论文标题
stochos:离岸风电场的随机机会维护时间表
STOCHOS: Stochastic Opportunistic Maintenance Scheduling For Offshore Wind Farms
论文作者
论文摘要
尽管有前途的前景,但海上风能的众多经济和环境益处仍然受到其高运营和维护(O&M)支出的损害。一方面,海上特定的挑战,例如现场偏远,恶劣的天气,运输要求和生产损失,相对于土地风电场的O&M成本大大膨胀。另一方面,天气条件,资产下降和电价中的不确定性在很大程度上限制了农场运营商确定可能维护的时间窗口的能力,更不用说最佳了。作为回应,我们提出了Stochos,这是随机整体机会调度程序的缩写 - 一种量身定制的维护调度方法,旨在应对海上风电场的独特挑战和不确定性。给定关键环境和操作参数的概率预测,Stochos通过利用由于有利的天气条件,现场维护资源和最大运营收入而产生的机会来最佳安排离岸维护任务。 Stochos被配制为两阶段的随机混合整数线性程序,我们使用基于方案的滚动式算法与工业实践相吻合。在美国北大西洋的现实世界数据中进行了测试,那里的几个离岸风电场正在开发中,相对于普遍的维护基准,在各种O&M指标上都显示出相当大的改善,包括总成本,停机时间,资源利用,资源利用率和维护中断。
Despite the promising outlook, the numerous economic and environmental benefits of offshore wind energy are still compromised by its high operations and maintenance (O&M) expenditures. On one hand, offshore-specific challenges such as site remoteness, harsh weather, transportation requirements, and production losses, significantly inflate the O&M costs relative to land-based wind farms. On the other hand, the uncertainties in weather conditions, asset degradation, and electricity prices largely constrain the farm operator's ability to identify the time windows at which maintenance is possible, let alone optimal. In response, we propose STOCHOS, short for the stochastic holistic opportunistic scheduler--a maintenance scheduling approach tailored to address the unique challenges and uncertainties in offshore wind farms. Given probabilistic forecasts of key environmental and operational parameters, STOCHOS optimally schedules the offshore maintenance tasks by harnessing the opportunities that arise due to favorable weather conditions, on-site maintenance resources, and maximal operating revenues. STOCHOS is formulated as a two-stage stochastic mixed integer linear program, which we solve using a scenario-based rolling horizon algorithm that aligns with the industrial practice. Tested on real-world data from the U.S. North Atlantic where several offshore wind farms are in-development, STOCHOS demonstrates considerable improvements relative to prevalent maintenance benchmarks, across various O&M metrics, including total cost, downtime, resource utilization, and maintenance interruptions.