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

ABSTRACT: Hydrogen energy storage is increasingly recognized as a key enabler for enhancing flexibility and reliability in smart microgrids with high shares of renewable energy. However, its practical deployment remains constrained by challenges such as real-time dispatch complexity, forecasting uncertainty, and nonlinear system dynamics. This study presents a novel AI-powered decision-support framework that integrates Long Short-Term Memory (LSTM) neural networks for short-term forecasting with the Krill Herd Algorithm (KHA) for optimizing hydrogen charging and discharging schedules. To preserve computational tractability, the photovoltaic (PV) array, electrolyzer, and fuel cell are modeled using simplified constant-efficiency assumptions that capture overall system behavior without representing de

SUBMITTER: Hassan M 

PROVIDER: S-EPMC12627455 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature