Research Article
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Year 2024, Volume: 8 Issue: 1, 92 - 106, 19.01.2024
https://doi.org/10.31127/tuje.1282429

Abstract

References

  • Aydin, M. (2016). Enerji verimliliğinin sürdürülebilir kalkınmadaki rolü: Türkiye değerlendirmesi. Yönetim Bilimleri Dergisi, 14(28), 409-441.
  • Akdag, O. (2022). A improved Archimedes optimization algorithm for multi/single-objective optimal power flow. Electric Power Systems Research, 206, 107796. https://doi.org/10.1016/j.epsr.2022.107796
  • Li, S., Gong, W., Wang, L., & Gu, Q. (2022). Multi-objective optimal power flow with stochastic wind and solar power. Applied Soft Computing, 114, 108045. https://doi.org/10.1016/j.asoc.2021.108045
  • Elattar, E. E., & ElSayed, S. K. (2019). Modified JAYA algorithm for optimal power flow incorporating renewable energy sources considering the cost, emission, power loss and voltage profile improvement. Energy, 178, 598-609. https://doi.org/10.1016/j.energy.2019.04.159
  • Akbari, E., Ghasemi, M., Gil, M., Rahimnejad, A., & Andrew Gadsden, S. (2022). Optimal power flow via teaching-learning-studying-based optimization algorithm. Electric Power Components and Systems, 49(6-7), 584-601. https://doi.org/10.1080/15325008.2021.1971331
  • Bakir, H., Guvenc, U., & Kahraman, H. T. (2022). Optimal operation and planning of hybrid AC/DC power systems using multi-objective grasshopper optimization algorithm. Neural Computing and Applications, 34(24), 22531-22563. https://doi.org/10.1007/s00521-022-07670-y
  • Houssein, E. H., Hassan, M. H., Mahdy, M. A., & Kamel, S. (2023). Development and application of equilibrium optimizer for optimal power flow calculation of power system. Applied Intelligence, 53(6), 7232-7253. https://doi.org/10.1007/s10489-022-03796-7
  • Ramesh, S., Verdú, E., Karunanithi, K., & Raja, S. P. (2023). An optimal power flow solution to deregulated electricity power market using meta-heuristic algorithms considering load congestion environment. Electric Power Systems Research, 214, 108867. https://doi.org/10.1016/j.epsr.2022.108867
  • Premkumar, M., Kumar, C., Dharma Raj, T., Sundarsingh Jebaseelan, S. D. T., Jangir, P., & Haes Alhelou, H. (2023). A reliable optimization framework using ensembled successive history adaptive differential evolutionary algorithm for optimal power flow problems. IET Generation, Transmission & Distribution, 17(6), 1333-1357. https://doi.org/10.1049/gtd2.12738
  • Kaur, M., & Narang, N. (2023). Optimal Power Flow Solution Using Space Transformational Invasive Weed Optimization Algorithm. Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 1-27. https://doi.org/10.1007/s40998-023-00592-y
  • Bakır, H., Guvenc, U., Duman, S., & Kahraman, H. T. (2023). Optimal power flow for hybrid AC/DC electrical networks configured with VSC-MTDC transmission lines and renewable energy sources. IEEE Systems Journal, 17(3), 3938 – 3949. https://doi.org/10.1109/JSYST.2023.3248658
  • Sonmez, Y., Duman, S., Kahraman, H. T., Kati, M., Aras, S., & Guvenc, U. (2022). Fitness-distance balance based artificial ecosystem optimisation to solve transient stability constrained optimal power flow problem. Journal of Experimental & Theoretical Artificial Intelligence, 1-40. https://doi.org/10.1080/0952813X.2022.2104388
  • Abd El-sattar, S., Kamel, S., Ebeed, M., & Jurado, F. (2021). An improved version of salp swarm algorithm for solving optimal power flow problem. Soft Computing, 25, 4027-4052. https://doi.org/10.1007/s00500-020-05431-4
  • Jangir, P., Manoharan, P., Pandya, S., & Sowmya, R. (2023). MaOTLBO: Many-objective teaching-learning-based optimizer for control and monitoring the optimal power flow of modern power systems. International Journal of Industrial Engineering Computations, 14(2), 293-308. https://doi.org/10.5267/j.ijiec.2023.1.003
  • Pandya, S. B., Ravichandran, S., Manoharan, P., Jangir, P., & Alhelou, H. H. (2022). Multi-objective optimization framework for optimal power flow problem of hybrid power systems considering security constraints. IEEE Access, 10, 103509-103528. https://doi.org/10.1109/ACCESS.2022.3209996
  • Premkumar, M., Jangir, P., Sowmya, R., & Elavarasan, R. M. (2021). Many-objective gradient-based optimizer to solve optimal power flow problems: analysis and validations. Engineering Applications of Artificial Intelligence, 106, 104479. https://doi.org/10.1016/j.engappai.2021.104479
  • Ghasemi, M., Akbari, M. A., Jun, C., Bateni, S. M., Zare, M., Zahedi, A., ... & Chau, K. W. (2022). Circulatory System Based Optimization (CSBO): An expert multilevel biologically inspired meta-heuristic algorithm. Engineering Applications of Computational Fluid Mechanics, 16(1), 1483-1525. https://doi.org/10.1080/19942060.2022.2098826
  • Wang, L., Cao, Q., Zhang, Z., Mirjalili, S., & Zhao, W. (2022). Artificial rabbits optimization: A new bio-inspired meta-heuristic algorithm for solving engineering optimization problems. Engineering Applications of Artificial Intelligence, 114, 105082. https://doi.org/10.1016/j.engappai.2022.105082
  • Abdollahzadeh, B., Gharehchopogh, F. S., & Mirjalili, S. (2021). African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems. Computers & Industrial Engineering, 158, 107408. https://doi.org/10.1016/j.cie.2021.107408
  • Talatahari, S., & Azizi, M. (2021). Chaos game optimization: a novel metaheuristic algorithm. Artificial Intelligence Review, 54, 917-1004. https://doi.org/10.1007/s10462-020-09867-w
  • Mohamed, A. A. A., Mohamed, Y. S., El-Gaafary, A. A., & Hemeida, A. M. (2017). Optimal power flow using moth swarm algorithm. Electric Power Systems Research, 142, 190-206. https://doi.org/10.1016/j.epsr.2016.09.025
  • Biswas, P. P., Suganthan, P. N., Mallipeddi, R., & Amaratunga, G. A. (2018). Optimal power flow solutions using differential evolution algorithm integrated with effective constraint handling techniques. Engineering Applications of Artificial Intelligence, 68, 81-100. https://doi.org/10.1016/j.engappai.2017.10.019
  • Zimmerman, R.D., Murillo-Sánchez, C.E., Thomas, R.J., (2023). Matpower. http://www.pserc.cornell.edu/matpower.
  • MATLAB, T. U. S. G. (2022). Natick, Massachusetts: The MathWorks Inc.
  • Zimmerman, R. D., Murillo-Sánchez, C. E., & Thomas, R. J. (2010). MATPOWER: Steady-state operations, planning, and analysis tools for power systems research and education. IEEE Transactions on power systems, 26(1), 12-19. https://doi.org/10.1109/TPWRS.2010.2051168
  • Shaheen, A. M., Farrag, S. M., & El‐Sehiemy, R. A. (2017). MOPF solution methodology. IET Generation, Transmission & Distribution, 11(2), 570-581. https://doi.org/10.1049/iet-gtd.2016.1379
  • Mohamed, A. A. A., Mohamed, Y. S., El-Gaafary, A. A., & Hemeida, A. M. (2017). Optimal power flow using moth swarm algorithm. Electric Power Systems Research, 142, 190-206. https://doi.org/10.1016/j.epsr.2016.09.025
  • Bouchekara, H. R., Chaib, A. E., Abido, M. A., & El-Sehiemy, R. A. (2016). Optimal power flow using an Improved Colliding Bodies Optimization algorithm. Applied Soft Computing, 42, 119-131. https://doi.org/10.1016/j.asoc.2016.01.041
  • Abaci, K., & Yamacli, V. (2016). Differential search algorithm for solving multi-objective optimal power flow problem. International Journal of Electrical Power & Energy Systems, 79, 1-10. https://doi.org/10.1016/j.ijepes.2015.12.021
  • Mahdad, B., & Srairi, K. (2016). Security constrained optimal power flow solution using new adaptive partitioning flower pollination algorithm. Applied Soft Computing, 46, 501-522. https://doi.org/10.1016/j.asoc.2016.05.027
  • Ghasemi, M., Ghavidel, S., Rahmani, S., Roosta, A., & Falah, H. (2014). A novel hybrid algorithm of imperialist competitive algorithm and teaching learning algorithm for optimal power flow problem with non-smooth cost functions. Engineering Applications of Artificial Intelligence, 29, 54-69. https://doi.org/10.1016/j.engappai.2013.11.003
  • Kumar, A. R., & Premalatha, L. (2015). Optimal power flow for a deregulated power system using adaptive real coded biogeography-based optimization. International Journal of Electrical Power & Energy Systems, 73, 393-399. https://doi.org/10.1016/j.ijepes.2015.05.011
  • Ghasemi, M., Ghavidel, S., Gitizadeh, M., & Akbari, E. (2015). An improved teaching–learning-based optimization algorithm using Lévy mutation strategy for non-smooth optimal power flow. International Journal of Electrical Power & Energy Systems, 65, 375-384. https://doi.org/10.1016/j.ijepes.2014.10.027
  • Roy, P. K., & Paul, C. (2015). Optimal power flow using krill herd algorithm. International Transactions on Electrical Energy Systems, 25(8), 1397-1419. https://doi.org/10.1002/etep.1888
  • Derrac, J., García, S., Molina, D., & Herrera, F. (2011). A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation, 1(1), 3-18. https://doi.org/10.1016/j.swevo.2011.02.002

Optimal power flow analysis with circulatory system-based optimization algorithm

Year 2024, Volume: 8 Issue: 1, 92 - 106, 19.01.2024
https://doi.org/10.31127/tuje.1282429

Abstract

Optimal power flow (OPF) is a challenging optimization problem with a large number of variables and constraints. To overcome the OPF issue, high-performance optimization algorithms are needed. In this direction, this paper has been centered on the optimization of the OPF with the circulatory system-based optimization (CSBO) algorithm. The performance of the algorithm was evaluated on the IEEE 57- and 118-bus power networks for the optimization of non-convex OPF objectives, i.e., fuel cost, power loss, voltage deviation, and enhancement of voltage stability. The solution quality of CSBO is compared with state-of-the-art metaheuristic algorithms such as Artificial Rabbits Optimization (ARO), African Vultures Optimization Algorithm (AVOA), and Chaos Game Optimization (CGO). Based on the OPF results, it is seen that the best fuel cost and voltage deviation results are calculated to be 41666.2344 $/h and 0.5871 p.u with the CSBO algorithm for the IEEE 57-bus power system. The CSBO algorithm obtained the best objective function results for the IEEE 118-bus power network with a fuel cost of 134934.3140 $/h and a power loss of 16.4688 MW. In conclusion, the present paper reports that the CSBO is a powerful and efficient metaheuristic algorithm to solve the OPF problem.

References

  • Aydin, M. (2016). Enerji verimliliğinin sürdürülebilir kalkınmadaki rolü: Türkiye değerlendirmesi. Yönetim Bilimleri Dergisi, 14(28), 409-441.
  • Akdag, O. (2022). A improved Archimedes optimization algorithm for multi/single-objective optimal power flow. Electric Power Systems Research, 206, 107796. https://doi.org/10.1016/j.epsr.2022.107796
  • Li, S., Gong, W., Wang, L., & Gu, Q. (2022). Multi-objective optimal power flow with stochastic wind and solar power. Applied Soft Computing, 114, 108045. https://doi.org/10.1016/j.asoc.2021.108045
  • Elattar, E. E., & ElSayed, S. K. (2019). Modified JAYA algorithm for optimal power flow incorporating renewable energy sources considering the cost, emission, power loss and voltage profile improvement. Energy, 178, 598-609. https://doi.org/10.1016/j.energy.2019.04.159
  • Akbari, E., Ghasemi, M., Gil, M., Rahimnejad, A., & Andrew Gadsden, S. (2022). Optimal power flow via teaching-learning-studying-based optimization algorithm. Electric Power Components and Systems, 49(6-7), 584-601. https://doi.org/10.1080/15325008.2021.1971331
  • Bakir, H., Guvenc, U., & Kahraman, H. T. (2022). Optimal operation and planning of hybrid AC/DC power systems using multi-objective grasshopper optimization algorithm. Neural Computing and Applications, 34(24), 22531-22563. https://doi.org/10.1007/s00521-022-07670-y
  • Houssein, E. H., Hassan, M. H., Mahdy, M. A., & Kamel, S. (2023). Development and application of equilibrium optimizer for optimal power flow calculation of power system. Applied Intelligence, 53(6), 7232-7253. https://doi.org/10.1007/s10489-022-03796-7
  • Ramesh, S., Verdú, E., Karunanithi, K., & Raja, S. P. (2023). An optimal power flow solution to deregulated electricity power market using meta-heuristic algorithms considering load congestion environment. Electric Power Systems Research, 214, 108867. https://doi.org/10.1016/j.epsr.2022.108867
  • Premkumar, M., Kumar, C., Dharma Raj, T., Sundarsingh Jebaseelan, S. D. T., Jangir, P., & Haes Alhelou, H. (2023). A reliable optimization framework using ensembled successive history adaptive differential evolutionary algorithm for optimal power flow problems. IET Generation, Transmission & Distribution, 17(6), 1333-1357. https://doi.org/10.1049/gtd2.12738
  • Kaur, M., & Narang, N. (2023). Optimal Power Flow Solution Using Space Transformational Invasive Weed Optimization Algorithm. Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 1-27. https://doi.org/10.1007/s40998-023-00592-y
  • Bakır, H., Guvenc, U., Duman, S., & Kahraman, H. T. (2023). Optimal power flow for hybrid AC/DC electrical networks configured with VSC-MTDC transmission lines and renewable energy sources. IEEE Systems Journal, 17(3), 3938 – 3949. https://doi.org/10.1109/JSYST.2023.3248658
  • Sonmez, Y., Duman, S., Kahraman, H. T., Kati, M., Aras, S., & Guvenc, U. (2022). Fitness-distance balance based artificial ecosystem optimisation to solve transient stability constrained optimal power flow problem. Journal of Experimental & Theoretical Artificial Intelligence, 1-40. https://doi.org/10.1080/0952813X.2022.2104388
  • Abd El-sattar, S., Kamel, S., Ebeed, M., & Jurado, F. (2021). An improved version of salp swarm algorithm for solving optimal power flow problem. Soft Computing, 25, 4027-4052. https://doi.org/10.1007/s00500-020-05431-4
  • Jangir, P., Manoharan, P., Pandya, S., & Sowmya, R. (2023). MaOTLBO: Many-objective teaching-learning-based optimizer for control and monitoring the optimal power flow of modern power systems. International Journal of Industrial Engineering Computations, 14(2), 293-308. https://doi.org/10.5267/j.ijiec.2023.1.003
  • Pandya, S. B., Ravichandran, S., Manoharan, P., Jangir, P., & Alhelou, H. H. (2022). Multi-objective optimization framework for optimal power flow problem of hybrid power systems considering security constraints. IEEE Access, 10, 103509-103528. https://doi.org/10.1109/ACCESS.2022.3209996
  • Premkumar, M., Jangir, P., Sowmya, R., & Elavarasan, R. M. (2021). Many-objective gradient-based optimizer to solve optimal power flow problems: analysis and validations. Engineering Applications of Artificial Intelligence, 106, 104479. https://doi.org/10.1016/j.engappai.2021.104479
  • Ghasemi, M., Akbari, M. A., Jun, C., Bateni, S. M., Zare, M., Zahedi, A., ... & Chau, K. W. (2022). Circulatory System Based Optimization (CSBO): An expert multilevel biologically inspired meta-heuristic algorithm. Engineering Applications of Computational Fluid Mechanics, 16(1), 1483-1525. https://doi.org/10.1080/19942060.2022.2098826
  • Wang, L., Cao, Q., Zhang, Z., Mirjalili, S., & Zhao, W. (2022). Artificial rabbits optimization: A new bio-inspired meta-heuristic algorithm for solving engineering optimization problems. Engineering Applications of Artificial Intelligence, 114, 105082. https://doi.org/10.1016/j.engappai.2022.105082
  • Abdollahzadeh, B., Gharehchopogh, F. S., & Mirjalili, S. (2021). African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems. Computers & Industrial Engineering, 158, 107408. https://doi.org/10.1016/j.cie.2021.107408
  • Talatahari, S., & Azizi, M. (2021). Chaos game optimization: a novel metaheuristic algorithm. Artificial Intelligence Review, 54, 917-1004. https://doi.org/10.1007/s10462-020-09867-w
  • Mohamed, A. A. A., Mohamed, Y. S., El-Gaafary, A. A., & Hemeida, A. M. (2017). Optimal power flow using moth swarm algorithm. Electric Power Systems Research, 142, 190-206. https://doi.org/10.1016/j.epsr.2016.09.025
  • Biswas, P. P., Suganthan, P. N., Mallipeddi, R., & Amaratunga, G. A. (2018). Optimal power flow solutions using differential evolution algorithm integrated with effective constraint handling techniques. Engineering Applications of Artificial Intelligence, 68, 81-100. https://doi.org/10.1016/j.engappai.2017.10.019
  • Zimmerman, R.D., Murillo-Sánchez, C.E., Thomas, R.J., (2023). Matpower. http://www.pserc.cornell.edu/matpower.
  • MATLAB, T. U. S. G. (2022). Natick, Massachusetts: The MathWorks Inc.
  • Zimmerman, R. D., Murillo-Sánchez, C. E., & Thomas, R. J. (2010). MATPOWER: Steady-state operations, planning, and analysis tools for power systems research and education. IEEE Transactions on power systems, 26(1), 12-19. https://doi.org/10.1109/TPWRS.2010.2051168
  • Shaheen, A. M., Farrag, S. M., & El‐Sehiemy, R. A. (2017). MOPF solution methodology. IET Generation, Transmission & Distribution, 11(2), 570-581. https://doi.org/10.1049/iet-gtd.2016.1379
  • Mohamed, A. A. A., Mohamed, Y. S., El-Gaafary, A. A., & Hemeida, A. M. (2017). Optimal power flow using moth swarm algorithm. Electric Power Systems Research, 142, 190-206. https://doi.org/10.1016/j.epsr.2016.09.025
  • Bouchekara, H. R., Chaib, A. E., Abido, M. A., & El-Sehiemy, R. A. (2016). Optimal power flow using an Improved Colliding Bodies Optimization algorithm. Applied Soft Computing, 42, 119-131. https://doi.org/10.1016/j.asoc.2016.01.041
  • Abaci, K., & Yamacli, V. (2016). Differential search algorithm for solving multi-objective optimal power flow problem. International Journal of Electrical Power & Energy Systems, 79, 1-10. https://doi.org/10.1016/j.ijepes.2015.12.021
  • Mahdad, B., & Srairi, K. (2016). Security constrained optimal power flow solution using new adaptive partitioning flower pollination algorithm. Applied Soft Computing, 46, 501-522. https://doi.org/10.1016/j.asoc.2016.05.027
  • Ghasemi, M., Ghavidel, S., Rahmani, S., Roosta, A., & Falah, H. (2014). A novel hybrid algorithm of imperialist competitive algorithm and teaching learning algorithm for optimal power flow problem with non-smooth cost functions. Engineering Applications of Artificial Intelligence, 29, 54-69. https://doi.org/10.1016/j.engappai.2013.11.003
  • Kumar, A. R., & Premalatha, L. (2015). Optimal power flow for a deregulated power system using adaptive real coded biogeography-based optimization. International Journal of Electrical Power & Energy Systems, 73, 393-399. https://doi.org/10.1016/j.ijepes.2015.05.011
  • Ghasemi, M., Ghavidel, S., Gitizadeh, M., & Akbari, E. (2015). An improved teaching–learning-based optimization algorithm using Lévy mutation strategy for non-smooth optimal power flow. International Journal of Electrical Power & Energy Systems, 65, 375-384. https://doi.org/10.1016/j.ijepes.2014.10.027
  • Roy, P. K., & Paul, C. (2015). Optimal power flow using krill herd algorithm. International Transactions on Electrical Energy Systems, 25(8), 1397-1419. https://doi.org/10.1002/etep.1888
  • Derrac, J., García, S., Molina, D., & Herrera, F. (2011). A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation, 1(1), 3-18. https://doi.org/10.1016/j.swevo.2011.02.002
There are 35 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Hüseyin Bakır 0000-0001-5473-5158

Early Pub Date September 15, 2023
Publication Date January 19, 2024
Published in Issue Year 2024 Volume: 8 Issue: 1

Cite

APA Bakır, H. (2024). Optimal power flow analysis with circulatory system-based optimization algorithm. Turkish Journal of Engineering, 8(1), 92-106. https://doi.org/10.31127/tuje.1282429
AMA Bakır H. Optimal power flow analysis with circulatory system-based optimization algorithm. TUJE. January 2024;8(1):92-106. doi:10.31127/tuje.1282429
Chicago Bakır, Hüseyin. “Optimal Power Flow Analysis With Circulatory System-Based Optimization Algorithm”. Turkish Journal of Engineering 8, no. 1 (January 2024): 92-106. https://doi.org/10.31127/tuje.1282429.
EndNote Bakır H (January 1, 2024) Optimal power flow analysis with circulatory system-based optimization algorithm. Turkish Journal of Engineering 8 1 92–106.
IEEE H. Bakır, “Optimal power flow analysis with circulatory system-based optimization algorithm”, TUJE, vol. 8, no. 1, pp. 92–106, 2024, doi: 10.31127/tuje.1282429.
ISNAD Bakır, Hüseyin. “Optimal Power Flow Analysis With Circulatory System-Based Optimization Algorithm”. Turkish Journal of Engineering 8/1 (January 2024), 92-106. https://doi.org/10.31127/tuje.1282429.
JAMA Bakır H. Optimal power flow analysis with circulatory system-based optimization algorithm. TUJE. 2024;8:92–106.
MLA Bakır, Hüseyin. “Optimal Power Flow Analysis With Circulatory System-Based Optimization Algorithm”. Turkish Journal of Engineering, vol. 8, no. 1, 2024, pp. 92-106, doi:10.31127/tuje.1282429.
Vancouver Bakır H. Optimal power flow analysis with circulatory system-based optimization algorithm. TUJE. 2024;8(1):92-106.
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