What question did this study set out to answer?

The aim is to optimize the operation of hybrid renewable power plants to maximize economic benefits while minimizing costs and emissions.

February 2, 2026PLoS ONEOpen Access

A HLBDA, GA, and COA for optimal operation of distributed energy resources

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The article demonstrates improved economic benefits in microgrids using energy management systems and novel algorithms.

The aim is to optimize the operation of hybrid renewable power plants to maximize economic benefits while minimizing costs and emissions.
Introduced an energy management system incorporating various renewable technologies.
Formulated optimization problems using Hyper Learning Binary Dragonfly Algorithm (HLBDA), Genetic Algorithms (GA), and Crayfish Optimization Algorithm (COA).
Considered a stochastic framework to address uncertainties in microgrid operations.
HLBDA achieved a 12.4% cost saving over GA and a 9.54% reduction in carbon emissions.
COA achieved a 3.24% improvement in cost reduction and 2.40% in emission reduction.
Overall improvements were noted in system costs and emissions across all algorithms.

Alhasnawi et al. (2026) studied this question.

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