Impact of different time series aggregation methods on optima1 energy system design

摘要

AbstractModeling renewable energy systems is a computationally-demanding task due to the high fluctuation of supply and demand time series. To reduce the scale of these, this paper discusses different methods for their aggregation into typical periods. Each aggregation method is applied to a different type of energy system model, making the methods fairly incomparable.To overcome this, the different aggregation methods are first extended so that they can be applied to all types of multidimensional time series and then compared by applying them to different energy system configurations and analyzing their impact on the cost optimal design.It was found that regardless of the method, time series aggregation allows for significantly reduced computational resources. Nevertheless, averaged values lead to underestimation of the real system cost in comparison to the use of representative periods from the original time series. The aggregation method itself e.g., k-means clustering plays a minor role. More significant is the system considered: Energy systems utilizing centralized resources require fewer typical periods for a feasible system design in comparison to systems with a higher share of renewable feed-in. Furthermore, for energy systems based on seasonal storage, currently existing models integration of typical periods is not suitable.Highlights•Time series aggregation significantly reduce the computational load of energy system optimization models.•Cluster algorithms perform much better in comparison to averaged monthly profiles, for example.•The choice of the clustering algorithm itself has a minor impact on the quality of time series aggregation, but cluster medoids as representative periods produces more accurate results than cluster centroids.•The impact of the aggregation level is highly system-specific and not generalizable.•The aggregation methods are published as open source python package:https://github.com/FZJ-IEK3-VSA/tsam.