Maximizing energy storage in Microgrids with an amended multi-verse optimizer.

Microgrids have emerged as a possible alternative to overcome the difficulties of the combined cooling, heating, and power (CCHP) system in power networks. Energy storage devices are vital for the stable and effective functioning of Microgrids. In this paper, a new modified metaheuristic technique, called the Amended Multiverse Optimizer algorithm (AMVOA) is used to suggest a new method of Microgrid design with energy storage. The Multiverse theory notion served as the inspiration for the metaheuristic optimization method known as the AMVOA. The suggested strategy takes into account the load demand, energy storage technologies, and architecture of a Microgrid with renewable energy sources. The goal is to keep the Microgrid's overall cost as low as possible while preserving its dependability and sustainability. To validate the efficiency of the proposed method, two HRES scenarios are put out, the first of which relies on PV, wind, diesel, and battery power, and the second of which uses PV, diesel, and battery power. To validate the superiority of the proposed method, the method has been compared with five state-of-the-art algorithms, including the Evolutionary Algorithm (EA), Modified Grasshopper Optimization Algorithm (MGOA), Improved Gray Wolf Optimization Algorithm (IGWOA), Improved Arithmetic Optimization Algorithm (IAOA), and the original MVOA. The study compares two scenarios: one with wind, PV, diesel, and battery power and the other with only PV, diesel, and battery power. In scenario 1 (Wind/PV/DG/BESS), the AMVOA algorithm achieves optimal results, resulting in a Net Present Cost (NPC) of $299,010 and an energy cost of $0.2309 per kilowatt-hour. The proposed technique successfully integrates 84.86 % renewable energy sources while meeting defined limitations. The optimal sizing for scenario 2 (PV/DG/BESS) is $333,800 with an energy cost of $0.3451 per kilowatt-hour. The AMVOA algorithm outperforms other algorithms in convergence and provides efficient power management. However, further analysis and evaluation are necessary to assess the robustness, practicality, and reliability of the proposed Microgrid configurations. The outcomes show how the suggested AMVO-based strategy may be used to create the best Microgrid architecture with energy storage. The recommended method may be applied as a decision-making tool for Microgrid planning and design, especially for the integration of renewable energy.