AI-Driven Integrated Energy Systems: Emerging Trends, Research Gaps, and Global Roadmap
TL;DR
The review offers a state-of-the-art, comprehensive basis for co-designing algorithms, market designs, and regulatory protections that expedite bankable, real-world AI-IES deployments across various regions worldwide by specifically bridging AI methodologies, storage technologies, and multi-scale planning horizons.
Abstract
The review integrates disparate knowledge on forecasting, optimal scheduling, degradation-aware control, and resilience enhancement across power, heat, mobility, and water–energy–food nexuses, drawing on 153 recent studies covering batteries, hydrogen, thermal storage, and sector-coupled microgrids. Digital-twin and surrogate-based optimisation, deep and reinforcement learning for multi-energy dispatch, physics-informed and hybrid models, and privacy-preserving or federated analytics that respect data sovereignty while facilitating cross-asset learning are all covered by a single classification. While pointing out enduring gaps in cross-regional validation, cyber-secure implementation, and socially just deployment in vulnerable grids, the analysis quantifies typical performance gains reported for AI-enabled storage coordination, such as reductions in unmet load, curtailed renewable generation, operating cost, and emissions. Drawing from these observations, the paper lays out a three-phase global roadmap for AI-IES in energy storage from 2030 to 2050. These phases move from reliable pilots and benchmark datasets to human-centric, interoperable ecosystems and, finally, autonomous, resilience-optimized, and justice-oriented infrastructures. The roadmap provides practical advice for researchers, system operators, regulators, and investors looking to match AI innovation with net-zero, reliability, and equity targets in future integrated energy systems. It does this by connecting algorithmic decisions to technology pathways for batteries, power-to-hydrogen, long-duration storage, and hybrid storage architectures. The review offers a state-of-the-art, comprehensive basis for co-designing algorithms, market designs, and regulatory protections that expedite bankable, real-world AI-IES deployments across various regions worldwide by specifically bridging AI methodologies, storage technologies, and multi-scale planning horizons.