Wind Energy: The Need for a Data Strategy

Key themes

Wind will be the leading low-carbon energy source that can help the UK and EU reach their net-zero targets. Offshore wind is becoming more popular, it is projected with the highest growth over the next decade in many EU countries; offshore wind blows more consistently and has a higher generating potential. The drawback is that it faces reliability and maintenance challenges. These challenges are costly and determine the economic lifespan of a wind turbine. With 50% of current cumulative installed wind capacity reaching the end of their lifecycle by 2030, addressing maintenance and reliability challenges can enable wind turbines to operate for longer and reduce overall project costs. To do this, the wind industry needs to mature its data strategy in order to unlock the value that is trapped in its data and make wind energy more commercially competitive.

Market Trends: Modern Renewables

Q2 of 2020 observed renewable energy breaking records in terms of its contribution to the UK’s electricity mix, at some points providing more than 44% of the UK’s power. Part of this can be linked to COVID-19 decreasing demand and shifting electricity consumption from business to residential locations. The UK and the EU have set ambitious targets to increase renewable energy’s contribution to the electricity mix by 2030. Modern renewable energy sources such as solar and wind are outpacing the growth of conventional renewables such as hydropower. Solar and wind are believed to be complementary energy sources, where one source smooths out the generation profile of the other – in the UK and many parts across the globe, electricity generated from wind is higher during colder months and relatively lower during to warmer months which is the opposite trend for solar.

With many countries shifting to low-carbon energies, solar and wind capacity will both grow significantly, wind projected with the highest growth in the next decade primarily from the commissioning of new windfarms in the EU, North America, and China. China dominates this market and will continue to dominate the market through to 2030 and 2040 with the EU and US in second and third place. The wind sector currently generates the majority of its wind energy from onshore wind, however the industry is seeing a surge in offshore installations which will further diversify this market. Its growing popularity is linked to its generation potential; offshore wind blows faster and more consistently throughout the year compared to onshore wind. The offshore wind market is led by the UK and aims to more than double its total wind capacity to 40GW by 2030.

The Path to Net-Zero, Can Wind Help Meet Targets?

Wind energy will make a significant contribution to the energy mix with an estimated active capacity of 2TW by 2030 and is projected to increase to 7TW by 2050. To realise growth targets, continuous commissioning of wind turbines is required to offset the number of mature assets reaching the end of their lifecycle. Considering a lifetime of 20-25 years, approximately 50% of current cumulative installed capacity will reach the end of its operational life by 2030 in the EU. Extending the operational life of turbines can make a significant contribution in achieving these targets earlier and help alleviate the stresses associated with high maintenance costs and ending government subsidies.

To extend turbine lifecycles operators must reduce costs…

A balance in cost reductions and technological improvements have made the cost of electricity from wind commercially competitive. This is particularly true for mature markets such as the UK and some parts of Europe that benefit from a developed supply chain. Capital Expenditure (CAPEX) costs have seen steady declines over the years while Operational Expenditure (OPEX) costs, on the other hand, have seen increases due to larger turbines (2+ MW) becoming increasingly popular. Although popular, larger turbines experience more frequent breakdowns due to the higher stresses on the gearbox, rotors and drivetrain components. This is particularly devastating for offshore installations which favour larger turbines. Data reveals that the rate of breakdowns in large offshore turbines is significant from as early at 2 years and with almost 80% of offshore turbines experience at least one failure before 7 years of operation. The same data shows smaller turbines (0.5 - 1 MW) are the most reliable.

A significant portion of breakdowns in offshore wind can be linked to extreme weather conditions. The lack of suitable offshore sites with shallow or medium depths lead installations further out to deeper seas that are prone to harsher conditions. Their foundations are susceptible to corrosion and turbine blades tips can experience erosion over time. This is further exacerbated by accessibility and logistical challenges which can make up to 90% of cumulative maintenance costs.

The costs associated with operating and maintenance of offshore wind farms can contribute 30-40% of total costs. This puts offshore wind at a particular disadvantage; similar to offshore oil & gas platforms, OPEX costs determine the economic life of a wind turbine - lower OPEX costs not only increase the return-on-Investment (RoI) but enable assets to operate for longer, extending their lifespan.

Reducing OPEX costs means addressing maintenance & reliability…

Reducing Operations & Maintenance (O&M) costs means addressing reliability and reducing the number of maintenance visits to offshore windfarms. The industry is exploring data-driven solutions to help generate intelligent insights and improve their decision-making capabilities. 

Maintenance

Unscheduled maintenance poses a significant challenge for offshore wind; the time to inspect, diagnose and order replacement parts (which may not be locally available) means a turbine can be offline for several weeks. While unscheduled maintenance may not always be avoidable, the industry has taken steps to adopt preventative and predictive maintenance programs to help with condition-monitoring and decision-making. Preventative and predictive maintenance programs can cost up to 25% and 47% less than reactive maintenance programs, respectively. Real-time condition monitoring using IoT technologies can generate critical insights for maintenance programs that help with:

-         Predicting Failures

-         Reducing Non-Productive Time [NPT]

-         Optimising Maintenance Scheduling

Reliability

Wind turbines consist of many moving parts such as the gearbox, rotors, and other drivetrain components. Reliability of turbines are determined by historic failures, downtime and maintenance. The lack of maturity in data strategies within this sector limits further progress that can help drive down O&M costs and optimise performance.

The vast number of turbine models, technological improvements and operating conditions means that it is difficult to decide which data to collect, store and analyse. Now, industry leaders are working more closely with data scientists and design engineers to develop a ‘best-practice guide’ to improve data governance. This is one of the first crucial steps in addressing reliability challenges. The applications of analytics and machine learning can help analyse data from failure analysis and manufacturing quality to uncover critical insights into design reliability.

Unlocking Value from Data Requires a Data Strategy

While offshore turbines are larger and generate more electricity, they are generally more expensive, experiencing higher CAPEX and OPEX costs throughout their lifetime. From an economic perspective offshore wind is currently not a viable long-term solution especially without government subsidies and support schemes, especially in non-mature markets. The key here is to address CAPEX and OPEX. With OPEX being the larger of the two, optimising maintenance programs will make the single largest contribution in driving down OPEX costs.

Operators are exploring data-driven solutions like predictive maintenance to reduce NPT and help predict failures before they occur. A positive example of predictive maintenance in the wind sector is Tessella’s collaboration with a large wind operator to reduce the costs of its maintenance programs. Data scientists identified that the gearbox oil was operationally performant for up to 7 years, whereas the operator replaced theirs every 5 years. Extending the replacement interval alone enabled the operator to reduce maintenance costs by 10%, saving them millions of dollars each year.

The benefits of data science applications are not exclusive to maintenance programs. For example, a large European utility company implemented a machine learning solution to replace offshore measuring towers. The client was able to eliminate O&M costs associated with measuring towers and generate more accurate power-curve models and power forecasts.

Other industry players such as Siemens and GE are also exploring data-driven solutions, particularly in digital twin technologies and their applications in asset management, training, and health & safety. BEPA and digital twin developer Akselos recently partnered to combine Reduced Basis Finite Element Analysis with digital twin technologies in aims to reduce offshore structural costs.

Collaboration is key…

The underlying factor to delivering value from data-driven solutions is developing a comprehensive data strategy. The implementation of data science techniques like analytics, machine learning and AI depend on good and reliable data. The wind sector is a relatively new market and is challenged with an underdeveloped data strategy and management framework.

Collaboration between industry players can help accelerate this process and identify best data-practices that can drive further value in areas like condition-monitoring, performance optimisation and improving reliability of wind turbines. A positive example can be observed in the UK where the Office of Gas and Electricity Markets (OfGEM) is bringing together industry organisations to host a collaborative environment that addresses key challenges associated with siloes and underdeveloped data strategy.

The proprietary nature of operations and competition between companies means that data is not always shared, this is a challenge that hinders progression. Many industry players are turning to data consultancies or developing their data-strategies in-house. This is creating siloes and hindering progression in an industry that could benefit significantly from modern data strategy…

Summary

·      Wind is a favourable candidate to help meet net-zero targets. Onshore wind dominates the market, but offshore wind is projected with the highest growth.

·      Offshore wind has potential for high RoI with stronger and consistent wind speeds but faces reliability and maintenance challenges.

·      50% of current cumulative installed wind capacity in the EU will reach the end of its life by 2030 – many of these turbines being decommissioned for economic reasons.

·      Addressing reliability and maintenance is key to reduce OPEX costs and to extend the economic life of offshore turbines.

·      To improve maintenance programs, wider adoption of data-driven solutions is required to help provide greater insights and drive the right decisions. Predictive maintenance programs have proven to generate useful insights that reduce costs and NPT.

·      Reliability of offshore wind is a challenge that is facing many industry players. The industry does not fully understand which data to collect and how to use which is hindering progression.

·      The wind sector is a relatively new market and needs a data strategy to help operators understand the value of data and how to extract it.

·      Data is an integral part of improving asset management, technology development, health & safety, training, and the adoption of smart connected systems like digital twin.

·      Industry-wide collaboration can help accelerate the development of a data strategy. Positive examples can be seen in the UK’s utilities sector where OfGEM is leading data transparency initiatives.

·      Industry leaders are exploring data-driven solutions to tackle real challenges. Some are developing their own data strategies creating siloes in the industry.

·      The competitive nature of the industry makes data sharing and collaboration difficult; this will continue to hinder progress. 

Acknowledgements

I would like to thank Danica V.  Greetham (Analytics Consultant, Tessella) for her support and guidance.

References:

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