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Scheduling thermostatically controlled loads to provide regulation capacity based on a learning-based optimal power flow model
Chen, Ge1; Zhang, Hongcai2; Hui, Hongxun3; Dai, Ningyi4; Song, Yonghua5
2021-07-30
Source PublicationIEEE Transactions on Sustainable Energy
ISSN1949-3029
Volume12Issue:4
Abstract

Thermostatically controlled load (TCL, such as heating, ventilation, and air conditioning system) is a desirable demand-side flexibility source in distribution networks. It can participate in regulation services and mitigate power imbalances from fluctuating distributed renewable generation. To effectively utilize the load flexibility from spatially and temporally distributed TCLs in a distribution network, it is necessary to consider power flow constraints to avoid possible voltage or current violations. Published works usually adopt optimal power flow models (OPF) to describe these constraints. However, these models require accurate topology of the distribution network that is often unobservable in practice. To bypass this challenge, this paper proposes a novel learning-based OPF to optimize TCLs for regulation services. This method trains three regression multi-layer perceptrons (MLPs) based on the distribution network's historical operation data to replicate its power flow constraints. The trained MLPs are further equivalently reformulated into linear constraints with binary variables so that the optimization problem becomes a mixed-integer linear program that can be effectively solved. Numerical experiments based on the IEEE 123-bus system validate that the proposed method can achieve better TCL power scheduling performance with guaranteed feasibility and optimality than other state-of-art models.

KeywordBuildings Demand-side Feasibility Distribution Networks Hvac Network Topology Neural Network Optimal Power Flow Regulation Regulation Capacity Security Security Constraint Topology
DOI10.1109/TSTE.2021.3100846
URLView the original
Language英語English
Scopus ID2-s2.0-85111578769
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Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionTHE STATE KEY LABORATORY OF INTERNET OF THINGS FOR SMART CITY (UNIVERSITY OF MACAU)
Affiliation1.State Key Laboratory of Internet of Things for Smart City, University of Macau, 59193 Taipa, Macao, (e-mail: yb97431@connect.um.edu.mo)
2.State Key Laboratory of Internet of Things for Smart City, University of Macau, 59193 Taipa, Macao, (e-mail: hczhang@um.edu.mo)
3.State Key Laboratory of Internet of Things for Smart City, University of Macau, 59193 Taipa, China, 519000 (e-mail: hongxunhui@um.edu.mo)
4.Department of Electrical and Computer Engineering, University of Macau, 59193 Taipa, Macau, Macao, (e-mail: nydai@um.edu.mo)
5.State Key Laboratory of Internet of Things for Smart City, University of Macau, 59193 Taipa, Macau SAR, Macao, (e-mail: yhsong@um.edu.mo)
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Chen, Ge,Zhang, Hongcai,Hui, Hongxun,et al. Scheduling thermostatically controlled loads to provide regulation capacity based on a learning-based optimal power flow model[J]. IEEE Transactions on Sustainable Energy,2021,12(4).
APA Chen, Ge,Zhang, Hongcai,Hui, Hongxun,Dai, Ningyi,&Song, Yonghua.(2021).Scheduling thermostatically controlled loads to provide regulation capacity based on a learning-based optimal power flow model.IEEE Transactions on Sustainable Energy,12(4).
MLA Chen, Ge,et al."Scheduling thermostatically controlled loads to provide regulation capacity based on a learning-based optimal power flow model".IEEE Transactions on Sustainable Energy 12.4(2021).
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