Abstract
The integration of hydrogen, electrical, and thermal energy systems represents a crucial direction for deep decarbonization by enhancing energy flexibility, maximizing the use of renewable resources, and facilitating inter-sectoral energy exchanges aligned with consistent climate and energy availability objectives. This review consolidates recent developments in the integrated modeling and control of hydrogen-electricity-heat (HEH) coupling, emphasizing optimization, real-time operational strategies, and data-driven methodologies that enhance resource efficiency and system robustness. Empirical data from simulations and pilot investigations indicate that synchronized HEH operations can reduce renewable energy cutting, increase overall system and decrease CO2 emissions compared to uncoupled systems. We scrutinize deterministic, stochastic, dynamic, and hybrid physics-artificial intelligence (AI) frameworks, including novel AutoML-assisted methodologies, alongside hierarchical and predictive control strategies. Outstanding challenges in scalability, multi-time-scale coordination, uncertainty mitigation, interoperability, and market structuring are identified, and a strategic framework is proposed for hybrid physics-ML modeling, decentralized multi-agent control, and digital-twin-enabled optimization to encourage equitable, cost-effective, and climate-resilient HEH energy ecosystems across varied geographical and socio-economic contexts globally.
| Original language | English |
|---|---|
| Article number | 116756 |
| Journal | Renewable and Sustainable Energy Reviews |
| Volume | 231 |
| DOIs | |
| State | Published - May 2026 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
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SDG 8 Decent Work and Economic Growth
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SDG 12 Responsible Consumption and Production
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SDG 13 Climate Action
Keywords
- Energy flexibility
- Hybrid physics-AI modeling
- Hydrogen-electricity-heat coupling
- Optimization and control
- Sector integration
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