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The fuzzy model of dynamic production and maintenance planning in pumped-storage hydroelectricity | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 20، دوره 15، شماره 7، مهر 2024، صفحه 227-242 اصل مقاله (2.58 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2023.31113.4569 | ||
| نویسندگان | ||
| Farid Asgari1؛ Fariborz Jolai* 1، 2؛ Farzad Movahedisobhani1 | ||
| 1Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran | ||
| 2School of Industrial and Systems Engineering, College of Engineering, University of Tehran, Tehran, Iran | ||
| تاریخ دریافت: 13 بهمن 1401، تاریخ بازنگری: 11 تیر 1402، تاریخ پذیرش: 15 تیر 1402 | ||
| چکیده | ||
| Developing hydropower plants is a successful strategy for sustainable energy production in countries. On the other hand, due to the high capacity of energy production in the pumping power plant sector, the strategy of saving and continuous exploitation of these power plants is one of the successful policies of governments. Therefore, in this research, the optimization of energy production and maintenance costs in one of the large storage pump power plants in Iran has been discussed and investigated based on the optimization mathematical model strategy. Therefore, a Mixed Integer Nonlinear Programming mathematical model was developed in this field. Due to the uncertainty in the presented mathematical model, the fuzzification strategy was used in the mathematical model. On the other hand, in order to achieve the optimal production plan, an energy production cost optimization policy has been presented to reduce the difference in supply and demand in the energy production network. In order to evaluate the presented mathematical model, four meta-heuristic algorithms of Multi-objective Keshtel Algorithm, Multi-objective Simulated Annealing, Non-dominated Ranking Genetic Algorithm and Non-dominated Sorting Genetic Algorithm II were used with binary coding. The results of this research have shown that the solution of the meta-heuristic NRGA algorithm has been done despite the approximation of the optimal solutions in a suitable period of time, and the results of the research indicate the applicability of the presented model in the studied power plant. Therefore, according to the level of optimization performed in the case study, it has caused the improvement of planning by 7% to 12% and effective optimization processes. | ||
| کلیدواژه‌ها | ||
| production planning؛ maintenance؛ pump storage power plant؛ meta-heuristic algorithm؛ NSGA-II؛ NRGA؛ MOKA؛ MOSA | ||
| مراجع | ||
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