Valuation of wind energy projects:

A Real Options approach.

Luis Mª Abadie (BC3) José M. Chamorro (UPV/EHU)

2013 International Energy Workshop, 19-21 June, Paris

Background

Renewable resources: ever more relevant in electricity generation.

Intermittent generation: technical challenges (operation of the electric system).

From an economic point of view: irreversibility, flexibility, uncertainty (ROA).

Focus: wheteher to invest or not, and optimal time to invest in a new wind park ;

• Different payment settings: fixed feed-in-tariff over 20 years, market price of electricity,

constant premium over price, one-time subsidy, price plus renewable energy credits

• seasonal pattern in wind generation and electricity price (correlated)

Sources of risk:

• Physical: wind electricity

• Economic: electricity price, renewable energy credit (ROC)

Methodology: Risk-neutral valuation, Monte Carlo simulation, trinomial lattice

Example: UK data (DECC, ICE)

Output: value of the option to invest & trigger investment cost under different

support regimes, sensitivity analyses

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Some references Boomsma, T.K., Meade, N., Fleten S.-T. (2012), Renewable energy investments

under different support schemes: A real option approach, European Journal of Operational Research 220, 225-237.

Reuter W.H., Szolgayová J., Fuss S., Obersteiner M. (2012), Renewable energy investment: Policy and market impacts, Applied Energy 97, 249-254.

Klessmann C., Nabe C., Burges K. (2008), Pros and cons of exposing renewables to electricity market risks - A\ comparison of the market integration approaches in Germany, Spain, and the UK. Energy Policy 36, 3646-3661.

Daim T.U., Amer M., Brenden R., 2012. Technology roadmapping for wind energy: case of the Pacific Northwest. Journal of Cleaner Production 20(1), 27-37.

Outline Stochastic models

Estimation

Valuation: now-or-never & now-or-later

Sensitivity analysis

Conclusions

Stcohastic models

Long-term electricity price:

Wind electricity (load factor):

Certificate price (UK Renewables Obligation Certificates ROCs):

The first two are correlated (seasonality).

But they are uncorrelated with the third.

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Estimation: Electricity price

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Estimation: Wind load factor

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Estimation: ROC price

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To invest now or never: Monte Carlo simulation

Wind generation over a time period Dt:

If it is sold at market price, the present value of the revenues amounts to:

We generate s=1,2,…,m uncorrelated random samples of Et and Wt.

Each simulation comprises j=1,2,…,n time steps.

m=1,000 simulation runs; 60 steps/year (Dt=1/60) over 20 years: n=1,200 steps.

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Electricity market price

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For a wind farm with installed capacity C=50 MW we get:

If total investment cost is I=66,000,000 £ the Net Present Value is:

In a now-or-never setting, it is optimal to invest whenever NPV > 0.

What FFIT would provide the same NPV? Solution: p=70.52 £ /MWh

A constant feed-in-tariff

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Market price plus fixed premium

Market price plus ROC price

Under the above assumptions we get:

If ROC price decreases exponentially over time at a rate of 10%:

Valuation & investment timing: Trinomial lattice (m-r)

Electricity price: A simulation is run

at each of the nodes in the lattice.

Logarithmic transformation (X≡ln E)

At the option’s maturity:

At earlier times:

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Case studies: maturity at T=10, lattice with Dt=1/4

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Electricity market price

A constant feed-in-tariff

All values in Table 4 outweigh the continuation values in Table 7: Invest Now!

FITTs are effective at bringing forward investments in wind parks.

A one-time, initial subsidy

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Market price plus fixed premium

I=75: a premium p=10 raises NPV from 47.2 to 64.5; still it is better to wait.

In Table 8, an initial subsidy S=15 M£ pushes the trigger cost as high as I*=98.4.

Market price plus ROC price

From Table 8, this scheme is equivalent to a subsidy S between 15 and 20 M£.

At these levels of S the 5 M£ increase raises I* by a factor of 5: ROCs effective.

Sensitivity analysis: changes in option’s maturity

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Sensitivity analysis: changes in electricity volatility

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Conclusions

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Valuation model for investments in wind parks when there are futures markets.

Focus on developed electricity markets with ‘pure’ or ‘mixed’ payment schemes.

Model accounts for physical (wind load factor) and economic uncertainties.

Major features: mean reversion, seasonality, correlation, actual market data.

Numerical solution combines MC simulation with a trinomial lattice

Market prices seem to push for delaying investments. FITTs do the opposite. A

one-time initial subsidy can be particularly effective. The same holds for ROCs.

Shorter investment maturities entail lower continuation values, but still it is better

to wait. As for electricity volatility, lower levels do help, but not enough.