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

The increasing integration of renewable energy sources in Nano-grids has created a need for efficient energy management systems to optimize energy usage and minimize operational costs. Traditional optimization algorithms often struggle with balancing the complex trade-offs between different energy sources, such as wind, solar, natural gas generators, and batteries, resulting in suboptimal performance and higher costs. To address this challenge, this paper introduces the Improved Sinh Cosh Optimizer (ISCHO), a novel meta-heuristic algorithm designed to enhance the energy management system in Nano-grids. ISCHO mimics the characteristics of Sinh and Cosh functions to dynamically adjust the balance between exploration and exploitation, enabling more efficient search space exploration and convergence towards optimal solutions. By optimizing key parameters related to energy generation and storage, ISCHO minimizes the total operational cost of the Nano-grid. Simulation results show that ISCHO outperforms traditional methods by achieving a significant reduction in total costs, making it a robust solution for real-time energy management in Nano-grids. According to population sizes equal 500 and 1000, ISCHO gave the best fitness values, means, and standard deviations of 0, which refered to a significant reduction in total operational costs. For instance, at a population size of 500, ISCHO's fitness value of 0 was significantly lower than the highest fitness value of 23.768 × 10^{- 6} recorded by the Chimp algorithm. Furthermore, ISCHO maintained a competitive execution time (e.g., 3.00862 s for population 500), confirming its practical applicability for real-time energy management in Nano-grids. Additionally, results ensured that ISCHO outperformed other algorithms using five benchmark functions. Hence, ISCHO's competitive execution time further solidifies its effectiveness for real-time energy management in Nano-grids.