Yousu Chen

Risk Assessment of Cascading Outages: Methodologies and Challenges

Cascading outages can cause large blackouts, and a variety of methods are emerging to study this challenging topic. The Task Force on Understanding, Prediction, Mitigation, and Restoration of Cascading Failures, under the IEEE PES Computer Analytical Methods Subcommittee (CAMS), seeks to consolidate and review the progress of the field towards methods and tools of assessing the risk of cascading failure. This paper discusses the challenges of cascading failure and summarizes a variety of state-of-the-art analysis and simulation methods, including analyzing observed data, and simulations relying on various probabilistic, deterministic, approximate, and heuristic approaches. Limitations to the interpretation and application of analytical results are highlighted, and directions and challenges for future developments are discussed.

Massive contingency analysis with high performance computing

Contingency analysis is a key function in the Energy Management System (EMS) to assess the impact of various combinations of power system component failures based on state estimates. Contingency analysis is also extensively used in power market operation for feasibility test of market solutions. Faster analysis of more cases is required to safely and reliably operate todays power grids which have a less margin and more intermittent renewable energy sources. Enabled by the latest development in the computer industry, high performance computing holds the promise of meet the need in the power industry. This paper investigates the potential of high performance computing for massive contingency analysis. The framework of “N-x” contingency analysis is established, and computational load balancing schemes are studied and implemented with high performance computers. Case studies of massive 300,000-contingency-case analysis using the Western Electricity Coordinating Council power grid model are presented to illustrate the application of high performance computing and demonstrate the performance of the framework and computational load balancing schemes.

Survey of tools for risk assessment of cascading outages

This paper is a result of ongoing activity carried out by Understanding, Prediction, Mitigation and Restoration of Cascading Failures Task Force under IEEE Computer Analytical Methods Subcommittee (CAMS). The task forces previous papers [1, 2] are focused on general aspects of cascading outages such as understanding, prediction, prevention and restoration from cascading failures. This is the second of two new papers, which extend this previous work to summarize the state of the art in cascading failure risk analysis methodologies and modeling tools. The first paper reviews the state of the art in methodologies for performing risk assessment of potential cascading outages [3]. This paper describes the state of the art in cascading failure modeling tools, documenting the view of experts representing utilities, universities and consulting companies. The paper is intended to constitute a valid source of information and references about presently available tools that deal with prediction of cascading failure events. This effort involves reviewing published literature and other documentation from vendors, universities and research institutions. The assessment of cascading outages risk evaluation is in continuous evolution. Investigations to gain even better understanding and identification of cascading events are the subject of several research programs underway aimed at solving the complexity of these events that electrical utilities face today. Assessing the risk of cascading failure events in planning and operation for power transmission systems require adequate mathematical tools/software.

Performance evaluation of counter-based dynamic load balancing schemes for massive contingency analysis with different computing environments

Contingency analysis is a key function in the Energy Management System (EMS) to assess the impact of various combinations of power system component failures based on state estimation. Contingency analysis is also extensively used in power market operation for feasibility test of market solutions. High performance computing holds the promise of faster analysis of more contingency cases for the purpose of safe and reliable operation of todays power grids with less operating margin and more intermittent renewable energy sources. This paper evaluates the performance of counter-based dynamic load balancing schemes for massive contingency analysis under different computing environments. Insights from the performance evaluation can be used as guidance for users to select suitable schemes in the application of massive contingency analysis. Case studies, as well as MATLAB simulations, of massive contingency cases using the Western Electricity Coordinating Council power grid model are presented to illustrate the application of high performance computing with counter-based dynamic load balancing schemes.

Improving small signal stability through operating point adjustment

ModeMeter techniques for real-time small-signal stability monitoring continue to mature, and more and more phasor measurements are available in power systems. It has come to the stage to bring modal information into real-time power system operation. This paper proposes to establish a procedure for Modal Analysis for Grid Operations (MANGO). Complementary to PSS and other traditional modulation-based control, MANGO aims to provide suggestions such as redispatching generation for operators to mitigate low-frequency oscillations. Load would normally not be reduced except as a last resort. Different from modulation-based control, the MANGO procedure proactively maintains adequate damping at all times, rather than reacting to disturbances when they occur. The effect of operating points on small-signal stability is presented in this paper. Implementation with existing operating procedures is discussed. Several approaches for modal sensitivity estimation are investigated to associate modal damping and operating parameters. The effectiveness of the MANGO procedure is confirmed through simulation studies of several test systems.

Computational Challenges for Power System Operation

As the power grid technology evolution and information technology revolution converge, power grids are witnessing a revolutionary transition, represented by emerging grid technologies and large scale deployment of new sensors and meters in networks. This transition brings opportunities, as well as computational challenges in the field of power grid analysis and operation. This paper presents some research outcomes in the areas of parallel state estimation using the preconditioned conjugated gradient method, parallel contingency analysis with a dynamic load balancing scheme and distributed system architecture. Based on this research, three types of computational challenges are identified: highly coupled applications, loosely coupled applications, and centralized and distributed applications. Recommendations for future work for power grid applications are also presented.

Risk assessment of cascading outages: Part I — Overview of methodologies

This paper is a result of ongoing activity carried out by Understanding, Prediction, Mitigation and Restoration of Cascading Failures Task Force under IEEE Computer Analytical Methods Subcommittee (CAMS). The task forces previous papers are focused on general aspects of cascading outages such as understanding, prediction, prevention and restoration from cascading failures. This is the first of two new papers, which extend this previous work to summarize the state of the art in cascading failure risk analysis methodologies and modeling tools. This paper is intended to be a reference document to summarize the state of the art in the methodologies for performing risk assessment of cascading outages caused by some initiating event(s). A risk assessment should cover the entire potential chain of cascades starting with the initiating event(s) and ending with some final condition(s). However, this is a difficult task and heuristic approaches and approximations have been suggested. This paper discusses different approaches to this and suggests directions for future development of methodologies. The second paper summarizes the state of the art in modeling tools for risk assessment of cascading outages.

MANGO – Modal Analysis for Grid Operation: A Method for Damping Improvement through Operating Point Adjustment

Small signal stability problems are one of the major threats to grid stability and reliability in the U.S. power grid. An undamped mode can cause large-amplitude oscillations and may result in system breakups and large-scale blackouts. There have been several incidents of system-wide oscillations. Of those incidents, the most notable is the August 10, 1996 western system breakup, a result of undamped system-wide oscillations. Significant efforts have been devoted to monitoring system oscillatory behaviors from measurements in the past 20 years. The deployment of phasor measurement units (PMU) provides high-precision, time-synchronized data needed for detecting oscillation modes. Measurement-based modal analysis, also known as ModeMeter, uses real-time phasor measurements to identify system oscillation modes and their damping. Low damping indicates potential system stability issues. Modal analysis has been demonstrated with phasor measurements to have the capability of estimating system modes from both oscillation signals and ambient data. With more and more phasor measurements available and ModeMeter techniques maturing, there is yet a need for methods to bring modal analysis from monitoring to actions. The methods should be able to associate low damping with grid operating conditions, so operators or automated operation schemes can respond when low damping is observed. The work presented in this report aims to develop such a method and establish a Modal Analysis for Grid Operation (MANGO) procedure to aid grid operation decision making to increase inter-area modal damping. The procedure can provide operation suggestions (such as increasing generation or decreasing load) for mitigating inter-area oscillations.

Parallel implementation of power system dynamic simulation

Dynamic simulation of power system transient stability is important for planning, monitoring, operation, and control of electric power systems. However, modeling the system dynamics and network involves the computationally intensive time-domain solution of numerous differential and algebraic equations. This results in a transient stability simulation implementation that does not satisfy the real-time constraints of online dynamic security assessment. This paper presents a parallel implementation of the dynamic simulation on a high-performance computing platform using parallel simulation algorithms and architectures. It enables the simulation to run even faster than real time, enabling the “look-ahead” capability to study pending stability problems in the power grid.

Modern Grid Strategy: Enhanced GridLAB-D Capabilities Final Report

GridLAB-D is a software simulation environment that was initially developed by the US Department of Energy (DOE) Office of Electricity (OE) for the purpose of enabling the effective analysis of emerging smart grid technologies. In order to achieve this goal GridLAB-D was developed using an open source approach with the intent that numerous people and organizations would contribute to the ongoing development. Because of the breadth and complexity of the emerging smart grid technologies the inclusion of multiple groups of developers is essential in order to address the many aspects of the smart grid. As part of the continuing Modern Grid Strategy (MGS) the Pacific Northwest National Laboratory (PNNL) has been tasked with developing an advanced set of GridLAB-D capabilities. These capabilities were developed to enable the analysis of complex use case studies which will allow for multi-disciplinary analysis of smart grid operations. The advanced capabilities which were developed include the implementation of an unbalanced networked power flow algorithm, the implementation of an integrated transmission and distribution system solver, and a set of use cases demonstrating the capabilities of the new solvers.