Multiobjective Optimal Operation of Reservoirs Based on Water Supply, Power Generation, and River Ecosystem with a New Water Resource Allocation Model
Publication: Journal of Water Resources Planning and Management
Volume 146, Issue 12
Abstract
A multiobjective water resource allocation model (GWAS) was constructed to incorporate the objectives of socioeconomic water use, power generation, and river ecological flow. The new model can simulate both rule-based operating schemes and optimized operating schemes for reservoirs with two kinds of solving algorithms and was applied to the Fuhe River basin. Fifteen socioeconomic water use units, 23 ecological flow control sections in the river channel, and 22 reservoirs were considered in the case study. The advantages and disadvantages of each rule-based operating scheme and the global optimization were compared under four computational schemes with the GWAS model. The competitive relationship among socioeconomic water use, power generation, and ecological water use under empirical rules, and the global optimal operation, were revealed. The GWAS model is an improvement of the traditional water resource allocation model and enables managers to compare empirical rule-based schemes and form a better, globally-optimized scheme.
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Data Availability Statement
The water resource development and utilization data and the GWAS model used in the study are available from the corresponding author by request.
Acknowledgments
This study was supported by the National Key Research and Development Program of China (No. 2016YFC0402405), Jiangxi Project (KT201501 and KT201508), the National Natural Science Foundation of China (Nos. 51779270 and 51309248), Shenzhen Project (SZCG2016121595B), the Foundation of China Construction Water & Environment Co., LTD (CWEPC) (CSCEC-PSH-2017-03), Yunnan Project (YSZD-2014-001 and YNWRM-2012-01), and the Foundation of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin (SKL-WAC) (SKL2018TS04).
References
Babel, M. S., A. D. Gupta, and D. K. Nayak. 2005. “A model for optimal allocation of water to competing demands.” Water Resour. Manage. 19 (6): 693–712. https://doi.org/10.1007/s11269-005-3282-4.
Baltar, A. M., and D. G. Fontane. 2008. “Use of multi-objective particle swarm optimization in water resources management.” J. Water Resour. Plann. Manage. 134 (3): 257–265. https://doi.org/10.1061/(ASCE)0733-9496(2008)134:3(257).
Chang, L., and F. Chang. 2009. “Multi-objective evolutionary algorithm for operating parallel reservoir system.” J. Hydrol. 377 (1–2): 12–20. https://doi.org/10.1016/j.jhydrol.2009.07.061.
Chang, L., F. Chang, K. Wang, and Y. Dai. 2010. “Constrained genetic algorithms for optimizing multi-use reservoir operation.” J. Hydrol. 390 (1–2): 66–74. https://doi.org/10.1016/j.jhydrol.2010.06.031.
Chen, L., J. Qiu, G. Wei, and Z. Shen. 2015. “A preference-based multi-objective model for the optimization of best management practices.” J. Hydrol. 520: 356–366. https://doi.org/10.1016/j.jhydrol.2014.11.032.
Chen, N., Y. Li, and C. Xu. 2006. “Optimal deployment of water resources based on multi-objective genetic algorithm.” [In Chinese.] J. Hydraul. Eng. 37 (3): 308–313.
Chen, Y., Y. Mei, H. Cai, and X. Xu. 2018. “Multi-objective optimal operation of key reservoirs in Ganjiang River oriented to power generation, water supply and ecology.” [In Chinese.] J. Hydraul. Eng. 49 (5): 1–11.
China IWHR (China Institute of Water Resources and Hydropower Research). 2019. “User manual of general water allocation and simulation model (GWAS).” Accessed April 12, 2019. http://new.ewater.net.cn/szy/kxyj/kyzz/webinfo/2019/04/1552620813539729.htm.
Day, J., et al. 2012. “Ecological response of forested wetlands with and without large-scale Mississippi River input: Implications for management.” Ecol. Eng. 46 (Sep): 57–67. https://doi.org/10.1016/j.ecoleng.2012.04.037.
Deb, K., S. Agarwal, A. Pratap, and T. Meyarivan. 2000. “A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II.” In Vol. 1917 of Parallel problem solving from nature PPSN VI: Lecture notes in computer science, edited by M. Schoenauer, K. Deb, G. Rudolph, X. Yao, E. Lutton, J. J. Merelo, and H.-P. Schwefel, 849–858. Berlin: Springer.
Deb, K., J. Sundar, N. Udar, and S. Chaudhuri. 2006. “Reference point based multi-objective optimization using evolutionary algorithms.” Int. J. Comput. Intell. Res. 2 (3): 273–286. https://doi.org/10.5019/j.ijcir.2006.67.
Elferchichi, A., O. Gharsallah, I. Nouiri, L. Lebdi, and N. Lamaddalena. 2009. “The genetic algorithm approach for identifying the optimal operation of a multi-reservoirs on-demand irrigation system.” Biosyst. Eng. 102 (3): 334–344. https://doi.org/10.1016/j.biosystemseng.2008.12.009.
Fonseca, M., A. P. Guerreiro, M. Lopez-Ibanez, and L. Paquete. 2011. “On the computation of the empirical attainment function.” In Proc., Int. Conf. on Evolutionary Multi-Criterion Optimization, 106–120. Berlin: Springer.
Fuzhou Water Conservancy Bureau. 2017. Fuzhou City water resources bulletin. Fuzhou, China: Fuzhou Water Conservancy Bureau.
Galat, D. L., et al.1998. “Flooding to restore connectivity of regulated large-river wetlands.” Bioscience 48 (9): 721–733. https://doi.org/10.2307/1313335.
Hakimi-Asiabar, M., S. H. Ghodsypour, and R. Kerachian. 2010. “Deriving operating policies for multi-objective reservoir systems: Application of self-learning genetic algorithm.” Appl. Soft Comput. 10 (4): 1151–1163. https://doi.org/10.1016/j.asoc.2009.08.016.
Homa, E. S., R. M. Vogel, and M. P. Smith. 2005. “An optimization approach for balancing human and ecological flow needs.” In Proc., EWRI 2005 World Water and Environmental Resources Congress. Reston, VA: ASCE.
Kumar, D. N., and M. J. Reddy. 2006. “Ant colony optimization for multi-purpose reservoir operation.” Water Resour. Manage. 20 (6): 879–898. https://doi.org/10.1007/s11269-005-9012-0.
National Meteorological Information Center of China. 2012. “Chinas surface climate data daily value data set (V3.0).” Accessed September 1, 2018. http://data.cma.cn/data/cdcdetail/dataCode/SURF_CLI_CHN_MUL_DAY_V3.0.html.
Sang, X., H. Wang, J. Wang, Y. Zhao, and Z. Zhou. 2018. “Water resources comprehensive allocation and simulation model (WAS). I: Theory and development.” [In Chinese.] J. Hydraul. Eng. 49 (12): 1451–1459.
Sang, X., Z. Zhai, J. Wang, and Y. Zhao. 2017. “The development and application of water resources allocation model based on water balance closure.” [In Chinese.] J. China Inst. Water Resour. Hydropower Res. 15 (2): 81–88.
Sang, X., Y. Zhao, Z. Zhai, and H. Chang. 2019. “Water resources comprehensive allocation and simulation model (WAS). II: Application.” [In Chinese.] J. Hydraul. Eng. 50 (2): 201–208.
Saxena, K., J. A. Duro, A. Tiwari, K. Deb, and Q. Zhang. 2013. “Objective reduction in manyobjective optimization: Linear and nonlinear algorithms.” IEEE Trans. Evol. Comput. 17 (1): 77–99. https://doi.org/10.1109/TEVC.2012.2185847.
Shiau, J., and F. Wu. 2013. “Optimizing environmental flows for multiple reaches affected by a multipurpose reservoir system in Taiwan: Restoring natural flow regimes at multiple temporal scales.” Water Resour. Res. 49 (1): 565–584. https://doi.org/10.1029/2012WR012638.
Thiele, L., K. Miettinen, P. J. Korhonen, and J. Molina. 2009. “A preference-based evolutionary algorithm for multiobjective optimization.” Evol. Comput. 17 (3): 411–436. https://doi.org/10.1162/evco.2009.17.3.411.
Wang, J., X. Sang, Z. Zhai, Y. Liu, and Z. Zhou. 2014. “An integrated model for simulating regional water resources based on total evapotranspiration control approach.” Adv. Meteorol. 2014: 1–10. https://doi.org/10.1155/2014/345671.
Yan, Z., Z. Zhou, X. Sang, and H. Wang. 2018. “Water replenishment for ecological flow with an improved water resources allocation model.” Sci. Total Environ. 643 (Dec): 1152–1165. https://doi.org/10.1016/j.scitotenv.2018.06.085.
Yang, N., Y. Mei, and C. Zhou. 2012. “An optimal reservoir operation model based on ecological requirement and its effect on electricity generation.” Water Resour. Manage. 26 (14): 4019–4028. https://doi.org/10.1007/s11269-012-0126-x.
Yin, X., Z. Yang, and G. E. Petts. 2015. “A new method to assess the flow regime alterations in riverine ecosystems.” River Res. Appl. 31: (4) 497–504. https://doi.org/10.1002/rra.2817.
Zhai, Z. L., X. F. Sang, J. Chen, and M. Yang. 2017. “The total control of water supply and water consumption in Tianjin city based on WAS model.” In Proc., 3rd Int. Conf. on Green Materials and Environmental Engineering (GMEE 2017). Lancaster, PA: DEStech Publications, Inc.
Zheng, F., A. R. Simpson, and A. C. Zecchin. 2014. “An efficient hybrid approach for multiobjective optimization of water distribution systems.” Water Resour. Res. 50 (5): 3650–3671. https://doi.org/10.1002/2013WR014143.
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Published In
Journal of Water Resources Planning and Management
Volume 146 • Issue 12 • December 2020
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Sep 4, 2019
Accepted: Jun 22, 2020
Published online: Sep 19, 2020
Published in print: Dec 1, 2020
Discussion open until: Feb 19, 2021
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