Energy system modeling with aggregated time series: A profiling approach

Abstract

Energy system models with a high technical, spatial and temporal resolution are not always feasible and therefore require a complexity reduction through, for example, time series aggregation. Modeling results of aggregated time series are not always reliable but may show large deviations depending on the energy system configuration, model and selected data. By analyzing the interaction between time series and modeling results, this study contributes to an improved understanding of these deviations and identifies relevant time series characteristics such as extreme values or parameters describing the relation between time series. The proposed profiling embeds these parameters and adapts already aggregated time series to the characteristics of original time series within three iterative steps. Results of an analysis including three scenarios, time series from eleven years and eight aggregation variants show that profiling can reduce systematic overestimation or underestimation of installed capacities by an average of 86% to a maximum of 0.4 GW and decrease the standard deviation of the modeling results by 71%. Profiling can be used to better represent time series characteristics and minimize deviations of the modeling results, thus providing a complement to existing aggregation methods. Despite these substantial improvements in comparison to aggregation methods, analyses suggest that there is further potential to improve time series aggregation using a profiling approach. Potential directions for future research are discussed.