TY - GEN
T1 - Optimized Site Selection for New Wind Farm Installations Based on Portfolio Theory and Geographical Information
AU - Nishiyama, Kohei
AU - Iwamura, Kazuaki
AU - Nakanishi, Yosuke
N1 - Funding Information:
This work was supported by Japan Science and Technology Agency as part of the e-ASIA Joint Research Program (e-ASIA JRP). Kohei Nishiyama is with Graduate School of Environment and Energy in Waseda University, Japan, (e-mail: nishiyama-2480@akane.waseda.jp). Kazuaki Iwamura, is with Graduate School of Environment and Energy in Waseda University, Japan, (e-mail: k-iwam315@ruri.waseda.jp). Yosuke Nkanishi is with Graduate School of Environment and Energy in Waseda University, Japan, (e-mail: nakanishi-yosuke@waseda.jp).
Publisher Copyright:
© 2019 IEEE.
PY - 2019/2
Y1 - 2019/2
N2 - An automated process for selecting sites for new wind farm installations is proposed. The region of interest is divided into a 1-km-square mesh, and geographical data such as altitude and wind speed are used to sort the mesh cells into regions that are feasible for wind farm installations. Before grouping the meshes, feasible meshes for constructing wind farms are extracted using a set of constraints. We tested two different constraints for grouping the feasible areas, either by maximizing the annual mean wind speed or by minimizing the covariance between the power outputs of each cell in the group. The first strategy is more attractive if the goal is to meet an expected level of power output each year, while the second strategy is intended to supply the most-stable power. Portfolio theory was then applied to the evaluate efficient-frontier curves of the two site-selection results from the mean and variance of the total expected power outputs. The analysis showed that grouping unit areas to maximize average wind speed most effectively suppresses variance in the expected output of an installation, and efficiently distributes the optimum wind farm locations.
AB - An automated process for selecting sites for new wind farm installations is proposed. The region of interest is divided into a 1-km-square mesh, and geographical data such as altitude and wind speed are used to sort the mesh cells into regions that are feasible for wind farm installations. Before grouping the meshes, feasible meshes for constructing wind farms are extracted using a set of constraints. We tested two different constraints for grouping the feasible areas, either by maximizing the annual mean wind speed or by minimizing the covariance between the power outputs of each cell in the group. The first strategy is more attractive if the goal is to meet an expected level of power output each year, while the second strategy is intended to supply the most-stable power. Portfolio theory was then applied to the evaluate efficient-frontier curves of the two site-selection results from the mean and variance of the total expected power outputs. The analysis showed that grouping unit areas to maximize average wind speed most effectively suppresses variance in the expected output of an installation, and efficiently distributes the optimum wind farm locations.
KW - Efficient-Frontier
KW - Geographical Information
KW - Mean and Variance
KW - Portfolio Theory
KW - Wind Farm
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U2 - 10.1109/ISGT.2019.8791636
DO - 10.1109/ISGT.2019.8791636
M3 - Conference contribution
AN - SCOPUS:85071432406
T3 - 2019 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2019
BT - 2019 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2019
Y2 - 18 February 2019 through 21 February 2019
ER -