Multivariate continuous-time modeling of wind indexes and hedging of wind risk

Abstract

With the introduction of the exchange-traded German wind power futures, opportunities for German wind power producers to hedge their volumetric risk are present. We propose two continuous-time multivariate models for the wind power utilization at different wind sites, and discuss the properties and estimation procedures for the models. Employing the models to wind index data for wind sites in Germany and the underlying wind index of exchange-traded wind power futures contracts, the estimation results of both models suggest that they capture key statistical features of the data. We argue how these models can be used to find optimal hedging strategies using exchange-traded wind power futures for the owner of a portfolio of so-called tailor-made wind power futures. Both in-sample and out-of-sample hedging scenarios are considered, and, in both cases, significant variance reductions are achieved. Additionally, the risk premium of the German wind power futures is analysed, leading to an indication of the risk premium of tailor-made wind power futures.

Spatio-temporal smoothing and dynamics of different electricity flexibility options

“…For this, we generate daily capacity factors for wind power as introduced by Benth, Christensen, and Rohde in [BCR21] to obtain many statistically representative weather years. These data are synthesised from ERA5 reanalysis data ranging from 1980 to 2020 [Her+18] and possess similar moments and distributional qualities.…”

“…We build on the approach by Benth, Christensen, and Rohde in [BCR21] for generating daily wind capacity factors for 100 weather years in our case. They find that it is possible to represent wind capacity factors for different locations as a multi-dimensional Ornstein-Uhlenbeck process with Lévy jumps.…”

“…We use reanalysis data from ERA5 [Her+18] from 1980 until 2020 and the open source tool Atlite [Hof+21] to compute capacity factors for northern Norway (defined by the market zones NO3 and NO4) and southern Norway (NO1, NO2, and NO5). With these capacity factors, we follow the steps described in [BCR21] to estimate Λ, Σ, and . Using these estimates, we obtain a stochastic time-series model fitted to data whose simulation matches the first three moments of the original capacity factors as shown in Figure 10 We generate 100 calendar years of wind capacity time series on a daily basis, but with this approach, we can also generate much larger numbers, probably smoother results, and rely more on sampling than we do here (in particular to obtain the surfaces in Figure 2 based on the average of the same 100 years).…”

“…In this paper, we use methods that can generate distributionally consistent artificial data that satisfactorily fit historical data of production and demand based on [BP18;BCR21]. A good representation of production distribution is particularly important for intermittent renewable generation [ST21].…”

Managing the Intermittency of Wind Energy Generation in Greece

“…An other direction is the examination of different strategies to mitigate the volumetric risk of renewable energy such as hedging strategies. Wind Power Futures and other financial contracts are steadily launched by EEX and attract potential investors [8,[54][55][56]. The market is on a premature level and scientific research has the potential to pave the way for the development of new derivatives designed to capture various dimensions of volumetric risk [7,57].…”