World Academy of Science, Engineering and Technology
Published October 4, 2015 | Version 10002774
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A Review on Applications of Evolutionary Algorithms to Reservoir Operation for Hydropower Production

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Evolutionary Algorithms (EAs) have been used widely through evolution theory to discover acceptable solutions that corresponds to challenges such as natural resources management. EAs are also used to solve varied problems in the real world. EAs have been rapidly identified for its ease in handling multiple objective problems. Reservoir operations is a vital and researchable area which has been studied in the last few decades due to the limited nature of water resources that is found mostly in the semi-arid regions of the world. The state of some developing economy that depends on electricity for overall development through hydropower production, a renewable form of energy, is appalling due to water scarcity. This paper presents a review of the applications of evolutionary algorithms to reservoir operation for hydropower production. This review includes the discussion on areas such as genetic algorithm, differential evolution, and reservoir operation. It also identified the research gaps discovered in these areas. The results of this study will be an eye opener for researchers and decision makers to think deeply of the adverse effect of water scarcity and drought towards economic development of a nation. Hence, it becomes imperative to identify evolutionary algorithms that can address this issue which can hamper effective hydropower generation.

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  • Wang, C., et al., Long-term scheduling of large cascade hydropower stations in Jinsha River, China. Energy Conversion and Management, 2015. 90(0): p. 476-487.
  • Bazmi, A.A. and G. Zahedi, Sustainable energy systems: Role of optimization modeling techniques in power generation and supply—A review. Renewable and Sustainable Energy Reviews, 2011. 15(8): p. 3480-3500.
  • Lu, P., et al., Short-term hydro generation scheduling of Xiluodu and Xiangjiaba cascade hydropower stations using improved binary-real coded bee colony optimization algorithm. Energy Conversion and Management, 2015. 91(0): p. 19-31.
  • Xu, J. and Z. Tao, A class of multi-objective equilibrium chance maximization model with twofold random phenomenon and its application to hydropower station operation. Mathematics and Computers in Simulation, 2012. 85(0): p. 11-33.
  • Zhang, R., et al., Optimal operation of multi-reservoir system by multielite guide particle swarm optimization. International Journal of Electrical Power & Energy Systems, 2013. 48(0): p. 58-68.
  • Kumar, N.K., S. Raju, and B. Ashok, Optimal reservoir operation for irrigation of multiple crops using genetic algorithms. Journal of Irrigation and Drainage Engineering, 2006. 132(2): p. 123-129.
  • Yuan, X., et al., An enhanced differential evolution algorithm for daily optimal hydro generation scheduling. Computers & Mathematics with Applications, 2008. 55(11): p. 2458-2468.
  • Storn, R. and K. Price, Differential evolution- A simple effeicient adaptive scheme for global optimization over continuous spaces. International computer science institute, ed. T.R.N. TR-95-012. 1995, Calif: Berkley.
  • Kennedy, J. and R.C. Eberhart. Particle swarm optimization. in Proceedings of IEEE International Conference on Neural Networks. 1995. [10] Malekmohammadi, B., R. Kerachian, and B. Zahraie, Developing monthly operating rules for a cascade system of reservoirs: Applicationn of Bayesian networks. Environmental Modelling & Software, 2009. 24: p. 1420-1432. [11] Naresh, R. and J. Sharma, Short term hydro scheduling using two-phase neural network. International Journal of Electrical Power & Energy Systems, 2002. 24(7): p. 583-590. [12] Adeyemo, J.A., Reservoir operation using Multi-objective Evolutionary Algorithms-A Review. Asian Journal of Scientific Research, 2011: p. 1- 12. [13] Singh, A., Simulation–optimization modeling for conjunctive water use management. Agricultural Water Management, 2014. 141(0): p. 23-29. [14] Chung, T., Y. Li, and Z. Wang, Optimal generation expansion planning via improved genetic algorithm approach. Interantional Journal of Electrical Power, 2004. 26(8): p. 655-659. [15] Madani, K., Game theory and water resources. Journal of Hydrology, 2010. 381(3-4): p. 225-238. [16] Nicklow, J.W., et al., State of the art for genetic algorithm and beyond in water resources planning and management. Journal of Water Resource Planning and Management. ASCE, 2010. 136(4): p. 412-432. [17] Reddy, M.J. and D.N. Kumar, Multiobjective Differential Evolution with Application to Reservoir Optimization. Journal of Computer in Civil Engineering, 2007. 21(2): p. 136-146. [18] Azamathulla, H.M., et al., Comparison between genetic algorithm and linear programming approach for real time operation. Journal of Hydro-environment Research, 2008. 2: p. 172-181. [19] Chang, L. and F. Chang, Multi-objective evolutionary algorithm for operating parallel reservoir system. Journal of Hydrology, 2009. 377: p. 12-20. [20] Fogel, D.B., Evolutionary computation: principles and practice for signal processing. 2000, Bellingham, Washington: SPIE press. [21] Sarker, R. and T. Ray, An improved evolutionary algorithm for solving multi-objective crop planning models. Computers and Electronics in Agriculture, 2009. 68(2): p. 191-199. [22] Blickle, T., Theory of evolutionary algorithms and applications to system syntheis. 1997, Swiss Federal school of Technology: Zurich. [23] Reddy, M.J. and D.N. Kumar, Optimal reservoir operation using multiobjective evolutionary algorithm. Water Resources Management, 2006. 20: p. 861-878. [24] Chang, L.C., Guiding rational reservoir flood operation using penaltytype genetic algorithm. Journal of Hydrology, 2007. 354. [25] Chang, L., et al., Constrained genetic algorithms for optimizing multiuse reservoir operation. Journal of Hydrology, 2010. 390(1–2): p. 66- 74. [26] Regulwar, D.G. and R.U. Kamodkar, Derivation of Multipurpose Single Reservoir Release policies with Fuzzy Constraints. J. Water Resource and Protection, 2010. 2: p. 1030-1041. [27] Wang, K., L. Chang, and F. Chang, Multi-tier interactive genetic algorithms for the optimization of long-term reservoir operation. Advances in Water Resources, 2011. 34(10): p. 1343-1351. [28] Rahimi, I., K. Qaderi, and A.M. Abasiyan, Optimal Reservoir Operation Using MOPSO with Time Variant Inertia and Acceleration Coefficients. Universal Journal of Agricultural Research, 2013. 1(3): p. 74-80. [29] Afshar, M.H., Extension of the constrained particle swarm optimization algorithm to optimal operation of multi-reservoirs system. Electrical Power and Energy Systems, 2013. 51: p. 71-81. [30] Wardlaw, R. and M. Sharif, Evaluation of genetic algorithm for optimal reservoir system operstion. Journal of Water Resource Planning and Management. , 1999. 125(1): p. 25-33. [31] Bandyopadhyay, S. and S. Saha, Some Single- and Multiobjective Optimization Techniques. 2013: Springer Berlin Heidelberg. [32] Reddy, M.J. and D.N. Kumar, Computational algorithms inspired by biological processes and evolution. Current Science, 2012. 103(4): p. 370-380. [33] Cheng, C.T., W.C. Wang, and D.M. Xu, Optimizing Hydropower Reservoir Operation Using Hybrid Genetic Algorithm and Chaos. Water Resources Management, 2008. 22: p. 895-909. [34] Srinivas, N. and D. Kalyanmoy, Multiobjective optimisation using Nondominated Sorting in Genetic Algorithms. Journal of Evolutionary Computation, 1994. 2(3): p. 221-248. [35] Deb, K., et al., A fast and Elitist multiobjective Genetic Algorithm: NSGA-II. IEEE Ttransactions of evolutionary computation, 2002. 6(2): p. 182-197. [36] Adeyemo, J.A., Application of Differential Evolution to water resources management, in Department of civil engineering. 2009, Tshwane university of Technology: Tshwane, Gauteng. p. 1-242. [37] Rani, D. and M.M. Moreira, Simulation-Optimization modeling: a survey and potential application in reservoir systems operation. . Water Resources Management, 2010. 24: p. 1107-1138. [38] Li, X., et al., A parallel dynamic programming algorithm for multireservoir system optimization. Advances in Water Resources, 2014. 67: p. 1-15. [39] Zhang, Z., et al., An adaptive particle swarm optimiztion algorithm for reservoir operation optimization. Applied Soft Computing, 2014. 18: p. 167-177. [40] Chang, L., Guiding rational reservoir flood operation using penalty-type genetic algorithm. Journal of Hydrology, 2008. 354(1–4): p. 65-74. [41] Regulwar, D.G., S.A. Choudhari, and P.A. Raj, Differential evolution algorithm with application to optimal operation of multipurpose reservoir. Journal of Water Resource and Protection, 2010. 2: p. 560- 568. [42] Karamouz, M., A. Ahmadi, and A. Moridi, Probabilistic reservoir operation using bayesian stochastic model and support vector machine. Advances in water resources, 2009. 32(11): p. 1588-1600. [43] Zheng, F., A. Simpson, and A. Zecchin, Improving the efficiency of multi-objective evolutionary algorithms through decomposition: An application to water distribution network design. Environmental Modelling & Software, 2014(0). [44] Elferchichi, A., et al., The genetic algorithm approach for identifying the optimal operation of a multi-reservoirs on-demand irrigation system. Biosystems Engineering, 2009. 102(3): p. 334-344. [45] Chen, L., J. Mcphee, and W.W.G. Yeh, A diversified multiobjective GA for optimzing reservoir rule curves. Advances in Water Resources, 2007. 30: p. 1082-1093. [46] Zhou, J., et al., Integrated optimization of hydroelectric energy in the upper and middle Yangtze River. Renewable and Sustainable Energy Reviews, 2015. 45(0): p. 481-512. [47] Baños, R., et al., Optimization methods applied to renewable and sustainable energy: A review. Renewable and Sustainable Energy Reviews, 2011. 15(4): p. 1753-1766. [48] Kıran, M.S., et al., A novel hybrid approach based on Particle Swarm Optimization and Ant Colony Algorithm to forecast energy demand of Turkey. Energy Conversion and Management, 2012. 53(1): p. 75-83. [49] Li, C., et al., Improved decomposition–coordination and discrete differential dynamic programming for optimization of large-scale hydropower system. Energy Conversion and Management, 2014. 84(0): p. 363-373. [50] Yoo, J.H., Maximization of hydropower generation through the application of a linear programming model. journal of Hydrology, 2009. 372((1-2)): p. 182-187. [51] Kuby, M.J., et al., A multiobjective optimization model for dam removal: an example of salmon passage with hydropower and water storage in the Willamette basin. . Advances in Water Resources, 2005. 28(8): p. 845-855. [52] Perez-Diaz, J.I., J.R. Wilhelmi, and J.A. Sanchez-Fernandez, Short term operation scheduling of a hydropower plant in the day ahead electricity market. Electrical Power Systems Research, 2010. 80(12): p. 1535-1542. [53] Lee, T.Y., Short term hydroelectric power system scheduling with wind turbine generators using multi-pass iteration particle swarm optimization approach. Energy conversion and management, 2008. 49(4): p. 751-760. [54] Li, A., et al., Application of immune algorithm-based particle swarm optimization for optimized load distribution along cascade hydropower station. Compter and mathematics with applications, 2009. 57: p. 1785- 1791. [55] Doganis, P. and H. Sarimveis, Optimization of power production through coordinated use of hydroelectric and conventional power units. Applied Mathematical Modelling, 2014. 38(7–8): p. 2051-2062. [56] Cai, W., et al., Optimized reservoir operation to balance human and environmental requirements: A case study for the three Gorges and Gezhouba Dams, Yangtze River basin, China. Ecological Informatics, 2013. 18: p. 40-48. [57] Zhang, Z., et al., Use of parallel deterministic dynamic programming and hierarchical adaptive genetic algorithm for reservoir operation optimization. Computers & Industrial Engineering, 2013. 65: p. 310-321. [58] Zhang, H., et al., An efficient multi-objective adaptive differential evolution with chaotic neuron network and its application on long-term hydropower operation with considering ecological environment problem. International Journal of Electrical Power & Energy Systems, 2013. 45(1): p. 60-70.