THE EFFECT OF WEATHER-RELATED UNCERTAINTY ON THE

ADOPTION OF WIND-FARM TECHNOLOGIES

Alolo A Mutaka, University of Hull, Finance, Phone, +447760657934,email:,

Alcino Azevedo, University of Hull, and Finance, Phone + 44(0)1482463107, email:

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Overview

In recent decades, policymakers have been challenged to reduce carbon emissions in order to address the problem of global warming. As a consequence, renewable energy-related firms have expanded substantially their business under several uncertainties, such as those related to deregulation, carbon emission reduction policies,weather and technological progress.The classical investment appraisal techniques and the real options methodology, when applied to the optimization of the adoption of wind-turbine technologies, assume that the output production of the adopted technology is fully predictable. We provide empirical evidence about the Lynn/Inner Dowsing offshore (LIDO) wind-turbine project of Centric Energy, Plc, located at Skegness which shows, however, that this is not always the case. We adjust Paxson and Pinto (2005) model to a monopoly market and derive analytical solutions for the firm’s value function and investment threshold. We calibrate our model with parameters estimated from a dataset comprising daily electricity market prices and daily energy power production of the LIDO wind-turbine project of Centrica Energy for the period between January 2011 and December 2011, a unique contribution. Our empirical evidence illustrates,and quantifies, the relevance of considering weather uncertainty when evaluating the adoption of wind-turbine technologies, a variable which is neglected in the current investment appraisal literature. Our results show that the higher the energy market prices and the energy power production volatility the later is the adoption of the technology, and the more negative (positive) the correlation between the energy market prices and the energy power production, the earlier (later) is the investment.Our results are perhaps helpful to shed light on some relevant intricacies related to wind-farm investments and respective subsidy policies globally.

Methodology

We develop a real option model for a monopoly market which considers both the uncertainty about the energy power price and the wind-turbine energy power production.The two stochastic variables used in our model, energy market prices and energy power production per unit of time are assumed to follow independent but possibly correlated gBm processes. We calibrate our model with empirical data which comprises information about the UK energy power price and information about the energy power production of the Lynn/Inner Dowsing offshorewind-turbine project of Centric Energy, Plc, located at Skegness. We use the above empirical data and our model to conjecture about the optimal time to invest in the Lincs Offshore (LO) project of Centrica Energy and Dong Energy, Plc, located at the Lincolnshire coast, which is yet under contruction and based on a new wind-turbine technology promoted by Siemens (the developer) as more efficient than that used in the LIDO project,and planned to comprise 75 wind turbine.

Results

Our dataset shows that wind conditions are very volatile in the LIDO’s wind-turbines farm case affecting the energy power production. Weather conditions uncertainty affect the energy power production of the wind-turbine farms and are hardly predictable so it must be taken into account when evaluating wind-turbine investments.The sensitivity analysis show that investment threshold is very sensitive to changes in the volatility of the energy power production. The results further show that the investment threshold reacts sharply to the changes in both the volatility of the energy market prices and the energy power production per unit of time. The lower the volatility of the energy power market prices and the volatility of the energy power production per unit of time, the lower is the investment threshold.Our results also show that the more negative (positive) the correlation between energy power prices and energy power production the earlier (later) is the adoption of the wind-turbine technology.From our dataset, we found a negative correlation between the energy power production of the wind turbines and the energy power prices.

Conclusions

We conclude that if the market conditions that held for the LIDO wind-turbine project of Centrica Energy, Plc between Jan. 2011 and Dec. 20111, holds in the future for the LO wind-turbine farm under construction by the Centrica Energy & Dong Energy, Plc, namely the level of uncertainty for the energy market prices and for the energy power production, as well as for their respective growth rates, and the value of the risk-less interest rate and of the correlation between energy market prices and ex-post energy power production of the LO wind-turbine project, the investment should be deferred. Therefore, it came to us as a surprise that according to information released by Centrica Energy & Dong Energy, Plc. they decided to go ahead with the investment. The discrepancy between the optimal investment behaviour suggested by our results and that of Centrica Energy & Dong Energy, Plc may be due to government subsidies which is absent from our analyses and whose value was not yet revealed.

References

Azevedo A., and Paxson, D., (2012), “Rivalry with market and Efficiency uncertainty in the adoption of new technologies”, presented at London Business school, Real Options Conference.

Azevedo A., and Paxson, D., (2013). Rivalry with Output Price and Weather Uncertainty in theAdoption of Renewable Energy Technologies, Working Paper, Hull University Business School.

Cortazar G., Schwartz E. S. and Salinas M. (1998): "Evaluating environmental investments: A Real Options approach". Management Science, 44(8), 1059-1070.

Fuss, S., Szolgayova, J., Obersteiner, M., and Gusti, M., (2008), “Investment under market and climate policy uncertainty”, Applied Energy, 85 (8), 708-721.

Paxson, D., and Pinto, H., (2005), “Rivalry under Price and Quantity Uncertainty”, Review of Financial Economics, 14, 209-224.