A recent study is giving wind energy investors new tools to deal with unpredictable weather.
New research is providing a boost for wind energy investors through new methods to measure and manage the unpredictability of wind conditions.
The combined study, ‘An assessment of model risk in pricing wind derivatives’ from the University of Melbourne and University of Waterloo in Canada, has been published in the Annals of Actuarial Science through Cambridge University Press.
The study focuses on wind derivatives to manage risks from variable conditions at turbine sites.
“Wind power is a vital component of the global transition to clean and renewable energy sources, but harnessing the power of wind is challenging, mainly due to the unpredictable nature of wind conditions,” research paper authors Giovani Gracianti said.
“These challenges lead to uncertainty in revenue generation for energy producers, making wind power investments less attractive to potential investors.
“Ultimately, this is slowing down the world’s transition to renewable energy.”
In the financial sector, derivatives are used to mitigate losses when adverse conditions make it difficult to predict the performance of future investments. Similarly, wind derivatives are tools used to manage financial risks associated with variability of wind speed.
They offer a financial safety net to energy producers, ensuring they receive compensation when low wind power production reduces revenue.
“This financial protection stabilises operations and revenues for energy producers, making renewable energy projects more enticing to investors and helping the global shift towards cleaner energy sources,” research paper author Dr Rui Zhou said.
“Our latest research details ways to refine calculations and models that determine derivative pricing, recognising the imperfections in current methodologies.”
The researchers have discovered four ways in which innovations in wind derivative modelling can be improved.
• Generalised Hyperbolic Distribution (GHYP): The researchers propose the use of GHYP distribution to model wind speed data. This distribution can capture the leptokurtosis (characterised by heavier tails) observed in wind speed data. This is the first application of the GHYP distribution in wind speed modelling and allows accurate representation of wind behaviour.
• Risk-neutral pricing: The study develops risk-neutral pricing methods suitable for the GHYP-based wind speed model, as well as models proposed in previous research. These pricing approaches leverage the “conditional Esscher transform”, a tool developed and utilised in the actuarial and financial mathematics fields to calculate wind derivative prices accurately.
• Impact analysis: The research prices wind derivatives with different models and analyses the resulting price differences to assess the extent of “model risk”. This data reveals the importance of using accurate wind speed models for pricing wind derivatives effectively.
• Leveraging actuarial expertise: The study highlights the valuable contribution of actuarial expertise in addressing financial uncertainties associated with renewable energy and climate change mitigation. Actuaries, known for their expertise in creating models for complex financial and environmental data, have a critical role to play in the evolving landscape of renewable energy and climate risk management.
“By applying knowledge to develop innovative pricing methodologies for wind derivatives, actuaries can account for the characteristics of wind speed data and other factors influencing wind power production,” Zhou said.
“There is a growing urgency for actuarial expertise in tackling climate-related challenges and it inspires further research in this field.”
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