Reservoir Hedging Approach in Optimal Operation of Water Resources Systems of Doiraj Dam Reservoir Using MOICA Algorithm

Document Type : Research Paper

Authors

1 Ph.D. Candidate, Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran

2 Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran

3 water Dept., kermanshah branch, islamic azad university, kermanshah, iran

Abstract

In this research, the simulation and optimization models are integrated to apply the reservoir hedging policy. Simulation of the studied basin was executed using the WEAP model to operate the Doiraj Dam reservoir located on the Doiraj River. In addition, the multi-objective MOICA model was utilized to optimize the system, in which the first objective function (maximizing the percentage of supplying demands), and the second one (minimizing the violation of allowable capacities of the reservoir during the operation period) were considered. In this regard, the operation modeling from the reservoir was carried out based on the current condition for a 720-month period (October 1960 to September 2019). Finally, by defining the optimization scenario and applying the reservoir hedging policy, the operation optimization of the reservoir was done and the results were compared with the reference scenario results. In this study, by considering 24 decision variables including 12 hedging level variables and 12 hedging coefficient variables, the optimal answers were achieved after 1000 iterations. The results showed that the violation of the allowable capacities did not occurred in any periods of the optimization scenario, while in the reference scenario the reservoir level reached the dead level in sequent months with more water shortage which might lead to the lack of water supply in such months and serious damages to the system. Due to the application of hedging policy in the optimization scenario, the percentage of supplying the demands in the critical months is increased between 20 to 35% as compared to the reference scenario, which indicates a significant reduction in the failure rate in such months.

Keywords


Afshar, A., Emami Skardi, M. J., and Masoumi, F. (2015). Optimizing water supply and hydropower reservoir operation rule curves: an imperialist competitive algorithm approach. Engineering Optimization, 47(9), 1208-1225.‏
Atashpaz-Gargari, E., and Lucas, C. (2007). Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: Proceedings of the IEEE Congress on Evolutionary Computation, 4661–4667.
Azari, A., Hamzeh, S., and Naderi, S. (2018). Multi-objective optimization of the reservoir system operation by using the hedging policy. Water Resource Management, 32(6), 2061–2078.
Acharya, D.P., Panda, G., and Lakshmi, Y.V.S. (2010). Effects of finite register length on fast ICA, bacterial foraging optimization-based ICA and constrained genetic algorithm-based ICA algorithm. Digital Signal Processing. 20, 964–975.
Bayesteh, M., and Azari, A. (2021). Stochastic Optimization of Reservoir Operation by Applying Hedging Rules. Journal of Water Resources Planning and Management, 147(2), 04020091-9.
Biyanto, T.R., Khairansyah, M.D., Bayuaji, R., Firmanto, H., and Haksoro, T. (2015). Imperialist Competitive Algorithm (ICA) for Heat Exchanger Network (HEN) Cleaning Schedule Optimization. Procedia Computer Science. 72, 5 – 12.
Deb, k., Pratap, A., Agarwal, S., and Meyarivan, T. (2002). A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans Evolutionary Computing, 6(2), 182–197.
Draper, A.J., and Lund, J.R. (2004). Optimal hedging and carry over storage value. Water Resource Planning and Management, ASCE, 130(1), 83–87.
Enayatifar, R., Yousefi, M., Abdullah, A. H., and Darus, A.N. (2013). MOICA: A novel multi-objective approach based on imperialist competitive algorithm. Applied Mathematics and Computation, 219(17), 8829–8841.
Felfelani, F., Jalali Movahed, A., and Zarghami, M. (2013). simulating hedging rules for effective reservoir operation by using system dynamics: a case study of Dez Reservoir, Iran. Lake and Reservoir Management, 29(2), 126-140.
Gohardani, S.A., Bagherian, M. and Vaziri, H. (2019). A multi-objective imperialist competitive algorithm (MOICA) for finding motifs in DNA sequences. Mathematical biosciences and engineering: MBE, 16(3), 1575-1596.
Hosseini-Moghari, S.M., Morovati, R., Moghadas, M., and Araghinejad, S. (2015). Optimum operation of reservoir using two evolutionary algorithms: imperialist competitive algorithm (ICA) and cuckoo optimization algorithm (COA). Water resources management, 29(10), 3749-3769.‏
Karamouz, M., Nazif, S., Sherafat, M.A., and Zahmatkesh, Z. (2014). Development of an Optimal Reservoir Operation Scheme Using Extended Evolutionary Computing Algorithms Based on Conflict Resolution Approach: A Case Study. Water Resources Management, 28, 3539-54.
Li, X., Zhao, Y., Shi, C., Sha, J., Wang, Z.L., and Wang, Y. (2015). Application of Water Evaluation and Planning (WEAP) model for water resources management strategy estimation in coastal Binhai New Area, China. Ocean and Coastal Management, 106, 97-109.
Loucks, D.P., and van Beek, E. )2005(. Water Resources Systems Planning and Management, An Introduction to Methods, Models and Applications. UNESCO Publication, PP: 677.
Neelakantan, T.R. and Pundarikanthan, N. V. (1999). Hedging rule optimization for water supply reservoirs system. Water Resources Management, 13(6), 409–426.
Rajabioun, R., Hashemzadeh, F., Atashpaz-Gargari, E., Mesgari, B., and Salmasi, F.R. (2008). Identification of a MIMO evaporator and its decentralized PID controller tuning using colonial competitive algorithm. In be presented in IFAC World Congress.‏
Shenava, N., and Shourian, M. (2018). Optimal Reservoir Operation with Water Supply Enhancement and Flood Mitigation Objectives Using an Optimization-Simulation Approach. Water resources management, 32(13), 4393-4407.‏
Sherinov, Z. and Ünveren, A. (2017). Multi-Objective Imperialistic Competitive Algorithm with Multiple Non-Dominated Sets for the Solution of Global Optimization Problems. Soft Computing, 22(24), Springer Nature America, Inc, pp. 8273–88.
Shih, J.S., and ReVelle, C. (1994). Water-supply operations during drought: Continuous hedging rule. Water Resource Planning and Management, 120(5), 613–629.
Taghian, M., Rosbjerg, D., Haghighi, A., and Madsen, H. (2014). Optimization of Conventional Rule Curves Coupled with Hedging Rules for Reservoir Operation. Water Resources Planning and Management, 140(5), 693–698.
Tenant, D.L. (1976). Instream flow regimens for fish, wildlife, recreation and related environmental resources. Fisheries, 1(4), 6-10.