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

TL;DR

This paper evaluates how different time series aggregation methods affect the design and cost estimation of renewable energy systems, highlighting the importance of system type and storage considerations.

Contribution

It extends aggregation methods for multidimensional series and compares their impact across various energy system configurations.

Findings

Aggregation reduces computational resources significantly.

Averaged values underestimate system costs.

System type influences the number of typical periods needed.

Abstract

Modelling 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.