10.1007/s40095-022-00550-0

Economic energy scheduling through chaotic gorilla troops optimizer

  • Ashutosh Bhadoria*1,
  • Sanjay Marwaha1,
  1. Sant Longowal Institute of Technology and Engineering SLITE, Sangrur, Punjab, IN

Published in Issue 2022-12-12

How to Cite

Bhadoria, A., & Marwaha, S. (2022). Economic energy scheduling through chaotic gorilla troops optimizer. International Journal of Energy and Environmental Engineering, 14(4 (December 2023). https://doi.org/10.1007/s40095-022-00550-0
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Abstract

Abstract This research proposes a novel solution to the power generation scheduling problem based on the chaotic gorilla troop's optimizer algorithm (CGTO). Power system operational planning is a large-scale, highly constrained combinatorial optimization problem known as the Energy Generation Scheduling Problem. The Gorilla Troops optimizer is a bio-inspired heuristic optimizer that uses gorilla hierarchy and hunting notions to resolve challenging scheduling issues. The gorilla update method is initially obtain binary string of generators in order to determine the global best solution (s), which is followed by a chaotic operation. Chaotic search avoids local minima while GTO seeks out global optima, resulting in a better balance of exploitation and exploration. A simple and effective strategy for wind power integration placement and sizing is presented in this research. The messy behavior of the wind is predicted to follow the Weibull PDF. Examining the viability and effectiveness of units between 10 and 100, the results are compared to those attained using various techniques mentioned in the literature. The results unequivocally demonstrate that the recommended technique provides superior solutions than competing alternatives. The solution was improved further in the wind power sharing scenario. Convergence curve amply demonstrates the optimizer's robustness.

Keywords

  • Artificial gorilla troops optimizer,
  • Metaheuristic algorithm,
  • Generation Scheduling Problem,
  • Chaotic search,
  • Hybrid optimizer

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