Wind turbine icing: Causes, risks, and forecasts for short-term power traders

As temperatures drop, wind power production and electricity demand rise, promising attractive revenues for wind power producers and short-term traders. Yet winter also comes with a critical challenge: wind turbine icing. This phenomenon can quickly negate the season’s benefits by putting both turbine safety and market performance at risk.

In Northern and Western European markets with high shares of weather-dependent renewables, icing events, though infrequent, are a leading cause of overall imbalance. For instance, Nordic short-term power traders are likely aware of the increase in icing disruptions. Summing up the overall impact of icing, a forecasting analyst from a renewable energy company we work with said:

“For our onshore wind portfolio, the most expensive ten days caused 50% of the yearly imbalance costs. Five of those days are due to icing power losses.“

To help you step into winter prepared, this article explores the complexities of icing, from meteorological factors and turbine mechanics to mitigation strategies and advanced forecasting of icing events.

What causes icing on wind turbine blades?

Icing season typically occurs from November to March but can stretch as early as October and as late as April. For example, Germany has experienced icing events in late April.

Icing results from a combination of freezing temperatures (-2°C or lower) and moisture (rain or supercooled droplets). Droplets remain liquid in the atmosphere where they lack a nucleation point, i.e., a surface or particle that would trigger their freezing. As soon as they come into contact with surfaces – such as wind turbine blades – they rapidly freeze.

Meteorological conditions associated with icing in the Netherlands during the 2023-24 winter, with polar air coming from higher latitudes and probability of local precipitation. Source: ECMWF.

Depending on the turbine’s location and altitude, different combinations of temperature and moisture will create varieties or phases of icing:

In-cloud icing forms when supercooled cloud droplets reach turbine blades and freeze. It includes:

• Rime ice: A rough, frosty layer that forms at below-freezing temperatures (between -4°C and -12°C.), similar to the ice in the back of your freezer.
• Glaze ice: A smoother, heavier ice that forms during freezing rain or at temperatures just below 0°C to -4°C.

Precipitation icing occurs in freezing rain and wet snow, leading to wet snow accumulation or black ice.

Offshore wind farms are generally less affected because salt in the air lowers the freezing point, making it harder for ice to form. Still, a freezing sea remains problematic as drifting ice may reach turbine foundations.

Rising global temperatures from climate change don’t necessarily mean fewer icing events. More frequent storms and higher moisture levels may lead to more icing in certain regions.

What happens to a wind turbine during icing?

Wind turbine blades are designed in a wing shape to rotate primarily based on lift forces generated by the wind. When they operate in the freezing cold and humid conditions described above, the moisture in the air freezes onto the blades and starts to accumulate, a process known as ice accretion.

Any added mass from the ice alters the blade’s aerodynamics – its shape and weight – thus affecting the interaction with the wind. This adds mechanical strain on the rotor, causing the turbine to produce less electricity than if the blades were ice-free.

As wind speeds increase, the issue worsens. As rotors spin faster, the blades bend slightly under wind pressure. However, ice is rigid and doesn’t bend. Instead, it breaks off in large chunks, which can be dangerous for anything or anyone nearby. In severe cases, ice can unbalance the turbine, leading to damage or even a breakdown.

In the long term, repeated ice accretion can damage turbine components, increase maintenance costs, and reduce their lifespan due to the strain on internal mechanics. Beyond the operational impact, icing also has a considerable market impact, as the next section will show.

Iced wind turbines. Source: Barati-Boldaji, Reza & Komareji, Mohammad. (2017). Techniques of identifying icing and de-icing of wind turbines. Signal Processing and Renewable Energy. 8. 21-28.

What is the impact of icing on power markets?

The extra ice mass on the blades drastically reduces turbine efficiency. Even a light icing event can create enough surface roughness on turbine blades to significantly reduce efficiency. When turbines are coated with ice, their power production drops significantly.

Additionally, chunks of ice falling off turbines pose a serious safety hazard to personnel working on wind farms, visitors, or passers-by. To err on the side of caution, wind turbines are often just shut down, manually or automatically, for the entire icing period.

How much power is lost? A field study indicated up to 80% power loss during the icing event and over 20% loss of annual production.

Forecasted versus actual production during an icing event – the output of the turbine (blue) is much lower than forecasted (teal).

Furthermore, the sudden and widespread power production drops caused by icing – often across entire wind parks or even regions – can lead to considerable electricity shortages. To restore balance, grid operators must activate reserves, pushing imbalance prices up. The costs of these interventions are charged to the balancing responsible parties (BRPs). In these instances, the exact financial impact of icing on trading results depends on several factors, such as the availability of ancillary services to compensate for the power loss, the system’s residual load, or the behavior of other market players.

Overall, although widespread icing events affecting hundreds of turbines are rare, the resulting imbalance prices can be extreme. Consequently, short-term power traders can face immense revenue losses if their day-ahead power forecast does not account for icing. Based on customer insights, we learned that inaccurate icing forecasts can account for up to 25% of annual imbalance costs.

How to detect turbine icing?

To mitigate the impact of icing, most turbine operators offer additional sensors, coatings, or services to detect, prevent, or clear ice on blades, such as by heating rotor blades; one such example is an Ice Prevention System. Both fully automatic and human-monitored systems are available.

With only a minor risk of icing, a turbine operator might keep the turbine running, while with a severe risk, automatic systems may shut it down. Often, a human-in-the-loop decides when to resume operation.

While these systems help mitigate the performance and safety challenges of icing, they’re not ideal for short-term power traders, as they don’t forecast imbalance volume. Furthermore, on-blade sensor signals arrive after icing has occurred, and day-ahead bids have already been submitted. In some cases, asset managers will alert traders about a shutdown due to an icing event only if contractually obliged to do so.

Therefore, a robust day-ahead trading strategy should account for icing to minimize volumetric risk resulting from the power loss, ideally providing forecasts five days in advance. However, while such solutions are available in Nordic countries, they remain limited in Western Europe, where traders often rely on weather forecasts and personal experience to predict icing events.

Why are icing forecasts difficult to get right?

Forecasting icing is not commonly available because it is a challenging problem to solve. Three main factors contribute to this complexity.

Limitations of weather models

Weather conditions leading to icing are highly specific, localized, and rapidly changing. Most weather forecasts don’t capture these factors accurately, as they focus more on surface levels than on wind-turbine conditions. Moreover, while some weather models offer variables to automatically diagnose freezing rain, these aren’t always reliable; some models also raise many false alarms.

A perfect solution would require sensors and detailed modeling of turbine properties – aerodynamics, height, materials, energy density carried by the materials – as well as local conditions, which is prohibitively expensive.

Data scarcity

Because icing events are infrequent – only expected a few times each year during winter months – there’s not a lot of data covering their occurrence and resulting power losses at turbine or wind park levels.

These events also depend on turbine properties (is there blade heating installed?) and operational processes (are parks turned off preventatively to reduce ice build-up or for safety reasons?). Operational logs are often incomplete or inaccessible to forecasting desks, and historical uptime data usually does not differentiate between causes for lower capacity.

The variable duration of icing events

Finally, even if predicting icing was straightforward, it would not be sufficient to solve the problem for power producers or traders; they’d still need to know how long the icing event will last to determine when turbines can be on or at full capacity again. The length of an icing event covers the period of ice growth, retention, and melting or removal and can range anywhere from an hour to a day.

With all of the above accounted for, how do we know whether an icing forecast is actually valuable in a short-term trading operation?

How to evaluate an icing forecast?

An icing prediction is a binary signal; the event is either expected to occur or not. The matrix below provides a useful mathematical tool for evaluating this prediction. The formulas use the number of true/false positives or negatives to return three metrics, each providing insight into different aspects of a prediction model’s performance.

• Precision: How many of the things you predicted as “positive” (e.g., icing events) were correct?
• Recall: Out of all the actual positive cases, how many did you successfully predict?
• Accuracy: The overall correctness of your model, showing how often the predictions (positive or negative) were right.

Calculation of Precision, Recall, and Accuracy in the confusion matrix. Source: MA, Jun, et al. (2019). Analyzing the Leading Causes of Traffic Fatalities Using XGBoost and Grid-Based Analysis: A City Management Perspective.

Nevertheless, a well-performing icing model does not guarantee optimal short-term trading results. For trading decisions, a “good enough” icing forecast isn’t about being right on average; it’s about predicting high-stakes icing events in power markets. Large imbalance volumes during large price spreads are very costly, so having an accurate forecast precisely at these times is critical.

In other words, even if a forecast occasionally returns false positives, using it in a trading strategy is worth it as long as the cost of false positives is outweighed by the benefits of being right when it matters most. Thus, evaluating an icing forecast ultimately comes down to its value to the bottom line.

To explore the topic further, download our white paper on evaluating metering-point power forecasts.

How does Dexter forecast icing?

In October 2023, we launched an icing feature for our Wind Power Forecast, developed to support short-term trading decisions. This internal tool labels icing impact occurrence; it finds candidate periods for potential icing and displays them alongside everything else we know about the asset. It relies on over 500 TB of weather forecasts, turbine sensor data, park-level load measurements, and turbine-level weather parameters to deliver:

• (Probabilistic) icing forecasts;
• Impact forecasts with automated corrections for the expected power loss;
• Alerting to promote vigilance and risk management.

As mentioned, forecasting the impact of icing on short-term power markets remains too challenging for automation to fully address; thus, human intervention is still necessary. For more context on the human-machine balance, see this article on our human-in-the-loop approach to wind power forecasting. Furthermore, in the development of our icing feature, we also interviewed several operators to learn how they handle different icing situations.

What are the latest improvements?

Ahead of the 2026-2027 winter, we’ve made several major updates to our icing feature:

• Europe-wide coverage, including the Nordics;
• Ice accretion and ablation modeled over time, showing not just whether icing will occur but how long it will last;
• Full probability spreads per asset and per PTU, for a more nuanced view of icing risk;
• A range of impacts in kWh, from maximum to expected to no icing impact, alongside our point forecast;
• Clearer email alerts with redesigned visuals.

These updates come with w-NMAE improvements, meaning our customers can expect even better results this season.

Are you ready for icing season?

To summarize, icing on wind turbines occurs in colder climates between October and April because of freezing temperatures and moisture in the air. As ice accumulates on turbine blades, their aerodynamics change, affecting wind interaction and causing power production loss. The power loss shows up in short-term power markets as spikes in imbalance prices, driving up imbalance costs.

Icing events are difficult to predict because of limitations in weather models, limited data, and variable duration. Nevertheless, short-term wind power traders must incorporate icing forecasts into their strategy to minimize imbalance volumes during large price spreads.

At Dexter Energy, we know a successful trading cycle depends on making the right decision at the right market moment. Get in touch to ensure your strategy has a reliable power forecast to manage icing events this winter.