طراحی مدل برنامهریزی استوار یکپارچه تولید انرژی چند وجهی و تعمیرات تجهیزات در نیروگاه تلمبه ذخیرهای در راستای
فرید عسگری
1
(
دانشجوی دکتری گروه مهندسی صنایع، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران.
)
فریبرز جولای
2
(
استاد گروه مهندسی صنایع، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران.
)
فرزاد موحدی سبحانی
3
(
استادیار گروه مهندسی صنایع، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران.
)
کلید واژه: برنامهریزی تولید, نگهداری و تعمیرات, نیروگاه تلمبه ذخیرهای, الگوریتم فرا ابتکاری GA و ICA.,
چکیده مقاله :
تولید انرژی در بخش نیروگاههای تلمبهای، استراتژی ذخیره و بهرهبرداری دائم از این نیروگاهها یکی از سیاستهای موفق دولتها است. از این رو در این پژوهش حداقلسازی میزان هزینههای تولید انرژی و نگهداری و تعمیرات در یکی از نیروگاههای بزرگ تلمبه ذخیرهای در ایران در راستای سیاستگذاریهای سبز بر اساس راهبرد شبیهسازی- بهینهسازی پرداخته شده است. در مدل MINLP معرفی شده بدنبال بهینهسازی هزینه نگهداری و تعمیرات بر اساس میزان تولید، ساعت کارکرد نیروگاه، سطح کسری تولید انرژی با در نظر گرفتن عدم قطعیت در سطح تقاضای شبکه با استفاده از روش برنامهریزی امکانی ارائه شده است. جهت حل مدل ریاضی در ابعاد کوچک از الگوریتم حل دقیق CPLEX در نرم افزار GAMS حل شده است و در ابعاد بزرگ از دو الگوریتم فرا ابتکاری GA و ICA با کدنویسی دودویی در نرمافزار متلب بهرهگیری شد. نتایج این پژوهش نشان داده است که حل الگوریتم فرا ابتکاری با وجود تقریب جوابهای بهینه با ضریب اطمینان 95 درصد در مدت زمان مناسبی اجرا شده است و نتایج پژوهش به کاربردی بودن مدل ارائه شده در نیروگاه مورد مطالعه اشاره دارد.
چکیده انگلیسی :
Energy production in pumped power plants, reserve strategy, and continuous exploitation of these power plants are some of the successful policies of governments. Therefore, in this research, the minimization of the cost of energy production and maintenance and repairs in one of the large storage pump power plants in Iran in line with green policies has been discussed based on the simulation-optimization strategy. In the introduced MINLP model, optimization of the cost of maintenance and repairs based on the amount of production, operating hours of the power plant, and the deficit level of energy production, taking into account the uncertainty in the demand level of the network, is presented using the feasibility planning method. To solve the mathematical model in small dimensions, the CPLEX exact solution algorithm was solved in GAMS software, and in large sizes, two meta-heuristic algorithms GA and ICA were used with binary coding in MATLAB software. The results of this research have shown that the solution of the meta-heuristic algorithm has been implemented in a suitable period despite the approximation of optimal solutions with a confidence factor of 95%, and the results of the research indicate the applicability of the presented model in the studied power plant.
Achkar, V. G., Cafaro, V. G., Mendez, C. A., & Cafaro, D. C. (2019). Discrete-time MILP formulation for the optimal scheduling of maintenance tasks on oil and gas production assets. Industrial & Engineering Chemistry Research.
Aguirre, A. M., & Papageorgiou, L. G. (2018). Medium-term optimization-based approach for the integration of production planning, scheduling and maintenance. Computers & Chemical Engineering, 116, 191-211.
Aguirre, A. M., & Papageorgiou, L. G. (2018). Medium-term optimization-based approach for the integration of production planning, scheduling and maintenance. Computers & Chemical Engineering, 116, 191-211.
Ahmadi, R. (2018). An integrated approach to maintenance scheduling of multi-state production systems subject to deterioration. IMA Journal of Management Mathematics, 30(2), 235-264.
Akkaş, Ö. P., & Çam, E. (2019, October). Optimal Operation of Virtual Power Plant in a Day Ahead Market. In 2019 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) (pp. 1-4). IEEE.
Amiri, S., & Honarvar, M. (2018). Providing an integrated Model for Planning and Scheduling Energy Hubs and preventive maintenance. Energy, 163, 1093-1114.
B. Bouslah, A. Gharbi, and R. Pellerin, "Integrated production, sampling quality control and maintenance of deteriorating production systems with AOQL constraint," Omega, vol. 61, pp. 110-126, 2016.
Boudjelida, A. (2019). On the robustness of joint production and maintenance scheduling in presence of uncertainties. Journal of Intelligent Manufacturing, 30(4), 1515-1530.
Chansombat, S., Pongcharoen, P., & Hicks, C. (2019). A mixed-integer linear programming model for integrated production and preventive maintenance scheduling in the capital goods industry. International Journal of Production Research, 57(1), 61-82.
Cheng, G. Q., Zhou, B. H., & Li, L. (2018). Integrated production, quality control and condition-based maintenance for imperfect production systems. Reliability Engineering & System Safety, 175, 251-264.
Elgamal, A. H., Kocher-Oberlehner, G., Robu, V., & Andoni, M. (2019). Optimization of a multiple-scale renewable energy-based virtual power plant in the UK. Applied Energy, 256, 113973.
Ertogral, K., & Öztürk, F. S. (2019). An integrated production scheduling and workforce capacity planning model for the maintenance and repair operations in airline industry. Computers & Industrial Engineering, 127, 832-840.
F. Berthaut, A. Gharbi, and K. Dhouib, "Joint modified block replacement and production/inventory control policy for a failure-prone manufacturing cell," Omega, vol. 39, no. 6, pp. 642-654, 2011.
Gilabert, E., Konde, E., Sierra, B., & Arnaiz, A. (2018). A multi-stage optimization algorithm for standardization of maintenance plans. IFAC-PapersOnLine, 51(11), 520-524.
Glawar, R., Karner, M., Nemeth, T., Matyas, K., & Sihn, W. (2018). An approach for the integration of anticipative maintenance strategies within a production planning and control model. Procedia CIRP, 67, 46-51.
Guiras, Z., Hajej, Z., Rezg, N., & Dolgui, A. (2018). Comparative Analysis of Heuristic Algorithms Used for Solving a Production and Maintenance Planning Problem (PMPP). Applied Sciences, 8(7), 1088.
Hadayeghparast, S., Farsangi, A. S., & Shayanfar, H. (2019). Day-ahead stochastic multi-objective economic/emission operational scheduling of a large scale virtual power plant. Energy, 172, 630-646.
Hamrol, A. (2018). A new look at some aspects of maintenance and improvement of production processes. Management and Production Engineering Review, 9.
Hamrol, A. (2018). A new look at some aspects of maintenance and improvement of production processes. Management and Production Engineering Review, 9.
Haoues, M., Dahane, M., & Mouss, N. K. (2019). Outsourcing optimization in two-echelon supply chain network under integrated production-maintenance constraints. Journal of Intelligent Manufacturing, 30(2), 701-725.
Hunt, J. D., Freitas, M. A. V., & Junior, A. O. P. (2014). Enhanced-Pumped-Storage: Combining pumped-storage in a yearly storage cycle with dams in cascade in Brazil. Energy, 78, 513-523.
J. Kim and S. B. Gershwin, "Analysis of long flow lines with quality and operational failures," IIE transactions, vol. 40, no. 3, pp. 284-296, 2008.
J. Liu, J. Shi, and S. J. Hu, "Quality-assured setup planning based on the stream-of-variation model for multi-stage machining processes," IIE transactions, vol. 41, no. 4, pp. 323-334, 2009.
Jafari, L., & Makis, V. (2016). Optimal lot-sizing and maintenance policy for a partially observable production system. Computers & Industrial Engineering, 93, 88-98.
Kang, K., & Subramaniam, V. (2018). Joint control of dynamic maintenance and production in a failure-prone manufacturing system subjected to deterioration. Computers & Industrial Engineering, 119, 309-320.
Kopanos, G. M., & Puigjaner, L. (2019). Integrated Operational and Maintenance Planning of Production and Utility Systems. In Solving Large-Scale Production Scheduling and Planning in the Process Industries (pp. 191-244). Springer, Cham.
La Fata, C. M., & Passannanti, G. (2017). A simulated annealing-based approach for the joint optimization of production/inventory and preventive maintenance policies. The International Journal of Advanced Manufacturing Technology, 91(9-12), 3899-3909.
Liu, Q., Dong, M., & Chen, F. F. (2018). Single-machine-based joint optimization of predictive maintenance planning and production scheduling. Robotics and Computer-Integrated Manufacturing, 51, 238-247.
Liu, Q., Dong, M., Chen, F. F., Lv, W., & Ye, C. (2019). Single-machine-based joint optimization of predictive maintenance planning and production scheduling. Robotics and Computer-Integrated Manufacturing, 55, 173-182.
Martínez-Lucas, G., Pérez-Díaz, J. I., Chazarra, M., Sarasúa, J. I., Cavazzini, G., Pavesi, G., & Ardizzon, G. (2019). Risk of penstock fatigue in pumped-storage power plants operating with variable speed in pumping mode. Renewable energy, 133, 636-646.
Martinod, R. M., Bistorin, O., Castañeda, L. F., & Rezg, N. (2018). Maintenance policy optimisation for multi-component systems considering degradation of components and imperfect maintenance actions. Computers & Industrial Engineering, 124, 100-112.
Matyas, K., Nemeth, T., Kovacs, K., & Glawar, R. (2017). A procedural approach for realizing prescriptive maintenance planning in manufacturing industries. CIRP Annals, 66(1), 461-464.
Mennemann, J. F., Marko, L., Schmidt, J., Kemmetmüller, W., & Kugi, A. (2019). Nonlinear Model Predictive Control of a Variable-Speed Pumped-Storage Power Plant. IEEE Transactions on Control Systems Technology.
Özyön, S. (2020). Optimal short-term operation of pumped-storage power plants with differential evolution algorithm. Energy, 194, 116866.
Rivera-Gómez, H., Gharbi, A., Kenné, J. P., Montaño-Arango, O., & Hernández-Gress, E. S. (2018). Subcontracting strategies with production and maintenance policies for a manufacturing system subject to progressive deterioration. International Journal of Production Economics, 200, 103-118.
S. S. Sana, "Preventive maintenance and optimal buffer inventory for products sold with warranty in an imperfect production system," International Journal of Production Research, vol. 50, no. 23, pp. 6763-6774, 2012.
Schreiber, M., Klöber-Koch, J., Richter, C., & Reinhart, G. (2018). Integrated Production and Maintenance Planning for Cyber-physical Production Systems. Procedia CIRP, 72, 934-939.
Shafiee, M., Ghazi, R., & Moeini-Aghtaie, M. (2019). Day-ahead Resource Scheduling in Distribution Networks with Presence of Electric Vehicles and Distributed Generation Units. Electric Power Components and Systems, 1-14.
Sheikhalishahi, M., Eskandari, N., Mashayekhi, A., & Azadeh, A. (2019). Multi-objective open shop scheduling by considering human error and preventive maintenance. Applied Mathematical Modelling, 67, 573-587.
Vasconcelos, M. H., Beires, P., Moreira, C. L., & Lopes, J. A. P. (2019). Dynamic security of islanded power systems with pumped storage power plants for high renewable integration–A study case. The Journal of Engineering, 2019(18), 4955-4960.
Wu, Y., Zhang, T., Xu, C., Zhang, B., Li, L., Ke, Y., ... & Xu, R. (2019). Optimal location selection for offshore wind-PV-seawater pumped storage power plant using a hybrid MCDM approach: A two-stage framework. Energy Conversion and Management, 199, 112066.
Xiao, S., Chen, Z., & Sarker, B. R. (2019). Integrated maintenance and production decision for k-out-of-n system equipment with attenuation of product quality. International Journal of Quality & Reliability Management, 36(5), 735-751.
Yao, W., Deng, C., Li, D., Chen, M., Peng, P., & Zhang, H. (2019). Optimal Sizing of Seawater Pumped Storage Plant with Variable-Speed Units Considering Offshore Wind Power Accommodation. Sustainability, 11(7), 1939.
Yin, S., Ai, Q., Li, Z., Zhang, Y., & Lu, T. (2020). Energy management for aggregate prosumers in a virtual power plant: A robust Stackelberg game approach. International Journal of Electrical Power & Energy Systems, 117, 105605.
Zhang, L., Zhang, J., Yu, X., Lv, J., & Zhang, X. (2019). Transient Simulation for a Pumped Storage Power Plant Considering Pressure Pulsation Based on Field Test. Energies, 12(13), 2498.