Grid Integration of Wind Generation Considering Remote Wind Farms: Hybrid Markovian and Interval Unit Commitment

Bing Yan; Haipei Fan; Peter B. Luh; Khosrow Moslehi; Xiaoming Feng; Chien Ning Yu; Mikhail A. Bragin; Yaowen Yu

doi:10.1109/JAS.2017.7510505

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): 205-215

Bing Yan, Haipei Fan, Peter B. Luh, Khosrow Moslehi, Xiaoming Feng, Chien Ning Yu, Mikhail A. Bragin and Yaowen Yu, "Grid Integration of Wind Generation Considering Remote Wind Farms: Hybrid Markovian and Interval Unit Commitment," IEEE/CAA J. Autom. Sinica, vol. 4, no. 2, pp. 205-215, Apr. 2017. doi: 10.1109/JAS.2017.7510505

Citation:

Bing Yan, Haipei Fan, Peter B. Luh, Khosrow Moslehi, Xiaoming Feng, Chien Ning Yu, Mikhail A. Bragin and Yaowen Yu, "Grid Integration of Wind Generation Considering Remote Wind Farms: Hybrid Markovian and Interval Unit Commitment," IEEE/CAA J. Autom. Sinica, vol. 4, no. 2, pp. 205-215, Apr. 2017. doi: 10.1109/JAS.2017.7510505

Citation:

Bing Yan, Haipei Fan, Peter B. Luh, Khosrow Moslehi, Xiaoming Feng, Chien Ning Yu, Mikhail A. Bragin and Yaowen Yu, "Grid Integration of Wind Generation Considering Remote Wind Farms: Hybrid Markovian and Interval Unit Commitment," IEEE/CAA J. Autom. Sinica, vol. 4, no. 2, pp. 205-215, Apr. 2017. doi: 10.1109/JAS.2017.7510505

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Grid Integration of Wind Generation Considering Remote Wind Farms: Hybrid Markovian and Interval Unit Commitment

doi: 10.1109/JAS.2017.7510505

1.
Department of Electrical & Computer Engineering, University of Connecticut, Storrs, CT 06269-4157, USA
2.
University of Connecticut, Storrs, CT 06269-4157, USA
3.
ABB Inc., San Jose, CA 95134, USA
4.
ABB Inc., Raleigh, NC 27606, USA
5.
ABB Enterprise Software, San Jose, CA 95134, USA

Funds:

This work was supported in part by the Project Funded by ABB and U.S. National Science Foundation ECCS-1509666

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Author Bio:
Haipei Fan received her B.E. degree from East China University of Science and Technology in 2013, and M.S. from University of Missouri-Columbia in 2014. Her current research interests include power system optimization and grid integration of renewable energy.(e-mail: haipei.fan@uconn.edu)

Peter B. Luh (S'77–M'80–SM'91–F'95) received his B.S. degree from National Taiwan University, M.S. degree from M.I.T., and Ph.D. degree from Harvard University. He has been with the University of Connecticut since 1980, and is the SNET professor of communications & information technologies. His interests include smart power systems – smart grid, design of auction methods for electricity markets, effective renewable (wind and solar) integration to the grid, electricity load and price forecasting with demand response, and micro grid. He is a fellow of IEEE, was the vice president of publication activities for the IEEE Robotics and Automation Society.(peter.luh@uconn.edu)

Khosrow Moslehi received his Ph.D. degree from the University of California, Berkeley. He is the director of product management at ABB Network Control in Santa Clara, CA. Dr. Moslehi has 30 years of experience in R & D. His research interests include power system analysis and optimization, system integration and architecture, electricity markets, and the smart grid.(e-mails: khosrow.moslehi@us.abb.com)

Xiaoming Feng (M'91) received his B.S. degree from Xi'an Jiaotong University in 1982, and he obtained his M.S. and Ph.D. degree in electrical & computer engineering from Ohio University in 1986 and 1989, respectively. He is a principal consulting R & D engineer with ABB's research lab Electrical System Technology Institute, Raleigh, NC. He has more than 20 years of industry experience working as management consultant, R & D engineer, software developer, and software product manager. In recent years, he has been engaged in consulting and research in areas ranging from deregulated energy market analysis, using genetic methodology in transmission and distribution system planning, power quality, and distributed power and renewable energy. His research interests include simulation, analysis, planning, and optimization of electric power transmission and delivery systems using advanced simulation, optimization, and probabilistic techniques.(e-mail: xiaoming.feng@us.abb.com)

Chien Ning Yu received his B.S. degree from National Taiwan University in 1992, and he obtained his M.S. and Ph.D. degree in mechanical engineering from MIT in 1996 and 1999 respectively. He is currently a senior consulting engineer in ABB Inc., Santa Clara, CA. He has been actively involved in design and implementation of Texas ERCOT, Ontario IESO, South Korea KPX, East China ECG, Philippine WESM, and Western Australia IMO market applications. His research interests include power systems operation and economy, dynamics of complex networks and automatic control theories.(Chien-Ning.Yu@us.abb.com)

Mikhail A. Bragin (S'11-M'17) received his B.S. and M.S. degrees in mathematics from the Voronezh State University, Russia, in 2004, the M.S. degree in physics and astronomy from the University of Nebraska-Lincoln, USA, in 2006, and the M.S. and Ph.D. degree in electrical and computer engineering from the University of Connecticut, USA, in 2014 and 2016, respectively. He is an assistant research professor in electrical and computer engineering at the University of Connecticut. His research interests include mathematical optimization, operations, and economics of electricity markets.(mikhail.bragin@uconn.edu)

Yaowen Yu (S'12) received his B.S. degree in automation from Huazhong University of Science and Technology, Wuhan, China, in 2011, and Ph.D. degree from University of Connecticut in 2016. He is currently a senior application engineer at ABB Enterprise Software, San Jose, CA. His research interests include power system optimization, grid integration of renewable energy, and economics of electricity markets.(e-mail: yaowen.yu@us.abb.com)
Corresponding author: Bing Yan (S'11-M'17) received the B.S. degree from Renmin University of China in 2010, M.S. and Ph.D. degrees from University of Connecticut in 2012 and 2016, respectively. She is currently an assistant research professor in the Department of Electrical and Computer Engineering, University of Connecticut. Her research interests include power system optimization, grid integration of renewables (wind and solar), energy-based operation optimization of distributed energy systems, and scheduling of manufacturing systems. Corresponding author of this paper.(e-mail: bing.yan@uconn.edu)
Received Date: 2015-12-03
Accepted Date: 2016-05-25

Abstract

Abstract

Grid integration of wind power is essential to reduce fossil fuel usage but challenging in view of the intermittent nature of wind. Recently, we developed a hybrid Markovian and interval approach for the unit commitment and economic dispatch problem where power generation of conventional units is linked to local wind states to dampen the effects of wind uncertainties. Also, to reduce complexity, extreme and expected states are considered as interval modeling. Although this approach is effective, the fact that major wind farms are often located in remote locations and not accompanied by conventional units leads to conservative results. Furthermore, weights of extreme and expected states in the objective function are difficult to tune, resulting in significant differences between optimization and simulation costs. In this paper, each remote wind farm is paired with a conventional unit to dampen the effects of wind uncertainties without using expensive utility-scaled battery storage, and extra constraints are innovatively established to model pairing. Additionally, proper weights are derived through a novel quadratic fit of cost functions. The problem is solved by using a creative integration of our recent surrogate Lagrangian relaxation and branch-and-cut. Results demonstrate modeling accuracy, computational efficiency, and significant reduction of conservativeness of the previous approach.
- Branch-and-cut,
- interval optimization,
- Markov decision process,
- remote wind farms,
- surrogate Lagrangian relaxation (SLR),
- unit commitment

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References(37)

References

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Grid Integration of Wind Generation Considering Remote Wind Farms: Hybrid Markovian and Interval Unit Commitment

doi: 10.1109/JAS.2017.7510505

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