Research: Improving investor confidence in wind power through innovations in ‘wind derivatives’

Researchers from the University of Melbourne and University of Waterloo have devised new methods to measure and manage the unpredictability of wind conditions, which could boost investor confidence in wind power.

The research was published by Ms Giovani Gracianti, Dr Rui Zhou and Dr Xueyuan Wu from the University of Melbourne, and Dr Jonny Siu-Hang Li from the University of Waterloo in the Annals of Actuarial Science (Cambridge University Press) in September 2023.

“Wind power is a vital component of the global transition to clean and renewable energy sources, but harnessing the power of the wind is challenging, mainly due to the unpredictable nature of wind conditions,” said Ms Gracianti, who is undertaking a PhD under the supervision of her fellow authors.

“These challenges lead to uncertainty in revenue generation for energy producers, making wind power investments less attractive to potential investors. This ultimately is slowing down the world’s transition to renewable energy,” she said.

The role of ‘wind derivatives’

Derivatives are a tool used in the financial sector designed to mitigate losses when adverse conditions make it difficult to predict the performance of future investments. Wind derivatives are used by wind farms to manage the financial risks associated with the variability of wind speed and offer a financial safety net to energy producers, ensuring they receive compensation when low wind power production leads to reduced revenues.

“Wind derivatives are important because this financial protection stabilises operations and revenues for energy producers, making renewable energy projects more enticing to investors and helping the global shift toward cleaner energy sources,” said Dr Zhou.

“Our latest research details ways to refine the calculations and models that determine derivative pricing, recognising the imperfections in current methodologies,” she said.

Wind farm in the ocean

New innovations in wind derivative modelling

The researchers have found four ways they can be improved.

  • Generalized 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 a more accurate representation of wind behaviour.
  • Risk-neutral pricing: The study develops risk-neutral pricing methods suitable for both the new GHYP-based wind speed model and 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 sheds light on 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 the 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.

Dr Zhou said actuaries have an important role to play in the global transition to renewable energy.

“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. There is a growing urgency for actuarial expertise in tackling climate-related challenges and inspires further research in this field,” said Dr Zhou.

See the full journal article in Annals of Actuarial Science