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Bulk system reliability impacts of forced wind energy curtailment

Department of Electrical and Computer Engineering, University of Saskatchewan, 57 Campus Dr, Saskatoon, SK, Canada

Topical Section: Wind Energy

With rapid growth of wind power in power systems, it becomes important to accurately model the behavior of wind, its interaction with conventional sources and also with other wind resources in order to conduct a realistic assessment of system reliability and benefits from wind energy utilization. At low wind penetration levels, all the wind energy generated is utilized to serve the load. However, at higher penetration levels, wind energy is spilled due to limitations in the ramping capability of the scheduled generating units and transfer capability of transmission lines. The benefits from wind energy are reduced as its spillage increases. Hence, accurate wind models should be developed to include forced wind energy curtailment in the reliability modelling, considering factors such as the system load level, unit dispatch order, ramp rates of the generating units and wind profile diversity between multiple wind farms. A new technique is proposed in this paper to create a comprehensive wind absorption capability model, and embed it in the composite generation and transmission system reliability model. The presented methodology to evaluate bulk system adequacy and wind energy benefits considering wind curtailment due to both the generation and transmission constraints is illustrated on an example system.
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References

1. Burke DJ, O'Malley MJ (2011) Factors Influencing Wind Energy Curtailment. IEEE Trans Sustain Energy 2: 185–193.    

2. Martín-Martínez S, Gómez-Lázaro E, Molina-Garcia A, et al. (2014) Impact of Wind Power Curtailments on the Spanish Power System Operation. In Proc. 2014 IEEE PES General Meeting Conference & Exposition, pp. 1–5.

3. McKenna E, Grünewald P, Thomson M (2015) Going with the wind: temporal characteristics of potential wind curtailment in Ireland in 2020 and opportunities for demand response. IET Renew Power Gen 9: 66–77.    

4. Karki R, Dhungana D, Shimu S, et al. (2013) Reliability evaluation incorporating the load following capability of wind generation. In Proc. 2013 26th Annual IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), pp. 1–4.

5. Gu Y, Xie L (2014) Fast Sensitivity Analysis Approach to Assessing Congestion Induced Wind Curtailment. IEEE T Power Syst 29: 101–110.    

6. Pierre I (2011) Flexible Generation: Backing Up Renewables. Eurelectric Renewables Action Plan, Eurelectric, Brussels, D/2011/12.105/47.

7. Kirby B, Hirst E (1998) Generator Response to Intrahour Load Fluctuations. IEEE T Power Syst 13: 1373–1378.    

8. Billinton R, Allan RN (1996) Reliability Evaluation of Power Systems. 2nd ed. New York: Plenum.

9. Singh C, Lago-Gonzalez A (1985) Reliability modeling of generations systems including unconventional energy sources. IEEE T Power Appar Syst PAS-104: 1049–1055.    

10. Billinton R, Karki R, Gao Y, et al. (2012) Adequacy Assessment Considerations in Wind Integrated Power Systems. IEEE T Power Syst 27: 2297–2305.    

11. Billinton R, Wangdee W (2007) Reliability-Based Transmission Reinforcement Planning Associated With Large-Scale Wind Farms. IEEE T Power Syst 22: 34–41.    

12. Billinton R, Gao Y, Karki R (2009) Composite System Adequacy Assessment Incorporating Large-Scale Wind Energy Conversion Systems Considering Wind Speed Correlation. IEEE T Power Syst 24: 1375–1382.    

13. Holttinen H (2017) IEA Wind Task 25 - summary of experiences and studies for wind integration. Proceedings of WIW2017 workshop Vienna, 15–17.

14. Milligan M, Frew B, Ibanez E, et al. (2016) Capacity value assessments of wind power. Energy Environ 6(1).

15. WIREs Energy Environ 6: e226. Available from: https://doi.org/10.1002/wene.226.

16. Padhee M, Karki R (2016) Reliability/Environmental Impacts of Wind Energy Curtailment due to Ramping Constraints. Int J Syst Assur Eng Manag , Springer.

17. Aminifar F, Fotuhi-Firuzabad M, Shahidehpour M (2009) Unit Commitment With Probabilistic Spinning Reserve and Interruptible Load Considerations. IEEE T Power Syst 24: 388–397.

18. Arriagada E, Lópezet E, López M, et al, (2015) A probabilistic economic dispatch model and methodology considering renewable energy, demand and generator uncertainties. Electr Pow Syst Res 121: 325–332.    

19. Billinton R, Chen H, Ghajar R (1996) Time-series models for reliability evaluation of power systems including wind energy. Microelectron Reliab 36: 1253–1261.    

20. Environment Canada, Canadian Weather Energy and Engineering Datasets (CWEEDS). Available from: ftp://ftp.tor.ec.gc.ca/Pub/ Engineering_Climate_Dataset/Canadian_Weather_ Energy_Engineering_Dataset_CWEEDS_2005/ZIPPED FILES/ENGLISH/.

21. Billinton R, Karki R, Gao Y, et al. (2012) Adequacy Assessment Considerations in Wind Integrated Power Systems. IEEE T Power Syst 27: 2297–2305.    

22. Sturges HA (1926) The choice of a class interval. J Am Stat Assoc 21: 65–66.    

23. Li W (1998) Installation Guide and User's Manual for the MECORE Program. July Google Scholar .

24. Subcommittee APM (1979) IEEE Reliability Test System. IEEE T Power Appar Syst 98: 2047–2054.

25. Dhungana D, Karki R (2015) Data Constrained Adequacy Assessment for Wind Resource Planning. IEEE T Sust Energ 6: 219–227.    

© 2018 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

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