This study develops an optimized scientific framework to identify least-cost energy mixes while enabling scale-invariant energy security assessment for Puerto Rico’s clean-energy transition. A nonlinear programming model is formulated to minimize total energy cost, and a Gaussian Process Regression (GPR) surrogate with explainability is employed to identify key cost drivers and quantify techno-economic uncertainty. To address the complexity of hybrid energy systems, fifteen relevant Nuclear–Renewable Hybrid Energy System (N-RHES) features are systematically aggregated into six energy security variables representing system capacity, storage, renewable penetration, and demand characteristics. Using these variables, a dimensional-scaling framework based on the Buckingham -theorem is developed to construct three dimensionless -groups corresponding to Reliability, Resilience, and Renewability (3R). These metrics transform system-specific optimization outputs into transferable, scale-invariant engineering performance indicators suitable for comparing islanded energy systems of different sizes. The GPR surrogate provides posterior mean predictions and predictive variance to characterize uncertainty in Levelized Cost of Energy (LCOE) and energy security metrics. SHapley Additive exPlanations (SHAP) analysis indicates that nuclear capacity reduces LCOE by 1.4 ¢/kWh, whereas wind increases cost by 0.9 ¢/kWh in high-penetration scenarios. Under techno-economic uncertainty, the predicted LCOE is ¢/kWh, with the optimal nuclear–hybrid solution achieving 9.6 ¢/kWh while remaining below the 11.0 ¢/kWh policy constraint. Five hybrid configurations combining wind, solar PV, geothermal generation, battery storage, and hydrogen fuel-cell systems are analyzed, with selected cases integrating Small Modular Reactor (SMR) base-load supply. Optimization identifies three recommended configurations, with an SMR–renewables hybrid emerging as the least-cost solution. Configuration 5 achieves an LCOE of 10.0 ¢/kWh, delivers 70% renewable contribution, and reduces total energy cost by 18% relative to fossil-dominant mixes. By integrating techno-economic optimization with -based dimensional scaling, the proposed framework provides physically interpretable and transferable energy security metrics applicable to heterogeneous hybrid energy systems and hurricane-exposed island grids. ... Read more

Because the energy transition is a critical and urgent issue that is increasingly reliant on data, the Center for Energy System Intelligence (CESI), a Convergence collaboration between TU Delft and Erasmus University Rotterdam, has developed a platform where researchers on the energy transition can share, publish, and/or find energy-related datasets and algorithms: EnergySHR. This platform aims to accelerate energy transition research into intelligent, data-driven algorithms. In this demonstration, we present the EnergySHR platform as both a platform for storing, accessing, managing, and archiving datasets as well as a tool to conduct empirical research about platformization and data-driven decision-making about the energy transition. ... Read more