Contingency Scenarios Research Articles

Fault chain risk expectation-based resilient transmission hardening planning against hurricane

Hardening planning is an effective way to enhance the ability of the transmission system against hurricane attacks and further improve the resilience of power systems. However, the existing hardening method gives little consideration to the risk of multiple failures under hurricanes which may underestimate the real post-disaster damage. To fill the research gap, this paper proposes a resilience index, i.e. fault chain risk expectation (FCRE), which can measure the risk of multiple failures under hurricanes. Then the FCRE-based resilient transmission hardening planning (RTHP) model is proposed to minimize hardening and load shedding penalty costs under different hurricane scenarios. In this paper, hurricane simulation is finished by critical parameters firstly, then the failure probability curve is adopted to obtain the spatial–temporal failure probability of different components, and finally, the corresponding contingency scenarios under simulated hurricanes are obtained by Monte Carlo Simulation (MCS) and k-means clustering algorithms. Meanwhile, the set of fault chains is constructed to calculate the FCRE for each line, and the corresponding constraint is formed to ensure that the system resilience reaches a target level. The proposed model is verified on the modified RTS 24-bus system and the results demonstrate the effectiveness of the proposed hardening planning method.

Structure-Informed Graph Learning of Networked Dependencies for Online Prediction of Power System Transient Dynamics

Online transient analysis plays an increasingly important role in dynamic power grids as the renewable generation continues growing. Traditional numerical methods for transient analysis not only are computationally intensive but also require precise contingency information as input, and therefore, are not suitable for online applications. Existing online transient assessment studies focus on the determination of post-contingency system stability or stability margin. This paper develops a novel graph-learning framework, Deep-learning Neural Representation or DNR, for online prediction, of the time-series trajectories of the system states using initial system responses that can be measured by phasor measurement units (PMUs). The proposed DNR framework consists of two sequential modules: a Network Constructor that captures network dependencies among generators, and a Dynamics Predictor that predicts the system trajectories. The key to improved prediction performance is the introduction of the spatio-temporal message-passing operations into graph neural networks with structural knowledge. Its effectiveness and scalability are validated through comparative studies, demonstrating the prediction performance under different contingency scenarios for systems of different sizes. This framework provides a solution to online predicting post-fault system dynamics based on real-time PMU measurements. Additionally, it can also be applied to facilitate the offline transient simulation without simulating the entire trajectories.

Data recovery via covert cognizance for unattended operational resilience

One of the important premises of unattended operation, a highly promoted characteristic of fission batteries and advanced microreactors, is the ability to automate the analysis of sensors data used in support of operational monitoring and control. To meet this vision, this work proposes a new monitoring and data recovery paradigm to ensure resilience against data corruption which may be the result of malicious intrusion into the reactor operational network. This is paramount to ensure 100% availability under contingency scenarios such as cyberattacks. In support of this vision, earlier work has presented the concept of covert cognizance and demonstrated its mathematical ability to identify and embed cognizance parameters under the noise-dominated null space of the sensors data. This work extends this concept and applies it in real-time to demonstrate three key characteristics: zero-impact, zero-observability, and data recovery, where the first characteristic is to ensure no impact on operation, the second is immunity to discovery by pattern recognition techniques, and the third is to allow recovery of corrupt or falsified data. Recognizing that fission batteries are designed to operate under steady state most of the time, we elect to employ a small modular reactor model under transient operational conditions to demonstrate the operational resilience enabled by the covert cognizance paradigm. Specifically, the PI controller is augmented with the covert cognizance modules to develop self-awareness and enable automatic data recovery. The developed modules are expected to be equally applicable to a wide range of advanced reactor technologies relying on full or partial unattended control.

Optimal Operation of Integrated Water–Power Systems Under Contingencies

With the sharply growing complexity and rapid deployment of smart technologies in our modern society, risk-aware management and coordination in day-to-day operation of the interlinked critical infrastructures is urgently needed. In particular, the interconnected water and power systems (WaPS) are in need of joint and cooperative operation to maximize the economic benefits during normal operating conditions and resilience services during emergencies. While contingency analysis is used to assist the system operators in gaining knowledge of the system’s static security, such understanding is more challenging to achieve in the case of integrated WaPS. This article proposes a novel optimization model for under-emergency operation of the integrated WaPS, considering contingencies in both networks. In order to ensure the delivery of water demand, the proposed formulation considers the hydraulic constraints of the water networks, which is naturally a nonlinear model. The proposed nonlinear model is approximated using a piece-wise linearization approach to convert the optimization model into a mixed-integer linear programming formulation. The proposed analytics are applied to a modified IEEE 24-bus reliability test system that is jointly operated with two and three commercial-scale water networks. The proposed model is evaluated using various disaster severity levels (i.e., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N-k$</tex-math></inline-formula> contingency scenarios) and verify the promising performance of the proposed integrated WaPS model when facing failures and threatening high impact low probability emergencies.

Potential of harnessing operational flexibility from public transport hubs to improve reliability and economic performance of urban multi-energy systems: A holistic assessment framework

Growing penetration of renewable energy sources (RES) and emerging electrified loads (EEL) are bringing about increased difficulties for the power balancing and efficient operation of energy system, due to the impact of remarkable volatilities introduced. Public transport hub (PTH), as a new-style infrastructure of traffic service carriers, is regarded to offer a cogent solution to this problem, in terms of their potential operational flexibilities permitted by energy regulation, vehicular dispatch, and vehicle-to-grid (V2G) programs. As such, this paper carries out a comprehensive study to investigate the implication of harnessing PTH-enabled flexibility in a context of urban multi-energy system (UMES). The proposed methodology is established on a holistic reliability/economic analysis framework which is designed to indicate how UMES’s performances would vary with different utilization of PTH resources. In order to portray the real-time controllability of PTH during operation, a PTH model that takes into account the impacts of both energy- and service-related aspects has been developed, with particular focus on the interdependencies between the energy and transportation sector. The operational simulation of UMES in presence of PTHs is implemented by using a multi-modal-based optimization model, which captures the effects of PTH flexibility under both normal and contingency scenarios integratedly. By embedding the above formulation into a sequential Monte Carlo simulation-based assessment framework, the contribution of PTH to the reliability and economy of UMES can be determined. Numerical studies are conducted based on an illustrative electricity-gas-heat test case and the real PTH datasets in Beijing. The simulation results confirm the significance of PTH-enabled flexibility in improving the performances of UMES. Also, it is demonstrated that the reserving strategy adopted, the composition of vehicle model, and the travel demand profile of passengers are the noteworthy factors that influence the profitability of PTH exploitation.

Line outage distribution factors of a linearized AC model with reactive power and voltage magnitude for resilience-constrained economic dispatch

Research on preventive generation dispatch schemes for resilient power system operation is often based on the DC power flow model, ignoring the influence of reactive power and voltage magnitude. This paper presents an in-depth study of a linearized AC power flow (LAC) model with reactive power and voltage magnitude to derive sensitivity factors, including shift factors and line outage distribution factors, for pre-contingency and post-contingency power flow calculations for N-k contingencies. Based on the derived sensitivity factors, a resilience-constrained economic dispatch (LAC-RCED) strategy is developed, which considers the security constraints of N-1 contingency for all lines and N-2 contingency for the affected lines, as well as optimization objectives to improve the power flow distribution in the transmission system. To deal with the computational difficulties associated with the N-k contingency constraints, an iterative contingency filtering algorithm based on the derived line outage distribution factor is proposed for contingency screening and creating security constraints for violated contingency scenarios. In the case study, the accuracy of the power flow solution obtained from the derived sensitivity factors is investigated by comparison with the AC model. The proposed LAC-RCED model and the iterative contingency filtering algorithm are tested on the IEEE 30-bus and 118-bus systems.

Impact analysis of renewable energy Distributed Generation in deregulated electricity markets: A Context of Transmission Congestion Problem

In the present markets, transmission congestion is contemplated to play a crucial role in the initiation of cascading outages, which compels the system to collapse. It is to be alleviated as soon as possible to maximise profits; or else the cost effective supply will be restricted from reaching the end consumers. Furthermore, global warming, the most serious issue being overlooked by conventional congestion management approaches, causes severe environmental consequences like increase in temperature and declining water supplies. The incorporation of renewable energy (RE) or associated technologies is indeed an excellent countermeasure to global warming. Hence, in this work, optimal capacities of solar based Distributed Generation (DG) are inserted to acknowledge the transmission network's N-1 contingency scenario. A multi-objective framework utilising the Grey Wolf Optimizer (GWO) is presented to obtain the exact size of solar DG by minimizing the costs, voltage deviations and power losses of the network simultaneously. Also, a new heuristic ranking based on the MW flow and net power injections is proposed to determine the most critical buses for the optimal placement of the solar DG. To assess the viability of the proposed approach, it is evaluated using a variety of case studies, including constant and voltage dependent load models in both planning stage and real time operation stage. The capacities of unity and 0.9 lagging power factor (pf) DGs have been obtained for standard IEEE system to check the effectiveness of the approach. The final optimal solutions obtained by GWO are compared with the existing algorithms available in the literature to show the superiority and the potentiality in solving the multi-objective framework. The simulation results emphasise the importance of the proposed approach in terms of costs, voltages and losses as it delivers better performance when compared to existing models in the literature.

Fault current and fault voltage analysis of power transmission systems with high penetration of inverter-based wind generators

This paper highlights the evolution of wind power sources in the modern energy scenario, emphasizing the impacts of inverter-based generators, namely Full-Converter and DFIG (Doubly-Fed Induction Generator), on fault currents and fault voltages in transmission systems. The studies were carried out through computer simulations, with an EMTP (Electromagnetic Transients Program) type software. A power system with high penetration of such renewable generations was modeled and several contingency scenarios were simulated in a transmission line that performs the connection of the wind power plant to the grid. The scenarios included the variation of parameters such as fault type, inception instant angles, resistance, and distance, to explore the main differences between the contributions to the fault of a wind power plant and conventional generation. Among the atypical analyzed characteristics, the following can be highlighted: the absence of the DC (Direct Current) component for Full-Converter generation, independent of fault inception instant angle and distance variation; low levels of the fault contributions at the wind power plant terminal; low levels of voltages at the wind power plant terminal, evidencing the high SIR (Source Impedance Ratio) characteristic of these generations; atypical relations between the fault resistance and measured currents; and others. The obtained results show the importance of further studies on the impacts of inverter-based generations on fault currents and voltages, allowing developments that are able to improve control and protection systems for grids with high penetration of this wind generation topologies

Optimizing the operation of energy storage embedded energy hub concerning the resilience index of critical load

Dealing with high-impact events in energy hubs requires increasing resistance and restoration capability in multi-carrier energy systems. Assessing resilience as an essential and high priority measure must be done to ensure the optimal and economical operation of the energy hub. Thus, this paper introduces a cost-based optimization model of the energy hub that contains storage and considers resilience constraints and other general constraints. In this methodology, loads are categorized as critical and non-critical. The approach defines the highest priority for supplying the critical loads, whereas interruptions in non-critical loads must be minimized. That in this context, storages play a vital role. Four scenarios are investigated as normal situations and contingency cases. The contingency scenarios examine energy carrier interruptions such as electricity, gas, and heat input in an energy hub system. The mathematical model of this problem is founded based on a Mixed Integer Nonlinear Programming (MINLP), which DICOPT solver implements in GAMS. Mentioned model is evaluated by simulation of different case studies for the given energy hub test system. The trade-off between increasing critical load resiliency and reducing total costs is investigated. The results show that as critical load flexibility increases, total costs increase. In the worst-case scenario, only up to 13% of the total load can also be considered as critical load. Moreover, in different scenarios, the results demonstrate that using storage devices helps to increase resiliency Furthermore, in severe events, using storage devices has an increasing trend.

A Cooperative Approach of Frequency Regulation Through Virtual Inertia Control and Enhancement of Low Voltage Ride-through in DFIG-based Wind Farm

Doubly-fed induction generator (DFIG)-based wind farms (WFs) are interfaced with power electronic converters. Such interfaces are attributed to the low inertia generated in the WFs under high penetration and that becomes prevalent in a fault scenario. Therefore, transient stability enhancement along with frequency stability in DFIG-based WFs is a major concern in the present scenario. In this paper, a cooperative approach consisting of virtual inertia control (VIC) and a modified grid-side converter (GSC) approach for low voltage ride-through (LVRT) is proposed to achieve fault ride-through (FRT) capabilities as per the grid code requirements (GCRs) while providing frequency support to the grid through a synthetic inertia. The proposed approach provides LVRT and reactive power compensation in the system. The participation of the VIC in a rotor-side converter (RSC) provides frequency support to the DFIG-based WFs. The combined approach supports active power compensation and provides sufficient kinetic energy support to the system in a contingency scenario. Simulation studies are carried out in MATLAB/Simulink environment for symmetrical and unsymmetrical faults. The superiority of the proposed scheme is demonstrated through analysis of the performance of the scheme and that of a series resonance bridge-type fault current limiter (SR-BFCL).

An Efficient Homotopy Method for Solving the Post-Contingency Optimal Power Flow to Global Optimality

Optimal power flow (OPF) is a fundamental problem in power systems analysis for determining the steady-state operating point of a power network that minimizes the generation cost. In anticipation of component failures, such as transmission line or generator outages, it is also important to find optimal corrective actions for the power flow distribution over the network. The problem of finding these post-contingency solutions to the OPF problem is challenging due to the nonconvexity of the power flow equations and the large number of contingency cases in practice. In this paper, we introduce a homotopy method to solve for the post-contingency actions, which involves a series of intermediate optimization problems that gradually transform the original OPF problem into each contingency-OPF problem. We show that given a global solution to the original OPF problem, a global solution to the contingency problem can be obtained using this homotopy method, under some conditions. With simulations on Polish and other European networks, we demonstrate that the effectiveness of the proposed homotopy method is dependent on the choice of the homotopy path and that homotopy yields an improved solution in many cases. For at least 5% of the test cases, bad local minima were identified, and the homotopy method yielded a solution that was significantly better than state-of-the-art interior point methods in terms of reducing the violation cost during a catastrophic contingency scenario.

Streamlining Tactical Operator Handoffs During Multi-Vehicle Applications

Increased automation has shifted the operator control paradigm from a single operator controlling a single vehicle, to multiple operators collaborating to control multiple vehicles; this paradigm is known as m:N. Many questions remain unanswered in this new operational paradigm about the division of assets as workload for individual operators varies over time. This paper explores the management of workload by enabling operators to temporarily handoff vehicles among each other. A study was conducted to explore both a manual and assisted method for performing handoffs during manipulated contingency scenarios. The assisted handoff method allowed subjects to easily choose and group nominal and/or contingency vehicles. The number of contingencies was also manipulated to determine the effect workload had on how pilots utilized the ability to handoff vehicles. Results show subjects performed handoffs more often when there were more contingencies and when the assisted handoff tool was available. In addition, the assisted tool made subjects feel more comfortable, enabling them to feel like they could take longer to resolve contingency situations. Lastly, even during contingencies, subjects were able to successfully complete secondary tasks.

Challenges of COVID-19 pandemic: The postgraduate case in Sustainable Management of Natural Resources - UIEP

&lt;p&gt;A generalized preventive measure in Mexico against the COVID-19 pandemic was the suspension of in-person non-essential activities, including academic activities. The Universidad Intercultural del Estado de Puebla (Intercultural University of the State of Puebla - UIEP) suspended all classroom course from March 20th, 2019, until the present day. The program of Master of Science in Sustainable Natural Resource Management has remained active in a digital format, facing the following challenges: the speedy implementation of a virtual and distance teaching-learning process; the optimization of computer resources in the face of the digital gap and limited access to these technologies in the region; and an efficient traceability of research processes. COVID-19 displayed the social and digital gap present in the rural areas of Mexico, such as in the case of Sierra Norte de Puebla, in which UIEP is enclaved. However, it also displayed the ability of response and adaptation in the face of the contingency scenario caused by SARS-CoV-2.&lt;/p&gt;

Small-signal stability analysis in smart grids: An approach based on distributed decision trees

Accurate information on the dynamic behavior of a smart grid is essential for its effective control and reliable operation. Specifically, ensuring that the electromechanical oscillations are properly damped is needed for the dynamic security operation of an interconnected system. Since the model-based method to evaluate these oscillations is subject to uncertainties, the data from a wide-area measurement system can assist in this challenging task. In this context, this paper proposes a decentralized approach based on decision trees to classify the interarea oscillations through data measured in the generation buses. For each generation bus, there is an individual decision tree capable of evaluating the interarea oscillations damping. The decision trees presented a low computational burden and, consequently, they can be embedded in phasor measurement units with low-cost hardware. The results obtained with the 68-bus test system show that the classifications provided by individual decision trees are accurate, even when contingency scenarios were evaluated, reaching accuracies greater than 0.93. Thus, the possibility of analyzing the power system stability to small disturbances from local information is one of the main contributions of the proposed approach to advance the state-of-the-art, since the use of distributed decision trees is robust even when there is loss of information from a specific generator.

Individual Incremental loading factor based maximum loadability limit prediction using modern optimization tools

Infrastructure innovation in the power system industry encourages more partakers to participate in the electricity market which improvises the load utilization level. So, the maintenance of power system’s agility with respect to any dynamic update in terms of load level is necessary. Precise prediction of maximum allowable loading point helps to enhance the power system agility and also improvises the total allowable power transfer capability which in turn helps to supply continuous eminent power supply at the minimal cost to the customers by means of encouraging more contracts. Considering the above potential benefits, in this papear by using individual incremental loading factor (IILF) the precise prediction of total loadability limit (TLL) of the system is manipulated with the help of newly evolved meta-heuristic optimization algorithm such as Grey Wolf (GRW) optimizer and Flower Pollination Algorithm (FPA). The allowable single line contingency scenario is considering along with base case scenario to extract the more realistic TLL which helps to maintain the power system balance with respect to the dynamic nature of the load. The proposed maximum loading point extraction manipulation solution problem is tested with the help of three standard IEEE systems such as 30 Bus, 57 Bus and 118 bus systems. The extracted test results show that the predicted maximum allowable loading point enhances the load utilization level without affecting the system securities. The statistical performance measures of GRW and FPA confirmed the better balance of exploration and exploitation in extracting the optimal results.

MODEL CONSIDERATIONS FOR BLACK START STUDIES USING DISTRIBUTED ENERGY RESOURCES

Black Start (BS) is currently a transmission-led approach of starting large conventional generators with self-start capability and energising a skeleton transmission and distribution network. As large transmission connected fossil fuelled generators are decommissioned around the world to meet the emissions targets, system operators are looking at alternatives for BS services. With increasing penetration of DERs, particularly at 33kV and 11kV (in the UK), it becomes imperative to explore the possibility of using these to support whole system restoration. Different DER control systems such as AVR, Wind Farm (WF) control, etc. are tuned to operate in a strong grid condition with consideration for certain credible contingency scenarios. However, system restoration after a black out is an extreme condition. The short circuit power of the islanded system is limited to only the contribution from the ‘self-starting’ anchor generator. This paper explores the necessary model considerations for BS studies using DERs based on the learnings from the Distributed Restart innovation project.

Assuring Increasingly Autonomous Systems in Human-Machine Teams: An Urban Air Mobility Case Study

As aircraft systems become increasingly autonomous, the human-machine role allocation changes and opportunities for new failure modes arise. This necessitates an approach to identify the safety requirements for the increasingly autonomous system (IAS) as well as a framework and techniques to verify and validate that an IAS meets its safety requirements. We use Crew Resource Management techniques to identify requirements and behaviors for safe human-machine teaming behaviors. We provide a methodology to verify that an IAS meets its requirements. We apply the methodology to a case study in Urban Air Mobility, which includes two contingency scenarios: unreliable sensor and aborted landing. For this case study, we implement an IAS agent in the Soar language that acts as a copilot for the selected contingency scenarios and performs takeoff and landing preparation, while the pilot maintains final decision authority. We develop a formal human-machine team architecture model in the Architectural Analysis and Design Language (AADL), with operator and IAS requirements formalized in the Assume Guarantee REasoning Environment (AGREE) Annex to AADL. We formally verify safety requirements for the human-machine team given the requirements on the IAS and operator. We develop an automated translator from Soar to the nuXmv model checking language and formally verify that the IAS agent satisfies its requirements using nuXmv. We share the design and requirements errors found in the process as well as our lessons learned.

Transmission line protection algorithm based on voltage sequence component under high penetration of inverter-based resources

• Protection challenges under high penetration of inverter-based resources • A novel protection algorithm is proposed which uses voltage sequence components to identify faulty events occurrence in transmission line network. • The proposed protection algorithm reliability has been investigated under different network's impacts. • The proposed protection algorithm sensitivity is tested on a big transmission network under different contingency scenarios. A new protection algorithm based on sequence voltage components is proposed in this paper. The new algorithm helps overcome the sensitivity and maloperation issues of the traditional protection schemes in the transmission line network, especially with a high penetration level of inverter-based resources. In this algorithm, the differential fault sequence components are used to identify faulty events in the transmission network. The fault voltage component across the protected line is compared to a setting value of multiple parameters-based voltage makes the final decision of tripping for internal faults only. The proposed algorithm has been tested on a 400kV, 50 Hz single circuit transmission line network supplied with a mix of 90% wind turbines and 10% synchronous generators. The new algorithm simulation testing showed high accuracy when it comes to rapid trip activation with high discrimination reliability between internal and external events. High impedance with different fault locations has been tested to improve the proposed algorithm reliability. The study also performed on the IEEE 9-bus system to validate the proposed algorithm under various faults when strong and weak infeed performed into a bigger transmission network.

Decision support tool for enabling resiliency in an underground power distribution system

Natural disasters cause substantial damage to power distribution infrastructure with long duration of power outages. A resilient Power Distribution System (PDS) should be able to quickly absorb, adapt and recover from the catastrophic events. Enabling resiliency in the PDS is a new imperative for utilities to reduce financial losses, enhance customer satisfaction and supply critical loads in the network. Modern underground PDS comprise of underground feeders and overhead components. Though, these PDS are considered to be highly reliable; however, they still remain vulnerable to catastrophic events by virtue of their locations, age, maintenance and other factors. These factors have a direct impact on resiliency assessment of the system. Quick and accurate decision making by the network operator during and immediately after a natural disaster can make significant impact on the restoration of the system, particularly critical loads. This paper proposes a set of metrics for evaluation of resiliency for underground power distribution networks and a decision support tool for the network operator to enable resiliency in a PDS during network planning and operational contingency scenarios. It facilitates the decision making process to supply all critical loads in the network under all possible scenarios. The proposed decision support tool to enable resiliency is tested through simulation study on a real 110 Bus, 11 kV, underground PDS of CESC Limited, in Patuli area of Kolkata, India.

Power System Analysis of Seawater Desalination Plant in Algeria with Different Load Scenarios

The cost of electricity for the reverse osmosis desalination process is up to 50% of the cost per cubic meter of water produce. Currently, the reduction of energy consumption is the main objective of the research on reverse osmosis plants. This document presents a power system analysis of the seawater desalination plant in Algeria with different load scenarios with a power of 50 MW made available by the electricity company Sonelgaz and a distribution level of 220/11/0.69/0.4 kV and a 2 MW diesel generator at the 0.4 kV level. The objective of this study is to analyze and dimension a general distribution network of an industrial customer through the power flow with different load and contingency scenarios (full load, full load N-1, low load, emergency system) to know and control its optimal and flexible operation. In a second step, the dimensioning of different protective devices is planned through a short circuit analysis of this network in order to evaluate the performance of the system. The ETAP program is used to carry out our simulation of this industrial plant and the effectiveness of the results is proven by comparisons with real measurements for the power flow analysis on the one hand and on the other hand with the results obtained by the builder for the short circuit analysis.