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

使用基于测量的方法对大型电网进行建模、预测和控制

• 批准号: 1509624
• 负责人: Yilu Liu
• 金额: $ 28.97万
• 依托单位: University of Tennessee Knoxville
• 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: 2015-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1509624&HistoricalAwards=false
• 关键词: Using; Measurement; based; Approach; Model

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.

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了一项挑战。 2003年美国东北部/魁北克省和2012年印度的停电事件已经证明了大规模停电的灾难性后果。然而，如果能够更准确、更及时地监测和控制电力系统，则可以防止此类停电。该项目提出的变革性研究有可能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，以引导电网尽早避免大停电。拟议的项目还结合了强大的教育成分，以吸引来自代表性不足群体的学生，并广泛传播研究成果。基于通过同步相量测量对北美三大电网和全球主要电网十多年来的观察，大型电力系统已被观测到。这一观察结果也可以通过互连级动态仿真得到验证。不再受传统观念的束缚，即电网是一个非线性网络，应始终由基于高阶电路的模型表示，该项目提出了一种全新的基于测量的方法来建模、预测和控制大型电网。大规模互联电网，特别是在小信号动态行为方面。该项目将开发基于测量的电力系统分析和控制应用程序，充分利用这种未充分利用的系统线性度。具体来说，所提出的线性研究将表征大规模电网的强线性特征，这是可以理解的，作为第一步，研究界忽视了这一点。该研究将分析大规模电网线性的来源，并重新审视小信号的常规定义。其次，该项目将使用测量结果构建线性结构模型，以预测小信号扰动后大规模电网的动态行为。预测电网的行为对于大规模互连电力系统非常重要，这种预测能力将为系统运营商提供真正的前瞻能力。第三，大规模电网线性的另一个新颖应用涉及用基于测量的等效模型来表示基于大规模电路的模型的不太感兴趣的区域。这种混合电路和测量模型将轻松实现几个数量级的仿真速度，同时保持可接受的精度。最后也是最重要的是，与无法轻松频繁更新的基于电路的模型相比，这种测量衍生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，基于测量的振荡阻尼控制器可以实时更新，并且更加准确和稳健，从而提高互连电网的稳定性。随着更多高分辨率测量数据的出现，拟议的研究将对美国互联电力系统的建模、分析和控制产生直接和直接的影响；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。

项目成果

Dynamic Model Reduction for Large-Scale Power Systems Using Wide-Area Measurements

使用广域测量减少大型电力系统的动态模型

• DOI: 10.1109/access.2020.2992624
• 发表时间: 2020-01
• 期刊: IEEE Access
• 影响因子: 3.9
• 作者: Tong, Ning;Jiang, Zhihao;Zhu, Lin;Liu, Yilu
• 通讯作者: Liu, Yilu

A Comprehensive Method to Mitigate Forced Oscillations in Large Interconnected Power Grids

缓解大型互连电网受迫振荡的综合方法

• DOI: 10.1109/access.2021.3056123
• 发表时间: 2021-01
• 期刊: IEEE Access
• 影响因子: 3.9
• 作者: Zhu, Lin;Yu, Wenpeng;Jiang, Zhihao;Zhang, Chengwen;Zhao, Yi;Dong, Jiaojiao;Wang, Weikang;Liu, Yilu;Farantatos, Evangelos;Ramasubramanian, Deepak;et al
• 通讯作者: et al

Impact of Wide-Area Oscillation Damping Control using Measurement-Driven Approach on System Separation - Saudi Grid Case Study

使用测量驱动方法进行广域振荡阻尼控制对系统分离的影响 - 沙特电网案例研究

• DOI: 10.1109/td39804.2020.9299887
• 发表时间: 2020-10-12
• 期刊: 2020 IEEE/PES Transmission and Distribution Conference and Exposition (T&D)
• 作者: Ibrahim Altarjami;Lin Zhu;D. Lu;Xi;a Deng;a;Yilu Liu;E. Farantatos;D. Ramasubramanian;Mahendra Patel;Muhammad Ijaz;Ahmed H. Al;Salem Bashraheel
• 通讯作者: Salem Bashraheel