Seizing Opportunity: Maintenance Optimization in Offshore Wind Farms Considering Accessibility, Production, and Crew Dispatch
Seizing Opportunity: Maintenance Optimization in Offshore Wind Farms Considering Accessibility, Production, and Crew Dispatch
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DOI：
10.1109/tste.2021.3104982
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发表时间：
2020-12
影响因子：
8.8
通讯作者：
P. Papadopoulos;D. Coit;A. Ezzat
中科院分区：
文献类型：
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作者：
P. Papadopoulos;D. Coit;A. Ezzat
运营和维护（O&M）是海上风电能源成本的主要贡献者。由于海上涡轮机运行的恶劣和偏远环境，人们对海上风电场的机会性维护调度越来越感兴趣，其中在机会时激励分组维护任务。然而，我们对文献的调查表明，对于什么是海上维护的“机会”，还没有统一的共识。因此，我们提出了一种机会性维护调度方法，该方法将机会定义为基于人员派遣（由已派遣到相邻涡轮机的维护人员发起）、基于生产（由预计的低生产水平发起）或基于访问（由临时打开的涡轮机访问窗口发起）。我们制定的问题作为一个多阶段的滚动时域混合整数线性规划，并提出了一个迭代求解算法来确定最佳的每小时维护计划，这是发现有很大的不同，但实质上更好，比那些使用离岸不可知的策略。对实际风、浪和功率数据的大量数值实验表明，我们提出的方法在各种关键O&M指标上都有很大的改进余地。
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.