فرمان‌های نگهداشت تصادفی وابسته به زمان برای بهره‌برداری مخزن: مطالعه موردی مخزن سد بوکان

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی آبیاری و آبادانی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران

2 گروه آبیاری و آبادانی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران

چکیده

بهره­برداری مخزن سد برای تأمین کل نیاز گام زمانی جاری به دلیل احتمال مواجهه با کمبود آب شدید در آینده منطقی نیست و استفاده از فرمان­های نگهداشت می­تواند تأمین آب در آینده را بیمه کند. در بهره­برداری بلند- مدت مخزن سد برای تأمین نیاز آبیاری، عدم­قطعیت جریان ورودی به مخزن و نیاز آبیاری اثر قابل توجهی در نتایج رهاسازی از مخزن خواهد داشت. همچنین تغییرات حساسیت محصول به تنش آبی در دوره­های مختلف رشد باعث تغییر در شیب تابع عملکرد محصول می­شود، که در توابع عملکرد فصلی دیده نشده و مسئله را پیچیده­تر می­کند. در این مطالعه مزایای استفاده از مدل تصادفی و توابع عملکرد وابسته به گام زمانی نسبت به مدل قطعی و تابع عملکرد فصلی در بهره­برداری از مخزن سد بوکان با استفاده از فرمان­های نگهداشت نشان داده شده است. نتایج نشان می­دهد که بهره­برداری مخزن با فرمان­های نگهداشت نسبت به مدل بهره­برداری موجود، 8/46 درصد سود اقتصادی را افزایش می­دهد. همچنین توابع عملکرد وابسته به گام زمانی 19 درصد نتایج را نسبت به تابع عملکرد فصلی بهبود می­بخشد. نتایج مقایسه مدل تصادفی با مدل قطعی بهره­برداری مخزن نشان می­دهد که وارد کردن جداگانه عدم­قطعیت جریان ورودی به مخزن و نیاز آبیاری و ورود همزمان هر دو متغیر در محاسبات به ترتیبب 73/0، 95/4 و 99/12 درصد سود اقتصادی را افزایش خواهد داد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Time-dependent Stochastic Hedging Rules to Reservoir Operation: A Case Study of the Bukan Dam Reservoir

نویسندگان [English]

  • Shahram Zebardast 1
  • Masoud Parsinejad 2
1 , Department of Irrigation and Reclamation Engineering, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
2 Irrigation Engineering Department, Campus of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
چکیده [English]

In operation of dam reservoir, due to the possibility of severe water shortages in the future, supplying total demand of current step is not rational, and the use of hedging rules can provide insurance for water supply in the future. In the reservoir long-term operation to supply the irrigation water demand, uncertainty of reservoir inflow and uncertainty of irrigation water demand have a significant effect on release. Crop water stress sensitivity variation at different growth stages varies the crop production function slope, which is not seen in seasonal production functions. In this study, a stochastic planning model with time-dependent production functions and a deterministic planning model with seasonal production function, in operation of the Buchan dam reservoir by using hedging rules are compared. The results show the reservoir operation by hedging rules increases economic benefit by 46.8% compared to the existing operation model. The time-dependent production function can improve the results by 19% over seasonal production functions. Also, the results show using stochastic model with the inflow uncertainty, irrigation water demand uncertainty and both, inflow uncertainty and irrigation water demand uncertainty simultaneously, the economic benefit increase by 0.73, 4.95 and 12.99%, respectively.

کلیدواژه‌ها [English]

  • Reservoir operation
  • Hedging rules
  • Inflow uncertainty
  • irrigation water demand
  • Stochastic
Aasgård, E. K., Bolkesjø, T. H., Johnsen, R. I., Kristiansen, F., Larsen, T. J., Riddervold, H. O., ... & Skjelbred, H. I. (2015). Validating the SHARM model. SINTEF energy research. Postboks 4761 sluppen. No 7465. Trondheim, Norway. TR A7521- Unrestricted.
Amerian, M., Mohammadi, K. and Eslami, H. R. (2003). Optimal operation model of Buchan dam reservoir by dynamic programming method (artificial neural network), the first national conference of hydropower plants, Tehran, Iran Water and Power Resources Development Company. (In Farsi)
Belsnes, M. M., Wolfgang, O., Follestad, T., & Aasgård, E. K. (2016). Applying successive linear programming for stochastic short-term hydropower optimization. Electric Power Systems Research130, 167-180.
Bzorg haddad, O. (2014). Optimization of water resources systems, University of Tehran Publishing Institute, Publication No. 3561, Second Edition, 412 pages. (In Farsi)
Claxton, K., Sculpher, M., & Drummond, M. (2002). A rational framework for decision making by the National Institute for Clinical Excellence (NICE). The Lancet360(9334), 711-715.
Draper, A. J., & Lund, J. R. (2004). Optimal hedging and carryover storage value. Journal of water resources planning and management130(1), 83-87.
Emami, F., & Koch, M. (2017). Evaluating the water resources and operation of the Boukan Dam in Iran under climate change. Eur. Water59, 17-24.
Emami, F., & Koch, M. (2019). Modeling the impact of climate change on water availability in the Zarrine River Basin and inflow to the Boukan Dam, Iran. Climate7(4), 51.
Gavahi, K., Mousavi, S. J., and Ponnambalam, K. (2018). Comparison of Two Streamflow Forecast Approaches in an Adaptive Optimal Reservoir Operation Model.
Gavahi, K., Mousavi, S. J., & Ponnambalam, K. (2019). Adaptive forecast-based real-time optimal reservoir operations: application to Lake Urmia. Journal of Hydroinformatics21(5), 908-924.
Hubbard, D. (2011). How to Measure Anything: Finding the Value of" Intangibles" in Business. People and Strategy34(2), 58.
Karamouz, M., & Araghinejad, S. (2008). Drought mitigation through long-term operation of reservoirs: case study. Journal of irrigation and drainage engineering134(4), 471-478.
Lindo Systems Inc. (2003). Optimization modeling with LINGO. USA Makridakis S, Wheelwright SC, Hyndman RJ (1998) Forecasting: methods and applications, 3rd edn. Wiley, New York
Meira-Machado, L., de Uña-Álvarez, J., and Somnath, D. (2012). Conditional transition probabilities in a non-markov illness-death model. Discussion Papers in Statistics and Operation Research12(05).
Men, B., Wu, Z., Liu, H., Li, Y., & Zhao, Y. (2019). Research on Hedging Rules Based on Water Supply Priority and Benefit Loss of Water Shortage—A Case Study of Tianjin, China. Water11(4), 778.
Mirhasani, S. A. and Hoshmandkhaligh, F. (2019). Stochastic programming. Published by Amir Kabir University (polytechnic). 305-307. (In Farsi)
Moghaddasi, M., Araghinejad, S., & Morid, S. (2010). Long-term operation of irrigation dams considering variable demands: Case study of Zayandeh-rud reservoir, Iran. Journal of irrigation and drainage engineering136(5), 309-316.
Neelakantan, T. R., & Pundarikanthan, N. V. (1999). Hedging rule optimisation for water supply reservoirs system. Water resources management13(6), 409-426.
Palmer, R. N., Kersnar, J. M., and Choi, D., _1995_. “Estimating demand variability.” Proc., 22nd Annual ASCE Conf., Integrated Water Resources Planning for the 21st Century, Water Resources Planning and Management Division of ASCE, Reston, Va.
Seo, S. B., Kim, Y. O., & Kang, S. U. (2019). Time-Varying Discrete Hedging Rules for Drought Contingency Plan Considering Long-Range Dependency in Streamflow. Water Resources Management33(8), 2791-2807.
Sreekanth, J., Datta, B., & Mohapatra, P. K. (2012). Optimal short-term reservoir operation with integrated long-term goals. Water resources management26(10), 2833-2850.
Shiau, J. T., & Lee, H. C. (2005). Derivation of optimal hedging rules for a water-supply reservoir through compromise programming. Water resources management19(2), 111-132.
Shih, J. S., & ReVelle, C. (1995). Water supply operations during drought: A discrete hedging rule. European journal of operational research82(1), 163-175.
Shih, J. S., & ReVelle, C. (1994). Water-supply operations during drought: Continuous hedging rule. Journal of Water Resources Planning and Management120(5), 613-629.
Tu, M. Y., Hsu, N. S., & Yeh, W. W. G. (2003). Optimization of reservoir management and operation with hedging rules. Journal of Water Resources Planning and Management129(2), 86-97.
Tu, M. Y., Hsu, N. S., Tsai, F. T. C., & Yeh, W. W. G. (2008). Optimization of hedging rules for reservoir operations. Journal of Water Resources Planning and Management134(1), 3-13.
Wan, W., Zhao, J., & Wang, J. (2019). Revisiting Water Supply Rule Curves with Hedging Theory for Climate Change Adaptation. Sustainability11(7), 1827.
Yeh, W. W. G. (1985). Reservoir management and operations models: A state‐of‐the‐art review. Water resources research21(12), 1797-1818.
You, J. Y., & Cai, X. (2008a). Hedging rule for reservoir operations: 1. A theoretical analysis. Water Resources Research44(1).
You, J. Y., & Cai, X. (2008b). Hedging rule for reservoir operations: 2. A numerical model. Water resources research44(1).
Zareabyaneh, H., Abdolahzadeh, B. and Palangi, S. (2017). Development of control curves for reservoir operation of Buchan and Mahabad dams with PSO algorithm. Irrigation and Water Engineering of Iran 8 (2). (In Farsi)
Zhang, Z., Zhang, Q., Singh, V. P., and Shi, P. (2018). River flow modelling: comparison of performance and evaluation of uncertainty using data-driven models and conceptual hydrological model. Stochastic Environmental Research and Risk Assessment32(9), 2667-2682.
Zhao, T., Cai, X., and Yang, D. (2011). Effect of streamflow forecast uncertainty on real-time reservoir operation. Advances in water resources34(4), 495-504.