This article presents and discusses Operational Expenditure (OPEX) levels from 60 operational European offshore wind farms based on an analysis of publicly available accounts, i.e., it is based on actual operational figures.
These financial figures enable valuable intra-industry analyses and peer group benchmarking across offshore wind farms with either geographical or technical similarities. The article was originally published in May 2020 with data from 2018. The statistics below are based on publicly available annual accounts from 2020.
By the end of 2020, the overall installed capacity of offshore wind was around 34 GW. This represents an 11x increase from the installed capacity in 2010 (IRENA, 2021). It is expected that this number will reach between 270 and 330 GW by 2030 (Clean Energy News, 2021 & Renews, 2022). As the number of operational offshore wind farms increases rapidly around the world, OPEX will represent a larger proportion of the overall annual spending within offshore wind. The increase in installed capacity will lead to more substantiated benchmarks and operational performance reviews becoming a recurring activity for any diligent asset owner or operator in the industry.
The analysis highlights some of the mechanisms and trends related to OPEX levels, which account for around 25-35% of the Levelized Cost of Electricity (LCoE) of modern offshore wind farms. The exponential growth within the industry has led to increased operational synergies and benefits from economies of scale – both in the construction and operational phase of a project’s lifecycle. How these efficiencies and benefits are shared across the industry value chain is to be explored further but will undoubtedly be affected by the competitive landscape hereunder the new deep-pocketed market entrants, specific country characteristics, and a potential consolidation within the wind turbine producers.
Figure 1: Lifetime OPEX/MW level today and going forward (real 2020)
Many offshore wind farms are structured as separate companies which requires them to provide annual financial reports which are freely accessible for several markets. Hence, the data within these reports can be obtained, cleaned, consolidated, and analysed. The data for this analysis includes the annual accounts up until and including 2020. For obvious reasons, wind farms differ in characteristics which is important to emphasize when analysing and comparing their OPEX levels.
To improve the accuracy of the analysis and allow benchmarking, it is important to define the wind farm characteristics which drive the operational expenses. This is done by filtering the wind farms in the database on specific wind farm characteristics such as distance from O&M port, turbine size, installed capacity, turbine type, OPEX, and geography.
An overview of some key figures behind the benchmark is presented below:
Figure 2: Highlights of the dataset
This data presents countless opportunities for usage. This article will present a selection of the most relevant applications. Upon request, other analyses and Key Performance Indicators (KPIs) can be provided.
To enable a comparison across different project and turbine sizes, it is necessary to define metrics which eliminate the differences between the wind farms to the biggest extent possible. Several measures have been used in the analysis; however, the main two metrics are OPEX/MW/year and OPEX/MWh/year. Using these metrics enables a more accurate comparison between the projects. Regardless, there are still differences in the project characteristics which should always be considered when comparing OPEX levels. All the OPEX figures from the annual accounts have been converted to real 2020 figures to enable comparison across project from different time periods.
Figure 3 below, which shows the lifetime OPEX/MW profile for the 60-project sample, indicates a downward trend with an average year-on-year decrease of 2.5%. Visualised by the red line in Figure 3, the sample size decreases as the operational year increases, showcasing a relatively young industry. The small sample size on the right side of the graph brings more uncertainty to the OPEX figures as it is based on less observations.
Figure 3: Average OPEX/MW/year (kEUR) of entire sample based on operational year (real 2020)
The overall target of the offshore wind industry is to minimise the LCoE which can be done through several drivers. Focusing on the operational phase, the target must be to find the optimum between OPEX and energy production. This is a continuous process during the lifetime of the wind farm and decisions regarding O&M strategy must be re-evaluated throughout the asset lifetime. OPEX/MWh can be used as KPI to track productivity of the offshore wind industry i.e., the output per unit of input. This KPI should, as any KPI, not stand alone and should be supported and triangulated with other KPI’s and methods such as expected operational lifetime of the wind farm, revenue-based availability, and revenue/MWh.
Figure 4 below shows a downward trend in the OPEX/MWh level when looking at the 60-project dataset in the period from 2015 to 2020 with a total reduction of 15%. This trend is a result of several factors such as industrial efficiencies through operational experience, technological advancements, scalability, and improved ways of working. Further efficiencies are expected in the future and the OPEX/MWh metric should always be seen in a lifetime perspective. A “race-to-bottom” approach could indicate a high performing and productive wind farm, at the cost of expected lifetime and a jeopardization of the asset integrity of the wind farm. As the industry is now at a different stage of the maturity cycle it will be interesting to see how this metric develops over the next years.
Figure 4: Average OPEX/mwh/YEAR (kEUR) of entire sample based on calendar year (real 2020)
Variances in the OPEX/MW levels across wind farms can be explained by several factors. Figure 5 shows one of the explanations by visualising the average lifetime OPEX/MW/yr. on a country level. The numbers indicate that the OPEX/MW level is correlated with the country of origin of the wind farm. Showcased on the second Y-axis is the distance between the wind farm and the O&M harbour. Based on the four countries included in the dataset, it can be concluded that the order of the OPEX/MW levels fits 1:1 with the country order for average distance to shore. This clearly shows the correlation between the OPEX/MW level and the distance from shore. While distance to shore is one of the main drivers, note that other parameters also affect the OPEX/MW level including number of turbines, O&M strategy, logistical setup etc.
Figure 5: Average lifetime OPEX/MW/year (kEUR) based on country and distance from shore (real 2020)
When comparing OPEX levels across countries, it is important to be aware of the legislation and other country specific dynamics. UK wind farm owners must pay fees to the Offshore Transmission Owner (OFTO) and seabed lease fees to The Crown Estate. In Denmark, on the other hand, no such fees are paid. For this analysis, the OFTO transmission charges have been excluded from the UK OPEX/MW/year figures.
Based on the OPEX/MW levels for sites with geared and direct drive turbines, it is evident that the wind farms with direct drive turbines have a higher OPEX/MW than the sites with geared turbines (Figure 6). For the first 6 years of operations, the direct drive sites have been outspending the sites with geared turbines by an annual average of 12.7 kEUR/MW/year. corresponding to 9.4%.
Figure 6: OPEX/MW/year (kEUR) based on WTG technology (real 2020)
As the graph only shows the first six operational years for both geared and direct drive turbines. It will be interesting to see how this development unfolds when especially the direct drive sites enter more mature stages of their lifetime both from a cost perspective but certainly also regarding availability and outages.
This article has focused on explaining the OPEX/MW and OPEX/MWh level based on some of the parameters available in the dataset including operational year, country, distance from shore and turbine technology. But how do the individual projects compare?
Figure 7 presents an overview of the annual average OPEX/MW during the lifetime of all the wind farms in the dataset. From the figure it can be derived that the difference between the wind farms with the highest and lowest OPEX/MW/year. is a staggering 76%, however, WF_52 is an outlier in the dataset. The highest and the lowest OPEX/MW/year. observed in the dataset is 338 kEUR/year and 80 kEUR/year respectively, with a sample average of 135 kEUR/year. The sites with extremely high OPEX levels drive up the sample average and thereby have a big impact on the figures.
If the top five best performing wind farms measured on OPEX/MW/year. are bundled into one category the average annual OPEX/MW is 81.7 kEUR. When performing the same bundling of the five worst performing sites the average annual OPEX/MW is 252.6 kEUR. In Figure 7 below, the five wind farms with the lowest OPEX/MW/year are marked with green whereas the five wind farms with the highest observed OPEX/MW/year are marked with red.
Figure 7: Project lifetime OPEX/MW/year (real 2020)
Note: For this article the wind farms are anonymised, but the full range of project names is available on request.
During this article a variety of analyses and applications of the OPEX numbers in our database have been presented. The context of these analyses is key and a broader understanding of the OPEX levers driving these numbers are needed to fully conclude on the OPEX levels of offshore wind farms. At PEAK Wind we specialise in creating value-adding benchmarks across wind farms with similar characteristics but in analysing and understanding why deviations occur. The dataset provides endless opportunities for performance analyses across projects on a long list of parameters including power generation, revenue, EBITDA, CAPEX etc. The dataset is updated on an annual basis as new data becomes available and additional wind farm characteristics are added when deemed valuable or by request.
To gain further access to the data and other metrics, please feel free to reach out to discuss further. We are always happy to answer your questions and develop bespoke benchmark analysis.
Leoni Christensen | email@example.com | +45 24 20 94 07