Minimum Dinamik İletim Hat Derecelendirmesi Kullanarak Güç Sistemin Güvenilirliğini Artırma

Hem elektrik talebinde hem de yenilenebilir kaynakların kullanımındaki artış nedeniyle elektrik güç iletim altyapısı teknik sınırlarına yakın bir seviyede kullanılmaktadır. Dinamik iletim hat derecelendirmesi, mevcut iletim hatlarının daha verimli kullanılabileceği güç iletim yönetimindeki yeni uygulamalardan biridir. Literatürde dinamik derecelendirme genellikle güç sisteminin tüm hatlarına uygulanır. Bu yaklaşım, güç sistemi yöneticisini çok sayıda iletim hattını aynı anda izlemeye zorlar ve systemin ğüvenilirliğini tehlikeye sokar. Bu çalışmada, sistemin N-K güvenilirliğini etkili bir şekilde artırmak için çok az sayıda ve en uygun iletim hatlara dinamik derecelendirme uygulanması önerilmektedir. Dinamik derecelendirme için minimum sayıda iletim hatları bulmak için dayanıklı bir min-max-min üç katmanlı optimizasyon modeli geliştirilmiştir. Dualite teorisini kullanarak üç katmanlı problem iki katmanlı min-max probleme dönüştürülmüştür ve ardından iki katmanlı problemi çözmek için bir ayrıştırma (decomposition) yöntemi geliştirilmiştir. Çözüm yaklaşımı uygulamak ve sonuçları analiz etmek için iki test güç sistemi kullanılmıştır.

Anahtar Kelimeler:

Dayanaklı optimizasyon, karışık tamsayılı programlama, güç sistemi güvenilirliği, dinamik iletim hat derecelendirmesi, optimum güç akışı

Increasing Power System Reliability Using Minimum Dynamic Line Rating

Due to increase in electricity demand and renewable resources penetration, power transmission infrastructure is utilized close to its technical limits. Dynamic line rating is one of the new practices in power transmission management by which existing transmission lines can be more eﬃciently utilized. In the literature, dynamic rating is usually applied to all lines of the power system which is not desirable as it is risky to simultaneously monitor a high number of transmission lines. In this paper we propose to apply dynamic rating on few and most suitable lines to eﬀectively increase the system N–K reliability. We develop a robust min-max-min three-layer optimization model to find minimum number of lines for dynamic rating. We use duality theory and convert the three-layer problem into a two-layer min-max problem and then we develop a benders decomposition framework to solve the two-layer problem. We use two test power systems to demonstrate our solution approach and analyze the results.

Keywords:

Robust optimization, mixed integer programming, power system reliability, dynamic line rating, optimal power flow,

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Abboud, A. W., Fenton, K. R., Lehmer, J. P., Fehringer, B. A., Gentle, J. P., McJunkin, T. R., Le Blanc, K. L., Petty, M. A., Wandishin, M. S. (2019). Coupling computational fluid dynamics with the high resolution rapid refresh model for forecasting dynamic line ratings, Electric Power Systems Research, Volume 170, Pages 326-337, ISSN 0378-7796, Doi: https://doi.org/10.1016/j.epsr.2019.01.035
J. L. Aznarte and N. Siebert, (2017). Dynamic Line Rating Using Numerical Weather Predictions and Machine Learning: A Case Study, IEEE Transactions on Power Delivery, vol. 32, no. 1, pp. 335-343, Feb. 2017, Doi: https://doi.org/ 10.1109/TPWRD.2016.2543818
Banerjee, B., Jayaweera, D., and Islam, S. M. (2014). Optimal scheduling with dynamic line ratings and intermittent wind power. IEEE PES General Meeting, Conference Exposition, pages 1–5, Doi: https: //doi.org/10.1109/PESGM.2014.6939381
Banerjee, B., Jayaweera, D., Islam, S. (2015). Risk constrained short-term scheduling with dynamic line ratings for increased penetration of wind power, Renewable Energy, Volume 83, Pages 1139-1146, ISSN 0960-1481, Doi: https:// doi.org/10.1016/j.renene.2015.05.053
Bucher, M. A., Vrakopoulou, M., and Andersson, G. (2013). Probabilistic N−1 security assessment incorporating dynamic line ratings. IEEE Power & Energy Society General Meeting, pages 1–5, Doi: https:// doi.org/10.1109/PESMG.2013.6672679
Ciniglio, O. and Deb, A. (2004). Optimizing transmission path utilization in idaho power, IEEE Transactions on Power Delivery, vol. 19, no. 2, pp. 830-834, Doi: https: //doi.org/10.1109/TPWRD.2003.823186
Dupin, R., Kariniotakis, G., Michiorri, A. (2019). Overhead lines Dynamic Line rating based on probabilistic day-ahead forecasting and risk assessment, International Journal of Electrical Power & Energy Systems, Volume 110, Pages 565-578, ISSN 0142-0615, Doi: https://doi.org/ 10.1016/j.ijepes.2019.03.043
Fernandez, E., Albizu, I., Bedialauneta, M.T., Mazon, A.J., Leite, P.T. (2016). Review of dynamic line rating systems for wind power integration, Renewable and Sustainable Energy Reviews, Volume 53, Pages 80-92, ISSN 1364-0321, Doi: https://doi.org/10.1016/j.rser.2015.07.149
Fernandes, S. W., Rosa, M. A., Issicaba, D. (2020). A robust dynamic line rating monitoring system through state estimation and bad data analysis, Electric Power Systems Research, Volume 189, 106648, ISSN 0378-7796, Doi: https://doi.org/ 10.1016/j.epsr.2020.106648
Fisher, E. B., O’Neill, R. P., and Ferris, M. C. (2008). Optimal transmission switching. IEEE Transactions on Power Systems, 23(3):1346–1355, Doi: https://doi.org/ 10.1109/TPWRS.2008.922256
Garifi, K. and Baker, K. (2020). Considering integer chance constraints for enforcing flexible line flow ratings. American Control Conference (ACC), pages 3134–3139, Doi: https://doi.org/10.23919/ACC45564.2020.9147669
Hemparuva, R. J. C., Simon, S. P., Kinattingal, S., Padhy, N. P. (2018). Geographic information system and weather based dynamic line rating for generation scheduling, Engineering Science and Technology, an International Journal, Volume 21, Issue 4, Pages 564-573, ISSN 2215-0986, Doi: http s://doi.org/10.1016/j.jestch.2018.05.011
A. Kirilenko, M. Esmaili and C. Y. Chung, (2021). Risk-Averse Stochastic Dynamic Line Rating Models, IEEE Transactions on Power Systems, vol. 36, no. 4, pp. 3070-3079, Doi: https://doi.org/10.1109/ TPWRS.2020.3045589
Y. Li, K. Xie, R. Xiao, B. Hu, H. Chao and D. Kong, (2018). Network-Constrained Unit Commitment Incorporating Dynamic Thermal Rating and Transmission Line Switching, 2nd IEEE Conference on Energy Internet and Energy System Integration (EI2), 2018, pp. 1-6, Doi: https://doi.org/10.1109/EI2.2018.8582182
S. Madadi, B. Mohammadi-Ivatloo and S. Tohidi, (2020). Dynamic Line Rating Forecasting Based on Integrated Factorized Ornstein–Uhlenbeck Processes, IEEE Transactions on Power Delivery, vol. 35, no. 2, pp. 851-860, Doi: https:// doi.org/10.1109/TPWRD.2019.2929694
M. Nick, O. Alizadeh-Mousavi, R. Cherkaoui and M. Paolone, (2016). Security Constrained Unit Commitment With Dynamic Thermal Line Rating, IEEE Transactions on Power Systems, vol. 31, no. 3, pp. 2014-2025, Doi: https://doi.org/ 10.1109/TPWRS.2015.2445826
Numan, M., Feng, D., Abbas, F., Habib, S., Hao, S. (2021). Coordinated operation of reconfigurable networks with dynamic line rating for optimal utilization of renewable generation, International Journal of Electrical Power & Energy Systems, Volume 125, 106473, ISSN 0142-0615, Doi: https:// doi.org/10.1016/j.ijepes.2020.106473
Park, H., Jin, Y. G., Park, J. K. (2018). Stochastic security-constrained unit commitment with wind power generation based on dynamic line rating, International Journal of Electrical Power & Energy Systems, Volume 102, Pages 211-222, ISSN 0142-0615, Doi: https://doi.org/10.1016/j.ijepes. 2018.04.026
Power systems test case archive (1999), available: https://labs.ece.uw.edu/pstca/
Qiu, F. and Wang, J. (2015). Distributionally robust congestion management with dynamic line ratings. IEEE Transactions on Power Systems, 30(4):2198–2199, Doi: https://doi.org/10.1109/TPWRS.2014.2361012
Subcommittee, P. M. (1979). IEEE reliability test system. IEEE Transactions on Power Apparatus and Systems, PAS-98(6):2047–2054, Doi: https://doi.org/10.1109/TPAS.1979.319398
F. Teng, R. Dupin, A. Michiorri, G. Kariniotakis, Y. Chen and G. Strbac, (2018). Understanding the Benefits of Dynamic