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Application of a new hybridization to solve economic dispatch problem on an Algerian power system without or with connection to a renewable energy

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The most important contribution of this article is the use of four metaheuristic approaches to tackle the problem of economic dispatching, with the goal to study the influence of the injection of a renewable energy source on the electricity cost in the Algerian network, and minimizing the production cost of electrical energy while accounting for transmission losses. A Genetic Algorithm (GA) (a real coding) and Egyptian Vulture Optimization Algorithm (EVOA), as well as two hybridizations between the metaheuristics: Classic and Modern hybridization (C.H.GA-EVOA, M.H.GA-EVOA), are presented in this work. These techniques are used to address optimization difficulties of two Algerian electricity networks. The first has three system units, whereas the second has fifteen system units. The second electricity network is connected to a solar energy source. The findings obtained are compared with other techniques to validate the high performance of the suggested methods for addressing the economic dispatch issue. This study demonstrates that EVOA and C.H.GA-EVOA provide trustworthy results, and that M.H.GA-EVOA surpasses them.
Czasopismo
Rocznik
Strony
101--112
Opis fizyczny
Bibliogr. 44 poz., rys., tab.
Twórcy
  • Unité de Recherche Appliquée en Energies Renouvelables, URAER, Centre de Développement des Energies Renouvelables, CDER, 47133, Ghardaïa, Alegria
  • Faculty of electrical engineering, USTO-MB. B.P 1505 El M’naouar, Oran, 31000, Algeria, Laboratory of Sustainable Development of Electrical Energy LDDEE
  • Department of Electrical Engineering, Faculty of Applied Sciences, University Kasdi Merbah Ouargla, Street Ghardaia, 30000, Ouargla, Algeria
autor
  • Unité de Recherche Appliquée en Energies Renouvelables, URAER, Centre de Développement des Energies Renouvelables, CDER, 47133, Ghardaïa, Alegria
autor
  • Unité de Recherche Appliquée en Energies Renouvelables, URAER, Centre de Développement des Energies Renouvelables, CDER, 47133, Ghardaïa, Alegria
  • Faculty of electrical engineering, USTO-MB. B.P 1505 El M’naouar, Oran, 31000, Algeria, Laboratory of Sustainable Development of Electrical Energy LDDEE
  • Unité de Recherche Appliquée en Energies Renouvelables, URAER, Centre de Développement des Energies Renouvelables, CDER, 47133, Ghardaïa, Alegria
Bibliografia
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  • 18. B Larouci, L Benasla, A Belmadani. Cuckoo Search Algorithm for Solving Economic Power Dispatch Problem with Consideration of Facts Devices. UPB Sci. Bull, Series C. 2017; 79-81. https://doi: 10.13140/RG.2.2.17880.21766.
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  • 20. Zare K, Haque MT, Davoodi E. Solving non-convex economic dispatch problem with valve point effects using modified group search optimizer method. Electr Pow Syst Res. 2012; 84(1):83-9. https://doi.org/10.1016/j.epsr.2011.10.004.
  • 21. Secui DC. A modified symbiotic organisms search algorithm for large scale economic dispatch problem with valve-point effects. Energy. 2016; 113:366-84. https://doi.org/10.1016/j.energy.2016.07.056.
  • 22. Pradhan M, Roy PK, Pal T. Grey wolf optimization applied to economic load dispatch problems. Int J Electr Power. 2016; 83:325-334. https://doi.org/10.1016/j.ijepes.2016.04.034.
  • 23. Jayabarathi T, Raghunathan T, Adarsh BR, Suganthan PN. Economic dispatch using hybrid grey wolf optimizer. Energy. 2016; 111:630-41. https://doi.org/10.1016/j.energy.2016.05.105.
  • 24. Meng A, Li J, Yin H. An efficient crisscross optimization solution to large-scale non-convex economic load dispatch with multiple fuel types and valve-point effects. Energy. 2016; 113:1147-61. https://doi.org/10.1016/j.energy.2016.07.138.
  • 25. Dexuan Z , Steven L, Zongyan L, Xiangyong K. A new global particle swarm optimization for the economic emission dispatch with or without transmission losses. Energy Conversion and Management.2017;13945-70. http://dx.doi.org/10.1016/j.enconman.2017.02.035.
  • 26. Secui DC. A new modified artificial bee colony algorithm for the economic dispatch problem. Energy Convers Manage. 2015; 89:43-62. https://doi.org/10.1016/j.enconman.2014.09.034.
  • 27. Fraga ES, Yang L, Papageorgiou LG. On the modelling of valve point loadings for power electricity dispatch. Appl Energy. 2012; 91:301-3. https://doi.org/10.1016/j.apenergy.2011.10.001.
  • 28. Pothiya S, Ngamroo I, Kongprawechnon W. Ant colony optimisation for economic dispatch problem with non-smooth cost functions. Int J Electr Power Energy Syst. 2010; 32(5):478-87. https://doi.org/10.1016/j.ijepes.2009.09.016.
  • 29. Niknam T. Doagou M, H Zeinoddini, Meymand H. A new particle swarm optimization for non-convex economic dispatch. Int T Electr Energy. 2010; 21(1):656-79. https://doi: 10.1002/etep.468.
  • 30. Barisal AK, Prusty RC. Large scale economic dispatch of power systems using oppositional invasive weed optimization. Appl Soft Comput. 2015; 29:122-37. https://doi.org/10.1016/j.asoc.2014.12.014.
  • 31. Meng K, Wang HG, Dong Z, Wong KP. Quantuminspired particle swarm optimization for valve-point economic load dispatch. IEEE T Power Syst. 2010; 25(1):215-22. https://doi: 10.1109/TPWRS.2009.2036481.
  • 32. Subbraj P,Rengaraij R, Salivahanan S, Senthikumar TR. Particle swarm optimization with modified stochastic acceleration facrors solving large scale economic dispatch problem. Int J Elec Power. 2010; 32:1014-23. https://doi.org/10.1016/j.ijepes.2010.02.003.
  • 33. Al-Betar MA, Awadallah MA, Khader AT, Bolaji ALA. Tournament-based harmony search algorithm for non-convex economic load dispatch problem. Appl Soft Comput. 2016; 47:449-59. https://doi.org/10.1016/j.asoc.2016.05.034.
  • 34. Dilip K, Nandhini M. Adapting Egyptian Vulture Optimization Algorithm for Vehicle Routing Problem. International Journal of Computer Science and Information Technologies. 2016; 7(3):1199-204.
  • 35. Chiranjib S, Sanjeev S, Anupam S .Egyptian Vulture Optimization Algorithm - A New Nature Inspired Meta-heuristics for Knapsack Problem. IC2IT.2013 ;209:227-37. https://doi.org/10.1007/978-3-642-37371-8_26.
  • 36. Gaing, Z.-L. Particle swarm optimization to solving the economic dispatch considering the generator constraints. IEEE T Power Sys. 2003 ; (3):1187-95. https://doi: 10.1109/TPWRS.2003.814889.
  • 37. Victoire T, Jeyakumar A E. Discussion of particle swarm optimization to solving the economic dispatch considering the generator constraints. IEEE T Power Sys.2004;19(4):2121-23. https://doi: 10.1109/TPWRS.2004.831709.
  • 38. Victoire T, Jeyakumar A E. Hybrid PSO-SQP for economic dispatch with valve-point effect. Electr Pow Syst Res. 2004; 71(1):51-59. https://doi:10.1016/j.epsr.2003.12.017.
  • 39. Sinha N, Chakrabarti R, Chattopadhyay P K. Evolutionary programming techniques for economic load dispatch. IEEE T Evolut Comput. 2003;7(1):83- 94. https://doi: 10.1109/TEVC.2002.806788.
  • 40. Duman S, Güvenç U, Yörükeren N. Gravitational search algorithm for economic dispatch with valvepoint effects. int rev electr eng. 2010; 5(6):2890-5.
  • 41. Al-Sumait J S, Al-Othman A K, Sykulski J K. Application of pattern search method to power system valve-point economic load dispatch. Int J Elec Power. 2007; 29(10):720-30. https://doi:10.1016/j.ijepes.2007.06.016.
  • 42. Belmadani, A, Benasla L, Rahli M. The dynamic economic dispatch including wind power injection in the western algerian electrical power system. Acta Polytechnica Hungarica. 2011; 8(5), 191-204.
  • 43. Souag S, Benhamida F. A Dynamic Power System Economic Dispatch Enhancement by Wind Integration Considering Ramping Constraint - Application to Algerian Power System. international journal of renewable energy research. 2015; 5, 3.
  • 44. Yamina A G, Hamid B. Resolution of Economic Dispatch Problem of the Algerian Network using Hybrid Metaheuristic. Electrotehnică, Electronică, Automatică (EEA). 2017; 65(1),91-96.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a2c782d2-1da5-480e-9fa7-2f05795522d5
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