Among the main grid-based wind farm layout optimization studies addressed in the literature, 14 layouts have been recomputed by selecting the levelized cost of energy as a primary objective function. Relying on 120 wind turbine combinations, a previously developed optimization method targeting best turbine selection has then been applied. All literature layouts were optimized, as capacity factors were (slightly) increased (78.89-80.90 to 83.02-83.07%), while levelized costs of energy were (significantly) reduced (130.37-370.42 to 54.01-142.64 $/MWh). This study concluded that neither the discrete nor the continuous optimization model can be recommended in all scenarios. In general, a capacity factor increase does not necessarily imply a decrease in levelized cost of energy. The latter may be minimized by decreasing the overall wind farm capacity, the number of turbines, or selecting turbines with lower rotor diameters or rated powers. By contrast, capacity factor may be maximized by installing turbines with higher hub heights or lower rated speeds. Contradicting various findings, using turbines with different rotor diameters, rated powers or hub heights is not recommended to minimize the levelized cost of energy. Although addressed within several optimization studies, maximization of energy production is a misleading target, as involving the highest costs of energy.

Comparative analysis and improvement of grid-based wind farm layout optimization

Giovanni Gualtieri
2020

Abstract

Among the main grid-based wind farm layout optimization studies addressed in the literature, 14 layouts have been recomputed by selecting the levelized cost of energy as a primary objective function. Relying on 120 wind turbine combinations, a previously developed optimization method targeting best turbine selection has then been applied. All literature layouts were optimized, as capacity factors were (slightly) increased (78.89-80.90 to 83.02-83.07%), while levelized costs of energy were (significantly) reduced (130.37-370.42 to 54.01-142.64 $/MWh). This study concluded that neither the discrete nor the continuous optimization model can be recommended in all scenarios. In general, a capacity factor increase does not necessarily imply a decrease in levelized cost of energy. The latter may be minimized by decreasing the overall wind farm capacity, the number of turbines, or selecting turbines with lower rotor diameters or rated powers. By contrast, capacity factor may be maximized by installing turbines with higher hub heights or lower rated speeds. Contradicting various findings, using turbines with different rotor diameters, rated powers or hub heights is not recommended to minimize the levelized cost of energy. Although addressed within several optimization studies, maximization of energy production is a misleading target, as involving the highest costs of energy.
2020
Istituto per la BioEconomia - IBE
Wind farm layout optimization
Gridded layout
Literature case study
Wind turbine database
Levelized cost of energy
Self-organizing map
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/374688
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact