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AI-enabled digital twins for thermal energy storage in renewable power systems: Multi-scale modelling, power-system integration, and co-simulation frameworks
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DOI：10.1016/j.epsr.2026.112947.png)
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Abstract
En 中文
Achieving high levels of variable renewable energy (VRE) integration requires flexible resources that can operate across diverse temporal scales while ensuring system reliability, minimizing curtailment, and facilitating sector-coupled energy transfers. Thermal energy storage (TES) offers a cost-efficient, scalable solution for addressing these requirements; however, its value within the system increasingly hinges on sophisticated digitalization. This review consolidates recent advances in artificial intelligence (AI)-enabled digital twins and multi-scale modeling frameworks that enhance the monitoring, forecasting, and regulation of TES in renewable energy-dominated systems. We investigate the extent to which hybrid physics-machine learning (ML) models, reinforcement learning (RL) controllers, and physics-informed neural networks (PINNs) enhance TES efficacy in Concentrated Solar Power (CSP) facilities, district heating systems, industrial electrification, and power-to-heat (P2H) initiatives. Co-simulation methodologies that amalgamate TES models with unit commitment (UC), economic dispatch (ED), and capacity-expansion instruments are rigorously evaluated. Principal challenges, including data inter-operability, cyber-physical security, model validation, and interpretability, are recognized as impediments to extensive implementation. The review culminates in a structured research agenda aligned with Sustainable Development Goals (SDGs) 7, 9, and 13, delineating priorities for advancing next-generation autonomous and sustainable TES systems.
Keywords:
Thermal energy storage
Digital twins
Multi-scale modelling
Power system co-simulation
Unit commitment and economic dispatch
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2.2W
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