Collaborative Research: A Holistic Approach to Wind Energy Integration: From the Atmospheric Boundary Layer to the Power Grid

合作研究：风能整合的整体方法：从大气边界层到电网

• 批准号: 1609404
• 负责人: Sven Schmitz
• 金额: $ 12.5万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-15 至 2020-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1609404&HistoricalAwards=false
• 关键词: Collaborative; Research; Holistic; Approach; Wind

With the current trends towards higher levels of electric power produced using wind resources, the uncertainties and fluctuations of wind power output are increasingly challenging power grid operations and especially the so-called generation dispatch operations. Generation dispatch refers to scheduling traditional power plant output based on predicted demand, but this becomes difficult when wind energy becomes a major source of electric power. This is due to the unpredictability of wind as a resource. To seamlessly integrate wind energy to the power grid, it is imperative to improve the modeling of wind power output at the scale of wind farms, from seconds up to a day ahead. The quantitative characterization of wind fluctuations from the atmosphere and those induced by turbine-turbine interactions is a central component towards accurately predicting wind power output. Even with such predictions available to grid operators, the benefits associated with enhanced power system operations have neither been fully understood nor exploited. This problem is a fundamental issue of sustainable energy infrastructure related to the integration of fluctuating wind power into the power grid. This research aims to develop fundamental knowledge and guidelines to close the gap between atmospheric flow and wind farm electric power output, and to integrate this linkage to efficiently and reliably operate power grids. The holistic approach has the potential to allow the electric power grid to embrace higher levels of wind energy penetration. It will ultimately aid in decreasing the cost of wind power and help it become an attractive and viable option for the nation's renewable energy portfolio. The project will also fund the educational development of both graduate and undergraduate students, and significant efforts will be made to disseminate the results to the general public, the wind energy engineering community, and to the K-12 education.The research project aims to develop a holistic framework to close the gap between atmospheric turbulence and wind farm electric power output to increase the efficiency of power grid operations. To achieve this goal, an interdisciplinary team will synergize their analytical, experimental, and numerical expertise to address the foundational problems of improving wind power prediction and power grid operations. High-performance computing will be used to characterize and quantify the variations of the turbulence dynamics occurring over the diurnal cycle (i.e., 24 hour day/night cycle) and their ability to modulate power fluctuations at the wind-farm level. Furthermore, a generic approach to wind-farm power output parametrization will be developed to account for temporal power output variability over a range of time scales (from seconds to hours), along its variance and spectral structure. The state-of-the-art wind power predictions will be integrated to design advanced control and operational tools for power grids. The ultimate goal is to achieve higher levels of wind energy that can be integrated to the electric power systems, and to contribute towards a future sustainable energy infrastructure. The potentially transformative aspects of the research include the development of a general parametrization of wind farm power output, and robust and economic frequency control designs. The work will integrate the physical processes involved in wind energy systems, the power grid and the associated interface between the two, including i) uncertainties in wind power modeling associated with the atmospheric stability state and diurnal cycle; ii) short-term and hourly-ahead forecasting of electrical power output fluctuations at the wind farm scale; and iii) enhancing power grid operations using the wind output prediction. The facilities at UIUC and PSU as well as the diverse expertise of the PIs provide an ideal environment for conducting this research.

随着当前风电向高水平发展的趋势，风电出力的不确定性和波动性对电网运行尤其是所谓的发电调度运行提出了越来越大的挑战。发电调度是指根据预测的需求对传统发电厂的出力进行调度，但当风能成为电力的主要来源时，这就变得困难了。这是由于风能作为一种资源的不可预测性。为了将风能无缝集成到电网中，必须改进风电场规模的风电输出建模，从几秒钟到一天。对大气和涡轮机相互作用引起的风波动的定量表征是准确预测风力发电量的核心组成部分。即使电网运营商有了这样的预测，与增强电力系统运营相关的好处也没有得到充分的理解和利用。这一问题是关系到波动风电并网的可持续能源基础设施的根本性问题。这项研究的目的是发展基本知识和指导方针，以缩小大气流量和风电场发电量之间的差距，并整合这种联系，以高效和可靠地运行电网。这种整体的方法有可能让电网接受更高水平的风能渗透率。它最终将有助于降低风力发电的成本，并帮助它成为国家可再生能源投资组合中一个有吸引力和可行的选择。该项目还将资助研究生和本科生的教育发展，并将做出重大努力向公众、风能工程界和K-12教育传播成果。研究项目旨在建立一个整体框架，以缩小大气湍流和风电场发电量之间的差距，以提高电网运行的效率。为了实现这一目标，一个跨学科的团队将协同他们的分析、实验和数值专业知识，以解决改善风力发电预测和电网运营的基础问题。将使用高性能计算来表征和量化在日周期(即24小时昼夜周期)上发生的湍流动力学的变化，以及它们调节风电场水平上的功率波动的能力。此外，将开发一种通用的风电场功率输出参数化方法，以考虑在一系列时间尺度(从几秒到几小时)上的时间功率输出变化，以及其方差和频谱结构。最先进的风电功率预测将被整合起来，为电网设计先进的控制和操作工具。最终目标是实现能够整合到电力系统中的更高水平的风能，并为未来可持续的能源基础设施做出贡献。这项研究的潜在变革性方面包括开发风电场输出的通用参数，以及稳健和经济的频率控制设计。这项工作将整合风能系统、电网和两者之间的相关界面所涉及的物理过程，包括i)与大气稳定状态和日循环相关的风电建模的不确定性；ii)对风电场规模的电力输出波动的短期和每小时预测；以及iii)利用风能预测来增强电网运营。对外汉语大学和巴黎州立大学的设施以及私营部门的各种专业知识为开展这项研究提供了理想的环境。