Risk & contingencies - a brief introduction

One of the hot topics frequently discussed in the creation of the budget for big projects is the appropriate contingency level and how to estimate it.

My colleague Giuseppe had found an interesting paper online that has been the starting point for this post.

So, what are contingencies?

Some far reaching definitions consider different typologies of contingencies:

  1. Money in the budget
  2. Float in the schedule
  3. Tolerance in the technical specifications
  4. Tolerance in the quality
  5. Tolerance in the scope of work

Possibly this is a very broad approach, so I will focus only on the first point.

Monetary contingencies are added to the estimate “to allow for items and events for which the state, occurrence or effect is uncertain” (the definition is proposed by the Association for the Advancement of Cost Engineering).

Several concepts are usually excluded from the contingency budget:

  • Major changes in scope
  • Extraordinary events (such as the one indicated in the Force Majeure clauses)
  • Currency exchange risk (this is usually hedged or it is included in a different section of the budget)

Basically in the definition of contingencies the focus is on the “negative risks” that can create a loss if they materialize (“positive risks” is a fancy way to call the opportunities).

Identified vs unidentified risks

A key distinction should be made between identified risks and unidentified risks.

Identified risks are “known unknowns” – that is, risks you are aware of (a classic example would be the geotechnical risk.

Unidentified risks are more similar to “unknown unknowns” - risks that come from situations that are so unexpected and difficult to foreseen even to subject matter experts that they would not be considered.

Identified risks can (and should) be managed: they should be included in a risk register, with a quantification, a predefined plan if the risk materialize, an owner, etc.

Additionally, several strategies are possible for identified risk: they can be

Avoided (if a subcontractor has a poor financial status it can be removed from the bidders list)

Transferred (if the failure of the main transformer can put at risk the viability of the investment, a business continuity insurance can be purchased)

Shared (if a project is very big, possibly a Joint Venture could be a good choice)

Mitigated (if a construction technology is very complex it could be a poor choice in an emerging country, where an easier solution could be a less risky choice)

Accepted (in this case, usually a monetary reserve is created for the accepted risks).

Unidentified (“unknown”) risks are conceptually different. Even if a great effort has been made to identify all possible risks the experience show that when the project is finally built several unforeseen events will happen, impacting the budget.

The quantification of the contingency for these unknown risk is an hot topic.

 

BoP strikes back: the increasing relevance of Balance of Plant

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.

Automatic cost estimator - the Holy Grail of BoP

Yesterday I had the pleasure to drink an overpriced coffe (2,90€ for an Espresso? Really?) with my good friend José Ramón. He told me that I’m not writing on the blog often enough so I’ve decided to make an effort and find some time to write this post.

The subject I have selected is an evergreen topic, the Holy Grail of BoP – the possibility to create a tool that could calculate quickly the cost of the BoP of a wind farm.

There is already a good amount of material on the subject online, for instance this website of the University of Strathclyde (Glasgow) that present a model created in collaboration with SgurrEnergy (now part of the Wood Group's Clean Energy business).

You can download the tool from their web or from this link for your convenience: BoP estimator tool

I have decided to take it as starting point to show why the task is not so easy and probably me and the other engineers in the team will not be substituted by an Excel file anytime soon.

The ultimate purpose of such models is to pick a small number of input (to make the tool usable) without losing to much in accuracy. The guys at the University decided to go for an extreme simplification and selected only 6 inputs:

  1. Number of WTGs
  2. Turbines Rating
  3. Km of new roads
  4. Km of existing roads
  5. Km of cabling to substation
  6. Km if cabling to grid

That is a very, very extreme oversimplification.

For instance, the model doesn’t keep into account the topography of the area (flat, hilly, mountainous) or other relevant factors (poor soils, inundable areas, etc.) and link the cost only to the rated power of the turbine. As a consequence the calculation of the crane pads cost show a big dispersion in prices (from 5.000 to 42.000 Pounds) and a very low R Squared value (0.26 – that is, the model isn’t explaining the correlation).

Additionally, the model doesn’t consider any monetary input but the output is monetary. I believe it’s rather hard to accept this simplification: for instance, around 50% of the price of cables is in raw materials like copper, that have a high volatility. This could easily bring a multi million inaccuracy.

Also, there is no such a thing as a standard substation – and this is why we have very good electrical engineer in the team. Even without considering the peculiarities of the local grids (something hard to ignore when they are weak, like in Australia) different customers have also different needs. A customer interested in business certainty will ask for redundancy in the substation – 2 main transformer instead of one, emergency “cold” spare transformer, etc.

Same for the foundations: there are currently so many technical solution in the market (precast, with rock anchors, braced, P&H, etc.) that it is really hard to find a correlation between wind turbine MW and foundation cost. There is so much money in foundation and so much pressure on prices that project specific foundations nowadays are the norm, not the exception.

Wind farm optimization algorithms

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.

How much does it cost a wind turbine?

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 😉

Invest in wind energy option #1 – buy wind turbines

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%.

 

Invest in wind energy option #2 – stocks and ETFs

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).

Target Price for BoP: a basic introduction to a complex topic

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.

Google: powered by wind

One interesting fact that you might not know is that the Big G (that is, Google) decided several years ago to power 100% of its activities using renewable energy.

They reached their objective in 2017: what is surprising is that they started only in 2010, with a wind farm in the USA. Basically the strategy is to close Power Purchase Agreements with developers, aiming at investing in “additional” production.

“Additional” means that they don’t want only to buy renewable energy: they want to add this MW to the grid, building new plants and lowering the carbon footprint.

Another interesting fact is that they buy renewable plants connected to the same grid were the data centres are.

For instance their very first PPA was for a 114 MW windfarm in Iowa, one of the states with a data centre, while their 72 MW wind project in Sweden (2013) was intended to  “feed” the data centre in Finland.

The next step is to sell power to the grid at the spot price. Here is where the magic happen: Google is willing to sell it at a loss in case the spot price is lower than the price indicated in the PPA. The idea is that they wanted to use their financial power to give developers a steady cash flow, assuming the risk of fluctuations in prices.

They also get the famous “renewable energy credits”, and they use them to offset  the carbon footprints of the data centres.

A legitimate question would be “Why don’t you buy directly the renewable energy credits?”. The position of Google, as mentioned before, is that they want to help developers to create more and more renewable energy plants. They believe that the best way to do it is to  use their deep pockets to make more projects reality - "bankability", the possibility to get the money to finance a project from a panel of bank, is usually one of the critical point that kills many developments.

The good news, at least for people like me in the wind business, is that the vast majority of the investments (>95%) are in wind farms. The same apply to other business giants following Google on the renewable path, such as Amazon, Microsoft and Facebook.

The quest for scale: mergers and acquisitions in the wind industry

Mergers and acquisition are not a recent phenomenon in the wind business. My former manager Luis Miguel still remember vividly the merger in ’97 between Nordtank Energy Group (NEG) and Moerup Industrial Windmill Construction Company (Micon) – and the subsequent merger between NEG Micon and Vestas in 2004.

While in ’97 I was still enjoying the Golden Age of University, I had myself the pleasure of experiencing first-hand the merger between Nordex and Acciona Windpower 2 years ago. The same year Siemens merged with Gamesa, creating a new giant in the business. And that was not all, because GE’s completed the acquisition of Alstom.

What’s next?

Well, if you want my two cents on the topic the trend is going to continue in the next years. Wind turbine prices are free falling, and quite a lot of MW are awarded with an auction system were the cheaper takes all.

Every wind turbine manufacturer is working hard to lower the cost of energy, and for sure economies of scales help in the effort. I would say that Senvion is a good candidate for the next M&A: owned by the private-equity firms Centerbridge and Rapid Partners could be a good target for a Chinese manufacturer, for instance.

The acquisition can also be “vertical” in the value chain – turbine manufacturers are purchasing companies producing blades, blade moulds (Nordex with SSP Technology), or even providing Service (Vestas with the Operation and Maintenance company UpWind Solutions).

I see a consensus in the industry that this consolidation process will continue during the next years, somehow similar to the automotive industry.