By Gareth Brown, CEO, Clir Renewables

In February 2021, Texas faced a disastrous power failure as a result of an unexpectedly severe winter storm. Natural gas wells froze, transmission substations went offline, and the grid operator was forced to initiate rolling blackouts, leaving millions of homes and businesses without power for days. This extreme cold combined with humidity caused a number of wind turbines to visibly freeze over.

However, while 4 GW of wind energy did go offline, the majority of the state’s wind turbines stayed online, helping to mitigate the disaster with consistent, if low, power production.

The immediate reaction from regulators has been to call for winterization of power assets across Texas to prevent a repeat of this disaster. Many wind farm owners are considering whether it is worth following suit to increase winterization of their turbines to extend operational capacity and potentially take advantage of higher price production periods.

Here are the three key steps owners must consider in order to optimize their wind farms and give them the best opportunity to supply low-cost, clean energy despite extreme weather events.

  1. Predicting performance through cold weather

Icing can impact turbine performance in a number of ways. In the first instance, ice can build up on the turbine and directly reduce aerodynamic performance. As the ice and load on the blade builds to critical levels, the turbines are shut down to prevent mechanical damage.

In the week of the Texas disaster, expected output was already below average due to the low winds forecast – this meant that any additional generation loss due to the cold was minimized. However, in order to plan ahead for future extreme weather and prevent lost output during periods of high power prices, wind farm owners need to monitor and analyze asset performance in the context of both expected resource and environmental conditions, as well as turbine health. 

In order to model how turbines will perform through cold weather, the first step is to build a baseline for turbine performance in “normal” conditions. This enables wind farm owners to detect when icing is occurring, the money it is has cost them to date, and ultimately allows them to forecast future year performance and variability.

Advanced data analytics, such as the use of artificial intelligence and machine learning, are key to providing this full understanding of turbine performance and modeling “worst-case scenarios” based on all available data.

  1. Smart control to prevent ice buildup

Ice can cause long-term underperformance in cold conditions. A combination of freezing temperatures and humidity – for example, when a blade tip moves through freezing clouds – results in the formation of rime ice on the blade surface. This ice then propagates, reducing the aerodynamic performance of the turbine. The weight of ice on a blade triggers turbine shutdown – both as a safety measure and to protect the turbine from being overburdened. The turbine will stay offline until the ice melts, which can take weeks if low temperatures persist.

Preventing ice buildup at some sites can be as simple as powering down the turbine during periods of cloud coverage to prevent continued exposure of the blades to cold and humidity, as a spinning blade will accrue ice much faster than a stationary blade. Although this causes an immediate drop in power production, the turbine can then be powered up again once conditions clear and temperatures rise, while a more heavily iced-over turbine would stay offline for extended periods. This strategy can be incredibly useful for sites that experience long durations of cold but low frequency of icing.

  1. Retrofitting – when it makes sense

Anti-icing and de-icing systems have been developed to speed up the removal of ice from a turbine. Most commonly, these take the form of heaters in the blade that prevent or remove ice buildup, enabling turbines to operate in temperatures below freezing. Water-resistant coatings can also be applied to blades, preventing the adhesion of precipitation and the formation of ice crystals.

These retrofits are commonplace in northern Europe and North America as the climate is regularly cold and humid. Owners can be sure that the additional cost of installing these systems will pay off since they expect conditions to drop below freezing every year.

However, the relative frequency of extreme cold snaps in states like Texas has to be considered before owners invest in anti-icing systems, as this technology can raise the capex of projects by 6%. If an extreme cold weather event only takes place once in 25 years, retrofitting anti-icing systems could raise the cost of development – and ultimately, the levelized cost of energy for that project – without significant return on investment.

Additionally, anti-icing systems themselves need to be maintained in order to prevent mechanical or electrical failure when they are most needed. In northern regions where these systems are often in use, it is easy to detect when an anti-icing system has broken down and fix it immediately ahead of the next cold spell. However, if these systems are rarely used, a technical fault can be missed for years, preventing the technology from coming online when it is most needed.

In order to validate investment in anti- and de-icing systems, owners have to be careful to model wind farm performance through expected cold weather but also consider the likelihood that events such as the Texas winter storm will repeat in the lifetime of the project.

Keeping costs low has enabled Texas to lead the world in installing wind capacity – and any approach to mitigate extreme weather events has to ensure that additional costs will ultimately pay off.


Gareth Brown is CEO of Clir Renewables, a renewable energy AI software company. Gareth has over a decade of experience leading identification, development, construction, financing and operation of renewable energy assets for a world leading renewable energy technical consultancy. He is an entrepreneur, chartered engineer with the IMechE, and has degrees in mathematics and mechanical engineering.


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