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

Area of Science:

• Electrical Engineering
• Optimization Algorithms
• Renewable Energy Systems

Background:

• Microgrids offer a solution to power network challenges, with energy storage being crucial for their stability.
• Combined Cooling, Heating, and Power (CCHP) systems face integration difficulties in conventional power grids.

Purpose of the Study:

• To propose a novel microgrid design method using the Amended Multiverse Optimizer algorithm (AMVOA) for enhanced energy storage integration.
• To minimize the overall cost of microgrids while ensuring reliability and sustainability.
• To evaluate the performance of AMVOA against other state-of-the-art optimization algorithms.

Main Methods:

• Development and application of the Amended Multiverse Optimizer algorithm (AMVOA), inspired by multiverse theory.
• Design and analysis of two hybrid renewable energy system (HRES) scenarios for microgrids: one with PV, wind, diesel, and battery storage, and another with PV, diesel, and battery storage.
• Comparison of AMVOA with five other optimization algorithms: Evolutionary Algorithm (EA), Modified Grasshopper Optimization Algorithm (MGOA), Improved Gray Wolf Optimization Algorithm (IGWOA), Improved Arithmetic Optimization Algorithm (IAOA), and the original MVOA.

Main Results:

• The AMVOA algorithm achieved optimal results in Scenario 1 (Wind/PV/DG/BESS) with a Net Present Cost (NPC) of $299,010 and an energy cost of $0.2309/kWh.
• The proposed method successfully integrated 84.86% renewable energy sources while meeting system constraints.
• In Scenario 2 (PV/DG/BESS), the optimal sizing resulted in an NPC of $333,800 and an energy cost of $0.3451/kWh.
• AMVOA demonstrated superior convergence and efficient power management compared to other tested algorithms.

Conclusions:

• The AMVOA-based strategy is effective for designing optimal microgrids with energy storage.
• The proposed method can serve as a valuable decision-making tool for microgrid planning and design, particularly for renewable energy integration.
• Further research is recommended to assess the robustness, practicality, and reliability of the proposed microgrid configurations.