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Reliability constrained dynamic generation expansion planning using honey badger algorithm.

Adel A Abou El Ela1, Ragab A El-Sehiemy2, Abdullah M Shaheen3

  • 1Electrical Engineering Department, Faculty of Engineering, Menoufiya University, Shibîn el Kôm, Egypt.

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This study introduces a new framework for generation expansion planning (GEP) using the honey badger algorithm (HBA) to optimize technology mix while ensuring reliability and minimizing costs.

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Area of Science:

  • Optimization
  • Energy Systems Engineering
  • Computational Intelligence

Background:

  • Generation Expansion Planning (GEP) is a complex optimization problem.
  • Existing GEP models face challenges with computational time and search space reduction.

Purpose of the Study:

  • To present a novel framework for multi-stage, reliability-constrained GEP.
  • To develop and apply a new meta-heuristic algorithm, the Honey Badger Algorithm (HBA), for GEP.

Main Methods:

  • A reliability-constrained GEP model minimizing costs (capital, O&M, outage) and ensuring spinning reserve and fuel mix.
  • Proposed modifications: Virtual mapping, penalty factor, intelligent initial population.
  • Developed a novel Honey Badger Algorithm (HBA) inspired by animal foraging behavior.
  • Comparative analysis with Crow Search, Aquila Optimizer, Bald Eagle Search, and Particle Swarm Optimization.

Main Results:

  • The HBA demonstrated effectiveness in solving the reliability-constrained GEP problem.
  • Comparative studies showed the superiority of HBA over other tested meta-heuristic algorithms.
  • The framework was validated across short- and long-term planning horizons (6, 12, 24 years).

Conclusions:

  • The proposed HBA is a superior and effective method for solving complex GEP problems.
  • The novel framework successfully addresses multi-stage, reliability-constrained GEP.
  • The study provides a valuable tool for optimizing energy infrastructure development.