So, how good is the wind farm you are working at?

There are several parameters that can be used to assess a renewable energy project and to compare different projects.

Among the most used, it is worth mentioning the Capacity Factor, NPV, IRR and LCOE.

Capacity Factor is the ratio between the actual energy production of the wind farm (that is, GWh/year) compared with the theoretical production.

Expressed as a percentage it is usually a number somewhere between 20% and 50%. Wind farm with a capacity factor above 50% are usually regarded as quite exceptional.

It is basically function of two parameters, wind variability and wind turbine selected for the project. On top of that you will have several losses - for instance electrical losses, noise curtailment, wake losses, etc.

To calculate it you will simply divide the energy produced by the wind farm by the nameplate capacity by the number of hours. Due to the seasonal variability of the wind it makes sense to make an yearly calculation.

What is interesting is that Capacity Factor is fundamentally and economical decision. At the end of the day you want to improve your business case, so it could make sense to install wind turbines giving a lower capacity factor (but with an even lower total cost).

The Net Present Value (NPV) is today’s value of a future cash flow.

In the formula C is the cash flow (-C0 is the initial investment, C1 is the cash flow of the first year and so on until the last year, n) and r is the discount rate.

This metrics give priority to the absolute return of the investment. Basically it is useful if you have only one shot: if you put all your money in a single project you will prefer (ceteris paribus) the one bringing more money.

The discount rate reflect the fact that money in the future is worth less than money today – for inflation, cost of opportunity, etc.

Internal Rate of Return (IRR) is the discount rate that makes NPV = 0.

This metrics give priority to the percentage return. It could be useful for instance if you can pick several projects among many.

Levelized Cost of Energy (LCOE) is defined as (CAPEX + OPEX ) / AEP

CAPEX (Capital Expenditure) is the money that the wind farm developer will have to put in all the assets – not only the wind turbine itself but also the infrastructure (roads, foundations, substation, etc.), and the development costs (everything from land lease agreements to the engineering studies).

OPEX (Operational Expenditure) is what the wind farm owner will spend to have the wind farm up and running.

This include basically the maintenance of the wind turbines (they need new oil every now and then, pretty much like your car) and of the substation equipment. As the lifetime of such project is increasing from what used to be industry standard (20 years) to 25, 30 years and more.

Additionally the more the wind turbine gets older the more is likely that it would need major maintenance (for instance a new gear box).

LCOE makes a lot of sense when you are trying to compare energy produced by different technologies, for instance wind and solar photovoltaic.

Lazard (a huge private investment bank and financial advisory firm) distribute periodically a study on the evolution of LCOE. Currently available in version 13 it gives you some visibility on how much different forms of energy cost without subsidies.

I still remember when I started working in the renewables sectors about 10 years ago. Comparing the cost of solar and wind I believed that my colleagues who decided to work in solar were crazy as the cost per MWh of Solar PV was huge.

Well, it looks like I was wrong.

Workers in Renewable Energy. Copyright Statista (one of my favorite website)

It is no secret that the wind business is going through a turbulent period, with several players in the sectors experiencing a tough time.

I was wondering how many people are currently employed in Wind and I have found this interesting report from IRENA (the International Renewable Energy Agency).

I have discovered several interesting things:

• Only 11 million people are working in renewable energy job. Not that many, if you consider that we are 7.700.000.000.
• Out of these 11 million, only 1.1 million people work in wind. The biggest share is Solar PV, with over 3.5 milions.
• The majority of wind job are in China. With an incredible 44% of jobs in the People's Republic of China it looks like I will have to improve my Mandarin.
• One out of three is a woman, above all (45%) in administrative jobs but also (around 30%) in technical function. This is more of what I thought.

A key difference between combined cycle plants and wind or solar plants is the CAPEX / OPEX distribution. According on recent data of the American Department of Energy, for a combined cycle plant the CAPEX will be only around 25%, being the overwhelming majority of the investment in operational costs (that is, fuel) and maintenance.

The picture is different for wind farms and photovoltaic plants, were the fuel is free (still no taxes on wind and sun) and the majority of investment is needed upfront, with over 80% of CAPEX.

An interesting trend I am observing is the shift in the weight of Balance of Plant (BoP, more usually called Balance of Systems in the PV industry).

It is well known that the costs of photovoltaic modules and turbines are following a downwards trend. I do not see the same trend for BoP, with costs per MW decreasing at a slower pace.

In the figure above, you can see how the BoP share can be more than half of the CAPEX for rooftop solar. The numbers are coming from the Fraunhofer Institute, which include in BoP also land acquisition costs, permitting and legal cost, taxes, etc. Even if I disagree with this “broad” definition of BoP, the result is unchanged - the relative weigh of BoP in renewable is increasing.

What are the consequences?

In my opinion, the most relevant is that now BoP can really make of kill a deal. When it was approximately the 20% of the CAPEX even a big movement in the BoP budget was not really moving the numbers that much. However now the relative weight increased to over 40% in some project, and an expensive BoP can make a project economically inviable.

Is there something that we can do about it?

For several items probably not. For instance, wind farms on top of mountains will need expensive access roads, complicate earthworks with rock blasting, etc. While there is probably still some room to decrease the price of wind turbines (e.g. with a better supply chain) I do not see why civil works should be cheaper in the future.

The same apply to some electrical works items such as medium voltage cables, which are basically a commodity linked to the price of aluminium, steel, etc.

In conclusion, I think that in the next years we will see an increasing effort in engineering and optimization to lower the cost of BoP and insure the economic sustainability of projects.

I have always been amazed by the number of published papers, master thesis and documents focusing on the use of algorithms to optimize the layout of a wind farm. Some of them were proposed more than 25 years ago, showing a continuous, sustained interest in the topic.

I guess that the reason for such abundance is the stimulating difficulty of the problem and the fact that there are huge investments behind a wind farm.

From a mathematical perspective the problem is complex due to the type of variables involved, both discrete (you can have 30 or 31 turbine but not 30.5) and continuous (for instance, the length of cables). Additionally there are strong links between variables (for instance higher turbines = higher tower and foundation cost) so finding the “sweet spot” that maximize earnings is not a simple task.

Generally speaking, these algorithm try to maximize the profitability of the investment, usually expressed in terms of Net Present Value (NPV). Basically they compare the value of all expenditures during the life of the project “in today money” with all the earning “in today money” using a certain discount rate for cash flows in the future.

Expenses belong to two categories, capital expenses (CAPEX) and operational expenses (OPEX), while net earnings are function of the amount of power produced, the price of electricity and the electrical losses.

Therefore even a simplified model should try to minimize these expenses:

• Wind turbine
  • Model (power curve)
  • Tower
  • Installation
• Civil works
  • Foundations
  • Roads
• Electrical works
  • MV cables
  • Substation
• Operation & Maintenance

While maximizing the production, a mainly a function of:

• Wind
• Wind shear (of the speed of the wind increase with height)
• Wake effect (how turbine interact with each other creating turbulences)

The interaction between all these variables is what makes the problem interesting.

To give a few examples,

1. Packing the turbines densely in a small area will lower the cost of roads and cables but will create huge production losses due to the turbulences inducted by the turbines upwind.
2. Using a higher tower should increase the production – unless the wind shear is low, in which case the additional tower and installation costs would off weight the benefits
3. A certain position could be extremely productive – but it could be very far away from the substation (increasing the electrical losses ) or on the top of a steep hill (increasing the earthworks cost)

Additionally you have to decide the level of complexity of the model. For instance the foundation cost can be considered as:

• A lump sum, equal for all turbine models. Under such assumption, you would see a benefit decreasing the number of turbines but not switching to a different WTG model.
• A function if the wind turbine model (greater loads = greater foundation).
• A function of wind turbine model, geotechnical parameters of the soil and unit cost of concrete of still. This latter option, although more precise, would probably make the model very difficult to handle.

I believe that a reasonable compromise between complexity of the model and quality of the result can be achieved using nested algorithms as proposed by these researchers.

In the first steps, only the variables related to the turbines (power curve, wind resource, availability and cost) are considered. Once the turbine model and the layout are fixed the civil and electrical works can be considered, defining the optimum position of the substation (to minimize cable length) and the shortest roads connecting the wind turbines.

Onshore wind turbines - price per MW (Millions Euro)

The easy answer to this question is “Today it costs less then yesterday. And probably tomorrow it will cost less than today”.

Today (April 2019) the average price is around 700.000€ per MW – that is, expect to pay around 3 ML€ for a 4 MW wind turbine. That’s a huge reduction when you consider that some years ago the easy to remember formula was 1MW = 1ML€

If you are working in the wind industry you are probably aware of the huge pressure on wind turbine prices, driven by several factors and resulting in turbines cheaper than ever.

It is interesting to observe that, in the current market condition where wind turbines are very cheap, the majority of the main wind turbine manufacturers are reporting very solid order intake figures. However, net profit is still elusive and EBIT margin are very low.

For instance, Vestas reported 9.5% for 2018 while Siemens/Gamesa 7.6% (pre PPA and I&R costs) for the same period, with a guiding range for 2019 between 7% and 8.5%.

It looks like manufacturers are having more luck in the maintenance side of the business: margins there are significantly better.

One of the consequences of this situation is that several players are leaving the market (Senvion declared bankruptcy some weeks ago) and the consolidation of the sector continue: there are rumours about a possible purchase of Suzlon (heavily indebted) by Vestas, while Enercon absorbed the Dutch manufacturer Lagerwey some time ago.

In case you are wondering about the origin of the figures in this post I’ve taken the numbers for this post from the official annual statements of Siemens/Gamesa and Vestas and not from my friends working there 😉

This is the first of several post that I’d like to write in the next weeks about investing in wind energy.

There are several possible alternatives to invest in wind energy, or more broadly in renewables: stocks, managed funds, ETFs or even direct investment in the development of a project.

The option described in this post (buy your own turbines) is probably the most extreme but it’s not unseen. I’ve been personally involved in several projects owned not by utilities, mega corporations or professional developers but by private investors or small companies willing to pay out of their balance sheet.

Additionally, it has to be considered that the banks are usually willing to finance a relevant portion of projects. The percentage that can be financed is somewhere around 60% to 70%, in some cases even more.

The capital cost of wind projects are dominated by the cost of the turbine. In this blog you will find quite a lot of post detailing the other costs associated with the project, usually called “Balance of Plant” (BoP).

As a rule of thumb I would say that the turbines, fully installed and operational (that is, including transportation, installation and commissioning costs) will be somewhere between 60% to 80% of the total investment.

How much does an industrial, multi megawatt wind turbine cost?

It’s obviously not easy to answer this question as it’s dependant on several variables such as number of turbines purchased, transportation costs (marine and overland), financing, insurance and warranties, etc. Actually is so critical that companies in the wind business have usually specialized departments devoted to the gentle art of Pricing.

However several reliable sources (Bloomberg in primis - they are the source for the image above) are concordant on the fact that the cost per megawatt is steadily decreasing.

When I joined the wind industry (2010) a MW was somewhere around 1.4 to 1.6 million dollars -  that is, you could expect to pay around 3 ML$ for a 2 MW wind turbine.

Today (end of 2018) prices have dropped dramatically. Buying a turbine today, with delivery at the end of the next year, will probably cost around 1 M$ per MW.

There are several reasons behind this price drop. I believe that the main 2 are scale factor (today, 3 to 4 MW wind turbines are the norm while in the past the standard was 1.5 to 2 MW) and market pressure in the majority of developed markets (USA and Europe).

To summarize, to invest in wind energy building your own small wind farm (1 turbine around 3MW, no substation or other substantial BoP costs) you would need probably between 0.5 and 1 ML$. This very rough estimate consider a total cost of the project between 3.5 and 4 ML$, with the banks financing around 70%.

A second alternative to invest in wind energy is given by stocks and ETFs of companies in the energy.

There are many “renewable energies” ETF and a bunch of solar ETFs.

However the choice for wind ETF is much more limited.

There used to be one ETF form Invesco (PWND) specialized in pure wind players but it has been delisted due to very low trading. Yes, that is not a good sign.

So, as far as I know today (2018) the only wind ETF is First Trust ISE Global Wind Energy Index Fund (FAN – a very appropriate name).

Not all the companies in this ETD are 100% wind: for instance, the biggest share (almost  10%) is Ørsted (or Dong, if you prefer the old name like me: Dansk Olie og Naturgas).

You will, however, find the big players, including  Longyuan Power (probably the biggest wind power producer in Asia) and all the usual suspects such as Vestas, Siemens Gamesa, etc.

What you will buy is very high volatility today, but probably also long term growth.

An alternative is to do some cherry picking and select the stocks one by one. Almost all players are traded (with some exceptions, for instance Enercon).

There is an old joke that say something like “What happens when you put 10 economists in a room? You'll get 11 opinions.”

My experience with Target Price is similar: I’ve heard many opinions in favour and against it and probably in general it’s not “right” or “wrong", but it's a strategy that, depending on the context, can be more or less appropriate.

Basically, the idea is to share with the subcontractors the price level that they are supposed to reach – or if you want to see it the other way around how much you can afford to pay to build the wind farm.

On a smaller scale the idea is not new. It is what happen when you ask someone if they can meet a certain budget, for instance asking to an artist “Can you do me a portrait for 100$?”. The answer could be for instance something like “Yes, but the dimensions will be 20x20 cm”

There are indeed some arguments I can see in favour of it:

• BoP is (partially) a custom service with certain technical specifications that in some cases can be changed.

The implication is that the input of the subcontractor can be requested to hit the target, or some initial requirements can be changed. A classic example is the level of redundancy of the substation: fail proof solutions are not cheap.

• Material costs can be clearly identified (in some cases).

This is for instance the case when items like medium voltage cables are purchased - a key driver in the cost of cables is the spot price of the raw materials (copper, steel, aluminium) so it’s relatively easy to calculate how much you should pay.

However, it’s also easy to find arguments against it:

• To give a target price, the buyer should understands the cost structure.

This is not so easy as sit might seem: people dealing with BoP are usually operating in different markets, interacting with companies of different sizes and with different business models. Therefore having a clear view of the seller costs structure can be a daunting task.

• Price volatility should be low.

This is true in certain markets where it’s easy to find a steady supply of bidders. However, overheated markets with several competing projects executed at the same time can create price volatility: basically, the resources that you need to build the wind farm (for instance the crane, or the mobile batching plant) will go to another project – another wind farm nearby, or possibly something totally different.