Spatial dependencies of wind power and interrelations with spot price dynamics

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractWind power has seen strong growth over the last decade and increasingly affects electricity spot prices. Generation from wind energy is stochastic, and if there is lot of wind, prices tend to be lower. Therefore, for an investor, but also for the whole electricity system, it is important to assess the value of wind power at different locations. In this paper, we develop a stochastic simulation model that captures the full spatial dependence structure of wind power by using copulas, incorporated into a structural supply and demand based model for the electricity spot price. This model is calibrated with German data. We find that the specific location of a turbine -i.e., its spatial dependence with respect to the aggregated wind power in the system -is of high relevance for its value. Many of the locations analyzed show an upper tail dependence that adversely impacts the market value. Therefore, a model that assumes a linear dependence structure would systematically overestimate the market value of wind power in many cases. This effect becomes more important for increasing levels of wind power penetration and may render the large-scale integration into markets more difficult.

Does renewable energy generation decrease the volatility of electricity prices? An analysis of Denmark and Germany

“…When the installed VRE capacity increases, the available supply increases and the parallel shifts are larger, which contributes to the growing weekly volatility. In both countries, the impact can be amplified by highly clustered wind power farms (Elberg and Hagspiel, 2015). However, the average weekly solar power is not found to contribute to the weekly price volatility, which can be explained by the peak-price-decreasing impact of solar power in Germany.…”

“…The results for Denmark suggest that access to flexible capacity via adequate transmission capacity can reduce short-term volatility. In addition, measures such as i) capacity payments that incentivise flexible plants (Hach and Spinler, 2016), ii) dispersing wind and solar power farms (Elberg and Hagspiel, 2015), and iii) integration of adjacent markets (Farahmand et al, 2012) can be utilised. On the consumer side, enhanced understanding of the causes of volatility can be used to design tariffs that incentivise demand response (Dupont et al, 2014), which is likely to mitigate the costs of balancing caused by the intermittency of VRE.…”

An Illustrative Note on the System Price Effect of Wind and Solar Power: The German Case

“…Alternative empirical functions from hourly wholesale prices and (residual) load data are, for example, derived by Barlow (2002), Burger et al (2006) and Elberg and Hagspiel (2013).…”

A Multifactor Approach to Modelling the Impact of Wind Energy on Electricity Spot Prices

“…We looked at three variables: wind energy forecast F t , wind penetration index WPI t defined in Subsection 4.2 and the residual demand RD t defined as the difference between the total load and the forecasted wind energy production (motivated by [Elberg and Hagspiel, 2015]). We fitted linear and quadratic models involving different linear combinations of these variables.…”

“…Few exeptions include [Elberg and Hagspiel, 2015] (copula model for the spatial dependence structure of wind power in Germany), [Veraart, 2016] (impact of wind power generation on German spot prices modelled by regime-switching Lévy semistationary processes), [Ketterer, 2014] (GARCH model of wind power's impact on the electricity price level and volatility in Germany, taking into account changes in market regulations) or [Deschatre and Veraart, 2017] (the impact of wind energy production on the spikes in the spot prices). In this paper we attempt to fill this research gap by introducing a model for both spot and futures prices with wind energy production as an exogenous variable.…”