TY - JOUR
T1 - Wind farm layout optimization based on support vector regression guided genetic algorithm with consideration of participation among landowners
AU - Ju, Xinglong
AU - Liu, Feng
AU - Wang, Li
AU - Lee, Wei Jen
N1 - Publisher Copyright:
© 2019 Elsevier Ltd
PY - 2019/9/15
Y1 - 2019/9/15
N2 - Due to the existence of wake effect that causes the reduction of intake wind speed among wind turbines in the downwind direction, the wind farm efficiency is substantially discounted. An integral question to ask is how to find the optimal wind turbine layout given a wind farm. Inspired by the self-adjustment capability among individuals in the natural evolution process, a new algorithm called support vector regression guided genetic algorithm is proposed to solve the wind warm layout optimization problem which integrates the capability in each individual to adjust itself for a better “fitness” with guiding information sampled from a response surface approximated by support vector regression. It is also interesting to use the new proposed algorithm to evaluate the impact of the constraints imposed by landowners’ willingness whether to rent their land to the wind farm company. Extensive numerical experiments under different settings of wind distribution and wind farms with unusable cells are conducted to validate the proposed algorithm, shedding insights on the impact of landowners’ participation on the overall efficiency. The experiment showcases that the proposed algorithm outperforms two baseline algorithms under different conditions with improved efficiency. The proposed framework with consideration of landowners’ participation decision provide insights for wind farm planner on the different values of farm lands from the landowners.
AB - Due to the existence of wake effect that causes the reduction of intake wind speed among wind turbines in the downwind direction, the wind farm efficiency is substantially discounted. An integral question to ask is how to find the optimal wind turbine layout given a wind farm. Inspired by the self-adjustment capability among individuals in the natural evolution process, a new algorithm called support vector regression guided genetic algorithm is proposed to solve the wind warm layout optimization problem which integrates the capability in each individual to adjust itself for a better “fitness” with guiding information sampled from a response surface approximated by support vector regression. It is also interesting to use the new proposed algorithm to evaluate the impact of the constraints imposed by landowners’ willingness whether to rent their land to the wind farm company. Extensive numerical experiments under different settings of wind distribution and wind farms with unusable cells are conducted to validate the proposed algorithm, shedding insights on the impact of landowners’ participation on the overall efficiency. The experiment showcases that the proposed algorithm outperforms two baseline algorithms under different conditions with improved efficiency. The proposed framework with consideration of landowners’ participation decision provide insights for wind farm planner on the different values of farm lands from the landowners.
KW - Adaptive genetic algorithm
KW - Monte-Carlo simulation
KW - Support vector regression
KW - Surrogate model
KW - Wind farm layout optimization
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U2 - 10.1016/j.enconman.2019.06.082
DO - 10.1016/j.enconman.2019.06.082
M3 - Article
AN - SCOPUS:85068422613
SN - 0196-8904
VL - 196
SP - 1267
EP - 1281
JO - Energy Conversion and Management
JF - Energy Conversion and Management
ER -