A novel demand response framework for the optimal design of hydrogen–ammonia hybrid microgrids

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Mewafy, Abdelrahman and Ismael, Islam and Kaddah, Sahar and Sulaiman, Adam Y. and Deng, Fujin and Abulanwar, Sayed (2026) A novel demand response framework for the optimal design of hydrogen–ammonia hybrid microgrids. Applied Energy, 419. 128074. ISSN 0306-2619 (https://doi.org/10.1016/j.apenergy.2026.128074)

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Abstract

In standalone applications, hybrid microgrids are showing promise as a means of satisfying consumers’ combined needs for gas, heat, and power. While green hydrogen and green ammonia have been studied separately as energy carriers in earlier research, blended hydrogen-ammonia fuels that can directly feed gas loads or indirectly assist in the generation of electricity have received less attention. In order to achieve technical, economic, reliability, and environmental goals, this study proposes a novel demand response strategy for the optimal sizing and operation of a multiuse hybrid microgrid. The proposed strategy increases the viability of employing renewable resources to cover all energy demands by extending participation beyond electrical loads to include gas and heat loads. To address hourly variations in generation and demand, the 24-hour operation is divided into dynamic intervals in which selected power, gas, and heat loads are shifted from periods of low renewable generation to periods of surplus production, supported by customer incentives. In terms of the environment, the system collects oxygen that is generated as a byproduct of several energy conversion processes, making it suitable for use in industrial and medical settings. To fully substitute natural gas in meeting gas demand, a blended ammonia-hydrogen fuel is also introduced as a decarbonization approach. To reduce overall cost, unmet load, curtailed energy, and emissions, the optimization framework uses a multi-objective function that is weighted using the Analytical Hierarchy Process (AHP). Two optimization techniques, teaching-learning-based optimization and particle swarm optimization, are used to assess the efficacy of the proposed methodology under various circumstances.

ORCID iDs

Mewafy, Abdelrahman, Ismael, Islam, Kaddah, Sahar, Sulaiman, Adam Y., Deng, Fujin and Abulanwar, Sayed ORCID logoORCID: https://orcid.org/0000-0002-3396-4020;
CORE (COnnecting REpositories)