Digital Twin in Utilities: What It Is and Why It Matters for Renewable Energy

As utilities transition toward cleaner, decentralised energy systems, data-driven decision-making becomes not just a competitive advantage, but a necessity. In this context, one technology stands out for its ability to bridge the gap between physical assets and intelligent analytics: the digital twin.

From wind farms to solar PV installations, digital twins are transforming how renewable energy producers operate and optimise their portfolios. In this blog post, we’ll explore what a digital twin is, its applications in the renewable energy industry, the difference between physics-based and data-driven approaches, and how PowerFit, Enlitia’s virtual twin algorithm, empowers asset managers to uncover and solve underperformance issues without the need for complex physical modelling.

What Is a Digital Twin?

A digital twin is a virtual representation of a physical asset, system, or process that is continuously updated using real-world data. Think of it as a live, dynamic model that mirrors how an asset behaves, performs, and evolves over time.

In practical terms, this means operators can:

Simulate how the asset would respond to different conditions
Detect deviations from expected performance
Predict future behaviour based on historical and real-time data

Digital twins are widely used in industries like aerospace, manufacturing, and automotive — and they are increasingly becoming essential in utilities and energy production, particularly in wind and solar power.

Applications of Digital Twins in the Renewable Energy Industry

In the renewable energy sector, digital twins offer asset managers a smarter, more accurate way to monitor and optimise performance by turning raw operational data into actionable insights.

For Wind and Solar Operators, Digital Twins Enable:

Performance Benchmarking: Continuously compare actual output vs. expected output under given conditions (wind speed, irradiance, etc.)
Root Cause Analysis: Pinpoint causes of underperformance — whether it's due to curtailments, equipment inefficiencies, or environmental impacts like soiling and shading
Predictive Maintenance: Identify degradation trends in components and anticipate failures before they cause downtime
Scenario Simulation: Model how different operational strategies or environmental conditions would affect energy output or revenue
Portfolio Optimisation: Use asset-specific behavioural insights to adjust bidding, maintenance, and investment strategies across an entire fleet

In short, digital twins help turn data into diagnosis and strategy, empowering energy producers to maximise efficiency, reduce losses, and improve long-term asset value.

Types of Digital Twins: Physics-Based vs. Data-Driven

There are two main types of digital twins used in the energy industry, and understanding the difference is key to choosing the right approach for each use case.

Physics-Based Digital Twins

These rely on engineering models and physical equations to simulate asset behaviour. For example, a wind turbine twin might model fluid dynamics to estimate turbine output under different wind speeds or simulate drivetrain fatigue over time.

Strengths:

Highly accurate under controlled conditions
Based on known physical laws and mechanical relationships

Limitations:

Complex and time-consuming to develop
Require detailed technical data (e.g., turbine design parameters)
Struggle to adapt to real-world irregularities (e.g., sensor errors, undocumented curtailments)

Data-Driven Digital Twins

These use machine learning algorithms and historical data to model asset behaviour. They don’t require detailed physics or component design, instead, they learn how an asset should behave based on patterns in real-world operational data.

Strengths:

Fast to deploy, especially across diverse asset types
More flexible in handling incomplete or noisy data
Ideal for identifying subtle patterns and root causes of underperformance

Limitations:

Heavily dependent on data quality and completeness
May lack physical interpretability (though explainability tools are improving)

Comparison table

#	Feature	Physics-Based Digital Twin	Data-Driven Digital Twin
1	Model foundation	Physical equations and mechanical design	Machine learning from historical data
2	Input requirements	Detailed technical specifications	SCADA data, weather data, production history
3	Flexibility across asset types	Low	High
4	Ease of deployment	Complex and time-intensive	Fast and scalable
5	Adaptability to real-world noise	Limited	High
6	Ideal use case	Engineering simulations, fatigue analysis	Performance benchmarking, anomaly detection

Differences in Digital Twins for Wind vs. Solar PV

Digital twin applications and challenges can vary depending on the renewable energy technology. These differences highlight that digital twin strategies must be adapted to the unique characteristics of each technology.

In wind energy, the focus is often on capturing mechanical behaviour and reacting to dynamic environmental conditions, which adds complexity to modelling. In contrast, solar PV systems require digital twins that can accurately account for static but highly localised factors, such as shading patterns and soiling impact.

Understanding these distinctions ensures that asset managers apply the right modelling approach to maximise diagnostic accuracy and operational efficiency across their renewable portfolio.

Wind Energy

Greater mechanical complexity: More components (blades, gearboxes, yaw systems) introduce more failure points and simulation variables.
Stronger interaction with environmental data: Performance depends on wind direction, turbulence, and shear profiles, which are harder to model accurately.
Frequent curtailments: Grid-induced constraints must be accounted for in performance models to avoid misinterpreting availability.

Solar PV

Less mechanical complexity: Fewer moving parts, but performance is more sensitive to external factors like irradiance, shading, and soiling.
High dependence on local conditions: Panel tilt, orientation, and nearby obstructions heavily affect performance modelling.
Data granularity: SCADA systems in solar are often more limited or aggregated, making high-resolution performance modelling more challenging.

#	Feature / Challenge	Wind Energy	Solar PV
1	Mechanical Complexity	High – multiple moving components (blades, gearbox, yaw)	Low – minimal moving parts
2	Environmental Sensitivity	Wind direction, turbulence, and shear affect output	Irradiance, shading, and soiling heavily influence output
3	Curtailments and Grid Interactions	Frequent – often requires curtailment modelling	Less frequent, but may occur in congested grids
4	Performance Drivers	Aerodynamic efficiency, pitch/yaw systems, component wear	Soiling, shading, panel orientation, temperature
5	SCADA Data Resolution	Often granular and component-specific	Often aggregated or limited in detail
6	Modelling Complexity for Digital Twin	Higher – requires modelling multiple dynamic systems	Moderate – more influenced by external static factors

Introducing PowerFit: Enlitia’s Virtual Digital Twin

To make the power of digital twins accessible, Enlitia developed PowerFit: a virtual digital twin algorithm that learns the expected behaviour of each renewable energy asset using historical SCADA data, environmental inputs, and AI-powered performance modelling.

Unlike physics-based models, PowerFit is entirely data-driven, allowing it to be quickly deployed across diverse portfolios, regardless of asset age, technology, or manufacturer.

With PowerFit, asset managers can:

Detect underperformance by comparing actual output with expected performance under the same conditions
Identify root causes such as curtailments, equipment degradation, or soiling — without needing to build a physics model for each component
Adapt to changing conditions automatically as new data is ingested
Benchmark performance at the turbine, inverter, or site level to prioritise interventions and optimise asset value

Because PowerFit doesn’t rely on physical specifications, it can work even when sensor data is incomplete, or equipment documentation is limited, making it ideal for modern, complex portfolios that mix technologies, commissioning years, and geographies.

Final Thoughts

As the renewable energy sector grows more data-rich and performance-driven, digital twins are becoming essential tools for asset optimisation — and for utilities, their impact is increasingly strategic.

Whether you're managing wind turbines or solar PV systems, the ability to replicate, monitor, and simulate asset behaviour through a virtual twin unlocks new levels of efficiency, transparency, and control.

With PowerFit, Enlitia delivers all the benefits of a digital twin — without the complexity of physics-based modelling — helping asset managers get to the root of underperformance, benchmark efficiency, and maximise return on every megawatt.

Ready to see PowerFit in action? Schedule a Q&A session with our team and learn how digital twin technology can unlock deeper insights across your renewable portfolio.