Impact of Economic Indicators on the Integrated Design of Wind Turbine Systems

Abstract

This article presents a framework to integrate and optimize the design of large-scale wind turbines. Annual energy production, load analysis, the structural design of components and the wind farm operation model are coupled to perform a system-level nonlinear optimization. As well as the commonly used design objective levelized cost of energy (LCoE), key metrics of engineering economics such as net present value (NPV), internal rate of return (IRR) and the discounted payback time (DPT) are calculated and used as design objectives, respectively. The results show that IRR and DPT have the same effect as LCoE since they all lead to minimization of the ratio of the capital expenditure to the energy production. Meanwhile, the optimization for NPV tends to maximize the margin between incomes and costs. These two types of economic metrics provide the minimal blade length and maximal blade length of an optimal blade for a target wind turbine at a given wind farm. The turbine properties with respect to the blade length and tower height are also examined. The blade obtained with economic optimization objectives has a much larger relative thickness and smaller chord distributions than that obtained for high aerodynamic performance design. Furthermore, the use of cost control objectives in optimization is crucial in improving the economic efficiency of wind turbines and sacrificing some aerodynamic performance can bring significant reductions in design loads and turbine costs.

Design Optimization and Sizing for Fly-Gen Airborne Wind Energy Systems

“…Previous AWE literature includes LCOE estimation for a ground-gen farm vs. the number of units in the farm and the system scale [16], as well as estimating AWE system LCOE in order to compare to the best available renewable alternatives onshore [17] and to compare to other options for microgrids [18]. LCOE optimization for traditional wind turbines has been performed, to optimize blade length and hub height for systems in low wind speed areas using particle swarm optimization [19], to optimize rotor radius and rated speed for offshore systems [20], and to optimize rotor radius and rated speed for several wind conditions using a genetic algorithm [21].…”

Special Issue on Wind Turbine Aerodynamics

“…There are also 4 papers dealing with wind turbine design. Wu et al [11] presented a framework to optimize the design of large-scale wind turbines by using different design objectives, such as levelized cost of energy, net present value, internal rate of return, and discounted payback time, and found that the blade obtained with economic optimization objectives has a much large relative thickness and smaller chord distributions than obtained with high aerodynamic performance design. Yang et al [12] investigated the design of wind turbine in low wind speed areas by considering both blade length and hub height.…”