Optimal Power Flow Using a Hybrid Improved Harris Hawks Optimization Algorithm-Pattern Search Method

References

• S. Chandrasekaran, “Multiobjective optimal power flow using interior search algorithm: A case study on a real-time electrical network,” Comput. Intell., Vol. 36, no. 3, pp. 1078–96, 2020. DOI: 10.1111/coin.12312.
   Web of Science ®Google Scholar
• C. V. Suresh, and S. Sivanagaraju, “Analysis and effect of multi-fuel and practical constraints on economic load dispatch in the presence of Unified Power Flow Controller using UDTPSO,” Ain Shams Eng. J., Vol. 6, no. 3, pp. 803–17, 2015. DOI: 10.1016/j.asej.2014.12.011.
   Google Scholar
• C. V. Suresh, S. Sivanagaraju, and P. U. Reddy, “Economic concerns in optimal power dispatch in the presence of a generalized unified power flow controller under practical constraints,” J. Inst. Eng. Ser. B, Vol. 97, no. 3, pp. 319–31, 2016. DOI: 10.1007/s40031-015-0201-9.
   Google Scholar
• C. V. Suresh, and S. Sivangaraju, “Multi-objective optimal reactive power dispatch to maximize power system social welfare in the presence of generalized unified power flow controller,” Arch. Electr. Eng., Vol. 64, no. 3, pp. 405–26, 2015. DOI: 10.2478/aee-2015-0032.
   Google Scholar
• S. S. Chintalapudi, and V. Suresh, “Multi-objective optimized loadability estimation by considering power system objectives using NSPSO,” i-Manager’s J. Power Syst. Eng., Vol. 2, no. 4, pp. 7–20, 2015.
   Google Scholar
• V. Vasylius, A. Jonaitis, S. Gudžius, and V. Kopustinskas, “Multi-period optimal power flow for identification of critical elements in a country scale high voltage power grid,” Reliab. Eng. Syst. Saf., 107959, 2021. DOI: 10.1016/j.ress.2021.107959.
   Google Scholar
• T. N. Malik, A. Ul Asar, M. F. Wyne, and S. Akhtar, “A new hybrid approach for the solution of nonconvex economic dispatch problem with valve-point effects,” Electr. Power Syst. Res., Vol. 80, no. 9, pp. 1128–36, 2010. DOI: 10.1016/j.epsr.2010.03.004.
   Google Scholar
• W. Ongsakul, and T. Tantimaporn, “Optimal power flow by improved evolutionary programming,” Electr. Power Comp. Syst., Vol. 34, no. 1, pp. 79–95, 2006. DOI: 10.1080/15325000691001458.
   Web of Science ®Google Scholar
• S. Sayah, and K. Zehar, “Modified differential evolution algorithm for optimal power flow with non-smooth cost functions,” Energy Convers. Manag., Vol. 49, no. 11, pp. 3036–42, 2008. DOI: 10.1016/j.enconman.2008.06.014.
   Web of Science ®Google Scholar
• A. A. A. Mohamed, Y. S. Mohamed, A. A. M. El-Gaafary, and A. M. Hemeida, “Optimal power flow using moth swarm algorithm,” Electr. Power Syst. Res., Vol. 142, pp. 190–206, 2017. DOI: 10.1016/j.epsr.2016.09.025.
   Web of Science ®Google Scholar
• S. Duman, U. Güvenç, Y. Sönmez, and N. Yörükeren, “Optimal power flow using gravitational search algorithm,” Energy Convers. Manag., Vol. 59, pp. 86–95, 2012. DOI: 10.1016/j.enconman.2012.02.024.
   Web of Science ®Google Scholar
• C. Yaşar, and S. Özyön, “A new hybrid approach for nonconvex economic dispatch problem with valve-point effect,” Energy, Vol. 36, no. 10, pp. 5838–45, 2011. DOI: 10.1016/j.energy.2011.08.041.
   Web of Science ®Google Scholar
• S. H. A. Kaboli, and A. K. Alqallaf, “Solving non-convex economic load dispatch problem via artificial cooperative search algorithm,” Expert Syst. Appl., Vol. 128, pp. 14–27, 2019. DOI: 10.1016/j.eswa.2019.02.002.
   Google Scholar
• D. Aydin, and S. Özyön, “Solution to non-convex economic dispatch problem with valve point effects by incremental artificial bee colony with local search,” Appl. Soft Comput. J., Vol. 13, no. 5, pp. 2456–66, 2013. DOI: 10.1016/j.asoc.2012.12.002.
   Web of Science ®Google Scholar
• A. Bhattacharya, and P. K. Chattopadhyay, “Application of biogeography-based optimisation to solve different optimal power flow problems,” IET Gener. Transm. Distrib., Vol. 5, no. 1, pp. 70–80, 2011. DOI: 10.1049/iet-gtd.2010.0237.
   Web of Science ®Google Scholar
• A. A. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, and H. Chen, “Harris hawks optimization: algorithm and applications,” Futur. Gener. Comput. Syst., Vol. 97, pp. 849–72, 2019. DOI: 10.1016/j.future.2019.02.028.
   Web of Science ®Google Scholar
• M. Z. Islam, et al., “A Harris Hawks optimization based singleand multi-objective optimal power flow considering environmental emission,” Sustainability, Vol. 12, no. 13, 2020. DOI: 10.3390/su12135248.
   Google Scholar
• M. Z. Islam, et al. “Optimal power flow using a novel Harris Hawk optimization algorithm to minimize fuel cost and power loss,” 2019 IEEE Conf. Sustain. Util. Dev. Eng. Technol. CSUDET 2019, pp. 246–250, 2019, DOI:10.1109/CSUDET47057.2019.9214591.
   Google Scholar
• S. Tang, M. Jiang, R. Abbassi, H. Jerbi, and M. latifi, “A cost-oriented resource scheduling of a solar-powered microgrid by using the hybrid crow and pattern search algorithm,” J. Clean. Prod., Vol. 313, pp. 127853, 2021. DOI: 10.1016/j.jclepro.2021.127853.
   Google Scholar
• D. H. W. and W, and G. Macready, “No free lunch theorems for optimization,” IEEE Trans. Evol. Comput., Vol. 1, no. 1, pp. 67–82, 1997. DOI: 10.1109/4235.585893.
   Google Scholar
• E. Acha, C. R. Fuerte-Esquivel, H. Ambriz-Perez, and C. Angeles-Camacho. FACTS: modelling and simulation in power networks. NJ: John Wiley & Sons, 2004.
   Google Scholar
• M. A. Taher, S. Kamel, F. Jurado, and M. Ebeed, “Optimal power flow solution incorporating a simplified UPFC model using lightning attachment procedure optimization,” Int. Trans. Electr. Energy Syst., Vol. 30, no. 1, pp. 1–25, 2020. DOI: 10.1002/2050-7038.12170.
   Google Scholar
• S. Kamel, F. Jurado, Z. Chen, M. Abdel-Akher, and M. Ebeed, “Developed generalised unified power flow controller model in the Newton-Raphson power-flow analysis using combined mismatches method,” IET Gener. Transm. Distrib., Vol. 10, no. 9, pp. 2177–84, 2016. DOI: 10.1049/iet-gtd.2015.1247.
   Web of Science ®Google Scholar
• X.-P. Zhang, C. Rehtanz, and B. Pal. Flexible AC transmission systems: modelling and control. Berlin: Springer Science & Business Media, 2012.
   Google Scholar
• R. Upputuri, and N. Kumar, “Assessment and enhancement of static power system security with multi-line FACTS devices under congestion conditions,” Int. Trans. Electr. Energy Syst., 1–23, 2021. DOI: 10.1002/2050-7038.13129.
   Google Scholar
• R. Tiwari, K. R. Niazi, and V. Gupta, “Line collapse proximity index for prediction of voltage collapse in power systems,” Int. J. Electr. Power Energy Syst., Vol. 41, no. 1, pp. 105–11, 2012. DOI: 10.1016/j.ijepes.2012.03.022.
   Web of Science ®Google Scholar
• X. Yao, Y. Liu, and G. Lin, “Evolutionary programming made faster,” IEEE Trans. Evol. Comput., Vol. 3, no. 2, pp. 82–102, 1999. DOI: 10.1109/4235.771163.
   Web of Science ®Google Scholar
• J. G. Digalakis, and K. G. Margaritis, “On benchmarking functions for genetic algorithms,” Int. J. Comput. Math., Vol. 77, no. 4, pp. 481–506, Jan. 2001. DOI: 10.1080/00207160108805080.
   Web of Science ®Google Scholar
• V. K. Kamboj, A. Nandi, A. Bhadoria, and S. Sehgal, “An intensify Harris Hawks optimizer for numerical and engineering optimization problems,” Appl. Soft Comput. J., Vol. 89, pp. 106018, 2020. DOI: 10.1016/j.asoc.2019.106018.
   Google Scholar
• H. Pulluri, R. Naresh, and V. Sharma, “Application of stud krill herd algorithm for solution of optimal power flow problems,” Int. Trans. Electr. Energy Syst., Vol. 27, no. 6, pp. 9–12, 2017. DOI: 10.1002/etep.2316.
   Google Scholar
• S. Surender Reddy, P. R. Bijwe, and A. R. Abhyankar, “Faster evolutionary algorithm based optimal power flow using incremental variables,” Int. J. Electr. Power Energy Syst., Vol. 54, pp. 198–210, 2014. DOI: 10.1016/j.ijepes.2013.07.019.
   Web of Science ®Google Scholar
• K. Abaci, and V. Yamacli, “Differential search algorithm for solving multi-objective optimal power flow problem,” Int. J. Electr. Power Energy Syst., Vol. 79, pp. 1–10, 2016. DOI: 10.1016/j.ijepes.2015.12.021.
   Web of Science ®Google Scholar
• V. Yadav, and S. P. Ghoshal. “Optimal power flow for IEEE 30 and 118-bus systems using Monarch Butterfly optimization,” Int. Conf. Technol. Smart City Energy Secur. Power Smart Solut. Smart Cities, ICSESP 2018 – Proc., vol. 2018-Janua, no. 4, pp. 1–6, 2018, DOI:10.1109/ICSESP.2018.8376670.
   Google Scholar
• A. F. Attia, R. A. El Sehiemy, and H. M. Hasanien, “Optimal power flow solution in power systems using a novel sine-cosine algorithm,” Int. J. Electr. Power Energy Syst., Vol. 99, pp. 331–43, 2018. DOI:10.1016/j.ijepes.2018.01.024.
   Web of Science ®Google Scholar
• P. Jangir. “Transmission System (FACTS) Device Using Grey Wolf Optimizer,” vol. 5, pp. 1–5, 2017.
   Google Scholar
• A. A. Abou El Ela, M. A. Abido, and S. R. Spea, “Optimal power flow using differential evolution algorithm,” Electr. Power Syst. Res., Vol. 80, no. 7, pp. 878–85, 2010. DOI:10.1016/j.epsr.2009.12.018.
   Web of Science ®Google Scholar