Abstract
Multi-purpose advanced systems are considered a complex problem in water resource management, and the use of data-intelligence methodologies in operating such systems provides major advantages for decision-makers. The current research is devoted to the implementation of hybrid novel meta-heuristic algorithms (e.g., the bat algorithm (BA) and particle swarm optimization (PSO) algorithm) to formulate multi-purpose systems for power production and irrigation supply. The proposed hybrid modelling method was applied for the multi-purpose reservoir system of Bhadra Dam, which is located in the state of Karnataka, India. The average monthly demand for irrigation is 142.14 (106 m3), and the amount of released water based on the new hybrid algorithm (NHA) is 141.25 (106 m3). Compared with the shark algorithm (SA), BA, weed algorithm (WA), PSO algorithm, and genetic algorithm (GA), the NHA decreased the computation time by 28%, 36%, 39%, 82%, and 88%, respectively, which represents an excellent enhancement result. The amount of released water based on the proposed hybrid method attains a more reliable index for the volumetric percentage and provides a more effective operation rule for supplying the irrigation demand. Additionally, the average demand for power production is 18.90 (106 kwh), whereas the NHA produces 18.09 (106 kwh) of power. Power production utilizing the NHA’s operation rule achieved a sufficient magnitude relative to that of stand-alone models, such as the BA, PSO, WA, SA, and GA. The excellent proficiency of the developed intelligence expert system is the result of the hybrid structure of the BA and PSO algorithm and the substitution of weaker solutions in each algorithm with better solutions from other algorithms. The main advantage of the proposed NHA is its ability to increase the diversity of solutions and hence avoid the worst possible solutions obtained using BA, that is, preventing a decrease in local optima. In addition, the NHA enhances the convergence rate obtained using the PSO algorithm. Hence, the proposed NHA as an intelligence model could contribute to providing reliable solutions for complex multi-purpose reservoir systems to optimize the operation rule for similar reservoir systems worldwide.
Highlights
Water resource management attempts to control water scarcity during successive drought periods [1]
The results indicated that hydropower generation could be increased by approximately 12% and 15% using the particle swarm optimization (PSO) algorithm compared with the honey bee optimization algorithm (HBOA) and genetic algorithm (GA), respectively
Another study focused on the Karoon4 reservoir and utilized the water cycle algorithm (WCA) to increase the benefit of hydropower generation based on the released water, and the results showed that compared with the PSO algorithm and the GA, the WCA increased the annual benefit of hydropower generation by approximately 30% and 40%, respectively [30]
Summary
Water resource management attempts to control water scarcity during successive drought periods [1]. Most mathematical models, such as nonlinear programming, cannot be accurately adapted with multi-objective problems and perform the optimization procedure in a reasonable time period These models should be able to consider effective parameters that influence the optimization process, such as climate change conditions or uncertain inflow to reservoirs [10]. Water released from the reservoir is dependent on the physical characteristics of the dam and reservoir system, and these characteristics can be highly non-linear, such as the interrelationship among the elevation, surface area, and storage in the reservoir [18] In this context, generating optimal operation rules for water release based on nonlinear or linear objective functions with different constraints is considered an important problem for policymakers [20]
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