TY - JOUR
T1 - Seizing Opportunity
T2 - Maintenance Optimization in Offshore Wind Farms Considering Accessibility, Production, and Crew Dispatch
AU - Papadopoulos, Petros
AU - Coit, David W.
AU - Ezzat, Ahmed Aziz
N1 - Funding Information:
This work was supported in part by the Rutgers Energy Institute (REI) and in part by the National Science Foundation (NSF) under Grant ECCS-2114422.
Publisher Copyright:
© 2010-2012 IEEE.
PY - 2022/1/1
Y1 - 2022/1/1
N2 - Operations and Maintenance (O&M) constitute a major contributor to offshore wind's cost of energy. Due to the harsh and remote environment in which offshore turbines operate, there has been a growing interest in opportunistic maintenance scheduling for offshore wind farms, wherein grouping maintenance tasks is incentivized at times of opportunity. Our survey of the literature, however, reveals that there is no unified consensus on what constitutes an 'opportunity' for offshore maintenance. We therefore propose an opportunistic maintenance scheduling approach which defines an opportunity as either crew-dispatch-based (initiated by a maintenance crew already dispatched to a neighboring turbine), production-based (initiated by projected low production levels), or access-based (initiated by a provisionally open window of turbine access). We formulate the problem as a multi-staged rolling-horizon mixed integer linear program, and propose an iterative solution algorithm to identify the optimal hourly maintenance schedule, which is found to be drastically different, yet substantially better, than those obtained using offshore-agnostic strategies. Extensive numerical experiments on actual wind, wave, and power data demonstrate substantial margins of improvement achieved by our proposed approach, across a wide variety of key O&M metrics.
AB - Operations and Maintenance (O&M) constitute a major contributor to offshore wind's cost of energy. Due to the harsh and remote environment in which offshore turbines operate, there has been a growing interest in opportunistic maintenance scheduling for offshore wind farms, wherein grouping maintenance tasks is incentivized at times of opportunity. Our survey of the literature, however, reveals that there is no unified consensus on what constitutes an 'opportunity' for offshore maintenance. We therefore propose an opportunistic maintenance scheduling approach which defines an opportunity as either crew-dispatch-based (initiated by a maintenance crew already dispatched to a neighboring turbine), production-based (initiated by projected low production levels), or access-based (initiated by a provisionally open window of turbine access). We formulate the problem as a multi-staged rolling-horizon mixed integer linear program, and propose an iterative solution algorithm to identify the optimal hourly maintenance schedule, which is found to be drastically different, yet substantially better, than those obtained using offshore-agnostic strategies. Extensive numerical experiments on actual wind, wave, and power data demonstrate substantial margins of improvement achieved by our proposed approach, across a wide variety of key O&M metrics.
KW - Maintenance optimization
KW - mixed integer programming
KW - offshore wind energy
KW - operations & maintenance
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U2 - 10.1109/TSTE.2021.3104982
DO - 10.1109/TSTE.2021.3104982
M3 - Article
AN - SCOPUS:85113213218
SN - 1949-3029
VL - 13
SP - 111
EP - 121
JO - IEEE Transactions on Sustainable Energy
JF - IEEE Transactions on Sustainable Energy
IS - 1
ER -