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Demand response for industrial micro-grid considering photovoltaic power uncertainty and battery operational cost
Huang,Chao1; Zhang,Hongcai2; Song,Yonghua3; Wang,Long4; Ahmad,Tanveer5; Luo,Xiong4
2021
Source PublicationIEEE Transactions on Smart Grid
ISSN1949-3053
AbstractAn intelligent demand response (DR) program is developed for multi-energy industrial micro-grid consisting of manufacturing facilities, photovoltaic (PV) panels, and battery energy storage system (BESS). The proposed DR program tackles the practical challenges of components in the micro-grid including industrial process represented by a discrete manufacturing production model, uncertainty of PV generation, and operational cost of the BESS. The proposed DR program optimizes day-ahead production scheduling for manufacturing facilities and operation regime for the BESS in response to time of use electricity price and probabilistic forecasting of PV power. To capture the uncertainty of PV power, a data-driven PV power probabilistic forecasting model is developed and a copula-based approach is deployed for the sampling of temporally correlated scenarios of PV power over the scheduling horizon from probabilistic forecasts. The multi-energy management optimization problem is formulated as a scenario-based stochastic nonconvex mixed integer nonlinear programming (MINLP). A hybrid optimization method integrating the evolutionary algorithm and the branch-and-bound algorithm for mixed integer liner programming is proposed to solve the nonconvex MINLP. Simulation studies illustrate that the proposed DR program efficiently reduces the operational cost for manufacturing production and releases the stress of the main grid by making full use of flexibility of all the components in the micro-grid.
KeywordDemand response Forecasting industrial micro-grid Manufacturing mixed integer nonlinear programming. multi-energy Optimization Optimization methods Probabilistic logic Production Uncertainty
DOI10.1109/TSG.2021.3052515
URLView the original
Language英语
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Document TypeJournal article
CollectionUniversity of Macau
Affiliation1.State Key Laboratory of Internet of Things for Smart City and Department of Electrical and Computer Engineering, University of Macau, Macao S.A.R., 999078, China, and also with the School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 10083, China.
2.State Key Laboratory of Internet of Things for Smart City and Department of Electrical and Computer Engineering, University of Macau, Macao S.A.R., 999078, China. (e-mail: hczhang@um.edu.mo)
3.State Key Laboratory of Internet of Things for Smart City and Department of Electrical and Computer Engineering, University of Macau, Macao S.A.R., 999078, China.
4.School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 10083, China, and also with Shunde Graduate School of University of Science and Technology Beijing, Foshan, Guangdong, 528399, China.
5.State Key Laboratory of Internet of Things for Smart City and Department of Electrical and Computer Engineering, University of Macau, Macao S.A.R, 999078, China, and also with the Energy and Electricity Research Center, International Energy College, Jinan University, Zhuhai, Guangdong, 519070, China.
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Huang,Chao,Zhang,Hongcai,Song,Yonghua,et al. Demand response for industrial micro-grid considering photovoltaic power uncertainty and battery operational cost[J]. IEEE Transactions on Smart Grid,2021.
APA Huang,Chao,Zhang,Hongcai,Song,Yonghua,Wang,Long,Ahmad,Tanveer,&Luo,Xiong.(2021).Demand response for industrial micro-grid considering photovoltaic power uncertainty and battery operational cost.IEEE Transactions on Smart Grid.
MLA Huang,Chao,et al."Demand response for industrial micro-grid considering photovoltaic power uncertainty and battery operational cost".IEEE Transactions on Smart Grid (2021).
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