CyberSEES: Type 1: Collaborative Research: Large-Scale, Integrated, and Robust Wind Farm Optimization Enabled by Coupled Analytic Gradients

Cyber​​SEES：类型 1：协作研究：耦合分析梯度支持的大规模、集成和鲁棒的风电场优化

• 批准号: 1539384
• 负责人: Andrew Ning
• 金额: $ 19.99万
• 依托单位: Brigham Young University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1539384&HistoricalAwards=false
• 关键词: CyberSEES; Type; Collaborative; Research; Large

Wind provides a renewable source of energy and is one of the most cost-effective sources for new energy installations. Today, wind turbines are designed for an isolated environment, as are their power regulation strategies. When turbines are assembled into a wind farm their wakes significantly interfere with other turbines resulting in energy underproduction of 10-20% relative to expectations. This underproduction is a major barrier to increased wind energy growth. This project hypothesizes that a significant increase in power production is possible through simultaneous design of wind turbine layouts, power-regulation strategies, and the turbines themselves, all in the presence of stochastic inputs.Simultaneous layout-control-turbine design is challenging, especially when considering uncertain inputs. Current research and industry practices use simulation models that are non-differentiable or do not provide gradients. As a result, most wind farm layout optimizations are limited to around 10-100 variables, rely on sequential design processes, and only include uncertainty in simple ways if at all. To enable design problems of larger size and complexity, wake and turbine models must be reimplemented with scalable optimization in mind, and new methods for uncertainty quantification must be developed. The investigators' recent work suggests that by developing wind turbine wake models that provide exact derivatives, wind farm layout can be done effectively with 100 to 1,000 times more variables than those solved by the industry today. This scalability will enable wind farm optimization that includes a large number of design variables, integrates multiple disciplines, and incorporates uncertainty in the design process. These proposed contributions seek to advance energy sustainability, scientific computing, and education. The wake and turbine models will be large-scale-optimization ready to allow designers to solve problems that were previously out of reach. The new uncertainty quantification methodologies will be widely applicable to multiple disciplines, particularly as more industries move towards integrated system design. Finally, a dedicated website will serve as a teaching tool to introduce optimization and uncertainty quantification concepts to a general audience through interactive wind farm design problems.Concurrently, the investigators will focus on foundational methods for scalable uncertainty quantification that can be used for both forward uncertainty propagation and statistical inversion. The emphasis on scalability is required to address challenges related to the number of random input variables, the number of output quantities of interest, and the efficiency of parallel implementations on extreme-scale computers. As an example, the research team is developing new methodologies for scalable uncertainty quantification that take advantage of the exact derivatives provided by these turbine and wake models. The research plan focuses on three main goals: 1) develop new wake models with exact gradients, 2) perform integrated layout-control-turbine optimization, and 3) develop scalable uncertainty quantification methods to demonstrate expected performance improvements on robust wind farm layout problems.

Wind提供了可再生能源的来源，是新的势能装置最具成本效益的来源之一。如今，风力涡轮机是为孤立环境而设计的，其功率调节策略也是如此。当涡轮机组装到风电场中时，它们的唤醒会严重干扰其他涡轮机，从而相对于预期，能源生产不足10-20％。这种生产不足是风能增长增加的主要障碍。该项目假设，通过同时设计风力涡轮机布局，功率调节策略和涡轮机本身可以显着增加功率，这一切都是在存在随机输入的情况下。尤其是在考虑不确定输入的情况下，尤其是在考虑不确定输入的情况下。 当前的研究和行业实践使用的模拟模型不可差异或不提供梯度。结果，大多数风电场布局的优化仅限于约10-100个变量，依靠顺序设计过程，并且只能以简单的方式包括不确定性。为了实现较大尺寸和复杂性的设计问题，必须考虑到可扩展优化的唤醒和涡轮机模型，并且必须开发出新的不确定性量化方法。调查人员最近的工作表明，通过开发提供精确衍生品的风力涡轮机唤醒模型，风电场布局可以有效地完成，而变量比当今行业所解决的变量高100至1000倍。 这种可扩展性将使风电场优化能够包括大量设计变量，整合了多个学科，并将不确定性纳入设计过程。这些拟议的贡献旨在提高能源可持续性，科学计算和教育。尾流和涡轮机模型将是大规模优化的，准备允许设计师解决以前无法解决的问题。 新的不确定性量化方法将广泛适用于多个学科，尤其是随着越来越多的行业朝着集成的系统设计发展。 最后，一个专门的网站将作为一种教学工具，通过交互式风电场设计问题向普通受众介绍优化和不确定性量化概念。相关，研究人员将专注于可扩展不确定性量化的基础方法，可用于远期不确定性不确定性传播和统计反转。需要对可伸缩性的重点来解决与随机输入变量数量，关注数量的数量以及对极端计算机上的并行实现效率相关的挑战。例如，研究团队正在开发新的方法论，以利用这些涡轮机和唤醒模型提供的确切衍生物。研究计划的重点是三个主要目标：1）开发具有精确梯度的新唤醒模型，2）执行集成的布局 - 控制 - 控制式优化和3）开发可扩展的不确定性量化方法，以证明对强大的风电场布局问题的预期性能提高。