A new Data-Driven Approach for Comparative Assessment of Baseline Load Profiles Supporting the Planning of Future Charging Infrastructure

In order to achieve the worldwide set ambitious climate goals, the identification and characterization of flexibility in city districts can reduce grid loads and avoid grid congestion. Unlike other flexibility indicators in the literature, the present paper introduces a new flexibility indicator that uses a data-driven approach to determine flexibility from actual measured load profiles. We present this new indicator by considering flexibility in the context of planning charging infrastructure with a valley filling approach. For this use case, we introduce a data-analysis workflow to apply the presented flexibility indicator. The described data-analysis workflow is applied to data from a real-world city district. Based on the results from the real-world data, we show that the highest peak load and the least flexible peak are not always identical. Therefore, it is not sufficient to consider only the highest peak loads to adequately describe flexibility. Furthermore, we discuss that additional flexibility can be used as another degree of freedom to optimize the charging power or the charging duration. In the presented real-world data, we show that the maximum required charging power is determined by the most inflexible peak and can be the same or smaller for all peaks with a higher flexibility. Moreover, we highlight the difference between considering buildings individually and combining them as a district.