CAREER: An Integrated Hybrid Forecasting Framework for Increased Wind Power Penetration

职业生涯：提高风电渗透率的综合混合预测框架

• 批准号: 1254244
• 负责人: Mrinal Kumar
• 金额: $ 40万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-02-15 至 2017-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1254244&HistoricalAwards=false
• 关键词: CAREER; Integrated; Hybrid; Forecasting; Framework

The goal of the proposed research is to develop the next generation of algorithms to achieve significant improvement in short-term wind forecasting. Our inability to accurately capture non-Gaussian uncertainty of wind in high dimensional spaces translates to low predictability; high risk and the need for expensive balancing power resources. It is thus a primary objective of the proposed research is to significantly improve wind forecasts upto 48 hours in advance, leading to enhanced dispatch, scheduling and unit commitment operations in the day-ahead electricity market.Intellectual Merit: The proposed research will develop an integrated framework of randomized algorithms for scalable nonlinear uncertainty propagation. Algorithm output will be combined with measured on-site data in the sense of Bayesian fusion, leading to a hybrid forecasting structure. The main technical challenges are: (i) complex wind dynamics due to multiple temporal and spatial scales, turbulence and orographic effects; (ii) non-Gaussian wind uncertainty; (iii) need for scalable algorithms due to high dimensionality; and (iv) need for fusion of information arriving from multiple algorithms and heterogeneous measurement sources. The proposed framework will have the following key features to meet these challenges: (1) formulation of the wind state as a stochastic hybrid process, governed by multiple reduced ordermicro and mesoscale models; (2) a novel randomized particle uncertainty propagation approach based on the method of characteristics, Markov chain Monte-Carlo and the Karhunen-Lo`eve expansion. Practical effectiveness of developed algorithms will be measured against data from two wind-farms: the Cohocton Wind Farm/NY and the Roscoe Wind Farm/TX.Broader Impact: The proposed forecasting framework will lead to increased penetration of wind power by reducing the risks currently associated with it; and enable us to achieve our global targets of reducing dependence on fossil-fuel based electricity. The education plan includes training high school teachers in the multidisciplinary area of sustainable energy who will in turn reach thousands of students.

提出的研究目标是开发下一代算法，以实现短期风预报的重大改进。我们无法准确捕捉高维空间中风的非高斯不确定性，这意味着低可预测性；高风险和需要昂贵的平衡电力资源。因此，拟议研究的主要目标是显著改善提前48小时的风力预测，从而增强前一天电力市场的调度、调度和机组承诺操作。智力优势：提出的研究将为可扩展的非线性不确定性传播开发一个随机算法的集成框架。算法输出将以贝叶斯融合的方式与现场实测数据相结合，形成混合预测结构。主要的技术挑战是：(i)由于多个时空尺度、湍流和地形影响而导致的复杂风动力学；（ii）非高斯风不确定性；（iii）由于高维，需要可扩展的算法；（iv）需要融合来自多种算法和异构测量源的信息。提出的框架将具有以下关键特征来应对这些挑战：(1)将风状态表述为一个随机混合过程，由多个降阶微尺度和中尺度模式控制；(2)基于特征法、马尔可夫链蒙特卡罗和Karhunen-Lo 'eve展开的随机粒子不确定性传播方法。开发的算法的实际有效性将根据两个风电场的数据进行衡量：纽约州的科霍克顿风电场和德克萨斯州的罗斯科风电场。更广泛的影响：拟议的预测框架将通过降低目前与风电相关的风险，提高风电的渗透率；并使我们能够实现减少对化石燃料电力依赖的全球目标。该教育计划包括培训可持续能源多学科领域的高中教师，这些教师将反过来影响数千名学生。