Geomorphology-Guided Optimization of Hybrid Solar–Wind Systems: A Model with Comparative Evidence in Colombia

Geomorphological stratification of the territory is rarely integrated explicitly into sizing models for hybrid solar–wind energy matrices, even though relief modulates both resources in opposite ways. This work proposes a four-stage model that combines geomorphological stratification by elevation ranges, resource-specific solar and wind characterization, a decision-tree surrogate model that reproduces the coupled physical simulation (PVWatts and PyWake) with high fidelity (R2 > 0.99), and optimization through mixed-integer linear programming (MILP). MILP is subject to a hybridization constraint that prevents a single technology from occupying more than 80% of the installed area. The model was applied to a first location with four altitudinal strata and contrasted with a second one. For the first site, the wind resource was non-viable across all strata, and the model consistently selected exclusively solar configurations. At the second location, it selected a hybrid configuration with 73% wind and 27% solar energy, whose proportions are governed by the hybridization threshold. Uncertainty and sensitivity analyses showed that the technology selected is robust to the choice of representative point and to the out-of-sample behavior of the surrogate, whereas the wind non-viability of the first site depends on the reference height and shear assumptions of the reanalysis data.