An uncertainty-informed Digital Twin framework for optimal long-duration energy storage dispatch

This paper presents multiple data analytics and machine learning approaches to optimize energy storage for grid services. These approaches are critical components towards an information integration and decision-making framework as a Digital Twin for energy storage systems, especially those with long-duration storage capability. The Digital Twin framework integrates three complementary components: (i) unsupervised learning to capture joint patterns of energy storage system dispatch, system states, and environmental conditions; (ii) ensemble supervised learning for mechanistic understanding and classification of dispatch; and (iii) deep learning for mid-term electricity price forecasting to support multi-day decision-making. In contrast to recent forecasting- or optimization-centric studies, the proposed Digital Twin explicitly models nonlinear cross-dependencies, embeds uncertainty information into dispatch decisions, and unifies behavioral learning with predictive forecasting within a single modular architecture. The framework integrates effective feature selection, uncertainty reduction, and improved scheduling for inter-day energy shifting. We demonstrated the effectiveness of the framework and its three key components using multi-year datasets from independent system operators and a 10 h battery energy storage system. Results show that the Digital Twin predictions yield total revenues exceeding, by more than 10%, those achieved through conventional linear programming approaches, as demonstrated in the New York Independent System Operator studies. • Modular digital twin integrating unsupervised, ensemble, and deep learning. • Joint behavioral learning captures nonlinear cross-dependencies in energy storage. • Automatically optimized energy storage dispatch for long-duration storage systems. • Greatly reduced uncertainty and optimized multi-day energy storage scheduling. • Proposed framework yields >10% revenue gain over conventional benchmarks.