Natarajan, 2024. "Smart home energy management using demand response with uncertainty analysis of electric vehicle in the presence of renewable energy sources," Applied Energy, Elsevier, vol. 364(C).
- Song, Yuguang & Xia, Mingchao & Chen, Qifang, 2023. "The robust synchronization control scheme for flexible resources considering the stochastic and delay response process," Applied Energy, Elsevier, vol. 343(C).
- Xie, Jiahan & Ajagekar, Akshay & You, Fengqi, 2023. "Multi-Agent attention-based deep reinforcement learning for demand response in grid-responsive buildings," Applied Energy, Elsevier, vol. 342(C).
- Wang, Haibing & Zhao, Anjie & Khan, Muhammad Qasim & Sun, Weiqing, 2024. "Optimal operation of energy hub considering reward-punishment ladder carbon trading and electrothermal demand coupling," Energy, Elsevier, vol. 286(C).
- Wu, Jiaman & Lu, Chenbei & Wu, Chenye & Shi, Jian & Gonzalez, Marta C. & Wang, Dan & Han, Zhu, 2024. "A cluster-based appliance-level-of-use demand response program design," Applied Energy, Elsevier, vol. 362(C).
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Keywords
Demand-side management; Load modeling; Demand response; Mixed-integer linear programming (MILP) models;All these keywords.
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