Optimization of hydropower energy generation by 14 robust evolutionary algorithms

Abstract

The use of evolutionary algorithms (EAs) for solving complex engineering problems has been very promising, so the application of EAs for optimal operation of hydropower reservoirs can be of great help. Accordingly, this study investigates the capability of 14 recently-introduced robust EAs in optimization of energy generation from Karun-4 hydropower reservoir. The best algorithm is the one that produces the largest objective function (energy generation) and has the minimum standard deviation (SD), the minimum coefficient of variations (CV), and the shortest time of CPU usage. It was found that the best solution was achieved by the moth swarm algorithm (MSA), with the optimized energy generation of 19,311,535 MW which was 65.088% more than the actual energy generation (11,697,757). The values of objective function, SD and CV for MSA were 0.147, 0.0029 and 0.0192, respectively. The next ranks were devoted to search group algorithm (SGA), water cycle algorithm (WCA), symbiotic organism search algorithm (SOS), and coyote optimization algorithm (COA), respectively, which have increased the energy generation by more than 65%. Some of the utilized EAs, including grasshopper optimization algorithm (GOA), dragonfly algorithm (DA), antlion optimization algorithm (ALO), and whale optimization algorithm (WOA), failed to produce reasonable results. The overall results indicate the promising capability of some EAs for optimal operation of hydropower reservoirs.

Applying the new multi-objective algorithms for the operation of a multi-reservoir system in hydropower plants

“…The results revealed the superiority of FDB-MSA to GA and PSO; the smallest difference (3.41%) was observed between nominal and optimal power generation compared to the PSO (6.58%) and GA (33.89%). Also, Sharifi et al 30 investigated the capability of 14 new robust evolutionary algorithms (EAs) in optimizing energy generation from the Karun-4 hydropower reservoir in the Karun basin. The overall results indicated the promising capability of some EAs for optimal operation of hydropower reservoirs.…”

Research on Multi-Objective Optimization Model of Foundation Pit Dewatering Based on NSGA-II Algorithm

“…Reza introduced a variety of multi-objective optimization algorithms to optimize the power generation efficiency of the hydropower station reservoir [2]. Wang applied the NSGA-II algorithm to power grid optimization planning, established the design model of multi-objective power grid planning, and provided schemes for the trade-off analysis of various objectives.…”