Novel optimization technique of isolated microgrid with hydrogen energy storage

Area of Science:

• Electrical Engineering
• Renewable Energy Systems
• Optimization Techniques

Background:

• Isolated microgrids require efficient energy management strategies to integrate diverse Distributed Energy Resources (DERs).
• Balancing energy supply from sources like Photovoltaic (PV) arrays, Wind Turbine Generators (WTG), and Diesel Generators (DG) with load demand is crucial.
• Hydrogen storage systems offer a solution for short-term energy balancing in microgrids.

Purpose of the Study:

• To present a novel optimization technique for energy management in isolated microgrids.
• To compare the effectiveness of a non-dominated sorting genetic algorithm with a multi-objective flower pollination algorithm for microgrid optimization.
• To minimize operational costs and line losses while meeting load requirements under realistic constraints.

Main Methods:

• Modeling an isolated microgrid system using MATLAB.
• Implementing multi-objective optimization using a non-dominated sorting genetic algorithm.
• Validating results with a novel multi-objective flower pollination algorithm.
• Performing optimal load flow analysis and active/reactive power dispatch.

Main Results:

• The study successfully modeled and analyzed an isolated microgrid's performance under summer and winter conditions.
• Comparison of genetic algorithms and flower pollination algorithms provided insights into their respective advantages and disadvantages for microgrid optimization.
• Optimal load flow analysis demonstrated the capability to minimize fuel cost and line losses within system constraints.
• Validation on the modified IEEE 15 bus system confirmed the proposed algorithm's efficacy.

Conclusions:

• The proposed optimization technique effectively manages energy in isolated microgrids with multiple DERs.
• Both genetic algorithms and flower pollination algorithms show potential, with specific strengths for different aspects of microgrid optimization.
• The developed model and methods provide a robust framework for enhancing the economic and operational efficiency of isolated microgrids.