Use of Genetic Algorithm in Optimization of Irrigation Pumping Stations
Publication: Journal of Irrigation and Drainage Engineering
Volume 130, Issue 5
Abstract
Energy costs constitute the largest expenditure for nearly all water utilities worldwide and can consume up to 65% of a water utility’s annual operating budget. One of the greatest potential areas for energy cost savings is in the scheduling of pump operations. This paper presents a new management model, WAPIRRA Scheduler, for the optimal design and operation of water distribution systems. The model makes use of the latest advances in genetic algorithm (GA) optimization to automatically determine annually the least cost of pumping stations while satisfying target hydraulic performance requirements. Optimal design and operation refers to selecting pump type, capacity, and number of units as well as scheduling the operation of irrigation pumps that results in minimum design and operating cost for a given set of demand curves. The optimization process consists of three main steps: (1) generating randomly an initial set of pump combinations to start the optimization process for a given demand-duration curve; (2) minimizing the total annual cost, which consists of operation and maintenance costs and depreciation cost of the initial investment, by changing the set and discharge of pump sets based on the provided model; and (3) achieving the final criterion to stop the optimization process and reporting the optimized results of the model. Computational analysis is based upon one major objective function and solving it by means of a computer program that is developed following the GA approach to find the optimized solution of generated equations. Application of the model to a real-world project shows considerable savings in cost and energy.
Get full access to this article
View all available purchase options and get full access to this article.
References
1.
Ashofteh, J. ( 1999). “Optimal design of pumping stations and water delivery systems in steady state flow.” Proc., 2nd Conf. on Hydraulics of Iran, A. Afshar, ed., Tehran, Iran, 306–319.
2.
Boulos, P.F., Wu, Z.Y., Orr, C.H., and Ro, J.J. ( 2000). “Least cost design and rehabilitation of water distribution systems using genetic algorithms.” Proc. AWWA IMTech Conf., American Water Works Association, Denver.
3.
Boulos, P.F., Wu, Z.Y., Orr, C.H., and de Schaetzn, W. ( 2001a). “Using genetic algorithms for water distribution system optimization.” Proc., ASCE Environmental and Water Resources Institute’s (EWRI’s) World Water and Environmental Resource Congress, (CD-ROM), ASCE, New York, Sec. 1, Chap 472.
4.
Boulos, P.F., Wu, Z.Y., Orr, C.H., Moore, M., Hsiuing, P., and Thomas, D. ( 2001b). “Optimal operation of water distribution systems using genetic algorithms.” Proc., AWWA Distribution System Symposium, American Water Works Association, Denver.
5.
Deppo, L. D. A., and Datei, C. (1984). “Optimal diameters for pressure pipes of hydro-plants.“ Int. Water Power Dam Constr., 36(10), 31–47.
6.
Goldberg, D. ( 1989). Genetic algorithms in search optimization and machine learning, Addison-Wesley, Reading, Mass.
7.
Harrell, L.J., and Ranjithan, S.R. ( 1997). “Generating efficient watershed management strategies using a genetic algorithm-based method.” Proc., 24th Annual Water Resources Planning and Management Conf., ASCE, New York, 272–277.
8.
Holland, J.H. ( 1975). Adaptation in natural and artificial systems, University of Michigan, Ann Arbor, Mich.
9.
Mays, L.W., and Tung, Y.K. ( 1992). Hydrosystems engineering and management, McGraw-Hill, New York, 140–157.
10.
Moradi-Jalal, M., Mariño, M. A., and Afshar, A. (2003). “Optimal design and operation of irrigation pumping stations.” J. Irrig. Drain. Eng., 129(3), 149–154.
11.
Najmaii, N., and Movaghar, M. (1992). “Optimal design of run-of-river power plants.” Water Resour. Res., 28(4), 991–997.
12.
Ndiritu, J.G., and Daniell, T.M. ( 1996). “Time domain tuned rainfall runoff models optimized using genetic algorithms.” Proc., 6th Int. Conf. on the Development and Application of Computer Techniques in Environmental Studies, P. Zannetti and C. A. Brebbia, eds., Computational Mechanics, Billerica, Mass.
13.
Ormsbee, L. E., Walski, T. M., Chase, D. V., and Sharp, W. W. (1989). “Methodology for improving pump operation efficiency.” J. Water Resour. Plan. Manage. Div., Am. Soc. Civ. Eng., 115(2), 148–164.
14.
Reis, L. F. R., Porto, R. M., and Chaudhry, F. H. (1997). “Optimal location of control valves in pipe networks by genetic algorithm.” J. Water Resour. Plan. Manage. Div., Am. Soc. Civ. Eng., 123(6), 317–326.
15.
Rodin, S.I. ( 1998). “Use of genetic algorithms for optimal control of bulk water supply.” <http://stullia.t-k.ru/waterpump/waterpump.htm (May 5, 2001).
16.
Rodin, S.I., and Moradi-Jalal, M. ( 2002). “Use of genetic algorithm in optimization of irrigation pumping stations, WAPIRRA program.” <http://stullia.t-k.ru/irrpump/irr_pump.htm (June 10, 2002).
17.
Savic, D. A., and Walters, G. A. (1997). “Genetic algorithms for least-cost design of water distribution networks.” J. Water Resour. Plan. Manage. Div., Am. Soc. Civ. Eng., 123(2), 67–77.
18.
Simpson, A. R., Murphy, L. J., and Dandy, G. C. (1994). “Genetic algorithms compared to other techniques for pipe optimization.” J. Water Resour. Plan. Manage., 120(4), 423–443.
19.
Zessler, U., and Shamir, U. (1989). “Optimal operation of water distribution systems.” J. Water Resour. Plan. Manage., 115(6), 735–752.
Information & Authors
Information
Published In
Journal of Irrigation and Drainage Engineering
Volume 130 • Issue 5 • October 2004
Pages: 357 - 365
Copyright
Copyright © 2004 ASCE.
History
Published online: Oct 1, 2004
Published in print: Oct 2004
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.