The energy transition towards renewable and more distributed power production triggers the need for grid and storage expansion on all voltage levels. Today’s power system planning focuses on certain voltage levels or spatial resolutions. In this work we present an open source software tool eGo which is able to optimize grid and storage expansion throughout all voltage levels in a developed top-down approach. Operation and investment costs are minimized by applying a multi-period linear optimal power flow considering the grid infrastructure of the extra-high and high-voltage (380 to 110 kV) level. Hence, the common differentiation of transmission and distribution grid is partly dissolved, integrating the high-voltage level into the optimization problem. Consecutively, optimized curtailment and storage units are allocated in the medium voltage grid in order to lower medium and low voltage grid expansion needs, that are consequently determined. Here, heuristic optimization methods using the non-linear power flow were developed. Applying the tool on future scenarios we derived cost-efficient grid and storage expansion for all voltage levels in Germany. Due to the integrated approach, storage expansion and curtailment can significantly lower grid expansion costs in medium and low voltage grids and at the same time serve the optimal functioning of the overall system. Nevertheless, the cost-reducing effect for the whole of Germany was marginal. Instead, the consideration of realistic, spatially differentiated time series led to substantial overall savings.

The paradigm shift of large power systems to renewable and decentralized generation raises the question of future transmission and flexibility requirements. In this work, the German power system is brought to focus through a power transmission grid model in a high spatial resolution considering the high voltage (110 kV) level. The fundamental questions of location, type, and size of future storage units are addressed through a linear optimal power flow using today’s power grid capacities and a generation portfolio allowing a 66% generation share of renewable energy. The results of the optimization indicate that for reaching a renewable energy generation share of 53% with this set-up, a few central storage units with a relatively low overall additional storage capacity of around 1.6 GW are required. By adding a constraint of achieving a renewable generation share of at least 66%, storage capacities increase to almost eight times the original capacity. A comparison with the German grid development plan, which provided the basis for the power generation data, showed that despite the non-consideration of transmission grid extension, moderate additional storage capacities lead to a feasible power system. However, the achievement of a comparable renewable generation share provokes a significant investment in additional storage capacities.

The growing share of renewable energy results in a more challenging and computationally more demanding modelling of energy transmission systems. This is mainly due to the widely distributed weather-dependent electricity generation. This article evaluates two different methods to reduce the temporal complexity of a power grid model with extendable storage capacity. The goal of the reduction techniques is to accelerate the computation of the linear optimal power flow. The reduction is achieved by choosing a small number of representative time periods to represent one whole year. To select representative time periods, the hierarchical clustering is used to aggregate either adjacent hours or independently distributed days into clusters of time series. The efficiency of the aggregation is evaluated by means of the error of the objective value and the time reduction of the linear optimal power flow. Some statistical indicators are also introduced with the intention of anticipating the accuracy of the aggregations. Further, both the influence of the size of the network and the efficiency of parallel computation in the optimization process are analyzed. As a test case, the transmission network 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 and the extendability of the storage capacity.Index Terms-Power system modelling, energy system modelling, renewable energy, linear optimal power flow, time series aggregation, storage capacity expansion planning.

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