EPCN: Enhancing the Modeling, Simulation, and Visualization of Large-Scale Electric Grids Utilizing Detailed Synthetic Power Grids and Data Sets

高压电网在社会的日常运作和福利中发挥着关键作用，同时也支持其他主要基础设施。在面对不断变化的负荷和发电特性、自然灾害和不断发展的电力市场时，需要做出巨大的研究和开发努力来改进电网设计和运行，以确保可靠性。电网研究在很大程度上依赖于高保真电网模型和数据集的获取。然而，有关实际电网的设计和运行的许多关键信息都被认为是机密的，因此不能公开，甚至经常不能提供给该领域的研究人员。拟议的项目旨在通过开发和应用高质量的合成电网模型和数据集来满足这一需求，这些模型和数据集代表了实际电网的复杂性，但不包含关于真实电网的信息，因此可以公开提供。该项目的成果将有助于电力领域的研究人员和教育工作者，也可以帮助行业从业者培养新的工程师来解决电网面临的问题。该项目的另一个重要方面是，结果将公之于众，使许多有兴趣了解更多电网知识的不同群体受益，包括依赖电网的各种不同领域的研究人员和教育机构，以及公众。该项目的目标是通过提供与大规模合成电网的模拟相关的建模和计算分析来促进许多领域的研究和开发。现代电网不断地产生大量数据，例如来自相量测量单元(PMU)的数据。通过正确的分析和测量，可以监控系统运行状况、检测干扰并预测停机等事件。该项目由四个主要部分组成。首先，在网络创建方法的基础上建立详细的建模，以允许更长的时间动态模拟和生成真实的合成数据。详细的建模将包括子传输网络、保护系统元素、网络基础设施和增强的系统元素动态。其次，利用第一个任务中的合成网格来开发用于实时模拟的改进算法和模型。在这个阶段，将需要一些新的计算增强，目标是通过冒险的、潜在的变革性技术来创新，大大扩展传统的电力系统动态模拟。第三，创建反映复杂性和经常错误的合成数据集，这些数据集是从实际设备(例如PMU)获得的。最后，利用前三部分的结果，开发交互式、多用户的模拟场景。这将是一个重要的教育和研究部分。还将探索改进系统参数和结果的可视化，以帮助改进决策。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The high voltage electric grid plays a critical role in the day-to-day functioning and welfare of society, while supporting other major infrastructure as well. Tremendous research and development efforts are needed for improved grid design and operations to ensure reliability in the face of ever-changing load and generation characteristics, natural disasters, and evolving electricity markets. Electric grid research is heavily dependent upon access to high fidelity electric grid models and datasets. However, much of the crucial information about the design and operation of actual electric grids is considered confidential, and hence not publically available and often not even available to researchers in the field. The proposed project aims to meet this need by developing and applying high quality, synthetic electric grid models and datasets that represent the complexities of actual electric grids, but contain no information about real grids and hence can be made publicly available. The project results should be helpful to researchers and educators in the electric power area, and also to industry practitioners in training new engineers to solve problems facing the grid. Another important aspect of this project is the results will be made publicly available for the benefit of the many different groups of people interested in learning more about the electric grid including researchers and educations in a wide variety of different fields that depend upon the electric grid, and the public. The goal of this project is to advance research and development in many domains by providing modeling and computational analytics associated with simulations of large-scale synthetic electric grids. The modern power grid is continuously generating large volumes of data, such as from phasor measurement units (PMUs). With the right analytics and measurements, it is possible to monitor system health, detect disturbances, and predict events such as outages. The project has four main parts. First, to build on the network creation methodology with detailed modeling to allow for extended time dynamic simulations and generating realistic synthetic data. The detailed modeling will include sub-transmission networks, protection system elements, cyber infrastructure, and enhanced dynamics of system elements. Second, to leverage the synthetic grids from the first task to develop improved algorithms and models for a real-time simulation. Several new computational enhancements will be needed at this stage, with a goal being to innovate through adventurous, potentially transformative techniques that greatly extend conventional power system dynamic simulation. Third, to create synthetic and hence public data sets that reflect the complexity, and often errors, that would be obtained from actual devices such as from PMUs. Last, to take the outcomes of the first three parts and develop interactive, multi-user simulation scenarios. There will be a major education and research component to this. Improved visualization of the system parameters and results will also be explored to help with improved decision-making.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.