Stochastic Wind Curtailment Scheduling for Mitigation of Short-Term Variations in a Power System with High Wind Power and Electric Vehicle
Abstract
:1. Introduction
2. Modeling of Wind Curtailment and EV Charging
2.1. Mitigation of the Wind Power Forecast Uncertainty
2.2. Mitigation of the Wind Power Variation
2.3. Daily EV Charging Load
3. Formulation of the Wind Power Curtailment Scheduling Problem
3.1. Constraints for the Expected Wind Power and Load
3.1.1. Active Power Balance Equations
- 1.
- Operating limits of the conventional units:
- 2.
- Minimum up and down time limits:
- 3.
- Hourly ramp up/down limits of the conventional unit:
3.1.2. Constraints for the Uncertain Wind and Load Scenario
- 1.
- Active power balance for scenarios:
- 2.
- Absolute power limits of the wind farm for scenarios:
- 3.
- Generation limits of the conventional unit for scenarios:
- 4.
- Ramping capability limits of the unit for scenarios:
- 5.
- System ramping requirements for scenarios:
4. Scenario Based Wind Curtailment Scheduling
4.1. Random Variable Discretization for Absolute Power Limit
4.2. Decomposition of the Stochastic UC Problem
5. Numerical Results
5.1. Test System
5.2. Results of Wind Power Curtailment Scheduling
5.3. Influence of Wind and EV Penetration
6. Conclusions
Author Contributions
Acknowledgments
Conflicts of Interest
References
- Dragoon, K. Chapter 5-Representing Wind in Economic Dispatch Models. In Valuing Wind Generation on Integrated Power Systems, 1st ed.; Elsevier: Oxford, UK, 2010. [Google Scholar]
- Fink, S.; Mudd, C.; Porter, K.; Morgenstern, B. Wind Energy Curtailment Case Studies: May 2008−May 2009; National Renewable Energy Laboratory: Golden, CO, USA, 2009.
- Gu, Y.; Xie, L. Fast Sensitivity Analysis Approach to Assessing Congestion Induced Wind Curtailment. IEEE Trans. Power Syst. 2014, 29, 101–110. [Google Scholar] [CrossRef]
- Daneshi, H.; Srivastava, A.K. Security-Constrained Unit Commitment with Wind Generation and Compressed Air Energy Storage. IET Gener. Transm. Distrib. 2012, 6, 167–175. [Google Scholar] [CrossRef]
- Gu, Y.; Xie, L. Look-ahead Coordination of Wind Energy and Electric Vehicles: A Market-Based Approach. In Proceedings of the North American Power Symposium 2010, Arlington, TX, USA, 26–28 September 2010. [Google Scholar]
- Dui, X.; Zhu, G.; Yao, L. Two-Stage Optimization of Battery Energy Storage Capacity to Decrease Wind Power Curtailment in Grid-Connected Wind Farms. IEEE Trans. Power Syst. 2018, 33, 3296–3305. [Google Scholar] [CrossRef]
- Restrepo, J.; Galiana, F. Assessing the Yearly Impact of Wind Power Through a New Hybrid Deterministic/Stochastic Unit Commitment. IEEE Trans. Power Syst. 2011, 26, 401–410. [Google Scholar] [CrossRef]
- Wang, C.; Lu, Z.; Qiao, Y. A Consideration of the Wind Power Benefits in Day-Ahead Scheduling of Wind-Coal Intensive Power Systems. IEEE Trans. Power Syst. 2013, 28, 236–245. [Google Scholar] [CrossRef]
- Wang, C.; Liu, T.; Zhu, Z.; Cheng, J.; Wei, C.; Wu, Y.; Lin, C.; Bai, F. A Probabilistic Day-ahead Scheduling with Considering Wind Power Curtailment. In Proceedings of the IEEE Conference on Energy Internet and Energy System Integration, Beijing, China, 26–28 November 2017. [Google Scholar]
- Li, C.; Ahn, C.; Peng, H.; Sun, J. Synergistic Control of Plug-In Vehicle Charging and Wind Power Scheduling. IEEE Trans. Power Syst. 2013, 28, 1113–1121. [Google Scholar] [CrossRef]
- Bouffard, F.; Galiana, F.D. Stochastic Security for Operations Planning with Significant Wind Power Generation. IEEE Trans. Power Syst. 2008, 23, 306–316. [Google Scholar] [CrossRef]
- Wang, J.; Shahidehpour, M.; Li, Z. Security-Constrained Unit Commitment with Volatile Wind Power Generation. IEEE Trans. Power Syst. 2008, 23, 1319–1327. [Google Scholar] [CrossRef]
- Fu, Y.; Shahidehpour, M.; Li, Z. Security-Constrained Unit Commitment with AC Constraints. IEEE Trans. Power Syst. 2005, 20, 1538–1550. [Google Scholar] [CrossRef]
- Hillier, F.S.; Lieberman, G.J. Introduction to Operation Research, 9th ed.; McGraw-Hill: New York, NY, USA, 2010; pp. 464–536. ISBN 13: 978-0077298340. [Google Scholar]
- Tsili, M.; Papathanassiou, S. A Review of Grid Code Technical Requirements for Wind Farms. IET Renew. Power Gen. 2009, 3, 308–332. [Google Scholar] [CrossRef]
- Leon-Garcia, A. Probability and Random Processes for Electrical Engineering, 2nd ed.; Addison-Wesley: Boston, MA, USA, 1994; pp. 126–288. ISBN 13: 978-0201500370. [Google Scholar]
- Wan, Y.; Bucaneg, D. Short-Term Power Fluctuations of Large Wind Power Plants. J. Sol. Energy Eng. 2002, 124, 427–431. [Google Scholar] [CrossRef]
- Qian, K.; Zhou, C.; Allan, M.; Yuan, Y. Modeling of Load Demand Due to EV Battery Charging in Distribution Systems. IEEE Trans. Power Syst. 2011, 26, 802–810. [Google Scholar] [CrossRef]
- Rubinstien, R.Y.; Kroese, D.P. The Cross-Entropy Method, 1st ed.; Springer: New York NY, USA, 2004; pp. 59–128. ISBN 13: 978-0387212401. [Google Scholar]
- Grigg, C.; Wong, P.; Albrecht, P.; Allan, R.; Bhavaraju, M.; Billinton, R.; Chen, Q.; Fong, C.; Haddad, S.; Kuruganty, S.; et al. The IEEE Reliability Test System-1996. A report prepared by the Reliability Test System Task Force of the Application of Probability Methods Subcommittee. IEEE Trans. Power Syst. 1999, 14, 1010–1020. [Google Scholar] [CrossRef]
- Hedman, K.; Ferris, M.; O’Neill, R.; Fisher, E.; Oren, S. Co-Optimization of Generation Unit Commitment and Transmission Switching with N-1 Reliability. IEEE Trans. Power Syst. 2010, 25, 1052–1063. [Google Scholar] [CrossRef]
- 2011 Estimation of Vehicle Kilometers; Korea Transportation Safety Authority: Ansan, South Korea, December 2012; Available online: http://kiss.kstudy.com/public/public3-article.asp?key=60000130 (accessed on 19 August 2018).
Case without Wind Curtailment | Case with Wind Curtailment | |
---|---|---|
Generation Cost | 1,331,004 $/day | 812,309 $/day |
Reserve Cost | 138,529 $/day | 123,387 $/day |
Total Cost | 1,469,533 $/day | 935,696 $/day |
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Lee, J.; Lee, J.; Wi, Y.-M.; Joo, S.-K. Stochastic Wind Curtailment Scheduling for Mitigation of Short-Term Variations in a Power System with High Wind Power and Electric Vehicle. Appl. Sci. 2018, 8, 1684. https://doi.org/10.3390/app8091684
Lee J, Lee J, Wi Y-M, Joo S-K. Stochastic Wind Curtailment Scheduling for Mitigation of Short-Term Variations in a Power System with High Wind Power and Electric Vehicle. Applied Sciences. 2018; 8(9):1684. https://doi.org/10.3390/app8091684
Chicago/Turabian StyleLee, Jaehee, Jinyeong Lee, Young-Min Wi, and Sung-Kwan Joo. 2018. "Stochastic Wind Curtailment Scheduling for Mitigation of Short-Term Variations in a Power System with High Wind Power and Electric Vehicle" Applied Sciences 8, no. 9: 1684. https://doi.org/10.3390/app8091684