Spatial Dependence in Wind and Optimal Wind Power Allocation: A Copula Based Analysis
Oliver Grothe () and
Julius Schnieders ()
Additional contact information Oliver Grothe: Energiewirtschaftliches Institut an der Universitaet zu Koeln
Julius Schnieders: Energiewirtschaftliches Institut an der Universitaet zu Koeln
The investment decision on the placement of wind turbines is, neglecting legal formalities, mainly driven by the aim to maximize the expected annual energy production of single turbines. The result is a concentration of wind farms at locations with high average wind speed. While this strategy may be optimal for single investors maximizing their own return on investment, the resulting overall allocation of wind turbines may be unfavorable for energy suppliers and the economy because of large ﬂuctuations in the overall wind power output.
This paper investigates to what extent optimal allocation of wind farms in Germany can reduce these ﬂuctuations. We analyze stochastic dependencies of wind speed for a large data set of German on- and oﬀshore weather stations and ﬁnd that these dependencies turn out to be highly nonlinear but constant over time. Using copula theory we determine the value at risk of energy production for given allocation sets of wind farms and derive optimal allocation plans. We ﬁnd that the optimized allocation of wind farms may substantially stabilize the overall wind energy supply on daily as well as hourly frequency.