Earnings/Price Risk

Appendix C. Significance of Earnings Volatility for CfD vs. RO

In this section we present our analysis of how different factors contribute to the volatility of earnings for on- and off-shore wind. This analysis contributes to our overall assessment of the potential for CfDs to reduce hurdle rates as a result of their stabilisation of revenue volatility.

C.1. Earnings/Price Risk

By earnings/price risk we define changes in market condition which adversely affect the value of the asset driven by power market risk (incl. negative prices) and volume risk. To understand better the extent to which more stable revenues from electricity prices would affect wind generation, we have developed a set of modelling simulations that allows us to quantify the impact of stable revenues on both existing assets and new assets.

Figure C.1

Impact of Price Risk on WACC

Our findings are based on simulations of revenues using two techniques:

Historical simulation of existing assets: Using historical information on price and volumes, we analyse what revenue volatility would have been for the period 2005-2013 if the subsidy regime had been a CfD regime instead of a ROC regime; and

Scenario based analysis of hypothetical new assets: Using a set of different scenarios for the power price evolution we analyse the impact on the present value of a hypothetical new asset of various shocks, including price variation.

This section focuses on absolute risk and the importance of the main revenue volatility drivers of electricity generators, i.e. volume vs. price risk. It should be noted that although volume risk affects revenue volatility, and therefore possibly the level of achievable gearing, it is to some extent diversifiable, which means it is less important for the overall

determination of the hurdle rate.

Item Comment Impact

Exposure to Short Term (Annual) Volatility

• Power prices are one of several drivers of short term revenue volatility. Volatility coming from price is mitigated almost entirely

Exposure to Long Term Volatility

• Power prices are a key long term risk driver. The exposure to this macro-economic risk mitigated almost entirely

Exposure to Negative Prices

• Subsidy is capped at strike price such that the captured subsidy is reduced. However, the effect on present value is very limited.

Net Impact • Significant reduction of power price exposure

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In order to translate the reduction of absolute risk directly into an impact on beta, we would need to quantify the relationship between wholesale power prices and the market index, which we have not attempted here. This analysis can therefore be used only to assess qualitatively the direction of beta risk and the relative risk reductions between technologies and cannot be seen as stand-alone evidence of the magnitude of beta-risk reduction or the impact on the hurdle rate.

C.1.1. Historical Simulation: Revenue Volatility

The objective of the historical simulation is to analyse how the two regimes would have performed if applied to actual, observable market data and load factors, focussing on the revenue volatility.⁹⁰ We proceeded as follows:

1. Using historical hourly generation and power prices since 2005 we reconstructed the revenues as they would have been under ROC vs. CfD scheme for existing assets;

2. We then compare the volatility under the two regimes; and

3. Finally, we present a breakdown of the relative importance of volume and price risk.

Using this approach, we find that earnings volatility would have been significantly lower under the CfD system than under the ROC system. In particular, we find that market price risk, which is largely eliminated in the CfD regime, is much more important for onshore wind than offshore wind. In turn, we expect the reduction in discount factor to be larger for

onshore wind than offshore wind. Due to lack of data we have not analysed other sources of renewables such as biomass. For these asset classes, the reduction of market price risk is likely to be less important than onshore wind, because a larger proportion of costs are OPEX and therefore not sunk at the time of investment.

C.1.1.1. Summary of Historical Data Analysis

Table C.1 shows the simulated assets together with the date of the first reliable data. On the basis of these individual assets we constructed hypothetical onshore and offshore portfolios consisting of all of the assets (equally weighted).

90 The main benefit of this approach is that, rather than making subjective assumptions about scenarios for the future, we look at how revenues would have responded to historical events under the two schemes. The main drawback of this approach is that it only shows “one path” and that it only appropriate for assets which have been constructed.

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Table C.1 Simulated Assets

Source: Platts Powervision

Figure C.2 shows the achieved load factor for the onshore portfolio for the period 2005-2013.⁹¹ The average load factor is 25 percent but varies considerably between years, typically by +/-5 percent points.

Figure C.3 shows the reconstructed (ROC) and counterfactual (CfD) revenues for a portfolio of onshore portfolio assets under the two different subsidy regimes. The revenue stream varied by approximately +/- 20 percent around the mean (£40/kW), under the ROC scheme, and +/-12 percent around the mean (£25/kW), under the CfD scheme.⁹² This is affected significantly by the higher price in 2008.

91 The load factor is an average of the wind farms in the portfolio from the date where we have reliable data. 2005 is therefore based on only a single wind farm whilst 2012-2013 contained a portfolio of 5 wind farms.

92 For this analysis we assume that the CfD price was set such that the present value of the asset under CfD is exactly identical to the present value under the ROC regime, such that annual revenue volatility. In practice, of course, the power price cannot be forecasted so a ROC “revenue forecast” need not be the same as the outcome. This aspect is addressed separately in the next section.

Name Capacity (MW) Registration Date Data Since ROCs

Offshore

Walney Offshore Wind Farm Unit 1 182 01/11/2010 Feb 12 2

Greater Gabbard Offshore Windfarm 180 18/12/2009 Feb 11 2

Thanet Offshore Wind Unit 1 99 30/04/2010 Aug 10 2

Onshore

Kilbraur Windfarm 47.5 14/12/2007 Jun 08 1

An Suidhe Windfarm 19.4 05/05/2010 Nov 10 1

Black Law Wind Farm 134 01/04/2005 Apr 05 1

Beinn Tharsuinn 29.7 02/12/2005 Feb 06 1

Braes of Doune 74 04/09/2006 Sep 06 1

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Figure C.2