A physics-based Digital Twin models an asset’s behavior by using analytical formulas based on physical phenomena—like tools like PVSYST. Physics-based Digital Twins are often used in the design phase for estimating expected future behaviour. However, it also brings massive value in the operational phase of assets. But why should you consider adopting this technology, and how does it solve your key challenges?
What is a physics-based Digital Twin & how does it work?
At its core, a Digital Twin is a digital model that simulates the behaviour of a physical asset. The physics-based part refers to how this simulation is created: by using first-principle physics—the fundamental laws governing how an asset operates under various conditions. This means the Digital Twin can calculate how the asset should perform in real-world scenarios, based on its design and operational parameters.
During the operational phase, a Digital Twin uses real-time operating conditions as inputs to simulate expected system behaviour. These conditions are typically based on measurements like irradiation, ambient temperature and curtailment signals. The high-resolution simulation output can then be compared against actual measured time series data to assess performance. Any deviation between the two is flagged for further analysis, allowing for early detection of faults and inefficiencies.
Common challenges without a physics-based Digital Twin
- Reactive maintenance: Without a physics-based Digital Twin, O&M teams often find themselves doing reactive maintenance, addressing issues only after they have escalated. This leads to unplanned downtime, increased repair costs and reduced asset performance.
- Alarm overload without aggregation: When O&M teams are overwhelmed with unfiltered and non-aggregated alarms, it can result in “alarm fatigue,” where critical issues may be overlooked or mismanaged due to the sheer volume of alerts. This not only increases the time taken to diagnose and address faults but can also lead to human error. Over time, this inefficiency increases operational costs and risks unplanned downtime, as smaller issues may escalate if not addressed in a timely manner.
- Limited fault detection: Traditional models often fail to identify the root causes of performance losses, resulting in slower resolutions and misdiagnosed problems.
- Inaccurate benchmarking: Relying on machine-learning models or historical data for benchmarking can result in inaccurate performance predictions, especially when historical data includes underperformance. This affects long-term energy production forecasting, impacting revenue and investor confidence.
- Performance mismatch during construction: EPC teams face challenges in ensuring that an asset performs as designed during the construction phase. Without real-time verification, issues often arise, leading to rework, delays and potential downtime.
- Risk of liquidated damages: Failing to meet project timelines or performance guarantees during construction can lead to liquidated damages for EPCs. Without accurate real-time simulations, the risk of these penalties increases, threatening project profitability.
Why physics-based Digital Twins offer superior reliability
Physics-based Digital Twins provide more reliable insights because they don’t depend on historical data to make predictions. Instead, they use the physical laws governing an asset’s behavior, offering immediate accuracy and faster fault detection.
How does this enhance reliability?
- Clean reference from day one: Machine-learning models require a training period, during which underperformance issues become part of the model. In contrast, a physics-based Digital Twin approach assumes an ideal operational reference from day one, enabling more accurate detection of deviations.
- Failure root cause classification precision: Physics-based Digital Twin provides greater precision in identifying the root causes of failures, as they maintain a clean baseline and do not integrate existing issues into the model, unlike machine-learning approaches.
- Business model alignment: Since physics-based Digital Twins can be used from the engineering phase, they allow for more accurate business plan comparisons, improving financial forecasting and asset performance planning.
The benefits of a physics-based Digital Twin
- Monitoring and control room efficiency: With a physics-based Digital Twin, alarms are triggered as soon as performance deviates from the expected behaviour. This allows O&M & IPP teams to act fast and prevent prolonged downtime.
By intelligently filtering and consolidating alarms from various system components, the Digital Twin reduces the overall number of alerts, minimising alarm fatigue for O&M teams. This allows operators to focus on the most critical issues, speeding up fault identification and resolution.
- Root cause analysis: A Digital Twin helps O&M & IPP teams identify the exact cause of performance issues, rather than just symptoms, allowing for faster and more effective repairs.
- Long-term performance optimisation: By using the Digital Twin to forecast performance accurately, IPPs can optimise energy production over the asset’s lifetime, ensuring stable revenues and maintaining investor trust.
- Reduced operational risk : The twin allows IPPs to identify risks early, enabling them to mitigate issues before they affect financial performance.
- On-time project delivery: The Digital Twin ensures that asset performance aligns with design specifications during construction, helping EPCs meet timelines and avoid costly delays.
- Minimise liquidated damages: By providing real-time validation of performance targets, the Digital Twin reduces the likelihood of performance shortfalls and construction delays that could trigger liquidated damages.
Why building a physics-based Digital Twin in-house is complex
- Deep domain expertise required: Developing an accurate Digital Twin involves expertise in both the physical behavior of assets (like wind turbines, solar panels, etc.) and advanced modeling techniques. It requires a detailed understanding of physics, engineering and system interactions, which takes years to acquire.
- Integration of multidisciplinary data: A physics-based Digital Twin must integrate data from various sources—sensors, historical performance, environmental conditions, and more. Aligning these diverse datasets for real-time modeling and predictions requires sophisticated algorithms and extensive testing to ensure accuracy.
- High-precision simulations: To provide value, the Digital Twin needs to simulate real-world conditions and asset performance with precision. Developing and validating these models requires advanced computational techniques and substantial real-world data, which is both time-consuming and resource-intensive.
- Continuous updating and adaptation: Unlike static models, a Digital Twin must adapt continuously to changing conditions and operational data. Keeping it updated with real-time data streams while ensuring high performance and reliability presents significant technical challenges in data management and machine learning.
- Scalability and performance: As assets scale in number and complexity, the Digital Twin must handle large-scale simulations without losing accuracy or performance. Building a system that can scale effectively while maintaining real-time insights and operational value demands advanced software architecture and robust computational resources.
These complexities highlight why building a physics-based Digital Twin is a highly-specialised task best entrusted to experienced providers like 3E, who bring 100 man-years of expertise—the culmination of 25 years of dedicated work.
Conclusion
By adopting a physics-based Digital Twin, O&M teams, IPPs and EPC companies can optimise performance, mitigate risks and improve overall efficiency. Whether you are managing daily operations, overseeing long-term production or building a new asset, the Digital Twin provides actionable insights that help you stay ahead of challenges and maximise the value of your renewable assets.
