Time Aggregation Techniques Applied to a Capacity Expansion Model for Real-Life Sector Coupled Energy Systems

TL;DR

This paper evaluates clustering-based aggregation techniques for capacity expansion models in real-life sector-coupled energy systems, demonstrating significant solution time reductions and proposing new effective clustering methods.

Contribution

It systematically compares existing aggregation techniques, introduces two new clustering approaches, and provides first insights into the benefits of weighted cluster representations in energy system modeling.

Findings

Solution times reduced by 75-90% using aggregation techniques.

Aggregated solutions maintain high quality across diverse systems.

Weighted clustering improves representation and results.

Abstract

Simulating energy systems is vital for energy planning to understand the effects of fluctuating renewable energy sources and integration of multiple energy sectors. Capacity expansion is a powerful tool for energy analysts and consists of simulating energy systems with the option of investing in new energy sources. In this paper, we apply clustering based aggregation techniques from the literature to very different real-life sector coupled energy systems. We systematically compare the aggregation techniques with respect to solution quality and simulation time. Furthermore, we propose two new clustering approaches with promising results. We show that the aggregation techniques result in consistent solution time savings between 75% and 90%. Also, the quality of the aggregated solutions is generally very good. To the best of our knowledge, we are the first to analyze and conclude that a weighted representation of clusters is beneficial. Furthermore, to the best of our knowledge, we are the first to recommend a clustering technique with good performance across very different energy systems: the k-means with Euclidean distance measure, clustering days and with weighted selection, where the median, maximum and minimum elements from clusters are selected. A deeper analysis of the results reveal that the aggregation techniques excel when the investment decisions correlate well with the overall behavior of the energy system. We propose future research directions to remedy when this is not the case.