A novel nature-inspired meta-heuristic algorithm for solving the economic and environmental dispatch problems in power system

• Autorzy: Aroua Fatima Zohra , Salhi Ahmed , Mayouf Chiva , Naimi Djemai

Identyfikatory

DOI

10.15199/48.2024.07.55

Warianty tytułu

PL

Nowatorski, inspirowany naturą algorytm metaheurystyczny do rozwiązywania problemów ekonomicznych i środowiskowych w systemie elektroenergetycznym

• Języki publikacji: EN

Abstrakty

EN

n this paper, a novel nature-based meta-heuristic technique, named cheetah optimizer (CO) algorithm is suggested to solve the Optimal Power Flow (OPF) problem in electric power systems. The optimization method is inspired by the hunting behavior of cheetahs in the wild. The investigation process for optimal global solutions is based on three principal prey-hunting strategies, namely search, sit-and-wait, and attack. The presented technique is applied to solve two famous OPF problems, which are Economic and Environmental Dispatch (EED) by reducing total fuel cost and total gas emission level, respectively. The proposed approach was employed in the case of the IEEE 30-bus test system. The effectiveness of the CO method is justified based on a comparison report of its simulation results with those of other optimization algorithms recently developed in the literature.

PL

W artykule zaproponowano nowatorską meta-heurystyczną technikę opartą na naturze, nazwaną algorytmem optymalizatora geparda (CO), do rozwiązywania problemu optymalnego przepływu mocy (OPF) w systemach elektroenergetycznych. Metoda optymalizacji jest inspirowana zachowaniami łowieckimi gepardów na wolności. Proces poszukiwania optymalnych rozwiązań globalnych opiera się na trzech głównych strategiach polowania na ofiary, a mianowicie poszukiwaniu, siedzeniu i czekaniu oraz ataku. Zaprezentowana technika jest stosowana do rozwiązywania dwóch znanych problemów OPF, którymi są dyspozycja ekonomiczna i środowiskowa (EED) poprzez zmniejszenie całkowitego kosztu paliwa i całkowitego poziomu emisji gazów. Proponowane podejście zostało zastosowane w przypadku systemu testowego IEEE 30-bus. Skuteczność metody CO jest uzasadniona na podstawie raportu porównawczego jej wyników symulacji z wynikami innych algorytmów optymalizacyjnych opracowanych ostatnio w literaturze.

Słowa kluczowe

EN

cheetah optimizer algorithm; prey-hunting strategies; economic dispatch; environmental dispatch; electric power system

PL

algorytm optymalizatora Cheetah; dyspozycja ekonomiczna; dyspozycja środowiskowa; system elektroenergetyczny

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2024
• Tom: R. 100, nr 7
• Strony: 280--285
• Opis fizyczny: Bibliogr. 27 poz., rys., tab.

Twórcy

autor

Aroua Fatima Zohra

• Electrical department LGEB Laboratory, University of Biskra, BP 145, Biskra 07000, Algeria

• https://orcid.org/0009-0006-5744-902X

autor

Salhi Ahmed

• Electrical department LGEB Laboratory, University of Biskra, BP 145, Biskra 07000, Algeria

• https://orcid.org/0009-0005-4888-8365

autor

Mayouf Chiva

• University of Nouakchott, BP, 888, Mauritania

• https://orcid.org/0009-0000-3928-0941

autor

Naimi Djemai

• Electrical department LGEB Laboratory, University of Biskra, BP 145, Biskra 07000, Algeria

• https://orcid.org/0000-0002-5728-488X

Bibliografia

• [1] Ramavath, Dhakesh, and Manisha Sharma. “Optimal Power Flow Using Modified ALO.” 2020 International Conference on Renewable Energy Integration into Smart Grids : A Multidisciplinary Approach to Technology Modelling and Simulation (ICREISG). IEEE, 2020.
• [2] Abd El-sattar, Salma, et al. “An improved version of salp swarm algorithm for solving optimal power flow problem.” Soft Computing 25 (2021): 4027-4052.
• [3] Ouafa, Herbadji, Slimani Linda, and Bouktir Tarek. “Multi objective optimal power flow considering the fuel cost, emission, voltage deviation and power losses using Multi Objective Dragonfly algorithm.” Proceedings of the International Conference on Recent Advances in Electrical Systems, Hammamet, Tunusia. 2017.
• [4] Carpentier, J. L. “Optimal power flows: uses, methods and developments.” IFAC Proceedings Volumes 18.7 (1985): 11 21.
• [5] Dommel, Hermann W., and William F. Tinney. “Optimal power flow solutions.” IEEE Transactions on power apparatus and systems 10 (1968): 1866-1876.
• [6] Momoh, James A., Rambabu Adapa, and M. E. El-Hawary. “A review of selected optimal power flow literature to 1993. I. Nonlinear and quadratic programming approaches.” IEEE transactions on power systems 14.1 (1999): 96-104.
• [7] Momoh, James A., M. E. El-Hawary, and Ramababu Adapa. “A review of selected optimal power flow literature to 1993. II. Newton, linear programming and interior point methods.” IEEE transactions on power systems 14.1 (1999): 105-111.
• [8] Akbari, Mohammad Amin, et al. “The cheetah optimizer: A nature-inspired metaheuristic algorithm for large-scale optimization problems.” Scientific Reports 12.1 (2022): 10953.
• [9] Duman, Serhat. “Symbiotic organisms search algorithm for optimal power flow problem based on valve-point effect and prohibited zones.” Neural Computing and Applications 28 (2017): 3571-3585.
• [10] Khamees, Amr K., et al. “Solution of optimal power flow using evolutionary-based algorithms.” International Journal of Engineering, Science and Technology 9.1 (2017): 55-68.
• [11] Messaoudi, Abdelmoumene, and Mohamed Belkacemi. “Optimal Power Flow Solution using Efficient Sine Cosine Optimization Algorithm.” International Journal of Intelligent Systems and Applications 12.2 (2020): 34.
• [12] El Sehiemy, Ragab A., et al. “A novel multi-objective hybrid particle swarm and salp optimization algorithm for technical economical-environmental operation in power systems.” Energy 193 (2020): 116817.
• [13] Islam, Mohammad Zohrul, et al. “A Harris Hawks optimization based single-and multi-objective optimal power flow considering environmental emission.” Sustainability 12.13 (2020): 5248.
• [14] Bentouati, Bachir, et al. “An enhanced moth-swarm algorithm for efficient energy management based multi dimensions OPF problem.” Journal of Ambient Intelligence and Humanized Computing 12 (2021): 9499-9519.
• [15] Alhejji, Ayman, et al. “Optimal power flow solution with an embedded center-node unified power flow controller using an adaptive grasshopper optimization algorithm.” IEEE Access 8 (2020): 119020-119037.
• [16] Vijaya Bhaskar, K. “Modern swarm intelligence based algorithms for solving optimal power flow problem in a regulated power system framework.” Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12.2 (2021): 1786-1793.
• [17] Sarhan, Shahenda, et al. “Turbulent flow of water-based optimization for solving multi-objective technical and economic aspects of optimal power flow problems.” Mathematics 10.12 (2022): 2106.
• [18] Alanazi, Abdulaziz, et al. “Determining Optimal Power Flow Solutions Using New Adaptive Gaussian TLBO Method.” Applied Sciences 12.16 (2022): 7959.
• [19] Al-Kaabi, Murtadha, Virgil Dumbrava, and Mircea Eremia. “A Slime Mould Algorithm Programming for Solving Single and Multi-Objective Optimal Power Flow Problems with Pareto Front Approach: A Case Study of the Iraqi Super Grid High Voltage.” Energies 15.20 (2022): 7473.
• [20] Hardiansyah, Hardiansyah. “A novel bat algorithm for solving optimal power flow problem.” Engineering Review: Međunarodni časopis namijenjen publiciranju originalnih istraživanja s aspekta analize konstrukcija, materijala i novih tehnologija u području strojarstva, brodogradnje, temeljnih tehničkih znanosti, elektrotehnike, računarstva i građevinarstva 41.2 (2021): 41-53.
• [21] Reddy, S. Surender, and P. R. Bijwe. “An efficient optimal power flow using bisection method.” Electrical Engineering 100 (2018): 2217-2229.
• [22] Wolpert, David H., and William G. Macready. “No free lunch theorems for optimization.” IEEE transactions on evolutionary computation 1.1 (1997): 67-82.
• [23] Salhi, Ahmed, Djemai Naimi, and Tarek Bouktir. “Fuzzy multi objective optimal power flow using genetic algorithms applied to algerian electrical network.” Advances in Electrical and Electronic Engineering 11.6 (2013): 443-454.
• [24] Salhi, Ahmed, Djemai Naimi, and Tarek Bouktir. “Optimal power flow resolution using artificial bee colony algorithm based grenade explosion method.” Journal of Electrical Systems 12.4 (2016): 734-756.
• [25] Unies, Nations. “Protocole de Kyoto la convention-cadre des Nations Unies sur les changements climatiques.” Kyoto, Japon (1998).
• [26] Estes, Richard D. The behavior guide to African mammals: including hoofed mammals, carnivores, primates. Univ of California Press, 2012.
• [27] Christie, Rich, and Iraj Dabbagchi. “IEEE 30 bus test case.” Internet: www. ee. edu/research/pstca/pf30/pg_tca30bus. htm (1993).

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki i promocja sportu (2025).