What is sub-optimal performance, and is it losing you revenue?

In life, sub-optimal performance is never a good thing. For elite Olympic athletes and wind turbines the same principles apply. If each aren’t reaching their full potential, even by a very small percentage, the results at the end of the day can be disappointing.

In the last few years our industry has come a long way with our availability aims. For a long time, the norm was to push for higher time-based availabilities, but those now seem set to be left behind in-favour of production-based availabilities, and even revenue-based availabilities. The key is to incentivise revenue, not just the hours that the turbine can operate. Essentially, there is a desire to make turbines work smarter rather than harder.

But it’s not just up to the operations and maintenance (O&M) contractors to maximise production. We’ve typically left it to them to ‘do the right thing’, for example, schedule maintenance during low wind periods, and increasingly we are incentivising this behaviour. However, good technical asset management is all about maximising project yield, some of which means minimising operating losses.

RES also has a history as an asset owner. That means our default mindset has been to optimise the asset value from day one of takeover. We saw that RES operated sites were performing better than industry standard in availability and sub-optimal performance losses and so sought to investigate this further. We commissioned a study with DNV-GL to independently establish this. The results showed that wind farms managed by RES perform1% better than industry standard.

The standard assumption is that sub-optimal performance reduces yield by 0.5%, which, on a 40 MW wind farm could mean £40k in annual revenue.

But sub-optimal performance is not as well understood in the industry as downtime. Owners may be losing revenue without even realising even if the blades are turning

So what is sub-optimal performance and why is RES good at avoiding it? Sub-optimal performance losses occur whilst the turbine is running (not downtime) but not due to planned curtailments which would have been taken into account in the energy yield. Some examples are: a technician leaving an incorrect curtailment on a turbine after servicing, or a turbine automatically derating because of an overheated component.

• Turbine Downtime: loss of generation due to turbines not running
• BoP Downtime: loss of generation due to BoP not working e.g. transformer/HV outage.
• Sub-optimal performance: loss of generation due to turbines running, but not generating as much as they should e.g. accidental de-rating.

DNV GL has concluded that RES operated projects have much less sub-optimal performance than the industry standard benchmark”. In fact DNV GL can hardly detect any sub-optimal performance on our projects.

So why is RES industry leading in stopping these issues?

• Proactive power curve analysis – regular and intelligent analysis is fundamental.  Clients often come to us with a ”problem site” or a site that’s performing below budget and initiate an in-depth variance analysis. However, compound issues are hard to solve as it can be difficult to differentiate issues in the data, i.e. is that lost production due to incorrect curtailments or a power curve issue? A much better approach is to undertake regular analysis of site power curves.
• Understand normal behaviour – power curve analysis needs to be based on a robust understanding of the normal performance of the turbines. This means ditching the warranted power curves which refer to free-stream wind speed, not nacelle-measured wind speed which can quite different. It also means understanding the expected curtailment pattern as suboptimal performance can easily hide when there are complex curtailment power curves in play.
   

The industry is moving towards a new way of thinking about performance. Using downtime as a key indicator of performance is slowly melting away as we begin to embrace more sophisticated methods. At RES we have the expertise and experience to use data in ways which can detect and correct sub-optimal performance before it makes a major impact on the bottom line.

Author: Rosa Gindele, Head of Analysis and Optimisation

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