Digital Twin-Driven Energy Systems Optimization: From Algorithm Innovation to Low-Carbon Operation

Dear Colleagues,

The accelerating transition toward carbon-neutral energy systems necessitates transformative paradigms that integrate advanced modeling, real-time optimization, and lifecycle decarbonization. Digital twin technology, empowered by AI and IoT, emerges as a cornerstone for revolutionizing energy system design, operation, and governance. By creating high-fidelity virtual replicas of physical assets and networks, digital twins enable predictive analytics, scenario simulation, and closed-loop control, unlocking unprecedented potential for low-carbon energy transitions. This Special Issue will advance the frontiers of digital twin applications in energy systems, bridging algorithm innovation with sustainable operational outcomes.

From a systems engineering perspective, digital twins facilitate holistic co-optimization of generation, storage, and demand across multi-energy networks. They integrate heterogeneous data streams (e.g., weather, market prices, equipment status) to enhance renewable integration, balance supply–demand volatility, and orchestrate multi-timescale flexibility resources. From a device perspective, physics-informed neural networks and hybrid modeling techniques embedded in digital twins optimize component-level performance (e.g., battery health, turbine efficiency), extending asset lifetimes under dynamic operating conditions. From a cyberspace perspective, federated learning and edge-cloud synergy ensure the secure, scalable deployment of digital twins in distributed energy networks, fostering resilient and autonomous operations.

Despite their increased use, critical challenges persist in ensuring digital twins’ fidelity, computational scalability, and interoperability with legacy infrastructure. Bridging the gap between high-resolution modeling and real-world implementation remains vital. This Special Issue will curate pioneering research that addresses these barriers through novel algorithms, validation frameworks, and field demonstrations, advancing both theoretical rigor and industrial applicability. We welcome original contributions demonstrating measurable impacts on carbon reduction, operational efficiency, and energy equity.

Topics of interest include, but are not limited to, the following:​

• Physics-Informed Digital Twins for Renewable-Rich Grids: Modeling uncertainty in solar/wind forecasting and stability control;
• Multi-Scale Digital Twin Architectures: From device-level components (e.g., batteries, inverters) to city-scale energy hubs;
• AI-Enhanced Surrogate Models: Accelerating simulation of complex thermal-electrical systems using graph neural networks;
• Federated Learning for Privacy-Preserving Digital Twins: Collaborative training across decentralized energy assets;
• Digital Twin-Driven Predictive Maintenance: Reducing downtime via anomaly detection in wind farms/solar plants;
• Co-Simulation Platforms for Multi-Energy Systems: Integrating power, heat, gas, and mobility sectors;
• Blockchain-Empowered Digital Twins: Enabling transparent peer-to-peer energy trading in virtual power plants;
• Carbon-Aware Digital Twin Operations: Embedding life-cycle emission tracking into dispatch algorithms;
• Human-in-the-Loop Digital Twins: Interactive decision support for grid operators during extreme events;
• Standardization Frameworks for Interoperable Energy Digital Twins.

Dr. Lingxiao Yang
Prof. Dr. Shunyuan Xiao
Dr. Yushuai Li
Dr. Ning Zhang
Guest Editors

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• digital twin
• energy system optimization
• low-carbon operation
• AI-driven modeling
• federated learning
• predictive control
• multi-energy integration
• decentralized energy management