Optimization of hydropower energy generation by 14 robust evolutionary algorithms.

Area of Science:

• Engineering
• Computational Intelligence
• Renewable Energy Systems

Background:

• Evolutionary algorithms (EAs) show promise for complex engineering optimization.
• Optimal operation of hydropower reservoirs is crucial for efficient energy generation.
• Karun-4 hydropower reservoir serves as a case study for evaluating optimization techniques.

Purpose of the Study:

• To investigate the capability of 14 recently introduced robust EAs for optimizing energy generation in the Karun-4 hydropower reservoir.
• To identify the most effective EA for maximizing energy output while minimizing operational variability and computational time.
• To compare the performance of various EAs in the context of hydropower reservoir management.

Main Methods:

• Evaluated 14 recently introduced robust evolutionary algorithms (EAs).
• Optimized energy generation for the Karun-4 hydropower reservoir.
• Assessed algorithms based on objective function (energy generation), standard deviation (SD), coefficient of variation (CV), and CPU usage time.

Main Results:

• The Moth Swarm Algorithm (MSA) achieved the highest energy generation (19,311,535 MW), a 65.088% increase over the actual generation.
• MSA demonstrated excellent performance with low SD (0.0029) and CV (0.0192).
• Search Group Algorithm (SGA), Water Cycle Algorithm (WCA), Symbiotic Organism Search (SOS), and Coyote Optimization Algorithm (COA) also improved energy generation by over 65%, but ranked below MSA.

Conclusions:

• The Moth Swarm Algorithm (MSA) is highly effective for optimizing energy generation in hydropower reservoirs.
• Several other EAs, including SGA, WCA, SOS, and COA, show significant potential for similar applications.
• Some EAs, such as GOA, DA, ALO, and WOA, did not yield satisfactory results, highlighting the importance of algorithm selection for hydropower optimization.