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Optimal Sizing and Operation Strategies for Isolated Microgrid Integrating Green Hydrogen

20243 citationsHelwan University

Abstract

This study investigates the optimal sizing and operation scenarios for an island microgrid located in 6<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> of October, Egypt. The proposed microgrid integrates a combination of renewable and clean energy technologies, including photovoltaic (PV) panels, battery energy storage system (BESS), and a green hydrogen energy storage system (GHESS). The microgrid will also house a cable factory, adding an additional load profile to consider. Homer Pro software is employed to model and optimize the microgrid design. To achieve this goal, the various operation scenarios across different days throughout the year are simulated using Homer Pro. These simulated scenarios considered the fluctuating availability of solar energy from the PV system, along with the varying energy demands of the factory load. For each scenario, Homer Pro is intelligently selects the most cost-effective combination of energy sources to meet the demand. This might involve using the PV system during peak sunlight hours, the BESS during periods of high demand or low solar production, and the GHESS with its Electrolyser and fuel cell for extended periods of low solar availability. The green hydrogen system utilize an Electrolyser, powered by excess renewable energy from the PV system, to split water into hydrogen and oxygen where the hydrogen is stored in the GHESS. When renewable energy is insufficient, a fuel cell is employed to convert the stored hydrogen back into electricity, powering the microgrid and the factory. The results provide valuable insights into the feasibility and optimal design of an island microgrid utilizing green hydrogen for enhanced energy security and sustainability in the isolated microgrid.

Research topics

  • Microgrid Control and Optimization
  • Hybrid Renewable Energy Systems
  • Islanding Detection in Power Systems

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DOI: 10.1109/cgee62671.2024.10955904

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