A method for optimizing the location of wind farms

The optimal location and configuration of wind farms in a large region is important information for policy makers, electricity system planners and wind farm developers. The model developed in this paper uses wind resource data, population data and transmission line locations to find the configuration that produces electricity at minimum cost. Several economic and regulatory scenarios were used to demonstrate the importance of each factor in siting optimally siting wind farms. We demonstrate how gradient based optimization could be applied to discover optimal wind farm location and size. Although the use of gradient based optimization makes the model sensitive to local minima, numerical smoothing is used to reduce this sensitivity.

[1]  M. Y. Hussaini,et al.  Placement of wind turbines using genetic algorithms , 2005 .

[2]  N. Jensen.  A note on wind generator interaction , 1983 .

[3]  Ervin Bossanyi,et al.  Wind Energy Handbook , 2001 .

[4]  Serwan Mj Baban,et al.  Developing and applying a GIS-assisted approach to locating wind farms in the UK , 2001 .

[5]  van Kooten,et al.  Natural Gas, Wind and Nuclear Options for Generating Electricity in a Carbon Constrained World , 2012 .

[6]  R. Meentemeyer,et al.  A geographic analysis of wind turbine placement in Northern California , 2006 .

[7]  E. Pyrgioti,et al.  Optimal placement of wind turbines in a wind park using Monte Carlo simulation , 2008 .

[8]  Andrew Cruden,et al.  A GIS/PSS planning tool for the initial grid connection assessment of renewable generation , 2007 .

[9]  James F. Manwell,et al.  Book Review: Wind Energy Explained: Theory, Design and Application , 2006 .

[10]  I. J. Ramírez-Rosado,et al.  Promotion of new wind farms based on a decision support system , 2008 .

[11]  Dionysis Assimacopoulos,et al.  Evaluation of Renewable Energy potential using a GIS decision support system , 1998 .

[12]  Philippe Lejeune,et al.  Development of a decision support system for setting up a wind energy policy across the Walloon Region (southern Belgium) , 2008 .

[13]  Manuel Burgos Payán,et al.  An evolutive algorithm for wind farm optimal design , 2007, Neurocomputing.

[14]  Bryan A. Norman,et al.  Heuristic methods for wind energy conversion system positioning , 2004 .

[15]  Byungik Chang,et al.  Review of Computer-Aided Numerical Simulation in Wind Energy , 2013 .

[16]  Jian L. Zhou,et al.  User's Guide for CFSQP Version 2.0: A C Code for Solving (Large Scale) Constrained Nonlinear (Minimax) Optimization Problems, Generating Iterates Satisfying All Inequality Constraints , 1994 .