A novel method was developed to detect the optimal onshore wind farm layout driven by the characteristics ofall commercially-available wind turbines. A huge number of turbine combinations (577) was processed, resultingin 22,721 generated layouts. Various assumptions and constraints were considered, mostly derived fromthe literature, including site features, wind conditions, and layout design. For the latter, an irregularly staggeredturbine array configuration was assumed. Wake effects were simulated through the Jensen's model, while atypical turbine thrust coefficient curve as a function of wind speed was originally developed. A detailed costmodel was used, with levelized cost of energy selected as primary and capacity factor as secondary objectivefunction. The self-organizing maps were used to address a thorough analysis, proving to be a powerful means tostraightforwardly achieve a comprehensive pattern of wind farm layout optimization.In general, the two optimization functions basically match, while for higher wind potential sites, increasingcapacity factor did not necessarily result in decreasing levelized cost of energy. The latter may be minimised byreducing the total number of turbines or the overall wind farm capacity, as well as maximising rotor diameters orminimising rated wind speeds; increasing rated power or hub height is only beneficial for mid-potential sites.The mere maximisation of wind farm energy production is a misleading target, as corresponding to mid-to-highvalues of levelized cost of energy. In contrast to previous studies, the use of turbines with different rated power,rotor diameter or hub height should be avoided.

A novel method for wind farm layout optimization based on wind turbine selection

Gualtieri G
2019

Abstract

A novel method was developed to detect the optimal onshore wind farm layout driven by the characteristics ofall commercially-available wind turbines. A huge number of turbine combinations (577) was processed, resultingin 22,721 generated layouts. Various assumptions and constraints were considered, mostly derived fromthe literature, including site features, wind conditions, and layout design. For the latter, an irregularly staggeredturbine array configuration was assumed. Wake effects were simulated through the Jensen's model, while atypical turbine thrust coefficient curve as a function of wind speed was originally developed. A detailed costmodel was used, with levelized cost of energy selected as primary and capacity factor as secondary objectivefunction. The self-organizing maps were used to address a thorough analysis, proving to be a powerful means tostraightforwardly achieve a comprehensive pattern of wind farm layout optimization.In general, the two optimization functions basically match, while for higher wind potential sites, increasingcapacity factor did not necessarily result in decreasing levelized cost of energy. The latter may be minimised byreducing the total number of turbines or the overall wind farm capacity, as well as maximising rotor diameters orminimising rated wind speeds; increasing rated power or hub height is only beneficial for mid-potential sites.The mere maximisation of wind farm energy production is a misleading target, as corresponding to mid-to-highvalues of levelized cost of energy. In contrast to previous studies, the use of turbines with different rated power,rotor diameter or hub height should be avoided.
2019
Istituto di Biometeorologia - IBIMET - Sede Firenze
Wind farm layout optimization (WFLO)
Wake model
Wind turbine database
Wind turbine spacing
Levelized cost of energy (LCoE)
Self-organizing map (SOM)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/394715
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