@article{Gamst_Buchholz_Pisinger_2021, place={Aalborg, Denmark}, title={Time aggregation techniques applied to a real-life sector-coupled capacity expansion model}, volume={32}, url={https://journals.aau.dk/index.php/sepm/article/view/6400}, DOI={10.5278/ijsepm.6400}, abstractNote={<p>Simulating energy systems is vital for energy planning to understand the effects of fluctuating<br>renewable energy sources and integration of multiple energy sectors. Capacity expansion is a pow-<br>erful tool for energy analysts and consists of simulating energy systems with the option of investing<br>in new energy sources. In this paper, we apply clustering based aggregation techniques from the<br>literature to very different real-life sector coupled energy systems. We systematically compare<br>the aggregation techniques with respect to solution quality and simulation time. Furthermore,<br>we propose two new clustering approaches with promising results. We show that the aggregation<br>techniques result in consistent solution time savings between 75% and 90%. Also, the quality of<br>the aggregated solutions is generally very good. To the best of our knowledge, we are the first<br>to analyze and conclude that a weighted representation of clusters is beneficial. Furthermore,<br>to the best of our knowledge, we are the first to recommend a clustering technique with good<br>performance across very different energy systems: the k-means with Euclidean distance measure,<br>clustering days and with weighted selection, where the median, maximum and minimum elements<br>from clusters are selected. A deeper analysis of the results reveal that the aggregation techniques<br>excel when the investment decisions correlate well with the overall behavior of the energy system.<br>We propose future research directions to remedy when this is not the case.</p>}, journal={International Journal of Sustainable Energy Planning and Management}, author={Gamst, Mette and Buchholz, Stefanie and Pisinger, David}, year={2021}, month={Oct.}, pages={79–94} }