Introducing multi-criteria decision analysis for wind farm repowering: A case study on Gotland
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
Standard
Introducing multi-criteria decision analysis for wind farm repowering: A case study on Gotland. / Bezbradica, Marko; Kerkvliet, Hans; Borbolla, Ivan Montenegro; Lehtimäki, Pyry.
2016 International Conference on Multidisciplinary Engineering Design Optimization (MEDO). IEEE, 2016. p. 1-8.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
Harvard
APA
Vancouver
Author
Bibtex - Download
}
RIS (suitable for import to EndNote) - Download
TY - GEN
T1 - Introducing multi-criteria decision analysis for wind farm repowering: A case study on Gotland
AU - Bezbradica, Marko
AU - Kerkvliet, Hans
AU - Borbolla, Ivan Montenegro
AU - Lehtimäki, Pyry
N1 - INT=dee,"Lehtimäki, Pyry"
PY - 2016/11/17
Y1 - 2016/11/17
N2 - The aging of wind turbines worldwide, combined with substantial technological developments, implies an increasing number of upcoming end-of-service life decisions. Wind farm repowering may be financially viable as some of the decommissioning and installation expenses can be shared and due to the lower project risk given the well-known wind resource. However, the decision to repower a site is influenced by a list of factors and key stakeholders. Given the complexity of the decision-making process, the use of multi-criteria decision analysis provides a valuable tool for decision-makers, facilitating a structured framework to identify the best possible option for all stakeholders. In this study, PROMETHEE II method is applied to the case of Bockstigen offshore wind farm. Four scenarios were designed, varying the total capacity and the number of turbines, and evaluated against fourteen criteria and seven relevant stakeholders, including their preferences for all of the criteria. Application of the PROMETHEE II provided a ranking of repowering scenarios, offering several key conclusions, such as the link between wind park capacity and stakeholder preference. Moreover, the consensus likelihood analysis between stakeholders suggests one scenario with low possibility of consensus, two with medium, and one with high, making it the most likely to succeed.
AB - The aging of wind turbines worldwide, combined with substantial technological developments, implies an increasing number of upcoming end-of-service life decisions. Wind farm repowering may be financially viable as some of the decommissioning and installation expenses can be shared and due to the lower project risk given the well-known wind resource. However, the decision to repower a site is influenced by a list of factors and key stakeholders. Given the complexity of the decision-making process, the use of multi-criteria decision analysis provides a valuable tool for decision-makers, facilitating a structured framework to identify the best possible option for all stakeholders. In this study, PROMETHEE II method is applied to the case of Bockstigen offshore wind farm. Four scenarios were designed, varying the total capacity and the number of turbines, and evaluated against fourteen criteria and seven relevant stakeholders, including their preferences for all of the criteria. Application of the PROMETHEE II provided a ranking of repowering scenarios, offering several key conclusions, such as the link between wind park capacity and stakeholder preference. Moreover, the consensus likelihood analysis between stakeholders suggests one scenario with low possibility of consensus, two with medium, and one with high, making it the most likely to succeed.
KW - Decision analysis
KW - Production
KW - Stakeholders
KW - Wind
KW - Wind farms
KW - Wind turbines
KW - Multi-criteria decision analysis
KW - PROMETHEE II
KW - offshore wind power
KW - repowering
U2 - 10.1109/MEDO.2016.7746546
DO - 10.1109/MEDO.2016.7746546
M3 - Conference contribution
SP - 1
EP - 8
BT - 2016 International Conference on Multidisciplinary Engineering Design Optimization (MEDO)
PB - IEEE
ER -