Modeling, Simulation, and Analysis of Cascading Outages in Power Systems

Interconnected power systems are prone to cascading outages leading to large-area blackouts. Modeling, simulation, analysis, and mitigation of cascading outages are still challenges for power system operators and planners.Firstly, the interaction model and interaction graph proposed by [27] are demonstrated on a realistic Northeastern Power Coordinating Council (NPCC) power system, identifying key links and components that contribute most to the propagation of cascading outages. Then a multi-layer interaction graph for analysis and mitigation of cascading outages is proposed. It provides a practical, comprehensive framework for prediction of outage propagation and decision making on mitigation strategies. It has multiple layers to respectively identify key links and components, which contribute the most to outage propagation. Based on the multi-layer interaction graph, effective mitigation strategies can be further developed. A three-layer interaction graph is constructed and demonstrated on the NPCC power system.Secondly, this thesis proposes a novel steady-state approach for simulating cascading outages. The approach employs a power flow-based model that considers static power-frequency characteristics of both generators and loads. Thus, the system frequency deviation can be calculated under cascading outages and control actions such as under-frequency load shedding can be simulated. Further, a new AC optimal power flow model considering frequency deviation (AC-OPFf) is proposed to simulate remedial control against system collapse. Case studies on the two-area, IEEE 39-bus, and NPCC power systems show that the proposed approach can more accurately capture the propagation of cascading outages when compared with a conventional approach using the conventional power flow and AC optimal power flow models.Thirdly, in order to reduce the potential risk caused by cascading outages, an online strategy of critical component-based active islanding is proposed. It is performed when any component belonging to a predefined set of critical components is involved in the propagation path. The set of critical components whose fail can cause large risk are identified based on the interaction graph. Test results on the NPCC power system show that the cascading outage risk can be reduced significantly by performing the proposed active islanding when compared with the risk of other scenarios without active islanding

An Assessment of PIER Electric Grid Research 2003-2014 White Paper

This white paper describes the circumstances in California around the turn of the 21st century that led the California Energy Commission (CEC) to direct additional Public Interest Energy Research funds to address critical electric grid issues, especially those arising from integrating high penetrations of variable renewable generation with the electric grid. It contains an assessment of the beneficial science and technology advances of the resultant portfolio of electric grid research projects administered under the direction of the CEC by a competitively selected contractor, the University of California’s California Institute for Energy and the Environment, from 2003-2014

Cascading Failures and Contingency Analysis for Smart Grid Security

The modern electric power grid has become highly integrated in order to increase the reliability of power transmission from the generating units to end consumers. In addition, today’s power system are facing a rising appeal for the upgrade to a highly intelligent generation of electricity networks commonly known as Smart Grid. However, the growing integration of power system with communication network also brings increasing challenges to the security of modern power grid from both physical and cyber space. Malicious attackers can take advantage of the increased access to the monitoring and control of the system and exploit some of the inherent structural vulnerability of power grids. Therefore, determining the most vulnerable components (e.g., buses or generators or transmission lines) is critically important for power grid defense. This dissertation introduces three different approaches to enhance the security of the smart grid. Motivated by the security challenges of the smart grid, the first goal of this thesis is to facilitate the understanding of cascading failure and blackouts triggered by multi-component attacks, and to support the decision making in the protection of a reliable and secure smart grid. In this work, a new definition of load is proposed by taking power flow into consideration in comparison with the load definition based on degree or network connectivity. Unsupervised learning techniques (e.g., K-means algorithm and self-organizing map (SOM)) are introduced to find the vulnerable nodes and performance comparison is done with traditional load based attack strategy. Second, an electrical distance approach is introduced to find the vulnerable branches during contingencies. A new network structure different than the original topological structure is formed based on impedance matrix which is referred as electrical structure. This structure is pruned to make it size compatible with the topological structure and the common branches between the two different structures are observed during contingency analysis experiments. Simulation results for single and multiple contingencies have been reported and the violation of line limits during single and multiple outages are observed for vulnerability analysis. Finally, a cyber-physical power system (CPS) testbed is introduced as an accurate cyber-physical environment in order to observe the system behavior during malicious attacks and different disturbance scenarios. The application areas and architecture of proposed CPS testbed have been discussed in details. The testbed’s efficacy is then evaluated by conducting real-time cyber attacks and exploring the impact in a physical system. The possible mitigation strategies are suggested for defense against the attack and protect the system from being unstable

Preventing Wide Area Blackouts in Transmission Systems: A New Approach for Intentional Controlled Islanding using Power Flow Tracing

A novel method to reduce the impact of wide area blackouts in transmission networks is presented. Millions of customers are affected each year due to blackouts. Splitting a transmission system into smaller islands could significantly reduce the effect of these blackouts. Large blackouts are typically a result of cascading faults which propagate throughout a network where Intentional Controlled Islanding (ICI) has the advantage of containing faults to smaller regions and stop them cascading further. Existing methodologies for ICI are typically calculated offline and will form pre-determined islands which can often lead to excessive splits. This thesis developed an ICI approach based on real time information which will calculate an islanding solution quickly in order to provide a ‘just-in-time’ strategy. The advantage of this method is that the island solution is designed based on the current operating point, but well also be designed for the particular disturbance location and hence will avoid unnecessary islanding. The new method will use a power flow tracing technique to find a boundary around a disturbance which forms the island that will be cut. The tracing method required only power flow information and so, can be computed quite quickly. The action of islanding itself can be a significant disturbance, therefore any islanding solution should aim to add as little stress as possible to the system. While methods which minimise the power imbalance and total power disrupted due to splitting are well documented, there has been little study into the effect islanding would have on voltage. There a new approach to consider the effects that islanding will have on the voltage stability of the system is developed. The ICI method is based on forming an island specific to a disturbance. If the location of a source is known along with information that a blackout is imminent, the methodology will find the best island in which to contain that disturbance. This is a slightly different approach to existing methods which will form islands independent of disturbance location knowledge. An area of influence is found around a node using power flow tracing, which consists of the strongly connected elements to the disturbance. Therefore, low power flows can be disconnected. This area of influence forms the island that will be disconnected, leaving the rest of the system intact. Hence minimising the number of islands formed. Finally the methodology is compared to the existing methods to show that the new tool developed in this thesis can find better solutions and that a new way of thinking about power system ICI can be put forward

Risk-based security-constrained optimal power flow: Mathematical fundamentals, computational strategies, validation, and use within electricity markets

This dissertation contributes to develop the mathematical fundamentals and computational strategies of risk-based security-constrained optimal power flow (RB-SCOPF) and validate its application in electricity markets. The RB-SCOPF enforces three types of flow-related constraints: normal state deterministic flow limits, contingency state deterministic flow limits (the N-1 criteria), and contingency state system risk, which depends only on contingency states but not the normal state. Each constraint group is scaled by a single parameter setting allowing tradeoffs between deterministic constraints and system risk. Relative to the security-constrained optimal power flow (SCOPF) used in industry today, the RB-SCOPF finds operating conditions that are more secure and more economic. It does this by obtaining solutions that achieve better balance between post-contingency flows on individual circuits and overall system risk. The method exploits the fact that, in a SCOPF solution, some post-contingency circuit flows which exceed their limits impose little risk while other post-contingency circuit flows which are within their limits impose significant risk. The RB-SCOPF softens constraints for the former and hardens constraints for the latter, thus achieving simultaneous improvement in both security and economy. Although the RB-SCOPF is more time-intensive to solve than SCOPF, we have developed efficient algorithms that allow RB-SCOPF to solve in sufficient time for use in real-time electricity markets. In contrast to SCOPF, which motivates market behavior to offload circuit flows exceeding rated flows, the use of RB-SCOPF provides price signals that motivate market behavior to offload circuit flows and to enhance system-wide security levels. Voltage stability testing has demonstrated that the dispatch result based on RB-SCOPF has higher reactive margins at normal state and after a contingency happens, thus has better static voltage stability performance

Risk based multi-objective security control and congestion management

Deterministic security criterion has served power system operation, congestion management quite well in last decades. It is simple to be implemented in a security control model, for example, security constrained optimal power flow (SCOPF). However, since event likelihood and violation information are not addressed, it does not provide quantitative security understanding, and so results in system inadequate awareness. Therefore, even if computation capability and information techniques have been greatly improved and widely applied in the operation support tool, operators are still not able to get rid of the security threat, especially in the market competitive environment.;Probability approach has shown its strong ability for planning purpose, and recently gets attention in operation area. Since power system security assessment needs to analyze consequence of all credible events, risk defined as multiplication of event probability and severity is well suited to give an indication to quantify the system security level, and congestion level as well. Since risk addresses extra information, its application for making BETTER online operation decision becomes an attractive research topic.;This dissertation focus on system online risk calculation, risk based multi-objective optimization model development, risk based security control design, and risk based congestion management. A regression model is proposed to predict contingency probability using weather and geography information for online risk calculation. Risk based multi-objective optimization (RBMO) model is presented, considering conflict objectives: risks and cost. Two types of method, classical methods and evolutionary algorithms, are implemented to solve RBMO problem, respectively. A risk based decision making architecture for security control is designed based on the Pareto-optimal solution understanding, visualization tool and high level information analysis. Risk based congestion management provides a market lever to uniformly expand a security VOLUME , where greater volume means more risk. Meanwhile, risk based LMP signal contracts ALL dimensions of this VOLUME in proper weights (state probabilities) at a time.;Two test systems, 6-bus and IEEE RTS 96, are used to test developed algorithms. The simulation results show that incorporating risk into security control and congestion management will evolve our understanding of security level, improve control and market efficiency, and support operator to maneuver system in an effective fashion

Methodologies for Simplified Lifeline System Risk Assessments

Natural hazards are a growing risk across the globe. As regions have urbanized, single events impact greater proportions of the population, and the populations within those regions have become more dependent on infrastructure systems. Regional resilience has become closely tied to the performance of infrastructure. For a comprehensive risk assessment losses caused by lifeline outage must be considered alongside structural and nonstructural risks. Many well developed techniques quantify structural and nonstructural risk; however, there are insufficient procedures to determine the likelihood of lifeline outages. Including lifelines in seismic assessments will provide a comprehensive risk, improving a decision maker’s capacity to efficiently balance mitigation against the full spectrum of risks. An ideal lifeline risk assessment is infeasible due to the large geographic scale of lifeline systems and their system structure; these same characteristics also make them vulnerable to disruption in hazard events. Probabilistic methods provide solutions for their analysis, but many of the necessary analysis variables remain unknown. Continued research and increased collection of infrastructure data may improve the ability of advanced probabilistic methods to study and forecast performance of lifelines, but many inputs for a complete probabilistic model are likely to remain unknown. This thesis recognizes these barriers to assessment and proposes a methodology that uses consequences to simplify analysis of lifeline systems. Risk is often defined as the product of probability of failure and consequence. Many assessments study the probability of failure and then consider the consequence. This thesis proposes the opposite, studying consequence first. In a theoretical model where all information is available the difference in approach is irrelevant; the results are the same regardless of order. In the real world however, studying consequence first provides an opportunity to simplify the system assessment. The proposed methodology starts with stakeholders defining consequences that constitute ruin, and then the lifeline system is examined and simplified to components that can produce such consequences. Previously large and expansive systems can be greatly simplified and made more approachable systems to study. The simplified methodology does not result in a comprehensive risk assessment, rather it provides an abbreviated risk profile of catastrophic risk; risk that constitutes ruin. By providing an assessment of only catastrophic lifeline risk, the risk of greatest importance is measured, while smaller recoverable risk remains unknown. This methodology aligns itself with the principle of resilience, the ability to withstand shocks and rebound. Assessments can be used directly to consider mitigation options that directly address stakeholder resilience. Many of the same probabilistic issues remain, but by simplifying the process, abbreviated lifelines assessments are more feasible providing stakeholders with information to make decisions in an environment that currently is largely unknown