Developing Optimization Models to Provide Maximum Energy Production by Creating Wind Power Plants with Experimental Simulation Design

This study presents an integrated experimental simulation and multi-objective optimization methodology that maximizes energy production and optimizes economic performance in the design of wind power plants (WPPs). The relationship between five fundamental design parameters (wind speed (XWS), hub height (XHH), rotor diameter (XRD), turbine spacing (XTS), and row spacing (XRS)) and five techno-economic outputs (annual AC energy (YAEP), net present value (YNPV), levelized cost of energy (YLCOE), net cost of capital (YNCCpw), and total BOS cost (YTBC)) is systematically investigated using a Multi-Level Full Factorial Experimental Design (DoE) for four different US regions (Southern Wyoming, Southern California, Northeastern West Virginia, and South Florida). The optimization was performed by applying a multi-objective desirability function to regression models derived from 1200 NREL SAM simulation data points, thereby simultaneously evaluating five design parameters across five techno-economic responses. ANOVA results revealed that 77.5% of the variability in annual energy production was due to wind speed and 21.4% to rotor diameter, clearly demonstrating the decisive role of resource quality in project feasibility. Optimization identified the optimal configuration (XRS = 5, XTS = 3, XWS = 10.157 m/s, XHH = 120 m, XRD = 70 m) that provided a balanced trade-off between conflicting objectives, achieving 575.16 GWh of YAEP, $42.02 million of YNPV, $43.66 million of YTBC, 2.368 cents/kWh of YLCOE, and $1.508/W of YNCCpw. The study emphasizes that resource evaluation precedes technological optimization in the planning phase of wind energy projects, demonstrating that integrating DoE, simulation, and multi-objective optimization provides a strong framework for achieving realistic, feasible, and economically sustainable WPPs. The novelty of this approach lies in its ability to simultaneously account for environmental stochasticity and economic feasibility, providing a robust computational roadmap for stakeholders to maximize energy efficiency while minimizing levelized costs.