Optimization of multi-reservoir operation with a new hedging rule: application of fuzzy set theory and NSGA-II

Abstract

The reservoir hedging rule curves are used to avoid severe water shortage during drought periods. In this method reservoir storage is divided into several zones, wherein the rationing factors are changed immediately when water storage level moves from one zone to another. In the present study, a hedging rule with fuzzy rationing factors was applied for creating a transition zone in up and down each rule curve, and then the rationing factor will be changed in this zone gradually. For this propose, a monthly simulation model was developed and linked to the nondominated sorting genetic algorithm for calculation of the modified shortage index of two objective functions involving water supply of minimum flow and agriculture demands in a long-term simulation period. Zohre multireservoir system in south Iran has been considered as a case study. The results of the proposed hedging rule have improved the long-term system performance from 10 till 27 percent in comparison with the simple hedging rule, where these results demonstrate that the fuzzification of hedging factors increase the applicability and the efficiency of the new hedging rule in comparison to the conventional rule curve for mitigating the water shortage problem.

Comparing Optimal Hedging Policies Incorporating Past Operation Information and Future Hydrologic Information

“…That is, a series of smaller deficits is allowed in advance to conserve more water to avoid high-percentage water shortages in pending droughts. Many studies in the literature (e.g., Shih and ReVelle 1995;Cancelliere et al 1998;You and Cai 2008;Shiau 2011;Ilich 2011;Taghian et al 2014;Peng et al 2015;Hu et al 2016;Ahmadianfar et al 2016;Ji et al 2016;Ding et al 2017;Ashofteh et al 2017;Ahmadianfar et al 2017;Xu et al 2017;Wang et al 2018;Kumar and Kasthurirengan 2018;Chang et al 2019;Wan et al 2019;Adeloye and Dau 2019;Men et al 2019;Xu et al 2019;Ahmadianfar and Zamani 2020;Li et al 2020;Zhao et al 2020;Ashrafi et al 2020;Xu et al 2020;El Harraki et al 2021, and others) have proposed various hedging policies to determine when and how to implement hedging.…”

Decentralized calibration process for distributed water resources systems using the self-adaptive multi-memory melody search algorithm

“…Most water resources management models in Iranian basins have been developed based on integrated modeling of demand centers regardless of their distribution across the basin (Kim & Heo 2006;Dariane & Sarani 2013;Abadi et al 2015;Ahmadianfar et al 2017Ahmadianfar et al , 2019Ahmadianfar et al , 2021Ehteram et al 2018;Karami et al 2019;Ashrafi et al 2020;Azizipour et al 2020;Rahimi et al 2020). However, for the exact determination of consequences of management policies, the distribution and scattering of demand sites at the basin level should be considered (Ashrafi & Dariane 2017).…”

Fuzzy Logic Model for Optimal Operation of Darbandikhan Reservoir, Iraq

“…Some researchers focused on fuzzy logic and stochastic programming to derive optimal operating policies for the hydropower reservoir system and solution of hydropower problems within the reservoir system [3,11,12]. Also, several studies were conducted for developing models to derive the optimal operating policies for single and multi-reservoir systems during flood and drought periods based on the combination of fuzzy logic with other techniques such as linear programming, dynamic programming, conventional hedging rule, and genetic algorithm [13][14][15][16][17].…”