Decision-Based vs. Distribution-Driven Clustering for Stochastic Energy System Design Optimization

Two-stage stochastic programming is widely used for energy system design optimization under uncertainty but can exponentially increase the computational complexity with the number of second-stage 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 first-stage 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 for scenario reduction in energy system design under second-stage uncertainty 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.