OPTIMISATION PAR FILTRES MORPHOLOGIQUES POUR RÉSOUDRE LE PROBLÈME COMBINÉ DE RÉPARTITION ÉCONOMIQUE DES ÉMISSIONS
Mots-clés :
Optimisation par filtre morphologique, Problème économique et d'émissions combinés, Emission dispatch, Envoi économiqueRésumé
Cet article propose une nouvelle approche d'optimisation stochastique appelée optimisation par filtre morphologique (OMF) pour résoudre le problème combiné de répartition des émissions économiques (CEED) avec l'effet de point de vanne et de multiples contraintes d'égalité et d'inégalité. Quatre systèmes de test standard, avec et sans pertes de transmission, sont optimisés pour démontrer les performances de l'OMF. La comparaison des résultats expérimentaux avec diverses méthodes rapportées dans la littérature prouve la haute qualité de l'OMF pour résoudre les problèmes CEED pour les systèmes à petite et grande échelle.
Références
(1) A. Talha, A. Kouzou, A. Guichi, F. Bouchafaa, Multilevel inverter for grid-connected photovoltaic systems, Rev. Roum. Sci. Techn. – Électrotechn. et Énerg., Rev. Roum. Sci. Techn. – Électrotechn. et Énerg, 67, 2, pp. 105–110 (2022).
(2) N. Kacimi, A. Idir, S. Grouni, M. Boucherit, A new combined method for tracking the global maximum power point of photovoltaic systems, Rev. Roum. Sci. Techn. – Électrotechn. Et Énerg., 67, 3, pp. 349–354 (2022).
(3) Z. Dekali, L. Baghli, A. Boumediene, Experimental implementation of the maximum power point tracking algorithm for a connected wind turbine emulator, Rev. Roum. Sci. Techn. – Électrotechn. et Énerg., 66, 2, pp. 111–117 (2021).
(4) M.A. Abido, A niched Pareto genetic algorithm for environmental/ economic power dispatch, Electr. Power Syst. Res. pp. 97–105 (2003).
(5) M.A. Abido, A novel multiobjective evolutionary algorithm for environmental/economic power dispatch, Electr. Power Syst. Res. pp. 71–81 (2003).
(6) M.A. Abido, Environmental/economic power dispatch using multiobjective evolutionary algorithms, IEEE Trans. Power Syst. pp. 1529–1537 (2003).
(7) J.S. Alsumait, J.K Sykulski, A.K. Al-Othman, A hybrid GA–PS–SQP method to solve power system valve-point economic dispatch problems, Applied Energy, 87, 3, pp. 1773-1781 (2010).
(8) P.K. Hota, A.K. Barisal, R. Chakrabarti, Economic-emission load dispatch through fuzzy based bacterial foraging algorithm, Electr. Power Energy Syst., 32, 7, pp. 794–803 (2010).
(9) B.K. Panigrahi, V. Ravikumar Pandi, S. Das, et al. Multiobjective fuzzy dominance based bacterial foraging algorithm to solve economic emission dispatch problem, Energy, 35, 12, pp. 4761–70 (2010).
(10) A. El Ela, M.A. Abido, S.R. Spea, Differential Evolution algorithm for emission constraint economic power dispatch problem. Electr. Power Syst. Res., 80, 10, pp. 1286-1292 (2010).
(11) Y.S. Zhu, J. Wang, Multi-objective economic emission dispatch considering wind power using evolutionary algorithm based on decomposition, Electr. Power Energy Syst. 63, pp. 434–445(2014)
(12) B.Y. Qu, J.J. Liang, Y.S. Zhu, Z.Y. Wang, P.N. Suganthan, Economic emission dispatch problems with stochastic wind power using summation based multi-objective evolutionary algorithm, Inform. Sci., 351, pp. 48–66 (2016).
(13) P.K. Roy, S. Bhui, Multi-objective quasi-oppositional teaching learning-based optimization for economic emission load dispatch problem, Int. J. Electr. Power Energy Syst., 53, 4, pp. 937–48 (2013).
(14) P.K. Roy, C. Paul, S. Sultana, Oppositional teaching learning based optimization approach for combined heat and power dispatch, Int. J. Electr. Power Energy Syst., 57, pp. 392–403 (2014).
(15) D. Zou, L. Steven, L. Zongyan, K. Xiangyong, A new global particle swarm optimization for the economic emission dispatch with or without transmission losses, Energy Conversion and Management, 139, pp. 45–70 (2017).
(16) M. Singh, J.S. Dhillon, Multiobjective thermal power dispatch using opposition based greedy heuristic search, Int. J. Electr. Power Energy Syst., 82, pp. 339–353 (2016).
(17) M. Modiri-Delshad, N.A. Rahim, Multi-objective backtracking search algorithm for economic emission dispatch problem, Appl. Soft. Comput., 40, pp. 479–494 (2016).
(18) D.C. Secui, A new modified artificial bee colony algorithm for the economic dispatch problem, Energy Conversion and Management, 89, pp. 43–62 (2015).
(19) S. Dhanalakshmi, S. Kannan, K. Mahadevan, S. Baskar, Application of modified NSGA-II algorithm to Combined Economic and Emission Dispatch problem, International Journal of Electrical Power & Energy Systems, 33, 4, pp. 992–1002 (2011).
(20) U. Güenç, Y. Sömez, S. Duman, N. Yöerükeren, Combined economic and emission dispatch solution using gravitational search algorithm, Sci. Iranica, 19, 6, pp. 1754–1762 (2012).
(21) M. Maamri, M.N. Tandjaoui, H. Bouzeboudja, La methode des essaims de particules et celle du loup gris pour l'optimisation d'un systeme hybride d'energie renouvelable en Algerie, Rev. Roum. Sci. Techn.– Électrotechnique et Énergétique, 65, 3–4, pp. 205–210, (2020).
(22) R. Kherfane, M. Khodja, N. Kherfane, Intelligent algorithm solutions to economic dispatch with multiple fuels and non-smooth cost function, Rev. Roum. Sci. Techn. – Électrotechn. et Énerg, 61, 1, p. 32–36 (2016).
(23) C. Khelifa, A. Belmadani, A new approach to continuous optimization: optimization by morphological filters, 7th International Conference on Metaheuristics and Nature Inspired computing, Merrakech (2018).
(24) B.T. Sagar, D. Kulhare, M.D. Nirmal, G. Prajapati, Image Processing (IP) through erosion and dilation methods, Int. J. of Emerging Technology and Advanced Engineering, 3, 7, pp. 285-289 (2013).
(25) S. Sayah, A Hamouda, A. Bekrar, Efficient hybrid optimization approach for emission-constrained economic dispatch with nonsmooth cost curves. Electric Power Energy Syst, 56, pp. 127–139 (2014).
(26) I. Ziane, F. Benhamida, A. Graa, Economic/emission dispatch problem with valve-point effect, Rev. Roum. Sci. Techn. – Électrotechn. et Énerg, 61, 3, pp. 269–272 (2016).
(27) M.Younes, M. Rahli, M.Abid, M.Kandouci, Optimal power flow by intelligent algorithms, Rev. Roum. Sci. Techn. – Électrotechn. et Énerg., 52, 1, pp. 3–12 (2007).
(28) A.S. Tomar, H.M. Dubey, M. Pandit, Advances in intelligent systems and computing, Venkata Rao R., Taler J, Inc, Singapore, pp. 809–818 (2020).
(29) A.L. Stanzani, A. Balbo, L. Nepomuceno, E. Baptista, Solving the multi-objective environmental/economic dispatch problem using weighted sum and constraint strategies and a predictor-corrector primal-dual interior point method, J. Control Autom. Electr. Syst., 25, 4, pp. 503–15 (2014).
(30) A.M. Jubril, O.A. Olaniyan, O.A. Komolafe, P.O. Ogunbona, Economic emission dispatch problem: a semi-definite programming approach, Appl. Energy, 134, pp. 446–55 (2014).
(31) Q. Qin, S. Cheng, X. Chu, X. Lei, Y. Shi. Solving nonconvex/non-smooth economic load dispatch problems via an enhanced particle swarm optimization, Appl. Soft. Computing, 59, pp. 229–242 (2017).
(32) X. Xin Shen, D. Zou, X. Zhang, Q. Zhang, A phase-based adaptive differential evolution algorithm for the economic load dispatch considering valve-point effects and transmission losses, Mathematical Problems in Engineering (2018).
(33) S. Agrawal, B.K. Panigrahi, M.K. Tiwari, Multiobjective particle swarm algorithm with fuzzy clustering for electrical power dispatch, IEEE Trans. Evol. Comput., 12, 5, pp. 529–541 (2008).
(34) B. Mandal, P.K. Roy, S. Mandal, Economic load dispatch using krill herd algorithm, Int. J. Electr Power Energy Syst., 57, pp. 1–10 (2014).
(35) M. Basu, Economic environmental dispatch using multi-objective differential evolution, Appl. Soft. Comput., 11, 2, pp. 2845–53 (2011).
(36) P. Subbaraj. P, Rengaraj, R. Salivahanan. S, Enhancement of self-adaptive real-coded genetic algorithm using Taguchi method for economic dispatch problem, Appl. Soft. Computing, 11, pp. 83–92 (2011).
