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Related Concept Videos

Integrals of Powers of Sine and Cosine01:29

Integrals of Powers of Sine and Cosine

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An improved Sinh Cosh optimizer for optimizing energy management system in nano-grids.

Asmaa H Rabie1,2, Sally Elghamrawy3,4, Aboul Ella Hassanien5,6

  • 1Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt. asmaahamdy@mans.edu.eg.

Scientific Reports
|September 12, 2025
PubMed
Summary

The Improved Sinh Cosh Optimizer (ISCHO) significantly reduces Nano-grid operational costs by optimizing energy management. This novel algorithm outperforms traditional methods, ensuring efficient use of renewable energy sources.

Keywords:
Energy management systemsImproved sinh cosh optimizerMeta-heuristic algorithmsNano-gridRenewable energy

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Area of Science:

  • * Electrical Engineering
  • * Computer Science
  • * Optimization Algorithms

Background:

  • * Integrating renewable energy into Nano-grids necessitates efficient energy management systems.
  • * Traditional optimization methods face challenges in balancing diverse energy sources (wind, solar, natural gas, batteries), leading to suboptimal performance and increased costs.
  • * A need exists for advanced algorithms to optimize Nano-grid operations and reduce expenses.

Purpose of the Study:

  • * To introduce the Improved Sinh Cosh Optimizer (ISCHO), a novel meta-heuristic algorithm.
  • * To enhance the energy management system in Nano-grids for optimized energy usage and minimized operational costs.
  • * To demonstrate ISCHO's superiority over traditional methods in cost reduction and efficiency.

Main Methods:

  • * Development of the Improved Sinh Cosh Optimizer (ISCHO) algorithm, inspired by Sinh and Cosh functions.
  • * Dynamic adjustment of exploration-exploitation balance for efficient search space exploration and convergence.
  • * Optimization of key parameters for energy generation and storage within Nano-grids.

Main Results:

  • * ISCHO achieved optimal fitness values (0) at population sizes of 500 and 1000, indicating significant cost reduction.
  • * ISCHO demonstrated superior performance compared to the Chimp algorithm, with a fitness value of 0 versus 23.768 × 10-6.
  • * ISCHO exhibited competitive execution times (e.g., 3.00862 s for population 500) and outperformed other algorithms on benchmark functions.

Conclusions:

  • * ISCHO is a robust and effective solution for real-time energy management in Nano-grids.
  • * The algorithm significantly minimizes total operational costs, enhancing the economic viability of Nano-grids.
  • * ISCHO's efficiency and speed make it a practical choice for real-world Nano-grid applications.