مقایسه مدل برنامه ریزی خطی و الگوریتم ازدحام ذرات در بهینه سازی منحنی فرمان مخازن با اعمال سیاست جیره‌بندی (مطالعه موردی سد وشمگیر استان گلستان)

نویسندگان

1 دانشیار گروه مهندسی آب، دانشگاه علوم کشاورزی و منابع طبیعی گرگان

2 دانشجوی کارشناسی ارشد مهندسی منابع آب، دانشکده آب وخاک، دانشگاه زابل

چکیده

آب ذخیره شده در مخازن یکی از منابع مهم تامین آب در مناطق کم آب بشمار میرود. بنابراین بهرره برداری بهینه از آن مخصوصا در دوران خشکسالی از اهمیت زیادی برخوردار میباشد. در این تحقیق به بررسی بهینه‌سازی بهره‌برداری از مخزن سد وشمگیر واقع در استان گلستان با اعمال سیاست جیره‌بندی پیوسته با استفاده از الگوریتم ازدحام ذرات و برنامه ریزی خطی پرداخته شد. تابع هدف در این تحقیق کمینه سازی مقدار کمبود می باشد. به منظور بررسی اثر اعمال سیاست‌ مدیریتی جیره‌بندی، دوره خشکی سه ساله از سال 1380 تا 1382 در نظر گرفته شد. برای ارزیابی نتایج از شاخص‌های ارزیابی منابع آب همچون درصد تامین، درصد اعتمادپذیری، آسیب‌پذیری، برگشت‌پذیری، پایداری مورد استفاده قرار گرفت. میزان تغییرات حجم آستانه مخزن در هر ماه از مدل استخراج شده و منحنی قاعده جیره‌بندی ارائه گردید. ارزیابی نتایج دو مدل نشان داد شاخص پایداری مدل برنامه ریزی خطی (با مقدار78/41 ) از الگوریتم ازدحام ذرات (با مقدار 416/6) بسیار بالاتر بوده و نشان از کارایی بالاتر این مدل در بهینه سازی سیاست جیره بندی در سد وشمگیر می باشد.

کلیدواژه‌ها


عنوان مقاله [English]

Comparison of Linear Programming Model and Particle Swarm Optimization Algorithm in Optimization of Reservoir Rule Curves used Hedging Policy

نویسندگان [English]

  • O M 1
  • Z Mosavi Rasteger 2
1 Associated. Profprofessor, Water Engineering Departman, Gorgan University of Agricultural Sciences and Natural Resources.
2 M.Sc student in Water Resource Engin. Faculty. Of Water and Soil, Univ. of Zabol, Iran
چکیده [English]

Water stored in the reservoir is one of the main sources of water supply in arid regions. Therefore, optimal operation of it is of utmost importance, especially in drought duration. In this study, optimal operation of Voshmgir dam using continues hedging policy optimized by linear programming (LP) and Partial swarm optimization Algorithm (PSO) that located in Golestan province north of Iran was investigated. The objective function is minimization of the Shortage index. In order to examine the effects of used hedging policy, 3 continuous drought years was considered. To evaluate the results of the models, evaluation criteria such as the percentage of water supply, reliability, vulnerability and stability indicators was used. The threshold volume changes per month was extracted and the base hedging rule curve for reservoir was presented. Evaluating the results of two models showed stability index in linear programming with 41.78 model was much higher than particle swarm algorithm with 6.416. Which indicates the higher performance of this model in the optimization of hedging policy in Voshmgir reservoir.

کلیدواژه‌ها [English]

  • Operation
  • Reliability
  • Hedging Policy
  • optimization
  • reservoir
Ashofte PS and Bozorg Haddad O, 2015.  Use of multi-conditional functions in the field of reservoir management and under climate change. Iranian Journal of Soil and Water Research 45 (4): 397-404. (In Persian)
Azarafza  H,  Rezaei H,  Behmanesh J and  Besharat S, 2012. Results comparison of employing PSO, GA and SA algorithms in optimizing reservoir operation (Case Study: Shaharchai Dam, Urmia, Iran). Journal of Water and Soil 26(5):1101-1108. (In Persian)
karami F, Dariane A, 2014. Comparison of hedging policies in reservoir management under drought condition. Journal of Water and Wastewater 25(3):76-85. (In Persian)
Fallahmehdipour A, Blouri yazdli Y and Bozorghadad O, 2008. Dez dam command curve extraction with hedging policy.4th National Congress of Civil Engineering. May 6. University of Tehran. P.8.
Hogatti E, Faridhoseini e, Alizadeh A and Entezari M. 2013. Operation model of the reservoir with hedging policy and its application in preparing the rule curve of Dosti Dam. 7th National Congress of Civil Engineers, May 17-18. Faculty of Engineering, Sistan and Baluchestan University, Zahedan. Iran.
Hoseini H, Nagafi jilani E and Zakeri Niri M. 2013. Optimal model of reservoir operation with real, optimal and hedging conditions and its application in steering curve of Latian and Mamlou dam reservoirs. The 5th Iranian Water Resources Management Conference, February 29-30. Iranian Association of Water Resources Science and Engineering, Shahid Beheshti University, Tehran.
Razaghi P, Babazadeh H, Shorian M, 2013. Development of a hedging policy from multi-purpose reservoir under water resources limitation condition using MODSIM 8.1. Journal of Water and Soil Resources Conservation 3(2): 12-22. (In Persian)
Zeynali MJ, MohammadRezaPour O, Frooghi F, 2015. Evaluation of particle swarm, genetic and continuous ant colony algorithms in optimal operation of doroodzan dam reservoir. Journal of Water and Soil Sciences - University of Tabriz 25(3): 27-38. (In Persian)
Kamali N and Borhani E, 2000. Model of Shahid Madani dam reservoir operation in drought conditions. First National Conference on Measures to Combat Drought and Drought. March 9 and 10. Hsahid Bahonar University of Kerman, Kerman.
Moeini R and Afshar MH, 2008. Optimal Operation of the dam reservoir using the Max-Minimum Ant algorithm (MMAS). Sharif Scientific Research Journal 46:85 - 93. (In Persian)
Adeloye AJ, Soundharajan BS, Ojha CSP and Remesan R, 2015. Effect of hedging-integrated rule curves on the performance of the pong reservoir (India) during scenario-neutral climate change Perturbations. Water Resource Management 29: 3387–3407.
Baltar AM and Fontane DG, 2008. Use of multi-objective particle swarm optimization in water resources management. Journal of Water Resources Planning and Management 134(3): 265-275.
Dariane AB, 2003. Reservoir operation during drought. International Journal of Engineering 16(3): 209-216.
Fallah-Mehdipour E, Bozorg Haddad O and Mariño M A, 2011. MOPSO algorithm and its application in multipurpose multi-reservoir operations. Journal of Hydro-informatics 14(4): 794-811.
Goodarzi E, Ziaei M and Hossinipour E, 2015. Optimization analysis in hydrosystem engineering, topics in safety, risk, reliability and quality. Springer International Publishing Switzerland.
Kennedy J, Eberhart R, 1995. Particle swarm optimization. Pp.1942- 1948. Proceeding of International Conference on Neural Networks. Perth, Australia, 1995 IEEE,  Piscataway.
Kumar DN and Reddy J, 2007. Multipurpose reservoir operation using particle swarm optimization. Journal of Water Resources Planning and Management 133(3): 192-201.
Liu Y, 2009. Automatic calibration of a rainfall-runoff model using a fast and elitist multi-objective particle swarm algorithm. Expert Systems with Applications 36(14): 9533-9538.
Louks DP, Stedinger JR and Haith DA, 1981. Water Resource Systems Planning and Analysis. Prentice-Hall, New Jersey.Englewood Cliffs, N. J.
Marton D, Kapelan Z, 2014. Risk and reliability analysis of open reservoir water shortages using optimization. Procedia Engineering 89: 1478-1485.
Yan Sh.X, 2008. Firefly algorithm for multi-model optimization. Stochastic Algorithms: Foundations and Applications. Volume 5792 of the series Lecture Notes in Computer Science Pp. 169-178.
Rouzegari NY, Hassanzadeh Y and Sattari MT, 2018.  Optimization of reservoir operational policy using simulated annealing algorithm (Case Study: Mahabad reservoir).Water and Soil Sciences – University of Tabriz. 28(1): 173-185. (In Persion)
Moinaldini1 E, Mohamad Reza Pour O and Zeynali MJ, 2018. Application of imperialist competitive algorithm in optimizations of pipe diameters for urban water network (Case study: Shahrdari town, Kerman). Water and Soil Sciences – University of Tabriz. 28(2): 29-41. (In Persion)