Enhanced AC Quasi-steady State Cascading Failure Model for Grid Vulnerability Analysis under Wind Uncertainty

This paper presents several enhancements on a mixed OPF-stochastic cascading failure model to study the impacts of renewable energy resource uncertainty on grid vulnerability. The improved quasi-steady state (QSS) cascading failure model incorporates AC power flow calculations thus allowing us to simulate voltage-related failures in the grid. The under-voltage load shedding (UVLS) relays are modeled along with a stochastic time-inverse overload relay to accurately simulate the protective system response. In addition, more realistic assumptions are considered in the modeling of wind power penetration using geographical information of grid topology and wind potential map for a given geographical area. The effectiveness of the proposed framework is evaluated on a 500-bus synthetic network developed based on the footprints of South Carolina. The enhanced model allows us to more accurately simulate cascades in the power system with high penetration of erratic renewables and identify weak points

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

Modern Power System Dynamic Performance Improvement through Big Data Analysis

Higher penetration of Renewable Energy (RE) is causing generation uncertainty and reduction of system inertia for the modern power system. This phenomenon brings more challenges on the power system dynamic behavior, especially the frequency oscillation and excursion, voltage and transient stability problems. This dissertation work extracts the most useful information from the power system features and improves the system dynamic behavior by big data analysis through three aspects: inertia distribution estimation, actuator placement, and operational studies.First of all, a pioneer work for finding the physical location of COI in the system and creating accurate and useful inertia distribution map is presented. Theoretical proof and dynamic simulation validation have been provided to support the proposed method for inertia distribution estimation based on measurement PMU data. Estimation results are obtained for a radial system, a meshed system, IEEE 39 bus-test system, the Chilean system, and a real utility system in the US. Then, this work provided two control actuator placement strategy using measurement data samples and machine learning algorithms. The first strategy is for the system with single oscillation mode. Control actuators should be placed at the bus that are far away from the COI bus. This rule increased damping ratio of eamples systems up to 14\% and hugely reduced the computational complexity from the simulation results of the Chilean system. The second rule is created for system with multiple dynamic problems. General and effective guidance for planners is obtained for IEEE 39-bus system and IEEE 118-bus system using machine learning algorithms by finding the relationship between system most significant features and system dynamic performance. Lastly, it studied the real-time voltage security assessment and key link identification in cascading failure analysis. A proposed deep-learning framework has Achieved the highest accuracy and lower computational time for real-time security analysis. In addition, key links are identified through distance matrix calculation and probability tree generation using 400,000 data samples from the Western Electricity Coordinating Council (WECC) system

Defining the Simulation Scope for Extreme Events

This report investigates extreme events through the means of using three large, recent reports into different types of extreme event to determine what constitutes an extreme event, what data is likely to be necessary to model it, and what compromises are likely necessary to make such modelling possible. The discussions consider the use of weather data in power system analysis, the modelling of cascading outages within the power system, and the potential of attacks on the cyber-physical system which constitutes the power system. Recommendations are then made in terms of how weather data should be used, what types of power system simulations are necessary, and what metrics are likely to be useful in such analyses. It is suggested that an extreme event be defined as “any event that, without suitable mitigating actions, would cause, as a result of conditions arising from that event: interruptions to a large number of end users’ supply of energy, beyond those that could be expected due an outage of any single item of energy system plant; extraordinary energy market conditions, or; interruptions of energy supply to significant elements of critical national infrastructure ”. This captures all potential extreme events that are likely to be simulated; those associated with correlated weather events; those associated with an extended abnormal weather or operational conditions; those incurred by cyber-physical attacks on the power system. Different scenarios are proposed which would be appropriate to simulate to help define a clearer scope of what simulations to undertake, for example modelling an extreme windstorm similar to Storm Arwen

Modeling Cascading Failures in Power Systems in the Presence of Uncertain Wind Generation

One of the biggest threats to the power systems as critical infrastructures is large-scale blackouts resulting from cascading failures (CF) in the grid. The ongoing shift in energy portfolio due to ever-increasing penetration of renewable energy sources (RES) may drive the electric grid closer to its operational limits and introduce a large amount of uncertainty coming from their stochastic nature. One worrisome change is the increase in CFs. The CF simulation models in the literature do not allow consideration of RES penetration in studying the grid vulnerability. In this dissertation, we have developed tools and models to evaluate the impact of RE penetration on grid vulnerability to CF. We modeled uncertainty injected from different sources by analyzing actual high-resolution data from North American utilities. Next, we proposed two CF simulation models based on simplified DC power flow and full AC power flow to investigate system behavior under different operating conditions. Simulations show a dramatic improvement in the line flow uncertainty estimation based on the proposed model compared to the simplified DC OPF model. Furthermore, realistic assumptions on the integration of RE resources have been made to enhance our simulation technique. The proposed model is benchmarked against the historical blackout data and widely used models in the literature showing similar statistical patterns of blackout size

Quantification and mitigation of the impacts of extreme weather on power system resilience and reliability

Modelling the impact of extreme weather on power systems is a computationally expensive, challenging area of study due to the diversity of threats, complicatedness of modelling, and data and simulation requirements to perform the relevant studies. The impacts of extreme weather – specifically wind – are considered. Factors such as the distribution of outage probability on lines and the potential correlation with wind power generation during storms are investigated; so too is sensitivity of security assessments involving extreme wind to the relationships used between failures and the natural hazard being studied, specifically wind speed. A large scale simulation ensemble is developed and demonstrated to investigate what are deemed the most significant features of power system simulation during extreme weather events. The challenges associated with modelling high impact low probability (HILP) events are studied and demonstrate that the results of security assessments are significantly affected by the granularity of incident weather data being used and the corrections or interpolation being applied to the source data. A generalizable simulation framework is formulated and deployed to investigate the significance of the relationship between incident natural hazards, in this case wind, and its corresponding impact on system resilience. Based on this, a large-scale simulation model is developed and demonstrated to take consideration of a wide variety of factors which can affect power systems during extreme weather events including, but not limited to, under frequency load shedding, line overloads, and high wind speed shutdown and its impact on wind generation. A methodology for quantifying and visualising distributed overhead line failure risk is also demonstrated in tandem with straightforward methods for making wind power projections over transmission systems for security studies. The potential correlation between overhead line risk and wind power generation risk is illustrated visually on representations of GB power networks based on real world data.Open Acces