بهره‌برداری بهینه از سامانه سد تک‌مخزنه با الگوریتم اصلاحی کلونی زنبورعسل مصنوعی: (مطالعه موردی: سد دز در استان خوزستان)

نویسندگان

1 هیات علمی/دانشگاه اصفهان

2 دانشجو/ دانشگاه اصفهان

چکیده

در این تحقیق، الگوریتم کلونی زنبور عسل مصنوعی، برای حل مساله بهینه‌سازی بهره‌برداری از سیستم تک مخزنه استفاده شده است. بدین منظور، با ایجاد اصلاحاتی در الگوریتم پایه کلونی زنبور عسل مصنوعی، الگوریتم اصلاحی کلونی زنبور عسل مصنوعی معرفی شد. مساله بهرهبرداری بهینه ساده و برقابی از سیستم سد تک مخزنه سد دز (در دو دوره زمانی 5 و 20 ساله) حل و نتایج آن با سایر یافته‌های دیگران مقایسه ‌شد. برای حل دو فرمولبندی ارائه شد که در فرمولبندی اول، مقدار آب خروجی از مخزن سد‌ و در فرمولبندی دوم حجم ذخیره مخزن سد به عنوان متغیر تصمیم در نظر گرفته شد. نتایج نشان داد که در حل مساله بهره‌برداری ساده 5 و 20 ساله با استفاده از فرمولبندی اول، مقادیر تابع هدف الگوریتم اصلاحی کلونی زنبور عسل مصنوعی نسبت به الگوریتم پایه به ترتیب، %94/9 و %266/55 بهبود یافته و در حل مساله بهره‌برداری ساده 5 و 20 ساله با استفاده از فرمولبندی دوم، درصد بهبود مقادیر تابع هدف به ترتیب، برابر با %63/14 و %18/7 بود. علاوه بر این، در حل مساله بهره‌برداری برقابی 5 و 20 ساله با استفاده از فرمولبندی اول، مقادیر تابع هدف الگوریتم اصلاحی کلونی زنبور عسل مصنوعی نسبت به الگوریتم پایه به ترتیب %76/7 و %47/26 بهبود یافته و در حل مساله بهره‌برداری برقابی 5 و 20 ساله با استفاده از فرمولبندی دوم، درصد بهبود مقادیر تابع هدف به ترتیب، برابر با %79/3 و %49/25 بود.

کلیدواژه‌ها


عنوان مقاله [English]

Optimal Operation of Single Reservoir System Using Improved Artificial Bee Colony Algorithm (Case study: Dez Reservoir in Khozestan)

نویسنده [English]

  • ramtin moeini 1
چکیده [English]

In this research, artificial honey bee colony algorithm, is used to solve single reservoir operation optimization problem. For this purpose, improved artificial bee colony algorithm is proposed using some modification in the basic algorithm. The simple and hydropower operation problems of Dez reservoir over 5 and 20 year time periods are solved using the proposed algorithm and the outputs are compared with the other available research results. In order to solve these problems, two different formulations are proposed in which the water release and storage volumes are considered as decision variables in the first and second formulations, respectively. If the first formulation of the improved artificial bee colony algorithm is used to solve the simple reservoir operation over 5 and 20 years, the objective function values are improved %9.94 and %55.266 than basic artificial bee colony algorithm, respectively. If the second formulation is used to solve simple reservoir operation over 5 and 20 years, the objective function values are improved %14.63 and %7.18 than basic artificial bee colony algorithm, respectively. In addition, if the first formulation of improved artificial bee colony algorithm is used to solve hydropower reservoir operation over 5 and 20 years, the objective function values are improved %7.76 and %26.47 than basic artificial bee colony algorithm, respectively. If the second formulation is used to solve hydropower reservoir operation over 5 and 20 years, the objective function values are improved %3.79 and %25.49 than basic artificial bee colony algorithm, respectively.

کلیدواژه‌ها [English]

  • Dez reservoir
  • Hydropower
  • Improved artificial bee colony algorithm
  • Optimal operation
  • Single reservoir system
Afshar A, Bozorg Haddad O, Marino MA, Adams BJ, 2007. Honey-bee mating optimization (HBMO) algorithm for optimal reservoir operation. Journal of the Franklin Institute 344: 452-462.
Afshar MH, Moeini R, 2008. Partially and fully constrained ant algorithms for the optimal solution of large-scale reservoir operation problems. Water Resources Management 22: 1835-1857.
Afshar MH, 2012. Large scale reservoir operation by Constrained Particle SwarmOptimization algorithms. Hydro-environment Research 6: 75-87.
Bashiri-Atrabi H, Qaderi K, Rheinheimer D, Sharifi E, 2015. Application of harmony search algorithm to reservoir operation optimization. Water Resources Management 29(15):5729–5748
Bozorg Hadad O, Afshar A, Marino MA, 2008. Honey-Bee Mating Optimization (HBMO) algorithm in deriving optimal operation rules for reservoirs.  Journal of. Hydroinformatics 10(3): 257-264.
Castelletti A, Pianosi F, Restelli M, 2013. A multi objective reinforcement learning approach to water resources systems operation: Pareto frontier approximation in a single run. Water Resources Research 49: 3476-3486.
Chang L Ch, Chang FJ, Wang KW, Dai Sh Y, 2010. Constrained Genetic Algorithm for optimizing multi-use reservoir operation.  Journal of Hydrology  390: 66-74.
Choong SM, El-Shafie A, 2014. State-of-the-art for modelling reservoir inflows and management optimization.  Water Resources Management 20: 1-16.
Dinpazhoh V, Sattari MT, Ebrahimi S, and Darbandi S, 2016.Optimum operation of reservoir using Genetic Algorithm and particle Swarm optimization (Case study: Alavian dam). Soil and Water Science-University of Tabriz 27(2): 17-29. (In Persian)
Hossain MS, and El-Shafie A, 2014. Evolutionary techniques versus swarm intelligences: application in reservoir release optimization. Neural Computing and Applications 24: 1583-1594.
Hosseini Moghari M, Banihabib ME, 2014. Optimizing operation of reservoir for agricultural water supply using firefly algorithm. Journal of Soil and Water Resources Conservation 3(4): 17-31. (In Persian)
Karaboga D, Basturk B, 2007. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization  39:  459-471.
Mohammad Reza Pour O, Zeynali MJ, 2015. Application of an max-min ant system algorithm for optimal operation of multi – reservoirs (Case study: Golestan and Voshmgir Reservoir Dams). International Journal of Agriculture and Crop Sciences 8(1): 27-33.
Naveena S, Malathy S, Saranya D, Kumar DR, 2015. An Improved Artificial Bee Colony (IABC) algorithm for numerical function optimization. International Journal of Advanced Information in Engineering Technology  1: 13-17.
Norouzi B, Barani GH.A, Meftah Halaghi M,Dehghani AA, 2011. Amulti reservoir system operation optimization using multi population genetic algorithm (case study: Golstan and Voshmgir reservoirs). Journal of Soil and Water Resources Conservation 18(4): 43-62. (In Persian)
Rani D, Moreira MM, 2010. Simulation–optimization modeling: a survey and potential application in reservoir systems operation. Water Resources Management 24: 1107-1138.
Reddy MJ, Kumar DN, 2006. Ant colony optimization for multi-purpose reservoir operation. Journal of Water Resource Management 20: 879-889.
Wang KW, Chang LC, Chang FJ, 2011. Multi-tier interactive genetic algorithms for the optimization of long-term reservoir operation. Advances in Water Resources 34: 1343-1351.
Wardlaw R, Sharif M, 1999. Evaluation of genetic algorithm for the optimal reservoir system operation. Journal of Water Resources Planning and Management 125: 25-33
Yasar M, 2016. Optimization of reservoir operation using cuckoo search algorithm: Example of Adiguzel Dam, Denizli, Turkey. Mathematical Problems in Engineering 2016: 1-7
Zhang J Wu Zh, Cheng Ch, Zhang Sh, 2011. Improved particle swarm optimization algorithm for multi- reservoir system operation. Water Science and Engineering 4(1): 61-73.