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

输电和配电系统中的风力和光伏（PV）发电的高渗透率对使用现有方法维持保护系统的安全性和可靠性构成威胁。历史上，几次重大的连锁停电都涉及保护继电器的误操作或误协调。这样的停电表明，在压力系统条件下--并发高负荷需求，电路拓扑结构的变化，设备停电和短路故障条件下--继电器不协调可能导致脆弱的配电网络。在过去，变量生成处于比现在低得多的水平，因此不会导致继电器误协调。随着风能和光伏发电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，以便从根本上了解保护和控制系统在高度可变的可再生能源中应如何运作。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继电器智能，实时动态继电器设置和随机优化的基础上。在设想的框架中，继电器操作设置的计算被制定为一个随机优化问题，实时输入来自本地继电器智能，即，预测电路模型由继电器收集的数据被输入到电路仿真中，以便准确地预测继电器位置处的实时故障电流。然后，可以根据更新的发电配置文件，真实的实时调整设置。在文献中提出的最佳继电器设置的方法是有限的，并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，自然适应可再生能源发电的高渗透率所带来的随机性。从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输入。优化允许每个中继器基于已知信息（系统结构和本地测量）和估计值（非本地条件）做出最佳可能的决策，以真实的时间更新最优中继器响应。这项工作将为保护方法提供见解，这些方法将可再生能源引入的随机性纳入其中。这项工作将有助于增加可再生能源对电网的渗透，从而有助于减少碳排放。 开发的方法将在硬件中实施，以促进与工业界的讨论和技术转让。

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.