Multiobjective Cascade Reservoir Operation Rules and Uncertainty Analysis Based on PA-DDS Algorithm
Publication: Journal of Water Resources Planning and Management
Volume 143, Issue 7
Abstract
Pareto archived dynamically dimensioned search (PA-DDS) is one of the meta-heuristic methods available to solve multiobjective reservoir operation problems. This study uses this method to optimize reservoir operation rules with the objectives of maximizing the power generation and water supply. The performance of PA-DDS is compared with the nondominated sorting genetic algorithm–II (NSGA-II) in terms of a hypervolume indicator and the distribution of optimized nondominated solutions (NDSs) in a case study of Hanjiang cascade reservoirs in China. The results indicate that PA-DDS can increase the amount of power generation and water supply, respectively. Moreover, the uncertainty in reservoir operation optimized by both methods is analyzed in terms of the NDS distribution and the trade-off relationship between water supply and power generation. The results demonstrate that PA-DDS outperforms NSGA-II not only in the Pareto front approximation (NDS), but also with an increase in water supply by about for Hanjiang cascade reservoir operation.
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Acknowledgments
This study was financially supported by the National Natural Science Foundation of China (Grant No. 51539009 and 51379148) and the National Key Research and Development Plan of China (Grant No. 2016YFC0402206). The authors thank the editors and the anonymous reviewers for their valuable comments, which helped improve the quality of the paper. Sincere gratitude is extended to Professor Fi-john Chang for proofreading and grammatical corrections.
References
Afshar, A., Haddad, O. B., Mariño, M. A., and Adams, B. (2007). “Honey-bee mating optimization (HBMO) algorithm for optimal reservoir operation.” J. Franklin Inst., 344(5), 452–462.
Afshar, A., Massoumi, F., Afshar, A., and Mariño, M. A. (2015). “State of the art review of ant colony optimization applications in water resource management.” Water Resour. Manage., 29(11), 3891–3904.
Afshar, A., Shafii, M., and Haddad, O. B. (2010). “Optimizing multi-reservoir operation rules: An improved HBMO approach.” J. Hydroinf., 13(1), 121–139.
Afshar, M. (2013). “Extension of the constrained particle swarm optimization algorithm to optimal operation of multi-reservoirs system.” Int. J. Electr. Power Energy Syst., 51, 71–81.
Ahmadi, M., Haddad, O. B., and Marino, M. A. (2014). “Extraction of flexible multi-objective real-time reservoir operation rules.” Water Resour. Manage., 28(1), 131–147.
Asadzadeh, M., Razavi, S., Tolson, B. A., and Fay, D. (2014a). “Pre-emption strategies for efficient multi-objective optimization: Application to the development of Lake Superior regulation plan.” Environ. Modell. Software, 54, 128–141.
Asadzadeh, M., and Tolson, B. (2013). “Pareto archived dynamically dimensioned search with hypervolume-based selection for multi-objective optimization.” Eng. Optim., 45(12), 1489–1509.
Asadzadeh, M., and Tolson, B. A. (2009). “A new multi-objective algorithm, Pareto archived DDS.” Proc., 11th Annual Conf. Companion on Genetic and Evolutionary Computation Conf.: Late Breaking Papers, Association for Computing Machinery (ACM), New York, 1963–1966.
Asadzadeh, M., Tolson, B. A., and Burn, D. H. (2014b). “A new selection metric for multiobjective hydrologic model calibration.” Water Resour. Res., 50(9), 7082–7099.
Asadzadeh, M., Tolson, B. A., and McKillop, R. (2010). “A two stage optimization approach for calibrating water distribution systems.” Proc., 12th Water Distribution Systems Analysis Symp., ASCE, Reston, VA.
Ashofteh, P. S., Haddad, O. B., and Loáiciga, H. A. (2015). “Evaluation of climatic-change impacts on multiobjective reservoir operation with multiobjective genetic programming.” J. Water Resour. Plann. Manage., .
Basu, M. (2004). “An interactive fuzzy satisfying method based on evolutionary programming technique for multiobjective short-term hydrothermal scheduling.” Electr. Power Syst. Res., 69(2), 277–285.
Bolouri-Yazdeli, Y., Haddad, O. B., Fallah-Mehdipour, E., and Mariño, M. (2014). “Evaluation of real-time operation rules in reservoir systems operation.” Water Resour. Manage., 28(3), 715–729.
Chang, F. J., and Wang, K. W. (2013). “A systematical water allocation scheme for drought mitigation.” J. Hydrol., 507, 124–133.
Chang, F. J., Wang, Y. C., and Tsai, W. P. (2016). “Modelling intelligent water resources allocation for multi-users.” Water Resour. Manage., 30(4), 1395–1413.
Chang, L. C., and Chang, F. J. (2009). “Multi-objective evolutionary algorithm for operating parallel reservoir system.” J. Hydrol., 377(1), 12–20.
Chen, J., Guo, S., Li, Y., Liu, P., and Zhou, Y. (2013). “Joint operation and dynamic control of flood limiting water levels for cascade reservoirs.” Water Resour. Manage., 27(3), 749–763.
Deb, K. (2001). Multi-objective optimization using evolutionary algorithms, Wiley, Hoboken, NJ.
Deb, K., Agrawal, S., Pratap, A., and Meyarivan, T. (2000). “A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II.” Lect. Notes Comput. Sci., 1917, 849–858.
Delisle, F. J., Rochette, S., Pelletier, G., Asadzadeh, M., Tolson, B. A., and Rodriguez, M. J. (2015). “Minimizing water residence time in Quebec City’s main distribution network using hybrid discrete dynamically dimensioned search (HD-DDS): Part II.” J. Water Supply. Res. Technol. Aqua, 64(3), 378–390.
Elferchichi, A., Gharsallah, O., Nouiri, I., Lebdi, F., and Lamaddalena, N. (2009). “The genetic algorithm approach for identifying the optimal operation of a multi-reservoirs on-demand irrigation system.” Biosyst. Eng., 102(3), 334–344.
Fallah-Mehdipour, E., Haddad, O. B., Tabari, M. M. R., and Mariño, M. A. (2012). “Extraction of decision alternatives in construction management projects: Application and adaptation of NSGA-II and MOPSO.” Expert Syst. Appl., 39(3), 2794–2803.
Fonseca, C. M., Paquete, L., and López-Ibánez, M. (2006). “An improved dimension-sweep algorithm for the hypervolume indicator.” IEEE Int. Conf. on Evolutionary Computation, IEEE, Piscataway, NJ, 1157–1163.
Fu, X., Li, A., Wang, L., and Ji, C. (2011). “Short-term scheduling of cascade reservoirs using an immune algorithm-based particle swarm optimization.” Comput. Math. Appl., 62(6), 2463–2471.
Giuliani, M., Herman, J., Castelletti, A., and Reed, P. (2014). “Many-objective reservoir policy identification and refinement to reduce policy inertia and myopia in water management.” Water Resour. Res., 50(4), 3355–3377.
Goyal, M. K., Ojha, C., Singh, R., Swamee, P., and Nema, R. (2013). “Application of ANN, fuzzy logic and decision tree algorithms for the development of reservoir operating rules.” Water Resour. Manage., 27(3), 911–925.
Guo, S., Chen, J., Li, Y., Liu, P., and Li, T. (2011). “Joint operation of the multi-reservoir system of the Three Gorges and the Qingjiang cascade reservoirs.” Energies, 4(7), 1036–1050.
Guo, X., Hu, T., Wu, C., Zhang, T., and Lv, Y. (2013). “Multi-objective optimization of the proposed multi-reservoir operating policy using improved NSPSO.” Water Resour. Manage., 27(7), 2137–2153.
Haddad, O. B., and Marino, M. A. (2007). “Dynamic penalty function as a strategy in solving water resources combinatorial optimization problems with honey-bee mating optimization (HBMO) algorithm.” J. Hydroinf., 9(3), 233–250.
Hadka, D., and Reed, P. (2013). “Borg: An auto-adaptive many-objective evolutionary computing framework.” Evol. Comput., 21(2), 231–259.
Hall, W. A., Butcher, W. S., and Esogbue, A. (1968). “Optimization of the operation of a multiple-purpose reservoir by dynamic programming.” Water Resour. Res., 4(3), 471–477.
Huang, X., et al. (2014). “Parameter optimization of distributed hydrological model with a modified dynamically dimensioned search algorithm.” Environ. Modell. Software, 52, 98–110.
Karamouz, M., and Houck, M. H. (1982). “Annual and monthly reservoir operating rules generated by deterministic optimization.” Water Resour. Res., 18(5), 1337–1344.
Knowles, J. D., and Corne, D. W. (2000). “Approximating the nondominated front using the Pareto archived evolution strategy.” Evol. Comput., 8(2), 149–172.
Knowles, J. D., Corne, D. W., and Fleischer, M. (2003). “Bounded archiving using the Lebesgue measure.” IEEE Int. Conf. on Evolutionary Computation, IEEE, Piscataway, NJ, 2490–2497.
Labadie, J. W. (2004). “Optimal operation of multireservoir systems: State-of-the-art review.” J. Water Resour. Plann. Manage., 93–111.
Li, X., Guo, S., Liu, P., and Chen, G. (2010). “Dynamic control of flood limited water level for reservoir operation by considering inflow uncertainty.” J. Hydrol., 391(1), 124–132.
Li, X., Wei, J., Li, T., Wang, G., and Yeh, W. W. G. (2014). “A parallel dynamic programming algorithm for multi-reservoir system optimization.” Adv. Water Resour., 67, 1–15.
Liu, P., et al. (2015). “Optimal design of seasonal flood limited water levels and its application for the Three Gorges Reservoir.” J. Hydrol., 527, 1045–1053.
Ngoc, T. A., Hiramatsu, K., and Harada, M. (2014). “Optimizing the rule curves of multi-use reservoir operation using a genetic algorithm with a penalty strategy.” Paddy Water Environ., 12(1), 125–137.
Ostadrahimi, L., Mariño, M. A., and Afshar, A. (2012). “Multi-reservoir operation rules: Multi-swarm PSO-based optimization approach.” Water Resour. Manage., 26(2), 407–427.
Razavi, S., et al. (2013). “Evaluation of new control structures for regulating the Great Lakes system: Multiscenario, multireservoir optimization approach.” J. Water Resour. Plann. Manage., .
Salazar, J. Z., Reed, P. M., Herman, J. D., Giuliani, M., and Castelletti, A. (2016). “A diagnostic assessment of evolutionary algorithms for multi-objective surface water reservoir control.” Adv. Water Resour., 92, 172–185.
Schardong, A., and Simonovic, S. P. (2015). “Coupled self-adaptive multiobjective differential evolution and network flow algorithm approach for optimal reservoir operation.” J. Water Resour. Plann. Manage., .
Shokri, A., Haddad, O. B., and Mariño, M. A. (2012). “Reservoir operation for simultaneously meeting water demand and sediment flushing: Stochastic dynamic programming approach with two uncertainties.” J. Water Resour. Plann. Manage., 277–289.
Shokri, A., Haddad, O. B., and Mariño, M. A. (2013). “Algorithm for increasing the speed of evolutionary optimization and its accuracy in multi-objective problems.” Water Resour. Manage., 27(7), 2231–2249.
Soltanjalili, M. J., Bozorg Haddad, O., and Mariño, M. A. (2013). “Operating water distribution networks during water shortage conditions using hedging and intermittent water supply concepts.” J. Water Resour. Plann. Manage., 644–659.
Tolson, B. A., Khedr, A., and Asadzadeh, M. (2012). “The battle of the water networks (BWN-II): PADDS based solution approach.” Proc., 14th Water Distribution Systems Analysis Conf., Engineers Australia, Adelaide, Australia, 291–304.
Tolson, B. A., and Shoemaker, C. A. (2007). “Dynamically dimensioned search algorithm for computationally efficient watershed model calibration.” Water Resour. Res., 43(1), .
Tsai, W. P., Chang, F. J., Chang, L. C., and Herricks, E. E. (2015). “AI techniques for optimizing multi-objective reservoir operation upon human and riverine ecosystem demands.” J. Hydrol., 530, 634–644.
Wang, C., Yu, Y., Wang, P., Sun, Q., Hou, J., and Qian, J. (2016). “Assessment of the ecological reservoir operation in the Yangtze estuary based on the salinity requirements of the indicator species.” River Res. Appl., 32(5), 946–957.
Yang, G., Guo, S., Li, L., Hong, X., and Wang, L. (2016). “Multi-objective operating rules for danjiangkou reservoir under climate change.” Water Resour. Manage., 30(3), 1183–1202.
Yeh, W. W. G. (1985). “Reservoir management and operations models: A state-of-the-art review.” Water Resour. Res., 21(12), 1797–1818.
Yen, H., Jeong, J., and Smith, D. R. (2016). “Evaluation of dynamically dimensioned search algorithm for optimizing SWAT by altering sampling distributions and searching range.” JAWRA J. Am. Water Resour. Assoc., 52(2), 443–455.
Yoo, J. H. (2009). “Maximization of hydropower generation through the application of a linear programming model.” J. Hydrol., 376(1), 182–187.
Zhou, Y. L., and Guo, S. L. (2013). “Incorporating ecological requirement into multipurpose reservoir operating rule curves for adaptation to climate change.” J. Hydrol., 498, 153–164.
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Published In
Journal of Water Resources Planning and Management
Volume 143 • Issue 7 • July 2017
Copyright
©2017 American Society of Civil Engineers.
History
Received: Apr 9, 2016
Accepted: Jan 13, 2017
Published online: Mar 29, 2017
Published in print: Jul 1, 2017
Discussion open until: Aug 29, 2017
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