Using Measurement-based Approach to Model, Predict and Control Large-scale Power Grids

电网是所有现代社会的支柱。随着可再生能源发电量的增加，高效可靠地运行已经老化的美国电网成为一个挑战。2003年美国东北部/魁北克和2012年印度的停电已经证明了大规模停电的灾难性后果。然而，如果电力系统能够得到更准确和及时的监测和控制，这样的停电是可以防止的。该项目提出的变革性研究可能会充分利用电网中可用的高分辨率测量数据，并开发一种全新的基于测量的方法，使电网在早期远离大停电。通过对北美三大电网和全球主要电网十多年的观测，通过同步相量测量，观察到大规模电力系统的强线性。这一观察结果也可以通过互连级的动态仿真来验证。该项目不再局限于认为电网是一个非线性网络，应该始终用高阶电路模型来表示，提出了一种全新的基于测量的方法来对大规模互联电网进行建模、预测和控制，特别是关于小信号动态行为。该项目将开发基于测量的电力系统分析和控制应用程序，充分利用这种未得到充分利用的系统线性。具体地说，作为第一步，拟议的线性研究将表征大型电网的强线性，这是可以理解的，被研究界忽视了。本研究将分析大规模电网线性度的来源，并重新审视小信号的传统定义。其次，本项目将构建一个线性结构的模型，利用测量数据来预测大规模电网在小信号扰动后的动态行为？S。电网行为预测对于大规模互联电力系统是非常重要的，这种预测能力将为系统操作员提供真正的前瞻性能力。第三，大规模电网的另一个新应用？S线性涉及到用基于测量的等效模型来表示大规模电路模型中不太感兴趣的区域。这种混合电路和测量模型将很容易实现几个数量级的高仿真速度，同时保持可接受的精度。最后，也是最重要的是，与基于电路的模型不容易频繁更新相比，这种基于测量的模型可以使用实时流测量进行更新，并跟踪电网的连续变化。例如，基于测量的振荡衰减控制器可以实时更新，并且将更加准确和健壮，从而提高互联电网的稳定性。随着更多的高分辨率测量数据可用，拟议的研究将对如何对美国互联电力系统进行建模、分析和控制产生直接和立竿见影的影响；这种先进的方法将有助于美国电网基础设施的能源安全和效率。

The electric power grid is the backbone of all modern societies. With increasing renewable power generation, it becomes a challenge to operate the already aging U.S. power grids efficiently and reliably. The 2003 U.S. Northeast/Quebec and 2012 India blackouts have demonstrated the catastrophic consequences of a massive blackout. However, blackouts such as these could be prevented if the power system could be monitored and controlled more accurately and timely. The transformative research proposed in this project could potentially make full usage of the high-resolution measurement data available in the power grids and develop a completely new measurement-based approach to steer the power grids away from large blackouts early on. The proposed project is also coupled with a strong educational component to engage students from underrepresented groups and a broad dissemination of research findings.Based on over ten years of observation of the three major North American grids and major grids worldwide via synchrophasor measurement, strong linearity of large-scale power systems has been observed. This observation can also be verified by the interconnection-level dynamic simulations. No longer constricted by the habitual belief that the electric power grid is a nonlinear network that should be always represented by a high-order circuit-based model, this project proposes an entirely new measurement-based method to model, predict and control a large-scale interconnected power grid, especially in regard to small-signal dynamic behaviors. This project will develop measurement-based power system analysis and control applications that take full advantage of this underutilized system linearity. Specifically, the proposed linearity study will characterize the strong linearity of large-scale power grids, which has been understandably neglected by the research community, as the first step. The study will analyze the source of large-scale power grid linearity and re-examine the conventional definition of small signal. Secondly, this project will construct a linear-structured model using measurements to predict a large-scale power grid?s dynamic behavior following a small-signal disturbance. Predicting a power grid's behavior is very important for large-scale interconnected power systems and this predictive capability will provide system operators with true look-ahead capabilities. Thirdly, another novel application of a large-scale power grid?s linearity involves representing the less-interested areas of a large-scale circuit-based model with measurement-based equivalent models. This hybrid circuit and measurement model will easily achieve several orders of magnitudes higher simulation speed while maintaining acceptable accuracy. Finally and most importantly, compared to the circuit-based model that cannot be easily updated frequently, this measurement-derived model could be updated using real-time streaming measurements and keep track of the continuous change of power grids. For example, a measurement based oscillation damping controller could be updated in real time and would be much more accurate and robust, improving the stability of an interconnected power grid. With more high-resolution measurement data available, the proposed research will have a direct and immediate impact on how the U.S. interconnected power system should be modeled, analyzed, and controlled; and this advanced approach will contribute to the energy security and efficiency of the U.S. electric power grid infrastructure.