  The growing share of renewable energy makes the optimization of power flows
in power system models computationally more complicated, due to the widely
distributed weather-dependent electricity generation. This article evaluates
two methods to reduce the temporal complexity of a power transmission grid
model with storage expansion planning. The goal of the reduction techniques is
to accelerate the computation of the linear optimal power flow of the grid
model. This is achieved by choosing a small number of representative time
periods to represent one whole year. To select representative time periods, a
hierarchical clustering is used to aggregate either adjacent hours
chronologically or independently distributed coupling days into clusters of
time series. The aggregation efficiency is evaluated by means of the error of
the objective value and the computational time reduction. Further, both the
influence of the network size and the efficiency of parallel computation in the
optimization process are analysed. As a test case, the transmission grid of the
northernmost German federal state of Schleswig-Holstein with a scenario
corresponding to the year 2035 is considered. The considered scenario is
characterized by a high share of installed renewables.
