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Research Papers

Multi-Objective Wind Farm Layout Optimization Considering Energy Generation and Noise Propagation With NSGA-II

[+] Author and Article Information
Wing Yin Kwong

Institute for Aerospace Studies,
University of Toronto,
Toronto, ON M3H5T6, Canada
e-mail: penelope.kwong@mail.utoronto.ca

Peter Yun Zhang

Department of Mechanical and
Industrial Engineering,
University of Toronto,
Toronto, ON M5S3G8, Canada
e-mail: peteryun.zhang@mail.utoronto.ca

David Romero

Department of Mechanical and
Industrial Engineering,
University of Toronto,
Toronto, ON M5S3G8, Canada
e-mail: d.romero@utoronto.ca

Joaquin Moran

Renewable Power Division,
Hatch, Ltd.,
Niagara Falls, ON L2E7J7, Canada
e-mail: jmoran@hatch.ca

Michael Morgenroth

Renewable Power Division,
Hatch, Ltd.,
Niagara Falls, ON L2E7J7, Canada
e-mail: mmorgenroth@hatch.ca

Cristina Amon

Department of Mechanical and
Industrial Engineering,
University of Toronto,
Toronto, ON M5S3G8, Canada
e-mail: cristina.amon@utoronto.ca

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received January 25, 2013; final manuscript received May 29, 2014; published online July 3, 2014. Assoc. Editor: Michael Kokkolaras.

J. Mech. Des 136(9), 091010 (Jul 03, 2014) (11 pages) Paper No: MD-13-1029; doi: 10.1115/1.4027847 History: Received January 25, 2013; Revised May 29, 2014

Recently, the environmental impact of wind farms has been receiving increasing attention. As land is more extensively exploited for onshore wind farms, they are more likely to be in proximity with human dwellings, increasing the likelihood of a negative health impact. Noise generation and propagation remain an important concern for wind farm's stakeholders, as compliance with mandatory noise limits is an integral part of the permitting process. In contrast to previous work that included noise only as a design constraint, this work presents continuous-location models for layout optimization that take noise and energy as objective functions, in order to fully characterize the design and performance spaces of the wind farm layout optimization (WFLOP) problem. Based on Jensen's wake model and ISO-9613-2 noise calculations, single- and multi-objective genetic algorithms (GAs) are used to solve the optimization problem. Results from this bi-objective optimization model illustrate the trade-off between energy generation and noise production by identifying several key parts of Pareto frontiers. In particular, it was observed that different regions of a Pareto front correspond to markedly different turbine layouts. The implications of noise regulation policy—in terms of the actual noise limit—on the design of wind farms are discussed, particularly in relation to the entire spectrum of design options.

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References

Figures

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Fig. 1

Schematic representation of Jensen's wake model

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Fig. 2

Wind speed along a single wake's centerline, as a function of distance normalized with the turbine diameter (z = 60 m, zo = 0.3 m, R = 20 m, and Ct = 0.88)

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Fig. 3

SPL (A-weighted) as a function of distance from the source (LwA = 100 dBA, Dc = 0, receptor height hr = 1.5 m)

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Fig. 4

Distribution of wind speeds and directions for WR36 cases [8,24]

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Fig. 5

Schematic representation of a test case. Seven turbines are shown facing the wind direction. Noise receptors, represented by dots, are located along the edges of the wind farm every 50 m.

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Fig. 6

Sound pressure level contour maps generated by this work's implementation of the ISO-9613-2 standard [32]. Noise values are expressed in (dBA).

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Fig. 7

Sound pressure level contour maps generated by Openwind [41]. The black-box indicates the 2 km × 2 km wind farm area, for easier comparison with Fig. 6. Noise values are expressed in (dBA).

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Fig. 8

Predicted wind speed field for DuPont and Cagan's optimal 30-turbine layout (based on Fig. 9 in Ref. [24]), WR1 test case, zo = 0.5 m. Wind speed values are expressed in (m/s).

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Fig. 9

Convergence of the NDHV of the Pareto front during multiple runs of the NSGA-II multi-objective GA, for cases with (a) 39-turbines and (b) 40-turbines

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Fig. 10

Pareto frontiers for the WR1 cases. Note that the horizontal axis is reversed.

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Fig. 11

Pareto frontiers for the WR36 cases. Note that the horizontal axis is reversed.

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Fig. 12

Pareto frontiers for 39 - and 40-turbine layouts (WR36)

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Fig. 13

Alternative layouts with similar performance for 39 and 40 turbines, WR36, 2 km × 2 km wind farm, AEP ≃ 252.5 GW h, SPL ≃ 50.88 dBA

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Fig. 14

Effect of modeling simplifications on energy-noise Pareto fronts, 15 turbines, 3 km × 3 km wind farm, W24-V wind regime [15]. Note the difference in scale in the horizontal axes.

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