What Happens When it's Windy in Denmark? An Empirical Analysis of Wind Power on Price Variability in the Nordic Electricity Market

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 EconStor may AbstractThis paper attempts to test the effect that wind power production has on the variability of wholesale electricity prices in the spot market. I use a simple distributed lag econometric model and five years worth of hourly and daily data from Denmark, which is one of the few places with a long history of significant wind power penetration. I show that wind power has the effect of reducing intra-day variability but that this result only partially carries over to price variation over weekly time windows. I suggest that the reduction in price variability in turn is due to a steeper supply schedule at peak-load times.

Towards Definition of the Risk Premium Function

“…The mathematical derivation in Section III proves that the relation between the stochastic RP process and the exogenous variables can be described as a linear combination, via the KGB Linear Model, (24), and parameterized via MLR. Several exogenous variables are proposed in Section V. By using MLR we obtain regression parameters and other statistical quality measures of the estimation.…”

“…For validation of the measure change from to with such a choice of , please refer to [23], where the authors analyse measure change with state dependent Radon-Nikodym derivatives given by Ornstein-Uhlenbeck processes (including jumps) and RP is expressed as a weighted sum of the impact factors. This way, the KGB Linear Model is defined in (24).…”

“…To investigate the influence of the RES on RP development, we have correlated RP calculated using KGB Model with RES production, with the focus on PV and wind production. In the literature, expected price models related to several exogenous variables can be found that include drivers like wind production share, PV production share and consumption, [24], [24], [26]. Since RP is strongly related to time-to-maturity, [5], [6], it was chosen in addition to the three above already established exogenous variables.…”

“…To avoid correlation error between input variables, all inputs need to be exogenous to RP. Since wind and PV productions depend on weather conditions, and since their marginal production cost is near zero, the wind and PV power series can be treated as exogenous to price [24], consequently to RP. For the same reason, consumption can be treated as exogenous, too.…”

“…For the same reason, consumption can be treated as exogenous, too. To ensure that all exogenous variables are stationary, the Dickey-Fuller test, [24] was performed. The test results show that all exogenous variables used are stationary.…”