Wind Derivatives: Modeling and Pricing

Abstract

Abstract.Wind is considered to be a free, renewable and environmentally friendly source of energy. However, wind farms are exposed to excessive weather risk since the power production depends on the wind speed and the wind direction. This risk can be successfully hedged using a financial instrument called weather derivatives. In this study the dynamics of the wind generating process are modeled using a non-parametric non-linear wavelet network. Our model is validated in New York. The proposed methodology is compared against alternative methods, proposed in prior studies. We find that wavelet networks can model the wind process very well and consequently they constitute an accurate and efficient tool for wind derivatives pricing. Finally, we provide the pricing equations for wind futures.

Pricing analysis of wind power derivatives for renewable energy risk management

“…Noticeable, the distributions of the considered aggregations of the wind power index have similar characteristics as those of wind speed data in existing studies (e.g., Brown and Murphy, 1984;Bivona, Bonanno, Burlon, Gurrera, and Leone, 2011;Alexandridis and Zapranis, 2013). In the literature, several distributions have been fitted to average hourly wind speed data, including the gamma distribution (Sherlock, 1951), the inverse Gaussian distribution (Bardsley, 1980), the squared normal distribution (Carlin and Haslett, 1982), lognormal distributions (Luna and Church, 1974;Torres and De Francisco, 1998), the Chi-square (Dorvlo, 2002), and the Weibull distribution (Hennessey, 1978;Brown, Katz, and Murphy, 1984;Akpinar and Akpinar, 2005;Torres, Garcia, De Blas, and De Francisco, 2005;Bivona, Bonanno, Burlon, Gurrera, and Leone, 2011, among others).…”

“…Benth and Šaltytė Benth (2009) consider wind speed futures and suggest a pricing approach under the risk neutral measure where daily average wind speeds are dynamically modelled by a continuous-time autoregressive model with seasonal mean and volatility. Alexandridis and Zapranis (2013) present a pricing formula of futures contracts written on the cumulative average wind speed and the Nordix wind speed index also under the risk neutral measure. Benth and Pircalabu (2018) derive prices for wind power futures in the framework of no-arbitrage pricing by proposing a non-Gaussian Ornstein-Uhlenbeck model for the wind power load factor series.…”

Pricing Wind Power Futures

“…The logit‐normal transformation and its inverse are given by:where is the transformed power utilization with its seasonal mean component Λ t and short‐term variation around that mean Y t . In the literature, truncated Fourier expansions (Alexandridis & Zapranis, 2013) and local linear smoothing techniques are common practice to determine the seasonality. Benth and Šaltytė Benth (2009) and Härdle and López‐Cabrera (2012) suggest to use a sinusoidal truncated Fourier series (TFS).…”

A Prediction Market Trading Strategy to Hedge Financial Risks of Wind Power Producers in Electricity Markets

“…The WPPs, obviously, face volume risk due to the resource variations. Wind derivatives [28] are emerging as standard contracts to be traded in exchanges, such as in European Energy Exchange (EEX) [29] to manage wind-related risks. However, since they are available for long-term periods, e.g.…”