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：海上风电场规模与复杂性的快速增长，对风能资产的经济高效运营与管理提出了关键挑战。这一挑战的典型特征在于两个并行过程之间的经济权衡：电力生产（短期收入的主要驱动力）与资产退化（长期支出的主要决定因素）。传统上，风电场生产与维护的优化方法往往是孤立进行的。本文提出，通过严格建模这两个过程的短期与长期依赖关系，实现二者的联合优化，可为风电场运营商释放显著的经济效益。具体而言，我们构建了一个基于随机优化的决策理论框架，旨在合理权衡如何利用风荷载获取短期发电收益，与缓解风荷载以对冲长期维护成本。基于真实数据的大规模数值实验表明，相较于分别处理两个问题的孤立方法，本方法在多项运营性能指标上均表现出更优性能。