Imprecise Adequacy Assessment of Generating System Incorporating Wind Power Based on Interval Probability

Abstract:

Probability box (P-box) and interval probability (IP) were used to express both variability and imprecision of wind speed and output power of WTGs. The p-box of WTG's output power was constructed by empirical cumulative distribution function and K.S. confidence limits. The discrete IP distribution of WTG's output power was elicited from the p-box. The optimization model of imprecise generating capacity adequacy assessment incorporating wind power was established and solved by genetic algorithm (GA). Case study on RBTSW system shows the rationality of presented method.

• Periodical: Advanced Materials Research (Volumes 860-863)
• Pages: 2083-2087
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.860-863.2083
• Online since: December 2013
• Authors: Xian Jun Qi*, Jia Yi Shi, Xiang Tian Peng
• Keywords: Adequacy Assessment, Generating System, Interval Probability, Probability Box, Wind Power

References

[1] Billinton R, Gao Y, Huang D and Karki R. Adequacy assessment of wind-integrated composite generation andtransmission systems[M]. Innovations in Power Systems Reliability, Springer-Verlag London Limited. 2011: 142-167.

DOI: 10.1007/978-0-85729-088-5_4

[2] Billinton R, Bai G. Generating capacity adequacy associated with wind energy[J]. IEEE Transactions on Energy Conversion, 2004, 19(3): 641-646.

DOI: 10.1109/tec.2004.827718

[3] Billinton R, Gao Y. Multistate wind energy conversion system models for adequacy assessment of generating systems incorporating wind energy[J]. IEEE Transactions on Energy Conversion, 2008, 23(1): 163-170.

DOI: 10.1109/tec.2006.882415

[4] Wangdee W, Billinton R. Considering load-carrying capability and wind speed correlation of WECS in generation adequacy assessment[J]. IEEE Transactions on Energy Conversion, 2006, 21(3): 734-741.

DOI: 10.1109/tec.2006.875475

[5] Gao Y, Billinton R. Adequacy assessment of generating systems containing wind power considering wind speed correlation[J]. IET Renewable Power Generation, 2009, 3(2): 217-226.

DOI: 10.1049/iet-rpg:20080036

[6] Billinton R, Chen H, Ghajar R. A sequential simulation technique for adequacy evaluation of generating systems including wind energy[J]. IEEE Transactions on Energy Conversion, 1996, 11(4): 728-734.

DOI: 10.1109/60.556371

[7] Billinton R, Gao Y, Karki R. Composite system adequacy assessment incorporating large-scale wind energy conversion systems considering wind speed correlation[J]. IEEE Transactions on Power System, 2009, 24(3): 1375-1382.

DOI: 10.1109/tpwrs.2009.2023263

[8] Wangdee W, Billinton R. Reliability assessment of bulk electric systems containing large wind farms[J]. International Journal of Electrical Power & Energy Systems, 2007, 29(10): 759-766.

DOI: 10.1016/j.ijepes.2007.06.028

[9] Huang D, Billinton R. Effects of wind power on bulk system adequacy evaluation using the well-being analysis framework[J]. IEEE Transactions on Power System, 2009, 24(3): 1232-1240.

DOI: 10.1109/tpwrs.2009.2021232

[10] Campos L, Huete J, and Moral S. Probability intervals: a tool for uncertain reasoning[J]. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 1994, 2(02): 167-196.

DOI: 10.1142/s0218488594000146

[11] Zheng K, Lou H, and Huang Y. Sustainability under severe uncertainty: a probability-bounds-analysis-based approach[M]. Treatise on Sustainability Science and Engineering, Springer Netherlands, 2013: 51-66.

DOI: 10.1007/978-94-007-6229-9_4