Reducing wind energy production uncertainty: the power of high capacity factor wind turbines

Johannes Maidl, Commercial Senior Specialist
Mads Hovmøller Mortensen, Head of Value Management
Jan Vestergaard Knudsen, Senior Product Manager
Christoph Armbruster, Lead Specialist, Financial Engineering

Published on October 9th 2023


Wind is fuel – and by nature it is a force of constant change and unpredictability. In this world of uncertainty, the laws of physics and data-driven mathematical modelling can give a measure of predictability that businesses and banks can trust.  This article explores how high capacity factor wind turbines can provide that much-needed reliability - making it easier for everyone, from wind experts, investors to asset owners, to understand and trust wind energy economics. 


Measuring the wind resource

Before deciding whether to develop a wind farm, it is crucial to measure the wind resource over a certain period. This measurement helps estimate the Annual Energy Production (AEP) of a wind farm, which is a critical factor in project feasibility.

The standard process begins by categorising the measured wind speed data into “wind buckets”, i.e., 1 m/s, 2 m/s, and so on. This categorisation creates a frequency distribution that shows how many data points fall into each wind bucket. The frequency distribution is then fitted to a probability distribution, for which the Weibull distribution has established itself as an industry standard.

The challenge of limited data 

The catch is that wind speed data collected over time is limited, as it would be unfeasible to collect 30 years of measurement data before building a 30-year wind project. However, the probability distribution has the important function of enabling the calculation of an expected Annual Energy Production based on this sample of measured data. Naturally, such a distribution based on a limited amount of ‘real’ data comes with uncertainty. To account for this uncertainty, AEP is calculated at different probability levels. Investors commonly focus on the 50% probability level, known as “P50 AEP”, while lenders turn their attention to the 90% probability level, called “P90 AEP”. The P50 AEP value represents the average generation level projected to surpass 50% of the time throughout the project's lifespan. On the other hand, the P90 AEP figure indicates the generation level expected to be exceeded for 90% of the project's duration, and is a crucial input in financing, risk assessment, and long-term performance evaluation.


Understanding the difference

It is obvious that an AEP estimate with 90% probability is lower than an estimate with 50% likelihood. What matters is HOW MUCH lower the 90% estimate is compared to the 50% estimate. And here is where high capacity factor turbines make a difference. 


The power of high capacity factor turbines

High capacity factor turbines achieve lower sensitivity in AEP estimates by increasing the size of their rotors relative to their nominal rating. This higher rotor-to-rating ratio effectively increases the swept area on a wind park per MW of the turbine’s nominal rating, thereby capturing more wind energy and starting to produce more energy at lower wind speeds. Consequently, they improve AEP and revenue as well as reduce AEP sensitivity. In simpler terms, the higher the capacity factor of a turbine, the smaller the difference between P90 and P50 estimates. This means reduced uncertainty and a more robust business case for investors

Examplify this with two differently configured wind turbines, with nominal rating unchanged, the larger rotor wind turbine (163m rotor) representing a higher capacity factor machine captures more wind energy per MW of the nomimal rating than the smaller rotor (136m rotor). This effectively shifts the power curve to the left, allowing for more energy production where it matters more (at lower wind speeds).
High capacity factor turbines reduce risk because they capture more percentage of energy increase where the wind speed is lower, yielding more AEP in percentage at lower wind speeds, and achieving maximum production earlier in high winds.

Different P90 and P50 gaps

In addition to the gross AEP estimates, net AEPs are usually derived by deducting numerous potential losses, such as turbine availability, wake loss, transmission loss, loss attributed to blade shape/material, and blade degradation. It is worth noting that, usually, third-party certifying bodies are used to evaluate the financial viability and risk of a project by providing the net P50 and net P90 AEP estimates. However, there’s a varying degree to which the P50 and P90 gaps are observed depending on the evaluation methodology deployed, simply because there currently isn't an industry standard for how to assess losses and estimate P50 and P90 net AEP. Having a standardised method will ensure transparency among third parties, customers and OEMs, and ensure a more accurate reflection of the turbine’s true potential to provide a more accurate assessment of the project's financial viability, risk profile and overall bankability. Nevertheless, it is invariably true, that given the same net AEP evaluation methodology is applied, a higher capacity factor wind turbine will definitively reduce the AEP gap between P50 and P90 estimates compared with a lower capacity factor machine.


Benefits beyond AEP

With a high capacity factor wind turbine, investors not only gain higher AEP but also greater confidence that the actual AEP will better match the estimate than a lower capacity factor turbine does. Moreover, the more favourable P90 estimate can lead to more favourable loan terms from lenders. 

In conclusion, high capacity factor wind turbines are the key to reducing uncertainty in wind energy projects. They provide not only more energy but also greater certainty, making wind energy more attractive and accessible to investors and helping us harness the power of wind for the urgently needed expansion of renewable energy around the world.


Published: 09-10-2023