EAGER: Renewables: A New Framework in Power System Protection with High Levels of Renewable Generation

EAGER：可再生能源：高水平可再生能源发电电力系统保护的新框架

• 批准号: 1549769
• 负责人: Surya Santoso
• 金额: $ 29.5万
• 依托单位: University of Texas at Austin
• 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: 2015-09-15 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1549769&HistoricalAwards=false
• 关键词: EAGER; Renewables; New; Framework; Power

High penetration of wind and photovoltaic (PV) generation in transmission and distribution systems poses a threat to maintaining security and dependability of the protection system using existing approaches. Historically, several major cascading outages have involved mis-operation or mis-coordination of protective relays. Such outages demonstrate that relay mis-coordination during stressed system conditions---concurrent high load demand, changes in circuit topology, equipment outages, and short-circuit fault conditions---can result in a fragile distribution network. In the past, variable generation was at a significantly lower level than at present and thus did not contribute to relay mis-coordination. With levels of wind and PV projected only to increase in the future, large-scale variable generation can present an additional point of vulnerability to the existing protection system. The objective of this project is to pursue the development of new tools for the fundamental understanding of how protection and control systems should operate in the presence of highly variable renewable energy sources. The project aims to develop a radically different framework and potential solutions built upon on model-based distributed relay intelligence, real-time dynamic relay settings, and stochastic optimization.In the envisioned framework, the calculation of relay operating settings is formulated as a stochastic optimization problem with real-time inputs from local relay intelligence, i.e., the predictive circuit model. The data collected by a relay is input to circuit simulations in order to accurately predict real-time fault currents at the relay location. Settings can then be adapted in real time based on updated generation profiles. Methods proposed in the literature for optimal relay settings have been limited and did not account for the stochastic nature of renewable energy sources. This project will develop a probabilistic formulation of optimal relay settings that naturally adapts to the randomness introduced by the high penetration of renewable generation. Input is provided from the predictive circuit models in the form of estimated remote system parameters for each individual relay. The optimization allows each relay to make the best possible decision based on known information (system structure and local measurements) and estimated values (non-local conditions) to update the optimal relay response in real time. The work will provide insight for protection methodologies that incorporate randomness introduced by renewable energy sources. The work will contribute to allowing increased penetration of renewable generation sources to the grid, which contributes to reduction of carbon emissions. The developed methods will be implemented in hardware, facilitating discussions with and technology transfer to industry.

风电和光伏发电在输配电系统中的高度渗透对现有保护系统的安全性和可靠性构成了威胁。从历史上看，几次主要的级联停电都涉及保护继电器的误操作或不协调。这种中断表明，在系统压力条件下，继电保护不协调——并发高负载需求、电路拓扑变化、设备中断和短路故障条件——可能导致脆弱的配电网络。在过去，变量的产生水平明显低于现在，因此不会导致继电器失协调。由于风能和光伏发电的水平预计在未来只会增加，大规模的可变发电可能会给现有的保护系统带来额外的脆弱性。该项目的目标是开发新的工具，以便从根本上了解在高度可变的可再生能源存在的情况下，保护和控制系统应该如何运作。该项目旨在开发一个完全不同的框架和潜在的解决方案，建立在基于模型的分布式中继智能、实时动态中继设置和随机优化的基础上。在设想的框架中，继电器动作整定的计算被表述为一个随机优化问题，该问题具有来自局部继电器智能（即预测电路模型）的实时输入。将继电器采集的数据输入到电路仿真中，以便准确预测继电器位置的实时故障电流。然后可以根据更新的生成配置文件实时调整设置。文献中提出的最佳继电器设置方法受到限制，并且没有考虑到可再生能源的随机性。该项目将开发一种最佳继电器设置的概率公式，该公式自然地适应可再生能源发电的高渗透引入的随机性。输入来自预测电路模型，以每个单独继电器的估计远程系统参数的形式提供。优化允许每个继电器根据已知信息（系统结构和局部测量）和估计值（非局部条件）做出最佳决策，实时更新最优继电器响应。这项工作将为纳入可再生能源引入的随机性的保护方法提供见解。这项工作将有助于增加可再生能源向电网的渗透，从而有助于减少碳排放。开发的方法将在硬件中实施，促进与工业界的讨论和技术转让。