Simulation Optimization for Optimal Sizing of Water Transfer Systems
Water transfer development projects (WTDPs) could be considered in arid and semi-arid areas in response to uneven distribution of available water resources over space. This paper presents a simulation-optimization model by linking Water Evaluation and Planning System (WEAP) to particle swarm optimization (PSO) algorithm for optimal design and operation of the Karoon-to- Zohreh Basin WTDP in Iran. PSO searches for optimal values of design and operation variables including capacities of water storage and transfer components as well as priority numbers of reservoirs target storage levels, respectively; And WAEP evaluates the system operation for any combinations of the design and operation variables. The results indicate that the water transfer project under consideration can supply water for the development of Dehdash and Choram Cropland (DCCL) in an undeveloped area located in Kohkiloyeh Province.
KeywordsWater transfer systems Simulation-optimization WEAP PSO
Unable to display preview. Download preview PDF.
- 7.Wang, Y., Shi, H.S., Wang, J., Zhang, Y.: Research and application of water resources opti-mized distribution model in inter-basin water transfer project. In: Applied Mechanics and Materials. Trans. Tech. Publ., vol. 737, pp. 683–687 (2015)Google Scholar
- 8.Snaddon, C.D.: A global overview of inter-basin water transfer schemes, with an appraisal of their ecological, socio-economic and socio-political implications, and recommendations for their management. Water Research Commission (1999)Google Scholar
- 12.Mahab Ghods Consulting Engineering Company: Water Master Plan of Kohgilouye and Boy-erahmad province- Case study: Dehdasht and Choram Cropland, Preliminary Water Resources Planning Studies. Technical Report, Tehran, Iran (2012)Google Scholar
- 13.Sieber, J., Purkey, D.: Water Evaluation And Planning System, User Guide. Stockholm Envi-ronment Institute, U.S. Center, Somerville, MA (2011)Google Scholar
- 14.Kennedy, J. Eberhart, R.: Particle swarm optimization. In: Proceedings of 2004 International Conference on Machine Learning and Cybernetics, pp. 1942–1948. IEEE Press (1995)Google Scholar
- 15.Parsopoulos, K.E., Plagianakos, V.P., Magoulas, G.D., Vrahatis, M.N.: Stretching technique for obtaining global minimizers through particle swarm optimization. In: Proceedings of the Particle Swarm Optimization Workshop, Indianapolis, USA (2001)Google Scholar