Journal
RENEWABLE ENERGY
Volume 48, Issue -, Pages 276-286Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.renene.2012.04.052
Keywords
Wind farm micro-siting; Gaussian particle swarm optimization; Differential evolution; Local search
Funding
- National Natural Science Foundation of China [61075064]
- International Science and Technology Co-operation Program of China [2011DFG13020]
- National High-Tech Research and Development Program of China (863 Program) [2007AA05Z426]
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The micro-siting of wind farms has recently attracted much attention due to the booming development of wind energy. The paper aims to maximize the electrical power extracted from a wind farm while satisfying the required distance between turbines for operation safety. The micro-siting problem is by nature a constrained optimization problem, in which the coupling of wake effects is strong and the number of position constraints between turbines is large. An improved Gaussian particle swarm optimization algorithm is proposed to optimize the positions of turbines in the continuous space. To prevent the premature of the algorithm, a local search strategy based on differential evolution is incorporated to search around the promising region achieved by the particle swarm optimization. A simple feasibility-based method is employed to compare the performance of different schemes. Comprehensive simulation results demonstrate that the micro-siting schemes obtained by the proposed algorithm increase the power generation of the wind farm. Moreover, the execution time of the algorithm is significantly reduced, which is important especially for large-scale wind farms. (C) 2012 Elsevier Ltd. All rights reserved.
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