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The growth in volatile renewable energy (RE) generation is accompanied by an increasing network load and an increasing demand for storage units. Household storage systems and micro power plants, in particular, represent an uncertainty factor for distribution networks, as well as transmission networks. Due to missing data exchanges, transmission system operators cannot take into account the impact of household storage systems in their network load and generation forecasts. Thus, neglecting the increasing number of household storage systems leads to increasing forecast inaccuracies. To consider the impact of the storage systems on forecasting, this paper presents a new approach to calculate a substation-specific storage forecast, which includes both substation-specific RE generation and load forecasts. For the storage forecast, storage systems and micro power plants are assigned to substations. Based on their aggregated behavior, the impact on the forecasted RE generation and load is determined. The load and generation are forecasted by combining several optimization approaches to minimize the forecasting errors. The concept is validated using data from the German transmission system operator, 50 Hertz Transmission GmbH. This investigation demonstrates the significance of using a battery storage forecast with an integrated load and generation forecast.

To optimally design integrated energy systems a widely used approach is the Energy Hub. The conversion, storage and transfer of different energy vectors is represented by a coupling matrix. Yet, the coupling matrix restricts the configuration of the Energy Hub and the constraints, that can be included. This paper proposes a MILP based optimization framework, which allows a high variability and adaptability and is based on energy flows. The functionality of the developed framework is tested on four use cases depicting different system sizes and Energy Hub configurations. It is shown that the framework is able to simplify the design process of an Energy Hub.

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