Replication data and code for "Distributionally Robust Planning of Low-Carbon Industrial Parks: CCS–Power-to-Methanol Coupling under Source–Load Uncertainty in the Eastern Province of Saudi Arabia"

This repository contains the input data, model parameters, and supporting code for the two-stage distributionally robust optimization (DRO) capacity-planning model of a park-level integrated energy system (IES) coupling carbon capture (CCS) and power-to-methanol (P2M), described in theassociated paper. Contents:- data/: the two representative per-unit operating scenarios (24 hourly steps × 5 uncertainty variables) obtained by improved K-means clustering, their empirical probabilities (0.569 / 0.431), and the base values used for de-normalization.- parameters/: equipment investment/O&M/lifetime, thermodynamic and efficiency parameters, market and stepped-carbon-trading parameters, energy-storage parameters, and capacity bounds (all monetary values in USD).- code/: a working Python utility that loads, de-normalizes and plots the input scenarios, and an illustrative MATLAB/YALMIP template showing the structure of the column-and-constraint generation (C&CG) algorithm.- figures/: de-normalized scenario plot. The equipment cost and efficiency parameters were compiled from publicly available techno-economic literature. The operating profiles arerepresentative daily scenarios for an industrial park in the Eastern Province of Saudi Arabia. The complete production optimization model isavailable from the corresponding author on reasonable request.