Resilience-oriented Operation of Distribution Networks in Presence of Demand Response
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作者:
Aghdam, Farid Hamzeh
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Univ Oulu, Water Energy & Environm Engn Res Unit, Oulu, FinlandUniv Oulu, Water Energy & Environm Engn Res Unit, Oulu, Finland
Aghdam, Farid Hamzeh
[1
]
Zavodovski, Aleksandr
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Univ Oulu, Water Energy & Environm Engn Res Unit, Oulu, FinlandUniv Oulu, Water Energy & Environm Engn Res Unit, Oulu, Finland
Zavodovski, Aleksandr
[1
]
Adetunji, Adeleye
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Univ Oulu, Water Energy & Environm Engn Res Unit, Oulu, FinlandUniv Oulu, Water Energy & Environm Engn Res Unit, Oulu, Finland
Adetunji, Adeleye
[1
]
Pongracz, Eva
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Univ Oulu, Water Energy & Environm Engn Res Unit, Oulu, FinlandUniv Oulu, Water Energy & Environm Engn Res Unit, Oulu, Finland
Pongracz, Eva
[1
]
Rasti, Mehdi
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Univ Oulu, Water Energy & Environm Engn Res Unit, Oulu, Finland
Ctr Wireless Commun, Oulu, FinlandUniv Oulu, Water Energy & Environm Engn Res Unit, Oulu, Finland
Rasti, Mehdi
[1
,2
]
机构:
[1] Univ Oulu, Water Energy & Environm Engn Res Unit, Oulu, Finland
[2] Ctr Wireless Commun, Oulu, Finland
来源:
2024 INTERNATIONAL WORKSHOP ON ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FOR ENERGY TRANSFORMATION, AIE 2024
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2024年
The resilience of distribution networks is becoming increasingly critical in the face of growing uncertainties and challenges, including climate change impacts, aging infrastructure, and evolving energy consumption patterns. This paper explores the integration of demand response (DR) mechanisms into distribution network operations to enhance resilience. By leveraging DR, distribution networks can dynamically manage energy consumption in response to changing grid conditions and disruptions, thereby mitigating risks and improving system reliability. The paper examines the potential benefits, challenges, and best practices associated with implementing DR in distribution networks to enhance resilience. The insights presented in this paper contribute to the development of effective strategies for enhancing the resilience of distribution networks in the presence of demand response programs. Finally, simulations in the GAMS environment and IEEE 33-bus distribution test network are performed to validate the effectiveness of the proposed strategy.
机构:
Iowa State Univ, Dept Elect & Comp Engn, Ames, IA 50011 USAIowa State Univ, Dept Elect & Comp Engn, Ames, IA 50011 USA
Ma, Shanshan
Li, Shiyang
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CSG, State Key Lab HVDC, Elect Power Res Inst, Guangzhou 510663, Guangdong, Peoples R ChinaIowa State Univ, Dept Elect & Comp Engn, Ames, IA 50011 USA
Li, Shiyang
Wang, Zhaoyu
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Iowa State Univ, Dept Elect & Comp Engn, Ames, IA 50011 USAIowa State Univ, Dept Elect & Comp Engn, Ames, IA 50011 USA
Wang, Zhaoyu
Qiu, Feng
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机构:
Argonne Natl Lab, Div Energy Syst, 9700 S Cass Ave, Argonne, IL 60439 USAIowa State Univ, Dept Elect & Comp Engn, Ames, IA 50011 USA