System design of a wind turbine using a multi-level optimization approach

In this paper a multi-level system design (MLS) algorithm is presented and utilized for a wind turbine system analysis. The MLS guides the decision making process for designing a complex system where many alternatives and many mutually competing objectives and disciplines need to be considered and evaluated. Mathematical relationships between the design variables and the multiple discipline performance objectives are developed adaptively as the various design considerations are evaluated and as the design is being evolved. These relationships are employed for rewarding performance improvement during the decision making process by allocating more resources and influence to the disciplines which exhibit the improvement. Simulation tools developed by the National Renewable Energy Laboratory (NREL) are employed in the wind turbine design analysis. The Cost Of Energy (COE) comprises the overall system level objective, while performance improvements at two technical design disciplines are pursued at the same time. The optimal design of the blade geometry for maximum Annual Energy Production (AEP), and the structural design of the blade for minimum bending moment at the root of the blade comprise the two technical design disciplines. Scalar metamodels are developed for linking the design variables with the performance metrics associated with the design of the blade geometry. Main characteristics of the wind turbine, namely, the rotor diameter, the rotational speed, the maximum rated power, the hub height, the structural characteristics of the blade, and the geometric characteristics of the blade (distribution of thickness, twist angle, and chord) are employed as design variables for the overall design analysis. The optimization results and the physical insight which can be gained through a sensitivity analysis for the optimal configuration are presented and discussed.

► A wind turbine is optimized with a multi-level analysis to reduce the cost of energy.
► A metamodel technique is used to reduce the computational expense of the blade design.
► Our analysis reveals the design variables with the greatest influence on energy costs.