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

Authors:
; ;
Publication Date:
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Wind and Water Technologies Office (EE-4W)
OSTI Identifier:
1323954
Grant/Contract Number:
AC36-08GO28308
Resource Type:
Journal Article: Publisher's Accepted Manuscript
Journal Name:
Energy (Oxford)
Additional Journal Information:
Journal Name: Energy (Oxford); Journal Volume: 106; Journal Issue: C; Related Information: CHORUS Timestamp: 2017-10-06 21:54:06; Journal ID: ISSN 0360-5442
Publisher:
Elsevier
Country of Publication:
United Kingdom
Language:
English

Citation Formats

DuPont, Bryony, Cagan, Jonathan, and Moriarty, Patrick. An advanced modeling system for optimization of wind farm layout and wind turbine sizing using a multi-level extended pattern search algorithm. United Kingdom: N. p., 2016. Web. doi:10.1016/j.energy.2015.12.033.
DuPont, Bryony, Cagan, Jonathan, & Moriarty, Patrick. An advanced modeling system for optimization of wind farm layout and wind turbine sizing using a multi-level extended pattern search algorithm. United Kingdom. doi:10.1016/j.energy.2015.12.033.
DuPont, Bryony, Cagan, Jonathan, and Moriarty, Patrick. 2016. "An advanced modeling system for optimization of wind farm layout and wind turbine sizing using a multi-level extended pattern search algorithm". United Kingdom. doi:10.1016/j.energy.2015.12.033.
@article{osti_1323954,
title = {An advanced modeling system for optimization of wind farm layout and wind turbine sizing using a multi-level extended pattern search algorithm},
author = {DuPont, Bryony and Cagan, Jonathan and Moriarty, Patrick},
abstractNote = {},
doi = {10.1016/j.energy.2015.12.033},
journal = {Energy (Oxford)},
number = C,
volume = 106,
place = {United Kingdom},
year = 2016,
month = 7
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record at 10.1016/j.energy.2015.12.033

Citation Metrics:
Cited by: 5works
Citation information provided by
Web of Science

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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 eachmore » 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.« less
  • This work applies an enhanced levelized wind farm cost model, including landowner remittance fees, to determine optimal turbine placements under three landowner participation scenarios and two land-plot shapes. Instead of assuming a continuous piece of land is available for the wind farm construction, as in most layout optimizations, the problem formulation represents landowner participation scenarios as a binary string variable, along with the number of turbines. The cost parameters and model are a combination of models from the National Renewable Energy Laboratory (NREL), Lawrence Berkeley National Laboratory, and Windustiy. The system-level cost-of-energy (COE) optimization model is also tested under twomore » land-plot shapes: equally-sized square land plots and unequal rectangle land plots. The optimal COEs results are compared to actual COE data and found to be realistic. The results show that landowner remittances account for approximately 10% of farm operating costs across all cases. Irregular land-plot shapes are easily handled by the model. We find that larger land plots do not necessarily receive higher remittance fees. The model can help site developers identify the most crucial land plots for project success and the optimal positions of turbines, with realistic estimates of costs and profitability. (C) 2013 Elsevier Ltd. All rights reserved.« less