The rapid increase in scale and sophistication of offshore wind (OSW) farms poses a critical challenge related to the cost-effective operation and management of wind energy assets. A defining characteristic of this challenge is the economic trade-off between two concomitant processes: power production (the primary driver of short-term revenues), and asset degradation (the main determinant of long-term expenses). Traditionally, approaches to optimize production and maintenance in wind farms have been conducted in isolation. In this paper, we conjecture that a joint optimization of those two processes, achieved by rigorously modeling their short- and long-term dependencies, can unlock significant economic benefits for wind farm operators. In specific, we propose a decision-theoretic framework, rooted in stochastic optimization, which seeks a sensible balance of how wind loads are leveraged to harness short-term electricity generation revenues, versus alleviated to hedge against longer-term maintenance expenses. Extensive numerical experiments using real-world data confirm the superior performance of our approach, in terms of several operational performance metrics, relative to methods that tackle the two problems in isolation.
翻译:离岸风力农场的规模和复杂程度的迅速扩大,对风能资产的成本效益操作和管理提出了重大挑战。这一挑战的一个明显特点是两个同时发生的过程之间的经济权衡:电力生产(短期收入的主要驱动力)和资产退化(长期开支的主要决定因素),传统上,风力农场生产和维持的最佳方式是孤立进行的。在本文中,我们推测,通过严格模拟其短期和长期依赖性,联合优化这两个过程可以为风力农场经营者带来巨大的经济效益。具体地说,我们提出了一个决定理论框架,其根源是随机优化,力求在如何利用风力负荷来利用短期发电收入方面实现合理平衡,而不是在应付长期维修费用方面有所缓解。使用实际世界数据进行的广泛数字实验证实了我们方法的优异性表现,从若干业务性能衡量标准来看,相对于解决孤立两个问题的方法而言。</s>