Our earlier post, “LCOE’s to compare energy investments”, focused on levelized costs of energy. This post will more closely examine LCOE of wind energy. In doing so, we will seek to understand the calculations and their unique charateristics, and explore the differences between offshore and onshore projects. Finally, we will present an overview of some selected countries and their respective LCOE’s.
When talking about wind farm LCOE’s, a distinction between onshore and offshore assets needs to be made. Onshore wind farms outperform offshore farms in terms of costs, largely due to more expensive materials and marine technologies; higher engineering, procurement and construction and operations and maintenance; a more complex connection to the grid; and myriad logistical difficulties when building offshore.
However, offshore wind outperforms its onshore counterpart when it comes to load factors, as higher towers and bigger blades allow them capture more wind. In addition, technological improvements and advancements offer the opportunity for economies of scale to drive down costs, as discussed in one of our previous posts “Wind – worth the breeze?”.
Onshore wind already is one of the least expensive energy sources, closely competing with and even surpassing traditional sources such as coal and gas. Notably, the LCOE’s used to compare these different generating sources do not include the cost of pollution. Consequently, including a CO2 emission price would result in wind energy being even cheaper than already demonstrated by the levelized costs estimations. 
The very generalized formula for calculating the per megawatt costs for all kinds of energy-related projects is as follows:
LCOE = [Stn=1 (It + Mt + Ft) / (1+r)t] / [Stn=1 Et / (1+r)t]
where (i) It is the invested capital in period t, (ii) Mt are the costs of maintenance in period t, (iii) Ft is the cost of fuel in period t, and (iv) Et is the energy output in period t.
However, to better estimate the real costs of wind farms and projects, more detailed project-specific factors may be used, including Power Purchase Agreements (PPAs) and penalties, possible royalty fees, taxation (levies and production credits) and so on.
Before offering a formula that includes these additional factors, we need to properly understand PPA’s, and both why and how they affect the actual costs of energy produced by wind farms.
A PPA usually refers to a tailored long-term power purchase agreement between a producer (seller) and a customer (consumer or trader). It covers specifics like time of delivery, amount of electricity supplied, potential penalties, and so on. PPA’s are a commonly used tool within the renewable energy industry, substituting for feed-in tariffs, as utility consumers are more frequently looking for reducing their carbon footprint as well as their energy costs. Before the increased use of PPA’s, corporations of any kind had no say over which energy source would be tapped to meet their needs. Through these PPA’s, large corporations like Google, Amazon, etc. have the ability to first-hand pick their energy source of choice. Furthermore, private consumers, that rent their rooftops to solar installers, can enter into direct PPA’s as well. Such solutions are called behind the meter solutions. With the change in consumer behaviour and preferences; i.e.: a higher interest in environmental issues and wanting to be part of solutions that fight climate change, PPA’s allow for long-term price stability for the producer and easily forecastable energy expenses. Furthermore, such power agreements can reduce market risks primarily associated with planning and operating renewable power plants.
With that in mind, a possible revision to the above formula that better projects the costs per megawatt of wind energy produced is the equivalent of the sum of the total costs per year divided by the total output produced in the given year:
LCOE = [Sni=0 (Ii + OMi + Fi – TCi – Di – Ti + Peni + Ri) / (1+r)i] / [Sni=1 Ei / (1+r)i]
where (i) Ii is the invested capital in period i, (ii) Mi are the costs of maintenance in period i, (iii) Fi symbolizes the cost of fuel in period i, (iv) Ei is the energy output in period I, (v) TCi is the tax credit in year i, (vi) Peni is the sum of the production loss and the penalty (paid for non-compliance) in year I, (vii) Di is the depreciation in year i, (viii) Ti is the tax levy, and (iv) Ri) is royalties of the corresponding year. Tax credits and penalties are subject to each specific PPA.
When looking at the actual LCOE of onshore and offshore wind farms, we can see a strong correlation between annual full load hours and costs per megawatt. This should not come as a surprise, however. By looking at the formula above we can see that (assuming that all other costs remain the same) a greater output will decrease the costs per unit. This logic follows simple algebraic rules: If the numerator is constant and the denominator increases, then the quotient decreases.
Onshore wind prices have dropped from $100/MWh to $37/MWh in the US and to as low as $30/MWh in Brazil within the last 10 years. For almost two-thirds of the world, wind is already one of the cheapest energy sources. Projections suggest that prices will go as low as $20/MWh by 2030. The global weighted average LCOE for onshore and offshore wind in FY2019 were $53 and $115 per MWh, respectively. When looking more closely at different parts of the world, we can see a range in LCOE’s for onshore wind (all numbers in $/MWh):
As mentioned, Brazil accounted for one of the lowest average prices at $48/MWh, beaten only by China at $46/MWh. Other Asian countries recorded some of the highest LCOE’s with an average of $99/MWh.
One of our recent posts, “Renewable energy – just a drop in the bucket”, showed that onshore and offshore wind combined only contribute about 4% of our total worldwide electricity mix of energy produced.
Why is this, given wind energy’s low cost structure?