COYOTE OPTIMIZATION ALGORITHM FOR OPTIMAL REACTIVE POWER DISPATCH
Year 2020,
, 1 - 10, 29.12.2020
Uğur Güvenç
,
Okan Bingöl
,
Burçin Özkaya
Abstract
The optimal reactive power dispatch problem is a nonlinear and non-convex optimization problem containing both continuous and discrete control variables. In the study, coyote optimization algorithm is applied to optimal reactive power dispatch problem. Coyote optimization algorithm is tested on IEEE-30 and IEEE-57 bus test systems. The simulation results are compared with the results of SHADE-EC algorithm given in the literature. The comparison results demonstrate the superiority and accuracy of the coyote optimization algorithm to solve the optimal reactive power dispatch problem.
References
- AlRashidi, M. R., El-Hawary, M. E., 2009. Applications of computational intelligence techniques for solving the revived optimal power flow problem. Electric Power Systems Research, 79 (4), 694-702.
- Alsac, O., Bright, J., Prais, M., Stott, B., 1990. Further developments in LP-based optimal power flow. IEEE Transactions on Power Systems, 5(3), 697-711.
- Basu, M., 2016. Quasi-oppositional differential evolution for optimal reactive power dispatch. International Journal of Electrical Power & Energy Systems, 78, 29-40.
- Biswas, P. P., Suganthan, P. N., Mallipeddi, R., Amaratunga, G. A., 2019. Optimal reactive power dispatch with uncertainties in load demand and renewable energy sources adopting scenario-based approach. Applied Soft Computing, 75, 616-632.
- Bjelogrlic, M., Calovic, M. S., Ristanovic, P., Babic, B. S., 1990. Application of Newton's optimal power flow in voltage/reactive power control. IEEE Transactions on Power Systems, 5 (4), 1447-1454.
- Dai, C., Chen, W., Zhu, Y., Zhang, X., 2009. Seeker optimization algorithm for optimal reactive power dispatch. IEEE Transactions on Power Systems, 24 (3), 1218-1231.
- Duman, S., Sönmez, Y., Güvenç, U., Yörükeren, N., 2012. Optimal reactive power dispatch using a gravitational search algorithm. IET Generation, Transmission & Distribution, 6 (6), 563-576.
- Güvenç, U., Kaymaz, E., 2019. Economic Dispatch Integrated Wind Power Using Coyote Optimization Algorithm. In 2019 7th International Istanbul Smart Grids and Cities Congress and Fair (ICSG) IEEE, 179-183.
- Granville, S., 1994. Optimal reactive dispatch through interior point methods. IEEE Transactions on power systems, 9(1), 136-146.
- Khazali, A. H., Kalantar, M., 2011. Optimal reactive power dispatch based on harmony search algorithm. International Journal of Electrical Power & Energy Systems, 33 (3), 684-692.
- Khorsandi, A., Alimardani, A., Vahidi, B., Hosseinian, S. H., 2011. Hybrid shuffled frog leaping algorithm and Nelder–Mead simplex search for optimal reactive power dispatch. IET Generation, Transmission & Distribution, 5 (2), 249-256.
- Mahadevan, K., Kannan, P. S., 2010. Comprehensive learning particle swarm optimization for reactive power dispatch. Applied Soft Computing, 10 (2), 641-652.
- Mallipeddi, R., Jeyadevi, S., Suganthan, P. N., Baskar, S., 2012. Efficient constraint handling for optimal reactive power dispatch problems. Swarm and Evolutionary Computation, 5, 28-36.
- Mandal, B., Roy, P. K., 2013. Optimal reactive power dispatch using quasi-oppositional teaching learning based optimization. International Journal of Electrical Power & Energy Systems, 53, 123-134.
- Li, Y., Cao, Y., Liu, Z., Liu, Y., Jiang, Q., 2009. Dynamic optimal reactive power dispatch based on parallel particle swarm optimization algorithm. Computers & Mathematics with Applications, 57 (11-12), 1835-1842.
- Liang, R. H., Wang, J. C., Chen, Y. T., Tseng, W. T., 2015. An enhanced firefly algorithm to multi-objective optimal active/reactive power dispatch with uncertainties consideration. International Journal of Electrical Power & Energy Systems, 64, 1088-1097.
- Nguyen, T. T., Vo, D. N., 2019. Improved social spider optimization algorithm for optimal reactive power dispatch problem with different objectives. Neural Computing and Applications, 1-32.
- Pierezan, J., Coelho, L. D. S., 2018. Coyote optimization algorithm: a new metaheuristic for global optimization problems. In 2018 IEEE Congress on Evolutionary Computation (CEC), 1-8.
- Quintana, V. H., Santos-Nieto, M., 1989. Reactive-power dispatch by successive quadratic programming. IEEE Transactions on Energy Conversion, 4 (3), 425-435.
- Rajan, A., Malakar, T., 2015. Optimal reactive power dispatch using hybrid Nelder–Mead simplex based firefly algorithm. International Journal of Electrical Power & Energy Systems, 66, 9-24.
- Singh, R. P., Mukherjee, V., Ghoshal, S. P., 2015. Optimal reactive power dispatch by particle swarm optimization with an aging leader and challengers. Applied Soft Computing, 29, 298-309.
- Sulaiman, M. H., Mustaffa, Z., Mohamed, M. R., Aliman, O., 2015. Using the gray wolf optimizer for solving optimal reactive power dispatch problem. Applied Soft Computing, 32, 286-292.
- Yan, W., Lu, S., Yu, D. C., 2004. A novel optimal reactive power dispatch method based on an improved hybrid evolutionary programming technique. IEEE Transactions on Power Systems, 19(2), 913-918.
OPTİMAL REAKTİF GÜÇ DAĞITIMI İÇİN KIR KURDU OPTİMİZASYON ALGORİTMASI
Year 2020,
, 1 - 10, 29.12.2020
Uğur Güvenç
,
Okan Bingöl
,
Burçin Özkaya
Abstract
Optimal reaktif güç dağıtım problemi, sürekli ve ayrık kontrol değişkenlerini içeren doğrusal olmayan ve dışbükey olmayan bir optimizasyon problemidir. Bu çalışmada, kır kurdu optimizasyon algoritmasının optimal reaktif güç dağıtım problemine uygulaması sunulmuştur. Kır kurdu optimizasyon algoritması IEEE-30 ve IEEE-50 baralı sistemlerde test edilmiştir. Benzetim sonuçları, literatürde verilen SHADE-EC algoritmasının sonuçları ile karşılaştırılmıştır. Karşılaştırma sonuçları, optimal reaktif güç dağıtım problemini çözmek için kır kurdu optimizasyon algoritmasının üstünlüğünü ve doğruluğunu göstermiştir.
References
- AlRashidi, M. R., El-Hawary, M. E., 2009. Applications of computational intelligence techniques for solving the revived optimal power flow problem. Electric Power Systems Research, 79 (4), 694-702.
- Alsac, O., Bright, J., Prais, M., Stott, B., 1990. Further developments in LP-based optimal power flow. IEEE Transactions on Power Systems, 5(3), 697-711.
- Basu, M., 2016. Quasi-oppositional differential evolution for optimal reactive power dispatch. International Journal of Electrical Power & Energy Systems, 78, 29-40.
- Biswas, P. P., Suganthan, P. N., Mallipeddi, R., Amaratunga, G. A., 2019. Optimal reactive power dispatch with uncertainties in load demand and renewable energy sources adopting scenario-based approach. Applied Soft Computing, 75, 616-632.
- Bjelogrlic, M., Calovic, M. S., Ristanovic, P., Babic, B. S., 1990. Application of Newton's optimal power flow in voltage/reactive power control. IEEE Transactions on Power Systems, 5 (4), 1447-1454.
- Dai, C., Chen, W., Zhu, Y., Zhang, X., 2009. Seeker optimization algorithm for optimal reactive power dispatch. IEEE Transactions on Power Systems, 24 (3), 1218-1231.
- Duman, S., Sönmez, Y., Güvenç, U., Yörükeren, N., 2012. Optimal reactive power dispatch using a gravitational search algorithm. IET Generation, Transmission & Distribution, 6 (6), 563-576.
- Güvenç, U., Kaymaz, E., 2019. Economic Dispatch Integrated Wind Power Using Coyote Optimization Algorithm. In 2019 7th International Istanbul Smart Grids and Cities Congress and Fair (ICSG) IEEE, 179-183.
- Granville, S., 1994. Optimal reactive dispatch through interior point methods. IEEE Transactions on power systems, 9(1), 136-146.
- Khazali, A. H., Kalantar, M., 2011. Optimal reactive power dispatch based on harmony search algorithm. International Journal of Electrical Power & Energy Systems, 33 (3), 684-692.
- Khorsandi, A., Alimardani, A., Vahidi, B., Hosseinian, S. H., 2011. Hybrid shuffled frog leaping algorithm and Nelder–Mead simplex search for optimal reactive power dispatch. IET Generation, Transmission & Distribution, 5 (2), 249-256.
- Mahadevan, K., Kannan, P. S., 2010. Comprehensive learning particle swarm optimization for reactive power dispatch. Applied Soft Computing, 10 (2), 641-652.
- Mallipeddi, R., Jeyadevi, S., Suganthan, P. N., Baskar, S., 2012. Efficient constraint handling for optimal reactive power dispatch problems. Swarm and Evolutionary Computation, 5, 28-36.
- Mandal, B., Roy, P. K., 2013. Optimal reactive power dispatch using quasi-oppositional teaching learning based optimization. International Journal of Electrical Power & Energy Systems, 53, 123-134.
- Li, Y., Cao, Y., Liu, Z., Liu, Y., Jiang, Q., 2009. Dynamic optimal reactive power dispatch based on parallel particle swarm optimization algorithm. Computers & Mathematics with Applications, 57 (11-12), 1835-1842.
- Liang, R. H., Wang, J. C., Chen, Y. T., Tseng, W. T., 2015. An enhanced firefly algorithm to multi-objective optimal active/reactive power dispatch with uncertainties consideration. International Journal of Electrical Power & Energy Systems, 64, 1088-1097.
- Nguyen, T. T., Vo, D. N., 2019. Improved social spider optimization algorithm for optimal reactive power dispatch problem with different objectives. Neural Computing and Applications, 1-32.
- Pierezan, J., Coelho, L. D. S., 2018. Coyote optimization algorithm: a new metaheuristic for global optimization problems. In 2018 IEEE Congress on Evolutionary Computation (CEC), 1-8.
- Quintana, V. H., Santos-Nieto, M., 1989. Reactive-power dispatch by successive quadratic programming. IEEE Transactions on Energy Conversion, 4 (3), 425-435.
- Rajan, A., Malakar, T., 2015. Optimal reactive power dispatch using hybrid Nelder–Mead simplex based firefly algorithm. International Journal of Electrical Power & Energy Systems, 66, 9-24.
- Singh, R. P., Mukherjee, V., Ghoshal, S. P., 2015. Optimal reactive power dispatch by particle swarm optimization with an aging leader and challengers. Applied Soft Computing, 29, 298-309.
- Sulaiman, M. H., Mustaffa, Z., Mohamed, M. R., Aliman, O., 2015. Using the gray wolf optimizer for solving optimal reactive power dispatch problem. Applied Soft Computing, 32, 286-292.
- Yan, W., Lu, S., Yu, D. C., 2004. A novel optimal reactive power dispatch method based on an improved hybrid evolutionary programming technique. IEEE Transactions on Power Systems, 19(2), 913-918.