A novel simulation–optimization framework for techno-economic analysis and optimal design of wind energy systems with NPV maximization

The traditional techno-economic analysis of wind energy projects typically relies on commercially available turbines, often leading to suboptimal performance in low-to-medium wind speed regions. Moreover, wake losses increasingly reduce per-turbine output as wind farm size grows. This study proposes a modular two-phase simulation–optimization framework that sequentially optimizes wind turbine design and wind farm layout for site-specific, investment-oriented planning. In Phase 1, a nonlinear optimization model determines the NPV-maximizing turbine configuration, including hub height, rotor diameter, and rated power. In Phase 2, techno-economic simulations using the System Advisor Model (SAM) evaluate candidate layouts under wake effects and land-use constraints. Second-order regression surrogates, constructed within a systematically sampled spacing design space, approximate layout performance and guide a deterministic optimization model, with final layouts revalidated in SAM. The framework is applied to two onshore sites, Fayoum (Egypt) and Duqm (Oman), where the optimized turbine configurations are benchmarked against commercial alternatives using single-turbine SAM simulations. At both locations, the optimized turbine design outperforms all considered commercial machines in NPV. For the Fayoum site, the optimized turbine achieves a 28% capacity factor and 4.26 GWh annual energy production, while the optimized 42-turbine layout attains an NPV of $14.06 million and reduces wake losses by 34.2% relative to a dense baseline. A Monte Carlo analysis under wind-speed uncertainty supported the robustness of the optimized design. Overall, the framework provides a scalable, data-driven approach for integrated wind energy system design in wind-constrained environments.