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An advanced modeling system for optimization of wind farm layout and wind turbine sizing using a multi-level extended pattern search algorithm Public Deposited

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  • This paper presents a system of modeling advances that can be applied in the computational optimization of wind plants. These modeling advances include accurate cost and power modeling, partial wake interaction, and the effects of varying atmospheric stability. To validate the use of this advanced modeling system, it is employed within an Extended Pattern Search (EPS)-Multi-Agent System (MAS) optimization approach for multiple wind scenarios. The wind farm layout optimization problem involves optimizing the position and size of wind turbines such that the aerodynamic effects of upstream turbines are reduced, which increases the effective wind speed and resultant power at each turbine. The EPS-MAS optimization algorithm employs a profit objective, and an overarching search determines individual turbine positions, with a concurrent EPS-MAS determining the optimal hub height and rotor diameter for each turbine. Two wind cases are considered: (1) constant, unidirectional wind, and (2) three discrete wind speeds and varying wind directions, each of which have a probability of occurrence. Results show the advantages of applying the series of advanced models compared to previous application of an EPS with less advanced models to wind farm layout optimization, and imply best practices for computational optimization of wind farms with improved accuracy.
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  • DuPont, B., Cagan, J., & Moriarty, P. (2016). An advanced modeling system for optimization of wind farm layout and wind turbine sizing using a multi-level extended pattern search algorithm. Energy, 106, 802-814. doi:10.1016/j.energy.2015.12.033
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  • This work has been funded in part by the National Science Foundation under grants CMMI-0940730 and CMMI-0855326, and the NREL Research Participant Program. The NREL portion of the work was supported by the U.S. Department of Energy under Contract No. DE-AC36-08G028308 with the National Renewable Energy Laboratory. Funding for that work was provided by the DOE Office of Energy Efficiency and Renewable Energy, Wind and Water Power Technologies Office.
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