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Artificial intelligence powered intelligent energy management framework for hydrogen storage and dispatch in smart

Marwa Hassan1

  • 1Department of Electrical and Control, Arab Academy for Science, Technology & Maritime Transport (AASTMT), Cairo, Egypt. eng_marwa@aast.edu.

Scientific Reports
|November 18, 2025
PubMed
Summary

This study introduces an AI framework using Long Short-Term Memory (LSTM) and Krill Herd Algorithm (KHA) to optimize hydrogen storage in smart microgrids, improving energy management and reducing emissions.

Keywords:
AI-based energy optimizationHydrogen energy storageKrill Herd AlgorithmLSTM forecastingSmart microgrid

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Area of Science:

  • Renewable Energy Systems
  • Artificial Intelligence in Energy
  • Smart Grid Technologies

Background:

  • Hydrogen energy storage is crucial for smart microgrid reliability with high renewable energy integration.
  • Challenges include dispatch complexity, forecasting errors, and nonlinear dynamics hindering practical deployment.

Purpose of the Study:

  • To develop an AI-powered decision-support framework for optimizing hydrogen charging/discharging schedules in microgrids.
  • To integrate Long Short-Term Memory (LSTM) for forecasting and Krill Herd Algorithm (KHA) for optimization.

Main Methods:

  • Simplified constant-efficiency models for PV array, electrolyzer, and fuel cell.
  • LSTM neural networks for short-term solar power forecasting.
  • Krill Herd Algorithm (KHA) for optimizing hydrogen storage dispatch.

Main Results:

  • Achieved 4.8% MAPE forecasting accuracy.
  • Reduced average grid import by 35.6% and PV curtailment by 21.4%.
  • Improved energy self-sufficiency to 89.7% and reduced daily CO2 emissions by 7.76 kg.

Conclusions:

  • The AI framework effectively manages hydrogen storage in microgrids, enhancing efficiency and reducing environmental impact.
  • Combining deep learning (LSTM) with nature-inspired optimization (KHA) shows significant potential for intelligent microgrid energy management.