Enerji iletim sisteminde bara bölme problemi için çok amaçlı optimizasyon yöntemi

Öz

Artan enerji ihtiyacını karşılayabilmek için genişlemeye devam eden elektrik enerjisi iletim sistemi çeşitli güvenlik problemlerine sebep olmaktadır. Şebekeye entegre edilen yeni yatırımların bir sonucu olarak sistem eşdeğer empedansının azalması kısa devre akımlarının yükselmesine neden olmaktadır. Meydana gelebilecek kısa devre akımlarının mevcut kesicilerin kesme kapasitesini aşması önlem alınması gereken önemli bir problemdir. Kısa devre akımlarını sınırlandırmak amacıyla kullanılan yöntemlerden biri bara bölme tekniğini kullanmaktır. Bara bölme yöntemi iki bara bulunan transformatör merkezlerinde fiderleri uygun baralara dağıtarak baralar arasındaki bağlantının kesilmesi suretiyle uygulanmaktadır. Bara bölme yoluyla şebeke konfigürasyonunda yapılan değişiklikler neticesinde her ne kadar kısa devre akımları sınırlandırılabilse de N-1 güvenliğinde bozulmalar ve güç kayıplarında artışlar söz konusu olabilmektedir. Kısa devre akımlarını sınırlandırmak amacıyla sistem eşdeğer empedansını yükseltmenin bir sonucu olarak güç kayıpları da artmaktadır. Kısa devre akımları ile güç kayıpları arasında bulunan bu çıkar çatışması nedeniyle iki amaç arasında bir denge noktasının bulunması gerekmektedir. Bu doğrultuda çalışmamız kapsamında bara bölme optimizasyonu probleminin amaçları kısa devre akımları ve aktif güç kayıpları olarak seçilmiş ve N-1 güvenliği probleme kısıt olarak eklenmiştir. Problemin çözümünde Pareto-optimal kavramını Genetik Algoritmayla birleştiren kısıtlı NSGA-II algoritması çok amaçlı bara bölme optimizasyonu problemini çözmek için kullanılmıştır. Oluşturulan matematiksel model ve kullanılan algoritma IEEE RTS 96- baralı test sisteminde uygulanmıştır. Elde edilen sonuçlar önerilen yaklaşımın kısa devre akımlarını gerektiği kadar sınırlandırma, güç kayıplarının aşırı yükselmesini önleme ve N-1 güvenliğini sürdürme açısından şebekenin güvenli çalışma topolojilerini elde etmede başarılı sonuçlar verdiğini göstermektedir.

Anahtar Kelimeler

• Çok amaçlı optimizasyon
• Bara bölme optimizasyonu
• Sezgi-Üstü algoritma
• NSGA-II
• Kısıtlı optimizasyon
• Kısa devre akımları
• Güç kayıpları
• N-1 güvenliği

Multi-Objective optimization method for bus splitting problem in energy transmission system

Abstract

The electrical energy transmission system lasted to expand in order to compensate for growing energy demand, leads to a variety of security problems. The decreasing system equivalent impedance in the result of the new investments integrated into the network causes to raise the short-circuit currents. It is a crucial problem, which taking remedial action is a necessity, for the short-circuit currents to be occurred exceed the circuit-breaker capacity. One method used to contain short-circuit currents is to perform the bus splitting technique. The bus splitting method is implemented through distributing feeders to appropriate buses and disconnect the zero-impedance line between buses in substations. Even if the short-circuit currents are restricted thanks to alterations in the network configuration by using bus splitting, the violation of the N-1 security and the growth of the power losses might occur. Increasing the system equivalent impedance in order to limit the short-circuit currents results in greater power losses. A balance between power losses and short-circuit currents should be found out due to the conflict of interest between these objectives. The objective function is created with short-circuit currents and active power losses and N-1 security is implemented as a constraint in this study. The constrained NSGA-II algorithm integrating Pareto-optimality concept into Genetic Algorithm is performed to solve the multi-objective bus splitting optimization problem. Mathematical model and algorithm created are implemented to the RTS 96-bus test system. The results show that the proposed method is successive in obtaining the secure topological frameworks of the network in terms of restricting the short-circuit currents, preventing the over increase of the power losses and maintaining the N-1 security.

Keywords

• Multi-objective optimization
• Bus splitting optimization
• Meta-Heuristics
• NSGA-II
• Constrained optimization
• Short-Circuit currents
• Power losses
• N-1 security

Kaynakça

1. [1] Namchoat S, Hoonchareon N. “Optimal bus splitting for short-circuit current limitation in metropolitan area”. International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, Krabi, Thailand, 15-17 May 2013.
2. [2] Nasrolahpour E, Ghasemi H, Khanabadi M. “Optimal transmission congestion management by means of substation reconfiguration”. Iranian Conference on Electrical Engineering, Tehran, Iran, 15-17 May 2012.
3. [3] Yang Z, Zhong H, Xia Q, Kang C. “Optimal transmission switching with short-circuit current limitation constraints”. IEEE Transactions on Power Systems, 31(2), 1278-1288, 2016.
4. [4] Hedman KW, O’Neill RP, Fisher EB, Oren SS. “Optimal transmission switching with contingency analysis”. IEEE Transactions on Power Systems, 24(3), 1577-1586, 2009.
5. [5] Bacher R, Glavitsch H. “Network topology optimization with security constraints”. IEEE Transactions on Power Systems, 1(4), 103-111, 1986.
6. [6] Mazi A, Wollenberg BF, Hesse MH. “Corrective control of power system flows by line and bus-bar switching”. IEEE Transactions on Power Systems, 1(3), 258 - 264, 1986.
7. [7] Wrubel JN, Rapcienski SMPS, Lee KL. “Practical experience with corrective switching algorithm for on-line applications”. IEEE Transactions on Power Systems, 11(1), 415 - 421, 1996.
8. [8] Jacqueline G, Luiz R, Machado JB. “A study of the use of corrective switching in transmission systems”. IEEE Transactions on Power Systems, 14(1), 336-341, 1999.

9. [9] Heidarifar M, Ghasemi H. “A network topology optimization model based on substation and nodebreaker modeling”. IEEE Transactions on Power Systems, 31(1), 247 - 255, 2016.
10. [10] Heidarifar M, Doostizadeh M, Ghasemi H. “Optimal transmission reconfiguration through line switching and bus splitting”. IEEE PES General Meeting|Conference & Exposition, National Harbor, MD, USA, 27-31 July 2014.
11. [11] Heidarifar M, Andrianesis P, Ruiz P, Caramanis MC, Paschalidis IC. “An optimal transmission line switching and bus splitting heuristic incorporating AC and N-1 contingency constraints”. International Journal of Electrical Power & Energy Systems, 2021. https://doi.org/10.1016/j.ijepes.2021.107278.
12. [12] Xiao R, Xiang Y, Wang L, Xie K. “Power system reliability evaluation incorporating dynamic thermal rating and network topology optimization”. IEEE Transactions on Power Systems, 33(6), 6000-6012, 2018.
13. [13] Khodaei A, Shahidehpour M, Kamalinia S. “Transmission switching in expansion planning”. IEEE Transactions on Power Systems, 25(3), 1722-1733, 2010.
14. [14] Hedman KW, Ferris MC, O’Neill RP, Fisher EB, Oren SS. “Co-optimization of generation unit commitment and transmission switching with N-1 reliability”. IEEE Transactions on Power Systems, 25(2), 1052 - 1063, 2010.
15. [15] Zhou Y, Zhu H. “Bus split sensitivity analysis for enhanced security in power system operations”. 51st North American Power Symposium, Wichita, KS, USA, 13-15 October 2019.
16. [16] Mirjalili S, Mirjalili SM, Lewis A. “Grey wolf optimizer”. Advances in Engineering Software, 69, 46-61, 2014.
17. [17] Sherali HD, Driscoll PJ. “Evolution and state-of-the-art in integer programming”. Journal of Computational and Applied Mathematics, 124(1-2), 319-340, 2000.
18. [18] İşcan S, Kaplan O, Lokman G. “Güç sisteminde metasezgisel algoritmalarla güç kaybı ve gerilim kararlılığı optimizasyonu”. Pamukkale University Journal of Engineering Sciences, 27(2), 199-209, 2021.
19. [19] Güvenç U, Battal O. “Coyote optimization algorithm to solve energy hub economic dispatch problem”. International Journal of Technological Sciences, 12(1), 10-19, 2020.
20. [20] Gümüş ET, Sarıgüzel C, Turan M, Yalçın MA. “Genetik algoritmalar kullanılarak transformatörde kademe ayarı ile enerji verimliliği iyileştirme”. Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 10(4), 2486-2495, 2020.
21. [21] Alcan Y, Öztürk A, Dirik H, Demir M. “Güç şebekelerinde minimum kayıpları sağlayan STATCOM konumunun ve değerinin belirlenmesinde farklı sezgisel algoritmaların karşılaştırılması”. Pamukkale University Journal of Engineering Sciences, 23(5), 550-558, 2017.
22. [22] Hussain K, Mohd Salleh MN, Cheng S, Shi Y. “Metaheuristic research: a comprehensive survey”. Artificial Intelligence Review, 52(4), 2191-2233, 2019.
23. [23] Holland JH. “Genetic algorithms”. Scientific American, 267(1), 66-72, 1992.
24. [24] Srinivas N, Deb K. “Muiltiobjective optimization using nondominated sorting in genetic algorithms”. Evolutionary Computation, 2(3), 221-248, 1994.
25. [25] Deb K, Pratap A, Agarwal S, Meyarivan T. “A fast and elitist multiobjective genetic algorithm: NSGA-II”. IEEE Transactions on Evolutionary Computation, 6(2), 182-197, 2002.
26. [26] Kamarposhti MA, Kabalci E. “Optimal transmission expansion planning considering distributed generations by using non-dominated sorting genetic algorithm-ii (NSGAII)”. Journal of Power Technologies, 101(1), 70-77, 2021.
27. [27] Hu Y, Bie Z, Ding T, Lin Y. “An NSGA-II based multiobjective optimization for combined gas and electricity network expansion planning,” Applied Energy, 167, 280-293, 2016.
28. [28] Xiaoming W, et al. “Multi-objective transmission network planning based on multi-objective optimization algorithms”. IEEE Conference on Energy Internet and Energy System Integration, Beijing, China, 26-28 November 2017.
29. [29] Arabali A, Ghofrani M. “A multi-objective transmission expansion planning framework in deregulated power systems with wind generation”. IEEE Transactions on Power Systems, 29(6), 3003-3011, 2014.
30. [30] Eliassi M, Seifi H, Haghifam MR. “Multi-objective valuebased reliability transmission planning using expected interruption cost due to transmission constraint”. International Transactions on Electrical Energy Systems, 23(8), 1468-1489, 2013.
31. [31] Correa-Florez CA, Salcedo AS, Panesso AF. “Multiobjective transmission expansion planning considering uncertainty in wind power and demand”. IEEE PES Transmission and Distribution Conference and ExpositionLatin America, Morelia, Mexico, 20-24 September 2016.
32. [32] Correa CA, Bolanos R, Sanchez A, Garces A, Molina A. “Multiobjective transmission planning with security constraints”. IEEE EuroCon 2013, Zagreb, Croatia, 1-4 July 2013.
33. [33] Sadees M, Vijayakumar K, Roselyn JP. “MOGA based congestion management in deregulated power systems”. International Journal of Control Theory and Applications, 9(15), 7081-7093, 2016.
34. [34] Hernandez YR, Hiyama T. “Minimization of voltage deviations, power losses and control actions in a transmission power system”. 15th International Conference on Intelligent System Applications to Power Systems, Curitiba, Brazil, 8-12 November 2009.
35. [35] Belazzoug M, Boudour M. “FACTS placement multiobjective optimization for reactive power system compensation”. 7th International Multi-Conference on Systems, Signals and Devices, Amman, Jordan, 27-30 June 2010.
36. [36] Benabid R, Boudour M, Berizzi A, Bovo C, Ilea V. “Multiobjective optimization of static var compensator in the presence of secondary voltage regulation using NSGA-II”. IEEE International Energy Conference and Exhibition, Florence, Italy, 9-12 September 2012.
37. [37] Nguyen TT, Yousefi A. “Multi-Objective approach for optimal location of TCSC using NSGA II”. International Conference on Power System Technology, Zhejiang, China, 24-28 October 2010.
38. [38] Wartana IM, Singh JG, Ongsakul W, Buayai K, Sreedharan S. “Optimal placement of UPFC for maximizing system loadability and minimize active power losses by NSGA-II”. International Conference & Utility Exhibition on Power and Energy Systems: Issues and Prospects for Asia (ICUE), Pattaya, Thailand, 28-30 September 2011.
39. [39] Wartana IM. “A multi-objective problems for optimal integration of the DG to the grid using the NSGA-II”. International Conference on Quality in Research, Lombok, Indonesia, 10-13 August 2015.
40. [40] Moeini A, Kamwa I, de Montigny M, Lenoir L. “Application of battery energy storage for network vulnerability mitigation”. IEEE Power Engineering Society Transmission and Distribution Conference, Dallas, TX, USA, 3-5 May 2016.
41. [41] Li G, Shi D. “Risk-based maintenance schedule of transmission line using multi-objective evolutionary algorithm”. IEEE Power Engineering and Automation Conference, Wuhan, China, 8-9 September 2011.
42. [42] del Castillo MY, Song H. “Tabu search based topology modification for reduction of fault current level in power systems”. International Conference on Soft Computing and Intelligent Systems and International Symposium on Advanced Intelligence Systems, Kobe, Japan, 20-24 November 2012.
43. [43] Doğan E, Yörükeren N. “Optimal bus layout in transmission system by using meta-heuristic approaches”. Electric Power Components and Systems, 48(12-13), 1390-1400, 2020.
44. [44] Doğan E, Yörükeren N. “Binary pathfinder algorithm for bus splitting optimisation problem,” IET Generation, Transmission and Distribution, 14(26), 6613-6638, 2020.
45. [45] ILLINOIS Information Trust Institute. “IEEE 96-RTS test system”. https://icseg.iti.illinois.edu/power-cases/ieee-96-rtstest-system (27.07.2021).
46. [46] Grigg C, at all. “The IEEE reliability test system-1996”. IEEE Transactions on Power Systems, 14(3), 1010-1020, 1999.