JuliaCon Local Paris 2025

TulipaClustering.jl introduces a greedy hull clustering method designed to better preserve constraint-binding extremes in optimization problems, especially within energy systems modeling. This approach yields more accurate reduced models for improved downstream optimization performance.

We use DuckDB as a backend to generate JuMP variables and constraints using SQL. This enables memory-efficient slicing, reusable indices, and traceable constraints. In TulipaEnergyModel.jl, this reduced preprocessing time and memory usage by up to 50%, improving scalability and clarity.