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February 2, 2026PLoS ONEOpen Access

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

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Authors

BABilal Naji AlhasnawiSASabah Mohammed Mlkat AlmutokiHHHayder Khenyab Hashim

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Overview

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

Key Points

  • 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.

Cite This Study

Alhasnawi et al. (2026) studied this question.

synapsesocial.com/papers/6980ff19c1c9540dea811d89https://doi.org/10.1371/journal.pone.0340259
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