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Stochastic receding horizon control of active distribution networks with distributed renewables
Jiang,Yibao1; Wan,Can1; Wang,Jianhui2; Song,Yonghua1,3; Dong,Zhao Yang4
2019-03-01
Source PublicationIEEE Transactions on Power Systems
ISSN0885-8950
Volume34Issue:2Pages:1325-1341
Abstract

High penetration of distributed renewable energy introduces significant uncertainties to active distribution networks. Optimal control methods accounting for inherent uncertainties are needed to facilitate economic and reliable operation of active distribution networks. This paper proposes a stochastic receding horizon control method based on modified stochastic model predictive control framework to integrate high penetration of distributed generation. Multiple controllable resources are jointly optimized over a finite prediction horizon while ensuring relevant security restrictions. The simplified Z-bus sensitivity for active distribution networks is developed for computationally efficient estimation of system nonlinearity with high accuracy, and is combined with the sequential linear programming to iteratively derive the linear state space model for compensation of cumulative modeling errors. Furthermore, the voltage limitations are reformulated as chance constraints to indicate the probabilistic reliability index of voltage qualification rate, and achieve tradeoffs between cost reduction and voltage regulation. The affine-disturbance feedback control policy is leveraged here to enforce close-loop control performance and analytically transform intractable chance constraints into second-order cone constraints. Comprehensive case studies based on 33-bus and 123-bus distribution systems are carried out to demonstrate the capability and effectiveness of the proposed approach in terms of modeling accuracy, control performance, cost reduction, and method scalability. The proposed approach can effectively enforce voltage regulation against uncertainties with the prescribed probability level. Control costs and constraint violation can be reduced compared with deterministic model predictive control and open-loop control strategies.

KeywordActive Distribution Network Chance Constraint Distributed Renewable Generation Stochastic Model Predictive Control Uncertainty
DOI10.1109/TPWRS.2018.2879451
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaEngineering
WOS SubjectEngineering, Electrical & Electronic
WOS IDWOS:000459509400044
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Cited Times [WOS]:22   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionFaculty of Science and Technology
Corresponding AuthorWan,Can
Affiliation1.College of Electrical Engineering,Zhejiang University,Hangzhou,310027,China
2.Department of Electrical Engineering,Southern Methodist University,Dallas,75275,United States
3.Department of Electrical and Computer Engineering,University of Macau,Taipa, Macau,China
4.School of Electrical Engineering and Telecommunications,University of New South Wales,Sydney,NSW 2052,Australia
Recommended Citation
GB/T 7714
Jiang,Yibao,Wan,Can,Wang,Jianhui,et al. Stochastic receding horizon control of active distribution networks with distributed renewables[J]. IEEE Transactions on Power Systems,2019,34(2):1325-1341.
APA Jiang,Yibao,Wan,Can,Wang,Jianhui,Song,Yonghua,&Dong,Zhao Yang.(2019).Stochastic receding horizon control of active distribution networks with distributed renewables.IEEE Transactions on Power Systems,34(2),1325-1341.
MLA Jiang,Yibao,et al."Stochastic receding horizon control of active distribution networks with distributed renewables".IEEE Transactions on Power Systems 34.2(2019):1325-1341.
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