Wind turbine operators are always looking for ways to increase efficiency and decrease costs. One way to do this is by investing in a predictive maintenance AI solution.
Predictive maintenance allows operators to identify and fix potential issues before they cause downtime, i.e. time when the production is stopped for making repairs or upgrades. This results in significant cost savings and increased efficiency. Here we will outline how a wind turbine operator can quickly assess the return on investment (ROI) on investing in a predictive maintenance AI solution.
First, let us look at the cost savings attainable. A predictive maintenance solution allows operators to work on two very important and simultaneous goals:
Reduce the number of downtime incidents by identifying and fixing potential issues before they cause downtime.
Improve equipment lifespan by identifying and fixing potential issues before they cause damage to the equipment.
According to a study by the National Renewable Energy Laboratory (NREL), predictive maintenance can reduce downtime by as much as 30%, and extend the lifespan of wind turbine components by as much as 20%. These are industry level estimates from almost a decade ago. The current turnkey enterprise AI solutions are capable of showing these cost savings within weeks of implementation.
Improved productivity and output
Second, let us look at the increased efficiency and productivity attainable. Predictive maintenance allows operators to ensure that their turbines are running at optimal capacity, resulting in increased energy production and revenue. Again, a decade old estimate from the National Renewable Energy Laboratory is that the use of predictive maintenance can increase energy production by as much as 3%. In the context of maintaining uninterrupted operations, cutting down on unscheduled maintenance or avoiding catastrophic failures, this improvement is generally good. Also, it is not the percentage gain in productivity as much as the absolute gain in what the additional productivity leads to in terms of revenue opportunity.
Now, let us make a simple ROI calculation:
- To calculate the ROI of a predictive maintenance AI solution, operators can use the following formula: ROI = (Benefits – Costs) / Costs
- Assume that the cost of the predictive maintenance solution and any associated implementation costs is $50,000
- Benefits include cost savings from reduced downtime, improved equipment lifespan, increased efficiency and productivity
- Using the above formula, if an operator was able to save $100,000 per year in downtime and equipment replacement costs, then the ROI would be: ($100,000 – $50,000) / $50,000 = 100%
We can make this computation slightly more nuanced. Let us take into account that there is a wide range of turbines on a farm, and that their capacity ratings and cost of maintenance may vary. Even in such cases, there is still a simple way to measure the cost of overall maintenance. The International Energy Agency reported that the cost of operations and maintenance per MWh of energy produced by wind turbines is approximately in the range of $9-31, depending on the age of the turbine and any preventive maintenance in place. Let us say it is an average of $20 per MWh. So, if your farm produces 10 GWh of energy, then the cost of maintenance is on an average $200,000.
A 30% reduction in cost of maintenance as estimated by NREL implies that the savings will be $60,000 at the very least. This is till a significantly positive ROI for wind farm owners and operators, without even accounting for improved lifespan of the equipment.
Investing in a predictive maintenance AI solution is clearly a prudent decision for wind turbine owners and operators. By assessing the ROI and calculating the cost savings from reduced downtime, improved equipment lifespan, and increased efficiency, operators can make an informed decision on whether to invest in a predictive maintenance solution. Additionally, implementing a predictive maintenance AI solution can help to optimize the turbine performance and increase the turbine availability which will positively impact the ROI.