A Chance Constrained Optimal Reserve Scheduling Approach for Economic Dispatch Considering Wind Penetration

Yufei Tang; Chao Luo; Jun Yang; Haibo He

doi:10.1109/JAS.2017.7510499

Volume 4 Issue 2

Apr. 2017

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2017 > 4(2): 186-194

Yufei Tang, Chao Luo, Jun Yang and Haibo He, "A Chance Constrained Optimal Reserve Scheduling Approach for Economic Dispatch Considering Wind Penetration," IEEE/CAA J. Autom. Sinica, vol. 4, no. 2, pp. 186-194, Apr. 2017. doi: 10.1109/JAS.2017.7510499

Citation:

Yufei Tang, Chao Luo, Jun Yang and Haibo He, "A Chance Constrained Optimal Reserve Scheduling Approach for Economic Dispatch Considering Wind Penetration," IEEE/CAA J. Autom. Sinica, vol. 4, no. 2, pp. 186-194, Apr. 2017. doi: 10.1109/JAS.2017.7510499

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A Chance Constrained Optimal Reserve Scheduling Approach for Economic Dispatch Considering Wind Penetration

doi: 10.1109/JAS.2017.7510499

Yufei Tang^1
,,
Chao Luo^3
,,
Jun Yang^3
,,
Haibo He^{2
,
,}

1.
Department of Computer & Electrical Engineering and Computer Science, and the Institute for Sensing and Embedded Network Systems Engineering, Florida Atlantic University, Boca Raton, FL 33431, USA
2.
Department of Electrical, Computer and Biomedical Engineering, University of Rhode Island, Kingston, RI 02881, USA
3.
School of Electrical Engineering, Wuhan University, Wuhan 430072, China

Funds:

This work was supported in part by the National Science Foundation ECCS 1053717

This work was supported in part by the National Science Foundation CNS 1117314

the National Science Foundation of China 51529701

the National Science Foundation of China 51277135

More Information

Author Bio:
Yufei Tang (M'16) received the B. E. and M. S. degrees from Hohai University, Nanjing, China, in 2008 and 2011, respectively, and the Ph. D. degree in Electrical Engineering from the Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI, USA, in 2016. He is currently an Assistant Professor with the Department of Electrical, Computer, and Biomedical Engineering, and a Faculty Fellow with the Institute for Sensing and Embedded Network Systems Engineering, Florida Atlantic University, Boca Raton, FL, USA. His research interests include power systems stability and control, smart grid security, computational intelligence, and cyber-physical energy systems.(email: tangy@fau.edu)

Chao Luo received the B. S. degree from the Department of Electrical Engineering of Wuhan University, Wuhan, China, in 2010, where he is pursuing the Ph. D. degree in electrical engineering. He was a visiting Ph. D. student in the University of Rhode Island, Kingston, RI, USA. Currently, he is with the Smart Grid Research Institute of Wuhan University. His research interests include power system stability and optimization, cascading failure analysis and renewable energy.(email: luochao 2011@whu.edu.cn)

Jun Yang (M'12) received the B. S. degree in 1999, M. S. degree in 2002, and Ph. D. degree in 2006, all in electrical and electronic engineering from Huazhong University of Science and Technology, Wuhan, China. He is now an Associate Professor of the School of Electrical Engineering of Wuhan University. He was a visiting scholar in University of Rhode Island from March 2013 to March 2014. His research interests include the protection and control of renewable energy and V2G integrated power system, security risk assessment of large power grid.(JYang@whu.edu.cn)
Corresponding author: Haibo He (SM'11) received the B. S. and M. S. degrees in electrical engineering from Huazhong University of Science and Technology (HUST), Wuhan, China, in 1999 and 2002, respectively, and the Ph. D. degree in electrical engineering from Ohio University, Athens, in 2006. From 2006 to 2009, he was an Assistant Professor with the Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, USA. He is currently the Robert Haas Endowed Chair Professor with the Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI, USA. He has published one sole-author research book (Wiley), edited one book (WileyIEEE), and six conference proceedings (Springer), and authored and co-authored over 200 peer-reviewed journal and conference papers. His current research interests include adaptive dynamic programming, computational intelligence, self-adaptive systems, machine learning, and data mining. Prof. He was the recipient of the IEEE International Conference on Communications (ICC) Best Paper Award (2014), IEEE CIS Outstanding Early Career Award (2014), National Science Foundation (NSF) CAREER Award (2011) and Providence Business News (PBN) Rising Star Innovator Award (2011). He severed as the General Chair of the IEEE Symposium Series on Computational Intelligence. He is currently the Editor-in-Chief of the IEEE Transactions on Neural Networks and Learning Systems.(email:he@ele.uri.edu)
Received Date: 2015-11-13
Accepted Date: 2016-03-03

Abstract

Abstract

The volatile wind power generation brings a full spectrum of problems to power system operation and management, ranging from transient system frequency fluctuation to steady state supply and demand balancing issue. In this paper, a novel wind integrated power system day-ahead economic dispatch model, with the consideration of generation and reserve cost is modelled and investigated. The proposed problem is first formulated as a chance constrained stochastic nonlinear programming (CCSNLP), and then transformed into a deterministic nonlinear programming (NLP). To tackle this NLP problem, a three-stage framework consists of particle swarm optimization (PSO), sequential quadratic programming (SQP) and Monte Carlo simulation (MCS) is proposed. The PSO is employed to heuristically search the line power flow limits, which are used by the SQP as constraints to solve the NLP problem. Then the solution from SQP is verified on benchmark system by using MCS. Finally, the verified results are feedback to the PSO as fitness value to update the particles. Simulation study on IEEE 30-bus system with wind power penetration is carried out, and the results demonstrate that the proposed dispatch model could be effectively solved by the proposed three-stage approach.
- Chance constrained,
- day-ahead economic dispatch,
- optimal reserve scheduling,
- particle swarm optimization (PSO),
- wind power penetration

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References

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A Chance Constrained Optimal Reserve Scheduling Approach for Economic Dispatch Considering Wind Penetration

doi: 10.1109/JAS.2017.7510499

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