Digital twins set to redefine how solar, wind, and grids are managed worldwide

Rapid deployment of renewable energy has reduced emissions but introduced new operational challenges. Solar and wind power are inherently intermittent. Distributed generation complicates grid management. Maintenance costs remain high, particularly for offshore or remote installations. At the same time, energy systems must adapt to tighter climate targets, stricter regulations, and increasing electrification of transport and heating.

Digital twin technology is rapidly moving from experimental use to a key role in the management of clean energy systems, according to a new academic review that maps how virtual replicas of physical energy assets are reshaping power generation, grid stability, and urban sustainability worldwide. The findings come as governments and utilities face growing pressure to decarbonize energy systems while maintaining reliability in the face of climate volatility, aging infrastructure, and rising demand.

The study, titled Digital Twins for Clean Energy Systems: A State-of-the-Art Review of Applications, Integrated Technologies, and Key Challenges, and published in Sustainability, brings together evidence across solar, wind, hydropower, hydrogen, geothermal, bioenergy, nuclear power, and building-to-grid systems, offering one of the most detailed assessments to date of how digital twins are changing the energy sector .

From virtual replicas to operational intelligence

The study defines digital twins as dynamic digital representations of physical assets or systems that remain continuously synchronized with real-world operations through live data streams. Unlike traditional models, which rely on static assumptions or historical data, digital twins update in near real time, reflecting changing conditions, component wear, and external influences such as weather or demand fluctuations.

The review traces the evolution of this concept from early aerospace applications to its current role in Industry 4.0 and emerging Industry 5.0 frameworks. In clean energy systems, this evolution has been driven by the growing complexity of renewable infrastructure. Wind farms, solar parks, distributed storage, and smart grids generate massive volumes of data, while their performance is shaped by highly variable environmental factors. Conventional control methods struggle to manage this complexity at scale.

Across energy sectors, digital twins are being used to close this gap. In solar power, they support real-time performance tracking, fault detection, and output forecasting under changing irradiance and temperature conditions. In wind energy, they focus heavily on structural health monitoring, fatigue prediction, and maintenance planning for turbines exposed to constant mechanical stress. In hydropower, digital twins are applied to both turbine performance and dam safety, helping operators balance power generation with flood control and environmental constraints.

The review highlights a key distinction between three levels of digital integration: digital models, digital shadows, and full digital twins. Digital models operate offline with manual updates. Digital shadows receive real-time data but do not influence physical systems. True digital twins, by contrast, feature bidirectional data flows, enabling them not only to observe but also to recommend or execute control actions. The authors note that many projects labeled as digital twins still fall into the first two categories, creating confusion and overstated claims in both academic and industrial settings.

Despite this ambiguity, the overall trend is clear. The most advanced implementations are moving toward fully integrated digital twins that combine physics-based modeling with machine learning and artificial intelligence. These hybrid systems allow operators to predict failures, optimize performance, and simulate future scenarios with greater accuracy than either approach alone.

Clean energy systems under pressure

Rapid deployment of renewable energy has reduced emissions but introduced new operational challenges. Solar and wind power are inherently intermittent. Distributed generation complicates grid management. Maintenance costs remain high, particularly for offshore or remote installations. At the same time, energy systems must adapt to tighter climate targets, stricter regulations, and increasing electrification of transport and heating.

Digital twins are presented as a response to these pressures rather than a standalone solution. In hydrogen energy systems, for example, digital twins are used to model electrolyzers, monitor degradation, and assess safety risks across production, storage, and distribution. These capabilities are critical as hydrogen is increasingly positioned as a long-term energy carrier for sectors that are difficult to electrify.

In geothermal systems, digital twins address uncertainty beneath the surface, where incomplete data and complex geology raise financial and technical risks. By integrating seismic, thermal, and flow data, digital twins help operators predict reservoir behavior and manage long-term performance. In bioenergy, the focus shifts to process optimization and supply chain management, with digital twins used to stabilize biochemical reactions, improve yields, and track sustainability metrics across circular economy models.

Nuclear energy presents a different use case. Here, digital twins support safety-critical operations, real-time monitoring, and remote inspection in high-risk environments. The review highlights growing interest in using digital twins to enable autonomous or semi-autonomous control systems, particularly for small modular reactors and microreactors intended for localized, low-carbon power supply.

Across all these domains, the authors emphasize that digital twins are most effective when applied at the system level rather than as isolated tools. Their value increases when they integrate multiple assets, data sources, and decision layers, allowing operators to understand interactions across generation, storage, and distribution.

Cities, buildings, and the grid converge

As energy systems decentralize, buildings are no longer passive consumers. Rooftop solar, battery storage, electric vehicles, and smart appliances turn homes and commercial properties into active participants in energy markets.

Managing this complexity requires new forms of coordination. The review shows that digital twins are increasingly used as the control backbone for intelligent buildings and districts. By integrating data from sensors, energy management systems, and local generation assets, digital twins enable predictive control of heating, cooling, storage, and demand response. This allows buildings to shift energy use in response to grid conditions, price signals, and renewable availability.

At the district level, digital twins act as system-of-systems platforms. They coordinate multiple buildings, shared storage, and local generation within microgrids. In some cases, they also support peer-to-peer energy trading, allowing surplus power to be exchanged locally rather than exported to the wider grid. The review reports significant improvements in energy efficiency, peak load reduction, and cost savings in districts where digital twin-enabled control strategies are deployed.

These capabilities align closely with international policy goals. The authors link digital twin adoption to the United Nations Sustainable Development Goals, particularly those focused on clean energy and sustainable cities. By enabling higher penetration of renewables while maintaining stability and affordability, digital twins help bridge the gap between climate ambition and operational reality.

However, the review also sheds light on the limits of current approaches. Building-level digital twins face unique challenges due to human behavior. Occupant preferences, routines, and comfort needs introduce uncertainty that is difficult to model. The authors identify the “human-in-the-loop” problem as one of the most persistent barriers to reliable digital twin deployment in urban energy systems.

Barriers to scale and what comes next

Despite their growing prominence, digital twins are far from a mature or standardized technology in clean energy. The review identifies several structural barriers that could slow adoption if left unaddressed.

Interoperability stands out as the most significant technical challenge. Energy systems rely on a patchwork of proprietary platforms, data formats, and communication protocols. Without shared standards and open architectures, digital twins risk becoming siloed tools rather than integrated system enablers. The authors argue that progress depends on developing common data ontologies and modular frameworks that allow different systems to communicate seamlessly.

Cybersecurity and data governance present equally serious concerns. Digital twins increase connectivity between physical assets and digital platforms, expanding the potential attack surface for cyber threats. In building-to-grid applications, this risk extends to sensitive data about occupant behavior and energy use. The review calls for stronger security architectures and clearer ethical guidelines to ensure trust and regulatory compliance.

Economic feasibility is another unresolved issue. High-fidelity digital twins require substantial investment in sensors, data infrastructure, and computational resources. While large utilities and infrastructure operators may absorb these costs, smaller organizations and municipalities may struggle. The authors note a lack of robust life-cycle cost analyses and business models that clearly demonstrate return on investment across different scales.

Looking ahead, the review points to several emerging directions. Hybrid digital twins that combine physics-based models with data-driven learning are gaining momentum, offering a balance between interpretability and adaptability. Edge computing and federated learning may help reduce data transfer burdens and improve scalability. Integration with technologies such as blockchain could support decentralized energy markets, while immersive interfaces could enhance operator training and system oversight.

The authors stress that realizing these opportunities will require coordinated effort across research, industry, and policy. Digital twins must be treated not as isolated software tools but as critical infrastructure for the energy transition.