OPF Solution by the Hunger Games Search (HGS) Algorithm

Hasanain T. Kadhim, Nasser Y. Majed, Zuhair S. Al-Sagar

Abstract

Optimal power flow (OPF) is a significant problem in electrical engineering. The optimization method of the Hunger Games Search (HGS) algorithm was presented in this study, which finds the OPF under different case studies. These cases include reducing the total fuel cost, minimizing power losses in transmission lines, reducing the amount of pollutants generated by units, and minimizing voltage variation at load buses. MATLAB software was used to test the suggested algorithm using the IEEE 30-bus power system. The findings indicate that the suggested algorithm was effective in accomplishing the goals of obtaining a reduction of 11.30% in the use of fuel cost, a reduction of 46.90% in power loss, a reduction of 88.11% in voltage fluctuation, and a reduction of 3.38% in pollutants while simultaneously satisfying all of the restrictions. We compared the used and published optimization methods. Finally, the HGS presented good performance in terms of power loss and fuel cost compared with other techniques.

 

Keywords: Hunger Games Search, power flow, fuel cost, emission, power system, MATLAB.

 

https://doi.org/10.55463/issn.1674-2974.50.8.11


Full Text:

PDF


References


NGUYEN T. T. A high performance social spider optimization algorithm for optimal power flow solution with single objective optimization. Energy, 2019, 171: 218-240. https://doi.org/10.1016/j.energy.2019.01.021

KHAN A., HIZAM H., BIN ABDUL WAHAB N. I., and LUTFI OTHMAN M. Optimal power flow using hybrid firefly and particle swarm optimization algorithm. PLoS ONE, 2020, 15(8): e0235668. https://doi.org/10.1371/journal.pone.0235668

DUMAN S. Symbiotic organisms search algorithm for optimal power flow problem based on valve-point effect and prohibited zones. Neural Computing and Applications, 2017, 28: 3571-3585. https://doi.org/10.1007/s00521-015-1898-8

LI S., GONG W., WANG L., YAN X., and HU C. Optimal power flow by means of improved adaptive differential evolution. Energy, 2020, 198: 117314. https://doi.org/10.1016/j.energy.2020.117314

HOUSSEIN E. H., HOSNEY M. E., MOHAMED W. M., ALI A. A., and YOUNIS E. M. Fuzzy-based hunger games search algorithm for global optimization and feature selection using medical data. Neural Computing and Applications, 2023, 35(7): 5251-5275. https://doi.org/10.1007/s00521-022-07916-9

ABIDO M., & AL-ALI N. Multi-objective differential evolution for optimal power flow. Proceedings of the International Conference on Power Engineering, Energy and Electrical Drives, Lisbon, 2009, pp. 101-106. https://doi.org/10.1109/POWERENG.2009.4915212

WARID W., HIZAM H., MARIUN N., and ABDUL-WAHAB N. I. Optimal power flow using the Jaya algorithm. Energies, 2016, 9(9): 678. https://doi.org/10.3390/en9090678

EL-HANA BOUCHEKARA H. R., ABIDO M. A., and CHAIB A. E. Optimal power flow using an improved electromagnetism-like mechanism method. Electric Power Components and Systems, 2016, 44(4): 434-449. https://doi.org/10.1080/15325008.2015.1115919

MASKAR M. B., THORAT A., and KORACHGAON I. A review on optimal power flow problem and solution methodologies. Proceedings of the International Conference on Data Management, Analytics and Innovation, Pune, 2017, pp. 64-70. https://doi.org/10.1109/ICDMAI.2017.8073487

WOO J. H., WU L., PARK J.-B., and ROH J. H. Real-time optimal power flow using twin delayed deep deterministic policy gradient algorithm. IEEE Access, 2020, 8: 213611-213618. https://doi.org/10.1109/ACCESS.2020.3041007

SHILAJA C., & ARUNPRASATH T. Optimal power flow using moth swarm algorithm with gravitational search algorithm considering wind power. Future Generation Computer Systems, 2019, 98: 708-715. https://doi.org/10.1016/j.future.2018.12.046

ELATTAR E. E., & ELSAYED S. K. Modified JAYA algorithm for optimal power flow incorporating renewable energy sources considering the cost, emission, power loss and voltage profile improvement. Energy, 2019, 178: 598-609. https://doi.org/10.1016/j.energy.2019.04.159

KHUNKITTI S., SIRITARATIWAT A., PREMRUDEEPREECHACHARN S., CHATTHAWORN R., and WATSON N. R. A hybrid DA-PSO optimization algorithm for multiobjective optimal power flow problems. Energies, 2018, 11(9): 2270. https://doi.org/10.3390/en11092270

SALKUTI S. R. Optimal power flow using multi-objective glowworm swarm optimization algorithm in a wind energy integrated power system. International Journal of Green Energy, 2019, 16(15): 1547-1561. https://doi.org/10.1080/15435075.2019.1677234

KUMAR S., KUMAR V., KATAL N., SINGH S. K., SHARMA S., and SINGH P. Multiarea economic dispatch using evolutionary algorithms. Mathematical Problems in Engineering, 2021, 2021: 3577087. https://doi.org/10.1155/2021/3577087

JAYABARATHI T., RAGHUNATHAN T., ADARSH B., and SUGANTHAN P. N. Economic dispatch using hybrid grey wolf optimizer. Energy, 2016, 111: 630-641. https://doi.org/10.1016/j.energy.2016.05.105

BOUCHEKARA H., ABIDO M., and BOUCHERMA M. Optimal power flow using teaching-learning-based optimization technique. Electric Power Systems Research, 2014, 114: 49-59. https://doi.org/10.1016/j.epsr.2014.03.032

MENG A., ZENG C., WANG P., CHEN D., ZHOU T., ZHENG X., and YIN H. A high-performance crisscross search based grey wolf optimizer for solving optimal power flow problem. Energy, 2021, 225: 120211. https://doi.org/10.1016/j.energy.2021.120211

KUMAR A. R., & PREMALATHA L. Optimal power flow for a deregulated power system using adaptive real coded biogeography-based optimization. International Journal of Electrical Power & Energy Systems, 2015, 73: 393-399. https://doi.org/10.1016/j.ijepes.2015.05.011

MOHAMED A.-A. A., MOHAMED Y. S., EL-GAAFARY A. A., and HEMEIDA A. M. Optimal power flow using moth swarm algorithm. Electric Power Systems Research, 2017, 142: 190-206. https://doi.org/10.1016/j.epsr.2016.09.025

JIA Q.-S., XIE M., and WU F. F. Ordinal optimization based security dispatching in deregulated power systems. Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference, Shanghai, 2009, pp. 6817-6822. https://doi.org/10.1109/CDC.2009.5400740

ATTIA A.-F., EL SEHIEMY R. A., and HASANIEN H. M. Optimal power flow solution in power systems using a novel Sine-Cosine algorithm. International Journal of Electrical Power & Energy Systems, 2018, 99: 331-343. https://doi.org/10.1016/j.ijepes.2018.01.024

NIKNAM T., NARIMANI M., AGHAEI J., and AZIZIPANAH-ABARGHOOEE R. Improved particle swarm optimisation for multi-objective optimal power flow considering the cost, loss, emission and voltage stability index. IET Generation, Transmission & Distribution, 2012, 6(6): 515-527. https://doi.org/10.1049/iet-gtd.2011.0851

BISWAS P. P., SUGANTHAN P. N., MALLIPEDDI R., and AMARATUNGA G. A. Optimal power flow solutions using differential evolution algorithm integrated with effective constraint handling techniques. Engineering Applications of Artificial Intelligence, 2018, 68: 81-100. https://doi.org/10.1016/j.engappai.2017.10.019

TAHER M. A., KAMEL S., JURADO F., and EBEED M. Modified grasshopper optimization framework for optimal power flow solution. Electrical Engineering, 2019, 101: 121-148. https://doi.org/10.1007/s00202-019-00762-4

NADERI E., POURAKBARI-KASMAEI M., CERNA F. V., and LEHTONEN M. A novel hybrid self-adaptive heuristic algorithm to handle single-and multi-objective optimal power flow problems. International Journal of Electrical Power & Energy Systems, 2021, 125: 106492. https://doi.org/10.1016/j.ijepes.2020.106492


Refbacks

  • There are currently no refbacks.