This study investigates how winglet geometry influences wake dynamics and associated pressure fluctuations in horizontal-axis wind turbines and how these effects translate into changes in power production and wake recovery. Two winglet designs are evaluated relative to a baseline turbine using Unsteady Reynolds-Averaged Navier–Stokes simulations with the SST k–ω turbulence model. The results show that the optimized winglet slightly increases the near wake velocity deficit while reducing peak turbulence intensity. The extended winglet lowers the streamwise averaged velocity deficit to a distance of 1D, where D is the turbine diameter, and maintains turbulence levels close to the baseline. For both winglet geometries, differences in velocity deficit and turbulence intensity become negligible beyond x/D≈8. Winglets also promote faster decay of tip vortex rings, reducing wake vorticity and weakening coherent structures. Mean kinetic energy flux profiles indicate that winglets suppress turbulence production in the near wake while enhancing energy transport farther downstream. The optimized and extended winglets increase the power coefficient by 8.97% and 9.43%, respectively, and increase thrust by 14%. Root-mean-square pressure fluctuation (p_rms^′) reveals weaker and smoother tip vortex induced peaks that vanish over shorter streamwise distances. These results demonstrate that blade tip modifications based on winglets improve aerodynamic efficiency while reducing vortex induced instability, supporting improved downstream energy availability and wind farm power yield.

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.

Home Energy Management Systems (HEMS) play a key role in the electrification of the residential sector. In a future where different energy carriers must be converted and used seamlessly, HEMS are responsible for orchestrating all energy assets within a home. To determine the power flows, the HEMS must be aware of its impact on the aging of the system's batteries. On the other hand, since this is typically performed through optimization models, various variables are needed, such as ambient temperature, solar radiation, and initial conditions. These inputs are uncertain and require proper forecasting. This paper presents the operation of an aging-aware HEMS using probabilistic forecasts of exogenous information, directly relating them to the quality of the degradation control. The relationship is studied under deterministic and stochastic optimal control settings.