Modern Güç Sistemlerinde Tıkanıklık Yönetimi Probleminin Çözümü İçin Balçık Küfü Optimizasyon Algoritması
Öz
Günümüzde, yeniden yapılandırılmış elektrik güç sistemlerinde meydana gelen belirsizlikler rekabetçi elektrik piyasasında tıkanıklık oluşturmakta olup, bu durum güç sisteminde ekonomik kayba ve sistem güvenilirliğini zayıflatmaktadır. Bu durum göz önüne alındığında, tıkanıklık yönetimi (CM) modern güç sistemlerinin işletilmesi ve kontrolü açısından en önemli planlama problemlerden biri olarak ortaya çıkmaktadır. Bu makalede, tıkanıklık yönetimi problemini çözmek için son zamanlarda literatüre sunulan Balçık Küfü Algoritması (SMA) ve Tazmanya Canavarı Optimizasyon (TDO) algoritması kullanılmıştır. SMA ve TDO algoritmaları, generatörlerin ürettikleri aktif güç değerlerini yeniden planlayarak elektrik piyasasında meydana gelen tıkanıklığı belirtilen eşitlik ve eşitsizlik kısıtlamaları içerisinde hafifletmektedir. Ayrıca önerilen algoritmalar, toplam yeniden planlama maliyetini minimize ederken, benzetim çalışmalarında oluşturulan senaryolara göre meydana gelen tıkanıklığın giderilmesini sağlamaktadır. Benzetim çalışmaları değiştirilmiş IEEE 30- bara test sisteminde gerçekleştirilmiş ve elde edilen sonuçlar literatürdeki diğer sonuçlar ile karşılaştırılmıştır. Karşılaştırma sonuçlarına göre, SMA algoritmasının tıkanıklık yönetimi problemini çözmede literatürdeki diğer algoritmalardan daha başarılı olduğu görülmüştür.
Anahtar Kelimeler
Modern Güç Sistemleri Tıkanıklık Yönetimi Yeniden Planlama Optimizasyon
Kaynakça
- A. Narain, S.K. Srivastava, and S.N. Singh “Congestion management approaches in restructured power system: key issues and challenges”, The Electricity Journal, vol. 33, p. 106715, 2020.
- R. Peesapati, A. Yadav, V.K. Yadav, and N. Kumar “GSA-FAPSO-based generators active power rescheduling for transmission congestion management”, IEEE Systems Journal, vol. 13, no. 3, pp. 3266-3273, 2019.
- A. Pillay, S.P. Karthikeyan, and D.P. Kothari “Congestion management in power systems-a review”, International Journal of Electrical Power and Energy Systems, vol. 70, pp. 83-90, 2015.
- S. Balaraman, and N. Kamaraj “Transmission congestion management using particle swarm optimization”, J. Electrical Systems, vol. 7, no. 1, pp. 54-70, 2011.
- S. Verma, and V. Mukherjee “Firefly algorithm for congestion management in deregulated environment”, Engineering Science and Technology, an International Journal, vol. 19, pp. 1254-1265, 2016.
- S. Verma, and V. Mukherjee “Optimal real power rescheduling of generators for congestion management using a novel ant lion optimiser”, IET Generation, Transmission and Distribution, vol. 10, no. 10, pp. 2548-2561, 2016.
- S. Verma, S. Saha, and V. Mukherjee “Optimal rescheduling of real power generation for congestion management using teaching-learning-based optimization algorithm”, Journal of Electrical Systems and Information Technology, vol. 5, pp. 889-907, 2018.
- S. Verma, S. Saha, and V. Mukherjee “A novel symbiotic organisms search algorithm for congestion management in deregulated environment”, Journal of Experimental and Theoretical Artificial Intelligence, vol. 29, no. 1, pp. 59-79, 2017.
- K. Vijayakumar “Multiobjective optimization methods for congestion management in deregulated power systems”, Journal of Electrical and Computer Engineering, p. 962462, 2012.
- S. Balaraman, and N. Kamaraj “Application of differential evolution for congestion management in power system”, Modern Applied Science, vol. 4, no. 8, pp. 33-42, 2010.
- M. Kashyap, and S. Kansal “Hybrid apprach for congestion management using optimal placement of distributed generator”, International Journal of Ambient Energy, vol. 39, no. 2, pp. 132-142, 2018.
- S.T. Suganthi, D. Devaraj, K. Ramar, and S.H. Thilagar “An improved differential evolution algorithm for congestion management in the presence of wind turbine generators”, Renewable and Sustainable Energy Reviews, vol. 81, pp. 635-642, 2018.
- S.R. Salkuti, and S.C. Kim “Congestion management using multi-objective glowworm swarm optimization algorithm”, Journal of Electrical Engineering and Technology, vol. 14, pp. 1565-1575, 2019.
- C. Venkaiah, and D.M.V. Kumar “Fuzzy PSO congestion management using sensitivity-based optimal active power rescheduling of generators”, Journal of Electrical Engineering and Technology, vol. 6, no. 1, pp. 32-41, 2011.
- A. Sharma, and S.K. Jain “Gravitational search assisted algorithm for TCSC placement for congestion control in deregulated power system”, Electric Power Systems Research, vol. 174, 105874, 2019.
- K. Paul, P. Sinha, S. Mobayen, F.F.M. El-Sousy, and A. Fekih “A novel improved crow search algorithm to alleviate congestion in power system transmission lines”, Energy Reports, vol. 8, pp. 11456-11465, 2022.
- S. Li, H. Chen, M. Wang, A.A. Heidari, and S. Mirjalili “Slime mould algorithm: a new method for stochastic optimization”, Future Generation Computer Systems, vol. 111, pp. 300-323, 2020.
- M. Dehghani, S. Hubálovský, and P. Trojovský “Tasmanian devil optimization: a new bio-inspired optimization algorithm for solving optimization algorithm”, IEEE Access, vol. 10, pp. 19599-19620, 2022.
- F.S. Gharehchopogh, A. Ucan, T. Ibrikci, B. Arasteh, and G. Isik “Slime mould algorithm: a comprehensive survey of its variants and applications”, Archives of Computational Methods in Engineering.
- R.D. Zimmerman, C.E. Murillo-Sanchez, and R.J. Thomas “MATPOWER: steady-state operations, planning, and analysis tools for power systems research and education”, IEEE Transactions on Power Systems, vol. 26, no. 1, pp. 12-19, 2011.
- MATPOWER http://www.pserc.cornell.edu/matpower
Slime Mould Optimization Algorithm for Solving Congestion Management Problem in Modern Power Systems
Öz
Nowadays, the uncertainties that occur in reconstructed electrical power systems create a bottleneck in the competitive electricity market, which leads to economic loss in the power system and weakens the reliability of the system. Given this situation, the congestion management (CM) emerges as one of the most important planning problems for the operation and control of modern power systems. In this article, Slime Mould Algorithm (SMA) and Tasmanian Devil Optimization (TDO) algorithm, which have recently been presented to the literature, are used to solve the congestion management problem. SMA and TDO algorithms alleviate the congestion in the electricity market within the specified equality and inequality constraints by replanning the active power values produced by the generators. In addition, the proposed algorithms minimize the total replanning cost, while eliminating the congestion that occurs according to the scenarios created in the simulation studies. Simulation studies were performed on a modified IEEE 30-bar test system and the results were compared with other results in the literature. According to the comparison results, it has been seen that the SMA algorithm is more successful than other algorithms in the literature in solving the congestion management problem.
Anahtar Kelimeler
Modern Power Systems Congestion Management Replanning Optimization
Kaynakça
- A. Narain, S.K. Srivastava, and S.N. Singh “Congestion management approaches in restructured power system: key issues and challenges”, The Electricity Journal, vol. 33, p. 106715, 2020.
- R. Peesapati, A. Yadav, V.K. Yadav, and N. Kumar “GSA-FAPSO-based generators active power rescheduling for transmission congestion management”, IEEE Systems Journal, vol. 13, no. 3, pp. 3266-3273, 2019.
- A. Pillay, S.P. Karthikeyan, and D.P. Kothari “Congestion management in power systems-a review”, International Journal of Electrical Power and Energy Systems, vol. 70, pp. 83-90, 2015.
- S. Balaraman, and N. Kamaraj “Transmission congestion management using particle swarm optimization”, J. Electrical Systems, vol. 7, no. 1, pp. 54-70, 2011.
- S. Verma, and V. Mukherjee “Firefly algorithm for congestion management in deregulated environment”, Engineering Science and Technology, an International Journal, vol. 19, pp. 1254-1265, 2016.
- S. Verma, and V. Mukherjee “Optimal real power rescheduling of generators for congestion management using a novel ant lion optimiser”, IET Generation, Transmission and Distribution, vol. 10, no. 10, pp. 2548-2561, 2016.
- S. Verma, S. Saha, and V. Mukherjee “Optimal rescheduling of real power generation for congestion management using teaching-learning-based optimization algorithm”, Journal of Electrical Systems and Information Technology, vol. 5, pp. 889-907, 2018.
- S. Verma, S. Saha, and V. Mukherjee “A novel symbiotic organisms search algorithm for congestion management in deregulated environment”, Journal of Experimental and Theoretical Artificial Intelligence, vol. 29, no. 1, pp. 59-79, 2017.
- K. Vijayakumar “Multiobjective optimization methods for congestion management in deregulated power systems”, Journal of Electrical and Computer Engineering, p. 962462, 2012.
- S. Balaraman, and N. Kamaraj “Application of differential evolution for congestion management in power system”, Modern Applied Science, vol. 4, no. 8, pp. 33-42, 2010.
- M. Kashyap, and S. Kansal “Hybrid apprach for congestion management using optimal placement of distributed generator”, International Journal of Ambient Energy, vol. 39, no. 2, pp. 132-142, 2018.
- S.T. Suganthi, D. Devaraj, K. Ramar, and S.H. Thilagar “An improved differential evolution algorithm for congestion management in the presence of wind turbine generators”, Renewable and Sustainable Energy Reviews, vol. 81, pp. 635-642, 2018.
- S.R. Salkuti, and S.C. Kim “Congestion management using multi-objective glowworm swarm optimization algorithm”, Journal of Electrical Engineering and Technology, vol. 14, pp. 1565-1575, 2019.
- C. Venkaiah, and D.M.V. Kumar “Fuzzy PSO congestion management using sensitivity-based optimal active power rescheduling of generators”, Journal of Electrical Engineering and Technology, vol. 6, no. 1, pp. 32-41, 2011.
- A. Sharma, and S.K. Jain “Gravitational search assisted algorithm for TCSC placement for congestion control in deregulated power system”, Electric Power Systems Research, vol. 174, 105874, 2019.
- K. Paul, P. Sinha, S. Mobayen, F.F.M. El-Sousy, and A. Fekih “A novel improved crow search algorithm to alleviate congestion in power system transmission lines”, Energy Reports, vol. 8, pp. 11456-11465, 2022.
- S. Li, H. Chen, M. Wang, A.A. Heidari, and S. Mirjalili “Slime mould algorithm: a new method for stochastic optimization”, Future Generation Computer Systems, vol. 111, pp. 300-323, 2020.
- M. Dehghani, S. Hubálovský, and P. Trojovský “Tasmanian devil optimization: a new bio-inspired optimization algorithm for solving optimization algorithm”, IEEE Access, vol. 10, pp. 19599-19620, 2022.
- F.S. Gharehchopogh, A. Ucan, T. Ibrikci, B. Arasteh, and G. Isik “Slime mould algorithm: a comprehensive survey of its variants and applications”, Archives of Computational Methods in Engineering.
- R.D. Zimmerman, C.E. Murillo-Sanchez, and R.J. Thomas “MATPOWER: steady-state operations, planning, and analysis tools for power systems research and education”, IEEE Transactions on Power Systems, vol. 26, no. 1, pp. 12-19, 2011.
- MATPOWER http://www.pserc.cornell.edu/matpower
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Elektrik Mühendisliği |
| Bölüm | Araştırma Makaleleri |
| Yazarlar | |
| Erken Görünüm Tarihi | 18 Ekim 2023 |
| Yayımlanma Tarihi | 27 Ekim 2023 |
| Yayımlandığı Sayı | Yıl 2023 Cilt: 5 Sayı: 2 |
