Using wind velocity estimated from a reanalysis to minimize the variability of aggregated wind farm production over Europe

In this work we use the mean‐variance portfolio optimization, using as input the power derived from the wind and density results of meteorological model simulations (ERA‐Interim reanalysis), to minimize the variability of the wind power produced in a large region. The methodology involves selecting the placement of the wind farms on a high spatial resolution grid. We used the EU‐28 region to check the method and perform sensitivity tests. We studied the influence of the ratio between the total installed power of the whole domain (P_t) and the maximum power that can be installed per cell (P_mi) on the variability of wind power yield. The results show that the reliability of the electrical system improves when P_mi grows and worsens when P_t grows. A quadratic fit relates the variability of the system and the aforementioned ratio. The optimization procedure tends to select groups of terrain cells where wind farms should be installed. These groups grow when more energy production is demanded of the system, but they roughly maintain their location. There is some evidence that in a larger region greater system reliability could be achieved. Most of the selected cells have either a high or a low capacity factor and those with the latter are crucial in enhancing system reliability.