  Stochastic programming is widely used for energy system design optimization
under uncertainty but can exponentially increase the computational complexity
with the number of scenarios. Common scenario reduction techniques, like
moments-matching or distribution-driven clustering, pre-select representative
scenarios based on input parameters. In contrast, decision-based clustering
groups scenarios by similarity in resulting model decisions. Decision-based
clustering has shown potential in network design and fleet planning. However,
its potential in energy system design remains unexplored.
  In our work, we examine the effectiveness of decision-based clustering in
energy system design using a four-step method: 1) Determine the optimal design
for each scenario; 2) Aggregate and normalize installed capacities as features
reflecting optimal decisions; 3) Use these features for k-medoids clustering to
identify representative scenarios; 4) Utilize these scenarios to optimize cost
in stochastic programming.
  We apply our method to a real-world industrial energy system modeled as a
mixed-integer linear program. We incorporate uncertainty by scaling time series
with representative factors. We generate 500 single-year scenarios via Monte
Carlo sampling, which we reduce using decision-based clustering. For
benchmarking, we conduct distribution-driven k-medoids clustering based on the
representative factors. In our case studies, both clustering methods yield
designs with similar cost efficiency, although decision-based clustering
requires substantially more computational resources. To our knowledge, this is
the first application of decision-based clustering on energy system design
optimization. Future research should investigate the conditions under which
decision-based clustering yields more cost-efficient designs compared to
distribution-driven clustering.
