Optimal wind farm sitting using high-resolution digital elevation models and randomized optimization

John Koutroumpas; Konstantinos Koutroumpas; John Koutroumpas; Konstantinos Koutroumpas

doi:10.3934/energy.2015.4.505

AIMS Energy

2015, Volume 3, Issue 4: 505-524. doi: 10.3934/energy.2015.4.505

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Research article Special Issues

Optimal wind farm sitting using high-resolution digital elevation models and randomized optimization

infoMETRICS L.t.d, Megalou Aleksandrou 20, Spata, Athens, PO 19004, Greece

Received: 15 September 2015 Accepted: 23 September 2015 Published: 23 September 2015

We investigate the problem of wind farm design in isolated mountainous areas. We first describe a remote sensing approach for the terrain reconstruction of complex terrains. We then employ a well--known evolutionary optimization algorithm to find the optimal wind farm layout. Although the algorithm has been efficiently used for off--shore or smooth on--shore areas, we show that its performance is significantly affected by the complex topography. Moreover, we illustrate how a priori information can be exploited to improve both the computational time and efficiency of the optimization algorithm.
- remote sensing,
- wind farm layout optimization,
- complex terrain,
- randomized optimization
Citation: John Koutroumpas, Konstantinos Koutroumpas. Optimal wind farm sitting using high-resolution digital elevation models and randomized optimization[J]. AIMS Energy, 2015, 3(4): 505-524. doi: 10.3934/energy.2015.4.505

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Abstract

We investigate the problem of wind farm design in isolated mountainous areas. We first describe a remote sensing approach for the terrain reconstruction of complex terrains. We then employ a well--known evolutionary optimization algorithm to find the optimal wind farm layout. Although the algorithm has been efficiently used for off--shore or smooth on--shore areas, we show that its performance is significantly affected by the complex topography. Moreover, we illustrate how a priori information can be exploited to improve both the computational time and efficiency of the optimization algorithm.

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