CAREER: Risk-Aware Power System Operations with Significant Wind Power Penetration

风力发电已经以快速增长的速度融入国家的大容量电力系统。由于风力发电的快速性、波动性和不确定性，给电力系统的运行和规划带来了巨大的挑战。该项目旨在解决风电一体化中的关键挑战，包括备用采购、风电爬坡和非自愿风电限电。该项目的研究将产生以下内容：1）风电坡道风险评估和量化的系统方法; 2）极端天气事件引起的大型风电坡道的早期警报系统;以及3）从电网的发电和输电资源中获取储备的经济有效的操作协议。研究成果将被整合到开发新的多学科课程，以丰富国家风力研究所的风力工程课程，并通过在一个专门的风力发电研究实验室的设计和实验项目，使本科生参与研究，该实验室将作为该项目的成果而建立。该项目的成果预计将影响电力系统的运营，通过提高运营商的风力发电坡道的情况意识，并通过提高可靠性，安全性和大容量电力系统和批发电力市场的效率。该项目的研究旨在寻求在风电斜坡风险评估和减少限电方面的根本性突破，以实现大容量电力系统中增加的风电容量的有效利用。基于大型风力发电机组表现出由广义帕累托分布决定的尾部行为这一关键观察结果，开发了一种量化风力发电机组风险的系统方法，基于该方法可以以严格的方式确定足够的储备量。沿着不同的路径，使用来自Mesonet和分散风电场的真实世界数据的初步研究表明，Mesonet测量确实包含由极端天气事件（锋面，雷暴，结冰事件等）引起的大型风力发电斜坡的关键特征，现有技术的风力预测系统可能无法捕捉到这一点。有了这个洞察力，一个基于Mesonet的早期报警系统将被设计为提高电力系统运营商的风险意识，相对于大型风力发电坡道。此外，一个创新的概念，线路传输余量将开发从阻塞风险限制的观点，这有利于减少非自愿的风电限电抑制阻塞风险，并通过提高备用的可输送性。这些线路转移裕度可以很容易地预先计算，通过使用基本的统计信息，节点风力发电与电力网络的流量分布因素。该研究包括几种创新和非传统的方法，包括使用极值理论对风电坡道进行风险量化，通过网络数据分析进行风电坡道事件检测，以及从图论角度对动态备用分区进行探索性研究。

Wind power has been integrated into the nation's bulk power systems at a rapidly increasing pace. Due to its intermittency, volatility, and uncertainty, wind power generation has posed grand challenges for power system operations and planning. This project aims at addressing key challenges in wind power integration, including reserve procurement, wind power ramps, and involuntary wind power curtailment. The research of this project will produce the following: 1) a systematic approach for risk assessment and quantification of wind power ramps; 2) an early alarm system for large wind power ramps induced by extreme weather events; and 3) cost-effective operational protocols for acquiring reserves from generation and transmission resources of power networks. The research outcomes will be integrated to develop new multidisciplinary courses to enrich the curriculum of wind engineering program at the National Wind Institute, and to engage undergraduate students in research through design and experimental projects at a dedicated wind power research laboratory that is to be established as an outcome of the project. The project outcomes are expected to impact power system operations by enhancing the operator's situational awareness of wind power ramps, and by improving the reliability, security, and efficiency of bulk power systems and the wholesale electricity market. The integrated research and educational activities will contribute to training qualified engineers and researchers who can contribute to a thriving and sustainable wind energy industry.The project research seeks fundamental breakthroughs in wind power ramp risk assessment and curtailment reduction to enable efficient utilization of increased wind power capacity in bulk power systems. Motivated by a key observation that large wind power ramps exhibit tail behaviors dictated by generalized Pareto distributions, a systematic method for quantifying wind power ramp risk is developed, based on which the adequate amount of reserves can be determined in a rigorous manner. Along a different path, preliminary studies using real-world data from Mesonet and dispersed wind farms reveal that Mesonet measurements indeed contain critical signatures of large wind power ramps induced by extreme weather events (fronts, thunderstorms, icing events, etc.), which state-of-the-art wind power forecasting systems may fail to capture. With this insight, a Mesonet-based early alarm system will be designed to enhance the power system operator's risk awareness with respect to large wind power ramps. Further, an innovative concept of line transfer margin will be developed from a congestion risk-limiting viewpoint, which facilitates reduction of involuntary wind power curtailment by suppressing congestion risk and by improving the deliverability of reserves. These line transfer margins can be easily pre-computed by using basic statistical information on nodal wind power generation together with the flow distribution factors of power networks. The research encompasses several innovative and nontraditional approaches, including risk quantification of wind power ramps using extreme value theory, wind power ramp events detection through networked data analytics, and exploratory study of dynamic reserve zoning from a graph-theoretic perspective.