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Computational Intelligence for Sustainable Hydrogen Microgrids: A Nonlinear Optimization Framework

delete2026-05-08
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PRE
AI
S
Salman Habib *
M
Md Shafiullah
M
Muhammad Majid Gulzar
S
Sohaib Tahir Chauhdary
B
Bilal Khan
A
Ali Faisal Murtaza
DOI：10.1016/j.suscom.2026.101388delete
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Abstract

Abstract

En 中文
• Applies EAGOA to solve highly nonlinear hydrogen microgrid scheduling problems. • Formulates a unified nonlinear MILP for coupled hydrogen–electric systems. • Efficiently solves large-scale nonlinear models (365 days) within 207 s. • Achieves superior convergence and lower cost than PSO, GA, JAYA, and GWA. • Reveals nonlinear cost dominance of PV and hydrogen storage in system scaling.
Keywords:
Hydrogen microgrid
Nonlinear optimization
Mixed-integer linear programming
Computational intelligence
Sustainable energy

Journal

S
sustainable computing: informatics and systems
IF:
0
Papers:
126
Citations:
0

Organization

K
King Fahd University of Petroleum & Minerals
Scholars:
1.7K
Papers: 757
Citations: 1
Q
Qassim University
Scholars:
5.6K
Papers: 5.4K
Citations: 5.0K
D
Dhofar University
Scholars:
554
Papers: 682
Citations: 985
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