TY - GEN
T1 - Wind Farm Layout Optimization in Different Search Spaces
T2 - 2022 IEEE Power and Energy Society General Meeting, PESGM 2022
AU - Al Shereiqi, A.
AU - Al-Hinai, A.
AU - Mohandes, B.
AU - Al-Abri, R.
AU - Albadi, M.
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Wind farm layout is a vital subject due to its effect on power generation. Wind farm layout optimization (WFLO) is a complex optimization problem that cannot be solved by traditional optimization methods. Therefore, heuristic optimization techniques are used to solve a WFLO problem. With this problem, the search space plays a significant role in the results. Therefore, this study investigates the impacts of solving the WFLO problem in continuous and discrete search spaces using a genetic algorithm. Besides, Jensen's wake effect model is involved in this study to estimate the velocity deficit within the wind farm. A case study that features a wind profile with multi-speed and multi-direction is used to demonstrate how to get a wind farm layout in discrete and continuous search spaces. The results indicate for the superiority of a continuous search space in terms of compactness, whereas the discrete search space has higher output power, efficiency, and shorter computational time. These results are due to the limitations on the number of generations, population sizes, and the computational machine to get the optimality of the genetic algorithm.
AB - Wind farm layout is a vital subject due to its effect on power generation. Wind farm layout optimization (WFLO) is a complex optimization problem that cannot be solved by traditional optimization methods. Therefore, heuristic optimization techniques are used to solve a WFLO problem. With this problem, the search space plays a significant role in the results. Therefore, this study investigates the impacts of solving the WFLO problem in continuous and discrete search spaces using a genetic algorithm. Besides, Jensen's wake effect model is involved in this study to estimate the velocity deficit within the wind farm. A case study that features a wind profile with multi-speed and multi-direction is used to demonstrate how to get a wind farm layout in discrete and continuous search spaces. The results indicate for the superiority of a continuous search space in terms of compactness, whereas the discrete search space has higher output power, efficiency, and shorter computational time. These results are due to the limitations on the number of generations, population sizes, and the computational machine to get the optimality of the genetic algorithm.
KW - continuous search space
KW - discrete search space
KW - genetic algorithm
KW - layout optimization
KW - wind farm
UR - http://www.scopus.com/inward/record.url?scp=85141504220&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85141504220&partnerID=8YFLogxK
U2 - 10.1109/PESGM48719.2022.9916763
DO - 10.1109/PESGM48719.2022.9916763
M3 - Conference contribution
AN - SCOPUS:85141504220
T3 - IEEE Power and Energy Society General Meeting
BT - 2022 IEEE Power and Energy Society General Meeting, PESGM 2022
PB - IEEE Computer Society
Y2 - 17 July 2022 through 21 July 2022
ER -
