Optimal allocation of water resources using Water Cycle Algorithm (WCA) (Case study: Gorganrood basin)

Subject Areas : Article frome a thesis

saeid akbari fard ¹ , kourosh ghaderi ^{2 *} , bahram bakhtyari ³

1 - دانشجوی دکتری مهندسی منابع آب، گروه هیدرولوژی و منابع آب، دانشکده مهندسی علوم آب، دانشگاه شهید چمران اهواز، ایران
2 - دانشیار گروه مهندسی آب، دانشکده کشاورزی، دانشگاه شهید باهنر کرمان، ایران
3 - استادیار گروه مهندسی آب، دانشکده کشاورزی، دانشگاه شهید باهنر کرمان، ایران

Received: 2015-05-11 Accepted : 2018-09-02 Published : 2018-04-21

Keywords: Optimal allocation, Water Cycle Algorithm, Gorganrood basin, Golestan and Voshmgir Dams,

Abstract :

In this research, a metaheuristic algorithm, called Water Cycle Algorithm (WCA), was developed in MATLAB software, with the purpose of optimal allocation strategies of a multi-reservoirs system (Golestan and Voshmgir dams) located at Gorganrood basin (north of Iran), for a five year period (from 2007-2008 to 2011-2012). At the first step, the performance of the developed algorithm was successfully assessed through several benchmark functions. Next, it was applied to the monthly allocation of Gorganrood multi-reservoirs system. The objective function was defined as the minimizing of the total deficit for the study period. The results of all applied algorithms were evaluated by reliability and vulnerability criteria. The results of WCA were compared with other developed evolutionary algorithms including Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The WCA, GA and PSO were capable to supply 97.73, 87.07 and 94.3 percent of Golestan dam water demand, respectively. For the Voshmgir dam, the mentioned models could supply 97.06, 87.59 and 94.47 percent of water demand, in same order. The temporal reliability (α=0.9) for WCA, GA and PSO models, was obtained 95, 26.67 and 58.33 percent for Golestan dam and 91.67, 38.33 and 66.67 percent for Voshmgir dam, respectively, revealed that the WCA was superior in optimal allocation of multi-reservoirs system.

References:

1) اطلس منابع آب ایران. 1387 . گزارش مطالعات منابع
آب حوزه رودخانههای قرهسو و گرگانرود. آب منطقهای
استان گلستان.
2) شرکت آب منطقهای استان گلستان. 1392 . دفتر
مطالعات پایه منابع آب، مطالعات سیمای سدهای
گرگانرود.
3) شفیعی، م.، بزرگ حداد، ا.، و افشار، ع. 1386 . بررسی
ساختارهای جدید از الگوریتم ژنتیک در بهینهسازی بهره-
-122 :(1) برداری از مخازن. مجله فناوری و آموزش، 1
.117
. 4) قادری، ک.، زلقی، آ.، و بختیاری، ب. 1393
بهینهسازی بهرهبرداری از سیستم چند مخزنی با استفاده
مطالعه موردی: ) (SCE) از الگوریتم تکامل رقابتی جوامع
-228 :(2) حوضه کرخه). مجله مدیریت آب و آبیاری، 4
.215
5) مفتاح هلقی، م.، دهقانی، ا.ا.، مساعدی، ا.، و اسلامی،
ح.ر. 1390 . تعیین کمبود حجم بهینه مخزن سد وشمگیر
در سیستم بهرهبرداری چند سدی. مجله پژوهشهای
.215-230 :(1) حفاظت آب و خاک، 18
. 6) نجفی، م.ر.، هاشم پور، ج.، و خیاط خلقی، م. 1384
بهرهبرداری بهینه از مخزن با استفاده از مدل برنامهریزی
خطی و کاربرد آن در سد وشمگیر. مجله علوم کشاورزی و
.27-35 :(5) منابع طبیعی، 12
7) نوروزی، ب.، بارانی، غ.ع.، مفتاح هلقی، م.، دهقانی،
ا.ا. 1390 . بهینهسازی بهرهبرداری از یک سیستم چند
مخزنه به روش الگوریتم ژنتیک چند جمعیتی مطالعه
موردی (سدهای گلستان و وشمگیر). مجله پژوهشهای
.62-43 :(4) حفاظت آب و خاک، 18
8) Afshar, M.H. and Motaei, I. 2011. Constrained
Big Bang-Big Crunch algorithm for optimal
solution of large scale reservoir operation problem.
International Journal of Optimization in Civil
Engineering. 2: 357-375.
9) Ajibola, A.S. and Adewumi, A.O. 2014.
Review of population based metahueristics in
multi-objective optimization problems. Int'l Journal
of Computing. Communications & Instrumentation
Engg. 1(1): 126-128.
10) Ashofteh, P., Bozorg-Haddad, O. and
Loáiciga, H. 2015. Evaluation of climatic-change
impacts on multiobjective reservoir operation with
46 تخصیص بهینهی منابع آب با استفاده از الگوریتم چرخهی آب (مطالعهی موردی: حوضه آبخیز گرگانرود)
multiobjective genetic programming. Journal of
Water Resource Planning and Management.
10.1061/(ASCE)WR.1943-5452.0000540,
04015030.
11) Baghipour, R., Hosseini, S.M. and Boor, Z.
2014. A water cycle algorithm for optimal
allocation of DGs in distribution system
considering environmental profit. International
Journal of Mechatronics. Electrical and Computer
Technology. 4(11): 430-454.
12) Baltar, A.M. and Fontane, D.G. 2008. Use of
multi-objective particle swarm optimization in
water resources management. Journal of Water
Resource Planning and Management. 134(3): 265-
275.
13) Bozorg-Haddad, O., Moravej, M. and
Loáiciga, H.A. 2014. Application of the water cycle
algorithm to the optimal operation of reservoir
systems. Journal of Irrigation and Drainage
Engineering. 10.1061/(ASCE)IR.1943-
4774.0000832, 04014064.
14) Chang, J.X., Huang, Q. and Wang, Y.M. 2005.
Genetic algorithms for optimal reservoir
dispatching. Journal of Water Resources
Management. 19:321-331.
15) Cheng, M.Y. and Prayogo, D. 2014. Symbiotic
Organisms Search: A new metaheuristic
optimization algorithm. Journal of Computers &
Structures. 139: 98-112.
16) Esat, V. and Hall, M.J. 1994. Water resources
system optimization using genetic algorithm. Hydro
informatics. 94:225-231.
17) Eskandar, H., Sadollah, A., Bahreininejad, A.
and Hamdi, M. 2012. Water cycle algorithm -A
novel metaheuristic optimization method for
solving constrained engineering optimization
problems. Computers and Structures. 110(111):
151–166.
18) Fallah-Mehdipour, E., Bozorg-Haddad, O. and
Marino, M.A. 2013. Extraction of optimal operation
rules in an aquifer-dam system: genetic
programming approach. Journal of Irrigation and
Drainage Engineering. 139:872-879.
19) Garousi-Nejad, I., Bozorg-Haddad, O.,
Loáiciga, H. and Mariño, M. 2016. Application of
the firefly algorithm to optimal operation of
reservoirs with the purpose of irrigation supply and
hydropower production. Journal of Irrigation and
Drainage Engineering. 10.1061/(ASCE)IR.1943-
4774.0001064, 04016041.
20) Hashimoto, T., Stedinger, J.R. and Loucks,
D.P. 1982. Reliability, resilience, and vulnerability
criteria for water resource system performance
evaluation. Water Resources Research. 18(1):14-
20.
21) Labadie, J.W. 2004. Optimal operation of
multi-reservoir system: State of the art review.
Journal of Water Resources Planning and
Management. 130(2): 93-111.
22) Kumar, D.N. and Reddy, M.J. 2006. Ant
colony optimization for multi-purpose reservoir
operation. Journal of Water Resources
Management. 20(6): 879-898.
23) Oliveira, R. and Loucks, D.P. 1997. Operating
rules for multi-reservoir system. Journal of Water
Resources Research. 33(4): 839-852.
24) Pradhan, S.N. and Tripathy, U.K. 2013.
Optimization of the operating policy of the
multipurpose Hirakud Reservoir by genetic
algorithm. American Journal of Engineering
Research. 2(11): 260-266.
25) Reddy, M.J. and Kumar, D.N. 2007. Multiobjective
particle swarm optimization for
generating optimal trade-offs in reservoir operation.
Hydrological Processes. 21: 2897-2909.
26) Sonaliya, S. and Suryanarayana, T.M.V. 2014.
Optimal reservoir operation using genetic
algorithm: a case study of Ukai Reservoir project.
International Journal of Innovative Research in
Science Engineering and Technology. 3(6): 13681-
13687.
27) Tinoco, V., Willems, P., Wyseure, G. and
Cisneros, F. 2016. Evaluation of reservoir operation
strategies for irrigation in the Macul Basin,
Ecuador. Journal of Hydrology. Regional Studies.
5(2016) 213–225.
28) Wardlaw, R. and Sharif, M. 1999. Evaluation
of genetic algorithms for optimal reservoir system
operation. Journal of Water Resource Planning and
Management. 125(1): 25-33.
29) Wurbs, R.A. 1993. Reservoir-system
simulation and optimization models. Journal of
Water Resource Planning and Management. 119(4):
455-472.
30) Yeh, W.G. 1985. Reservoir management and
operation models: State-of-the-art review. Journal
of Water resource Research. 21(12): 1797-1818.
31) Zhang, X., Yu, X. and Qin, H. 2016. Optimal
operation of multi-reservoir hydropower systems
using enhanced comprehensive learning particle
swarm optimization. Journal of Hydro-environment
Research. 10(2016) 50–63.

_||_