If you are involved in acquiring or investing in wind energy projects, it is crucial for you to estimate the Annual Energy Production (AEP) as accurately as possible in your business case – independently of the seller. The AEP is a key factor in the calculation of your Internal Rate of Return (IRR) - and small changes in AEP can lead to large changes in the project IRR. Furthermore, it is important for you to be able to quantify the uncertainties in the AEP estimation in order to understand the associated investment risks.
I’ll talk more about how AEP and the associated uncertainties can be estimated in my next article, but first it’s important to understand the relevant terms.
AEP and nameplate capacity
The Annual Energy Production (AEP) of a wind turbine is the total amount of electrical energy it produces over a year, measured in kilowatt hours or megawatt hours (kWh or MWh). As I described in my previous blog article, this can be estimated by multiplying the power for each wind speed from the power curve with the wind speed frequency distribution experienced by the wind turbine, and the number of hours in a year. The total AEP of a wind farm can then be estimated by adding the individual AEPs of each wind turbine.
The nameplate capacity of a wind turbine – otherwise known as the rated power - is the maximum power that its generator can produce, measured in kilowatts or megawatts (kW or MW). The rated wind speed is the lowest wind speed at which the rated power is reached. This can be seen on the typical power curve of a wind turbine shown below, where the rated power is 2 MW and the rated wind speed is 12 m/s. The total installed capacity of a wind farm is then calculated by adding together the nameplate capacities of all the installed wind turbines.
It is important to understand the difference between AEP in MWh and nameplate capacity in MW, as explained in my previous blog article.
Availability - time-based and production-based
The actual (net) AEP of a wind turbine is usually less than the theoretical (gross) AEP, due to several reasons such as downtime for repairs, curtailment to protect the grid, inefficiencies of the rotor and drivetrain due to ageing, lower wind speeds compared to the expected wind speeds, wake effects, and shutdowns due to icing.
A number of different methods for quantifying the performance of operating wind turbines compared to the expected performance are used in the industry. This can get very confusing – just google “wind turbine availability” to see what I mean. So here I will base my definitions on the industry standard IEC 61400 part 26-1 (time-based availability for wind turbines) and part 26-2 (production-based availability for wind turbines).
The time-based availability of a wind turbine is defined as the time of operation divided by the total time. So a wind turbine that was running for a total of 340 days last year would have had an availability of 340/365 = 93%. The time-based availability can be divided further into separate availabilities – for example, the contractual availability considers only the downtime due to the manufacturer, whereas the turbine availability or technical availability considers only the downtime due to technical problems with the wind turbine. When you buy a wind turbine, a turbine availability of usually 97% is guaranteed by the manufacturer - and any more downtime is compensated for by them.
This value, however, doesn’t tell you how much energy the wind turbine has lost during this time. If the 25 days of downtime occurred during low wind periods, you would only have a very small loss in AEP; on the other hand, if the downtime occurred when the wind speed was higher, you would have a much larger loss in AEP.
In order to quantify the loss in energy during this time, the production-based availability is used. This is given by the actual energy produced divided by the energy that is expected. So if the expected (calculated) energy production was 10,000 MWh and the wind turbine actually only produced 9,500 MWh, the production-based availability would have been 95%. Depending on exactly how it is calculated (there are many different suggestions in the standard), it can be a measure of the total energy losses due the downtime, curtailment, operation inefficiencies as well as wind conditions. The production-based availability is also sometimes called the energetic availability.
The production-based availability is useful for understanding the overall losses of a wind turbine or wind farm and the time-based availability is useful for understanding the amount of downtime. They should not be confused with each other and the word “availability” should not be used without specifying time-based or production-based:
Capacity factor is not the same as nameplate capacity!
The capacity factor is also a term used frequently to indicate the performance of a wind turbine or wind farm – and here I'd like to differentiate between this and the nameplate capacity. The general definition of the capacity factor of a power plant is the ratio between the actual electrical energy output over a certain amount of time divided by the maximum possible electrical energy output over the same time period:
The capacity factor was originally introduced for comparing the performance of traditional power plants. These power plants basically produce rated power most of the time, so the capacity factor is only reduced from 100% when it is either not running, starting up, shutting down, damaged, curtailed or operating inefficiently. The difference between the capacity factor and the production-based availability is that the capacity factor takes into account losses when the power plant is running, but not at rated power, whereas the production-based availability also takes into account losses when the power plant is running below rated power. Typical capacity factors of traditional power plants are in the high 90s.
What about the capacity factor of wind turbines? Well, the maximum possible electrical energy output of a wind turbine in a year has been defined as the energy produced if it were operating at rated power 100% of the time. This is actually not possible because for this, the wind speed would have to remain between the rated and the cut-off wind speed (the wind speed above which the wind turbine is switched off and turned away from the wind in order to reduce the loads and avoid damage) for the entire year AND there would have to be no downtime or other losses. The plot below shows a typical wind speed distribution on the left and the required wind distribution to reach maximum possible energy on the right. Note: here the cut-off wind speed is marked as 20 m/s; in reality this value is usually 25 m/s. Refer to my previous blog article for an explanation about wind speed frequency distributions. It should be quite clear that this type of wind speed distribution is not possible.
But let’s use this definition to calculate the expected capacity factor of the 2 MW wind turbine introduced above by using the expected AEP in relation to the maximum possible energy if the wind turbine would be operating at rated power all year long. In my previous blog article, I calculated the AEP to be about 6,600 MWh for a standard wind speed distribution (second example). Let’s assume that the wind turbine has an expected production-based availability of 95%, so the expected AEP is now 6,600 * 95% = 6,270 MWh.
Then the expected capacity factor is 6,270 MWh divided by (365 days * 24 hours * 2 MW), giving 37%, which is a typical wind turbine capacity factor.
What does this value actually tell us? Well, not much, actually. Comparing the energy produced by the wind turbine with the maximum possible energy if the wind speed was constantly between the rated and cut-off wind speeds is not that useful, because (a) this never occurs and (b) even if it did, the wind turbine is not designed for such high loads. You’re actually calculating the production of your wind turbine compared to the production of a fictitious wind turbine operating in fictitious wind conditions. So the absolute value of capacity factor is not particular useful for characterising wind turbine performance, in my opinion.
What the capacity factor does give you is an indication of the variability of the energy production – the lower the value, the more variable the power production. The capacity factor is actually the same as the load factor, a term commonly used by electrical engineers to measure the variability of electricity consumption or generation - and not the performance of a power plant.
Although the capacity factor (or load factor) is not a particularly useful measure of wind turbine performance, it is widely used in the wind energy industry. Comparing the capacity factor of wind farms to traditional power plants is like comparing apples and oranges and should be avoided.
Levelized Cost of Electricity
For renewable energy sources, calculating the amount of energy produced compared to the amount of energy available has limited applicability, even if one finds a more useful number than the capacity factor. This is because, contrary to traditional power sources, the energy source doesn’t cost anything.
Of course, there are other costs associated with the production and operation of wind turbines. So the only way of comparing different types of power plants with each other fairly is to compare the energy produced with the total costs over the entire lifetime. The current industry standard way of doing this is to calculate the levelized cost of electricity (LCOE), which is given by the total cost to build and operate the power plant over its lifetime divided by the total energy output of the asset over that lifetime. It tells us the minimum cost at which electricity must be sold in order to break-even over the lifetime of the project:
Below I’ve taken a nice example of a LCOE comparison from the report Rethinking Energy 2017 from the International Renewable Energy Agency. It can be seen that the average LCOE for onshore wind projects in 2016 was very much in the lower part of the range given for fossil fuels:
So, as a conclusion, one has to be very careful when quantifying and comparing the performance of wind farms. To compare the performance in terms of energy losses, use production-based availability. To compare the performance in terms of downtime, used time-based availability. To compare overall performance, use the Levelized Cost of Electricity. And finally, make sure not to confuse nameplate capacity and capacity factor – and be cautious when comparing capacity factor values!
I am a wind energy engineer, lecturer and coach based in Zurich, Switzerland, aiming to empower people and organisations to understand wind energy technology better, in order for them to realise wind energy projects as effectively as possible.