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AMPS: Deep Stochastic Models for Space-Time Weather-Driven Grid Simulations

AMPS：用于时空天气驱动网格模拟的深度随机模型

基本信息

批准号：
1923062
负责人：
William Kleiber
金额：
$ 33.69万
依托单位：
University of Colorado at Boulder
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1923062&HistoricalAwards=false
关键词：
AMPS Deep Stochastic Models Space

项目摘要

The power system is changing rapidly with the introduction of large amounts of distributed energy resources such as distributed photovoltaics and loads with demand response potential. The addition of uncertain and variable distributed energy resources requires better understanding of potential impacts on grid operations. Direct testing on the grid is not possible, and the reliance on computer simulations for varying degrees of the distributed energy resource penetration is necessary. The companion mathematical challenge is in generating realistic scenarios of supplies and loads at the customer level, often at high spatial and temporal resolutions involving joint generation of tens of thousands of correlated space-time variables. Moreover, such processes are plagued by discontinuities due to electrical device switching in loads and microscale weather variations in distributed photovoltaics supply. This project represents an end-to-end effort developing new mathematical tools that will be applied to realistic electricity network testbeds.This research will develop a framework for space-time jump-diffusion processes that capture realistic non-Gaussian behavior of distributed energy resources and loads at high time frequencies. Such models will yield unprecedented insights into distributed energy resource implications on current and future grids. The testbed for the new framework and methodology is a state-of-the-art distribution simulation network dataset, Smart-DS. Results of this research will have potential impacts in infrastructure and energy planning by studying grid responses to varying levels of the distributed energy resource adoption. The statistical models will be applicable to a variety of fields including financial mathematics, environmetrics, geography and ecology. Moreover, this research will support student training at the intersection of statistics and energy science, who will gain interdisciplinary and collaborative experience with domain scientists during summer internships at the National Renewable Energy Laboratory.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.

随着分布式光伏发电和具有需求响应潜力的负载等大量分布式能源的引入，电力系统正在迅速变化。增加不确定和可变的分布式能源资源需要更好地了解对电网运营的潜在影响。在电网上直接测试是不可能的，依赖计算机模拟不同程度的分布式能源渗透是必要的。伴随的数学挑战是在客户层面上生成供应和负载的现实场景，通常是在高空间和时间分辨率下，涉及联合生成数万个相关的时空变量。此外，这样的过程受到由于负载中的电气设备切换和分布式光电子器件供应中的微尺度天气变化引起的不连续性的困扰。该项目代表了一个端到端的努力，开发新的数学工具，将被应用到现实的电力网络测试banks.This研究将开发一个框架的时空跳跃扩散过程，捕捉现实的非高斯行为的分布式能源和负载在高的时间频率。这些模型将对分布式能源对当前和未来电网的影响产生前所未有的见解。新框架和方法的测试平台是最先进的配电模拟网络数据集Smart-DS。这项研究的结果将通过研究电网对不同水平的分布式能源采用的反应，对基础设施和能源规划产生潜在的影响。这些统计模型将适用于各种领域，包括金融数学、计量学、地理学和生态学。此外，这项研究将支持学生在统计学和能源科学的交叉点的培训，谁将获得跨学科和合作的经验，与领域的科学家在夏季实习期间在国家可再生能源实验室。这个奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。