GOALI: Collaborative Research: Generation versus Degradation: Striking the optimal balance for wind farm profitability via digitization, predictive and prescriptive analytics

风电场规模和复杂性的快速增长对风能资产的成本效益管理提出了严峻挑战。这一挑战的一个明显特征是两个相伴进程之间的经济权衡：发电（短期收入的主要驱动力）和资产退化（长期费用的主要决定因素）。该NSF项目旨在制定一种决策理论方法，以共同优化风电场的发电和维护。该项目将为风电行业的资产管理现状带来变革性的变化，迄今为止，风电行业依赖于单方面的战略，在很大程度上忽视了风力涡轮机资产的发电和退化之间的依赖关系。该项目的智力优势包括制定新的数据和决策科学模型，在数字化平台中混合，以预测和共同优化运营和维护需求。该项目更广泛的影响包括通过课程、出版物、数据/软件和行业-学术研讨会传播研究成果。与工业合作伙伴共同开发的一系列用例演示将加速将科学知识转化为有形的工业影响，朝着实现到2050年美国风能占35%的目标迈出一步。夏季实习和本科生研究人员，特别是来自代表性不足群体的研究人员，将有助于培养下一代数据、决策和能源科学人才。在没有正式考虑发电和资产退化之间的内在依赖关系的情况下，风电场运营商从其运营和维护政策中获得次优收益。该项目旨在制定一个决策理论框架，寻求如何利用风力负荷来利用短期发电收入的最佳平衡，而减轻对冲长期维护费用。该框架包括决策感知的预测模型，用于电力和资产健康退化预测，集成在具有决策相关不确定性的混合整数规划中。新的重构和约束将确保有效的预测-规定耦合，从而使优化能够在预测空间内搜索最优预测-决策对。风电场的端到端数字孪生模型将在综合资产管理解决方案中结合拟议的预测-规范方法。该奖项反映了NSF的法定使命，通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The rapid increase in scale and sophistication of wind farms poses a critical challenge relating to the cost-effective management of wind energy assets. A defining characteristic of this challenge is the economic trade-off between two concomitant processes: electricity generation (the primary driver of short-term revenues) and asset degradation (the major determinant of long-term expenses). This NSF project aims to formulate a decision-theoretic approach to jointly optimize generation and maintenance in wind farms. The project will bring transformative change into the status-quo of asset management in the wind industry which, to-date, relies on single-faceted strategies that largely overlook the dependencies between the generation and degradation in wind turbine assets. The intellectual merits of the project include the formulation of novel data and decision science models, blended within a digitization platform, to predict and co-optimize operations and maintenance requirements. The broader impacts of the project include disseminating research findings via coursework, publications, data/software, and industry-academia workshops. A set of use case demonstrations, co-developed with industrial partners, will accelerate the translation of scientific knowledge into tangible industrial impact, in a step towards meeting the 35%-by-2050 U.S. wind energy target. Summer internships and undergraduate researchers, especially from underrepresented groups, will contribute towards educating the next-generation workforce in data, decision, and energy sciences.Without formally considering the intrinsic dependencies between electricity generation and asset degradation, wind farm operators reap sub-optimal benefits from their operations and maintenance policies. This project aims to formulate a decision-theoretic framework which seeks an optimal balance of how wind loads are leveraged to harness short-term generation revenues, versus alleviated to hedge against longer-term maintenance expenses. The framework comprises decision-aware predictive models for power and asset health degradation forecasting, integrated within mixed integer programs with decision-dependent uncertainty. New reformulations and constraints will ensure an effective predictive-prescriptive coupling, thereby enabling the optimization to search within the prediction space for an optimal prediction-decision pair. An end-to-end digital twin of the wind farm will bind the proposed predictive-prescriptive methodologies within an integrative asset management solution. Engagement of industrial partners and a national laboratory will ensure a sensible impact on asset management in the wind industry.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.