Contingency Scenarios Research Articles

A Survey on Human in the Loop for Self-Adaptive Systems

Adaptive systems possess the remarkable capability to assess and modify their behavior in real-time, particularly when the software deviates from its programmed course or when opportunities for enhanced functionality or performance arise. This adaptability proves invaluable in highly dynamic environments, where rapid changes occur, and human oversight alone falls short in effectively managing applications. However, in certain system types, achieving optimal performance through adaptation may necessitate human input, be it as a sensor providing unique information beyond the system’s reach, an actuator driving adaptation, or a fallback mechanism in contingency scenarios. In this context, the concept of ’human-in-the-loop’ harnesses the innate capabilities of humans to execute tasks and make decisions with greater efficiency and precision, thereby ensuring the security and reliability of these systems. Our primary objective in this study is to present a comprehensive analysis of the key research and contributions in this field. Additionally, we aim to pinpoint potential research avenues and unresolved challenges within the realm of Human-in-the-loop.

A Study on Maximizing Traffic Reachability in Contingency Scenarios Based on Genetic Algorithms

This paper presents a study on maximizing traffic demand accessibility in transportation networks under contingency scenarios using genetic algorithms. Transportation demand accessibility is defined as the proportion of allocated traffic that can reach its destination, considering potential disruptions in the network. The study begins by formulating a model for calculating transportation demand accessibility between different origin-destination pairs, taking into account the possibility of unforeseen events on any road segment. By applying genetic algorithms, the study aims to optimize the allocation of traffic flows in a way that maximizes the expected reachability across the network, even when multiple road sections experience contingencies. Several scenarios are considered, including the introduction of capacity constraints on road segments and varying traffic demands. The results demonstrate that the proposed method can effectively optimize traffic flow allocation, enhancing the robustness and efficiency of transportation networks under uncertain conditions. This research provides valuable insights for urban planners and traffic managers seeking to improve network resilience in the face of unexpected disruptions.

A fast calculation method for three-impulse contingency return trajectory during the near-moon phase based on differential algebra

Aimed at the demand of contingency return at any time during the near-moon phase in the manned lunar landing missions, a fast calculation method for three-impulse contingency return trajectories is proposed. Firstly, a three-impulse contingency return trajectory scheme is presented by combining the Lambert transfer and maneuver at the special point. Secondly, a calculation model of three-impulse contingency return trajectories is established. Then, fast calculation methods are proposed by adopting the high-order Taylor expansion of differential algebra in the two-body trajectory dynamics model and perturbed trajectory dynamics model. Finally, the performance of the proposed methods is verified by numerical simulation. The results indicate that the fast calculation method of two-body trajectory has higher calculation efficiency compared to the semi-analytical calculation method under a certain accuracy condition. Due to its high efficiency, the characteristics of the three-impulse contingency return trajectories under different contingency scenarios are further analyzed expeditiously. These findings can be used for the design of contingency return trajectories in future manned lunar landing missions.

УПРАВЛІННЯ РИЗИКАМИ ДОРОЖНЬОГО БУДІВНИЦТВА

The development of the road network plays a key role in the social, economic and environmental development of a country, region cities. A key aspect for achieving the set goals and ensuring the sustainability and efficiency of road construction projects is the management of risks that arise during project planning and execution. The purpose of the work is the research and classification of risks that arise during the implementation of road construction projects, the formation of a risk management system and the search for ways to minimize their negative consequences. The paper proposes a road construction risk management mechanism, the main purpose of which is to ensure the successful implementation of the road construction project, taking into account all possible risks that may affect the process of its implementation and results. The classification of road construction risks by origin, nature, stages of the project, impact on participants, scale, which will ensure the identification of potential risks, the probability of their occurrence and the establishment of the scale of consequences from their impact, is considered. The successful implementation of road construction projects is ensured by measures to minimize the negative consequences of the impact of risks. The authors identified the main areas of risk minimization during road construction, including detailed planning, risk analysis, use of technologies and innovations, reserve time planning, quality control, stakeholder participation, insurance, environmental management, creation of reserve funds, contingency scenario or plans. A contingency scenario of risk minimization in road construction is proposed, which makes it possible to define measures and strategies for the active management of risks that may arise during a road construction project. A contingency scenario allows you to prepare for unforeseen circumstances and negative events that may occur during the implementation of a road construction project. The application of this scenario is absolutely necessary in cases where there is a high probability of the occurrence of specific risks or when these risks can have a serious impact on the project

Mitigation of Photovoltaics Penetration Impact upon Networks Using Lithium-Ion Batteries

The paper conducts a comprehensive analysis of the impact of very large-scale photovoltaic generation systems on various aspects of power systems, including voltage profile, frequency, active power, and reactive power. It specifically investigates IEEE 9-bus, 39-bus, and 118-bus test systems, emphasizing the influence of different levels of photovoltaic penetration. Additionally, it explores the effectiveness of battery energy storage systems in enhancing system stability and transient response. The transition to PV generation alters system stability characteristics, impacting frequency response and requiring careful management of PV plant locations and interactions with synchronous generators to maintain system reliability. This study highlights how high penetration of photovoltaic systems can improve steady-state voltage levels but may lead to greater voltage dips in contingency scenarios. It also explores how battery energy storage system integration supports system stability, showing that a balance between battery energy storage system capacity and synchronous generation is essential to avoid instability. In scenarios integrating photovoltaic systems into the grid, voltage levels remained stable at 1 per unit and frequency was tightly controlled between 49.985 Hz and 50.015 Hz. The inclusion of battery energy storage systems further enhanced stability, with 25% and 50% battery energy storage system levels maintaining strong voltage and frequency due to robust grid support and sufficient synchronous generation. At 75% battery energy storage system, minor instabilities arose as asynchronous generation increased, while 100% battery energy storage system led to significant instability and oscillations due to minimal synchronous generation. These findings underline the importance of synchronous generation for grid reliability as battery energy storage system integration increases.

An efficient hydrogen-based water-power strategy to alleviate the number of transmission switching within smart grid

The main advantage of Transmission Switching (TS) is decreasing the cost function in a power system. Using TS requires a high number of transmission switching in the system, which can cause some problems in the long run. These problems include decreased lifetime and failure of circuit breakers (CBs), higher repair and maintenance costs, line outage, increased probability of load shedding, and lower reliability of the system. In this paper, congestion management is utilized in the unit commitment problem constrained to the security with the TS to decrease the number of switching. This methodology will resolve the mentioned problems and improve the overall security of the system. Besides, a grid-connected water-power package is suggested to make relaxation for the line congestion which results in the alleviation of the transmission switching. The proposed water-power system is restructured regarding the fuel cell based renewable resource considering the hydrogen tank. Indeed, such a restructured system utilizes the water grid to generate the hydrogen and then power with the aim of linking the electrical grid. Also, on the account of being uncertain of some parameters coming in the electrical grid, an uncertainty-based UT function is addressed to handle uncertainty impacts on the grid's performance. To make awareness-raising, we carry out an outage of the generators as a different contingency scenario of the problem. Finally, the introduced model is testified on two 6-bus and 118-bus grids and solved by Bender's decomposition method. The simulations are performed in GAMS software to confirm the introduced approach effectiveness. Inferred from the results that the proposed strategy can help the grid operator lessen the line congestion up to an acceptable level.

An Efficient Methodology to Identify Relevant Multiple Contingencies and Their Probability for Long-Term Resilience Studies

The selection of multiple contingency scenarios is a key task to perform resilience-oriented long-term planning analyses. However, the identification of relevant multiple contingencies may easily lead to combinatorial explosion issues, even for relatively small systems. This paper proposes an effective methodology for the identification of relevant multiple contingencies and their probabilities, suitable for the long-term resilience analysis of large power systems. The methodology is composed of two main pillars: (1) the clustering of lines that are more likely to fail together, to reduce the computational complexity of the analysis exploiting historical weather data and (2) the probability-based identification of multiple contingencies within each cluster, where the contingency probability is computed applying the copula theory. Tests performed on a portion of the Italian EHV transmission system confirm the validity of the clustering results compared against historical failure events. Moreover, the copula-based algorithm for contingency probability estimation passes the tests carried out on relatively large clusters with very low error tolerance. The method successfully pinpoints critical multiple contingency scenarios and their likelihoods, making it valuable for assessing power system resilience over long-term horizons in support of resilience-oriented planning activities.

A multilevel optimization approach for daily scheduling of combined heat and power units with integrated electrical and thermal storage

Renowned for their remarkable overall efficiencies ranging from 70% to 90%, combined heat and power systems stand as a pivotal strategy for optimizing energy consumption by capitalizing on the synergistic relationship between electricity and thermal energy production. However, achieving optimal performance in combined heat and power systems remains a formidable challenge due to the intricate interplay of numerous variables. This paper presents a novel approach to daily scheduling optimization for combined heat and power units, focusing on the integration of electrical and thermal storage systems and meticulous consideration of security constraints. The optimization of combined heat and power unit scheduling introduces a mixed-integer nonlinear programming challenge, replete with deterministic and random variables. Addressing this complexity requires resilient solutions. In this study, we employ a multilevel optimization technique, transforming the problem into a bilevel structure. The initial step involves mapping operating parameters and minimizing costs through a water cycle optimization algorithm, laying a robust foundation for combined heat and power unit operation. Subsequently, we immerse ourselves in the realm of stochastic contingency scenarios, acknowledging the myriad uncertainties inherent in real-world systems. To empirically validate the efficacy of our proposed algorithm, extensive simulations are conducted on IEEE 18-bus and 24-bus test systems that emulate practical power networks. The results unequivocally showcase the power of our approach in navigating the complexities of optimal CHP unit planning. This paper's contributions lie in its innovative multilevel optimization technique, adeptly addressing both deterministic and stochastic aspects, ultimately paving the way for increased energy efficiency and enhanced system reliability in practical applications.

Evaluation of power grid vulnerability indices accounting for wind power uncertainty

As power systems undergo transformative changes driven by the integration of renewable energy sources, smart grids, and advanced control systems, evaluating vulnerabilities becomes paramount for ensuring resilience. This article introduces a robust framework for computing vulnerability indices, encompassing both cascading indices and spectral graph metrics. Cascading indices are initially computed. Then these indices are compared with pure and extended spectral graph metrics to rank the lines under N−1 contingency conditions. The research also delves into N−k contingency scenarios, examining the system’s vulnerability across various degrees of damage size. The influence of uncertainties related to wind power generation and load demand on line vulnerability is addressed. The Point Estimate Method (PEM) is employed to effectively accommodate these uncertainties offering a reduction in computational time. To validate the proposed framework and analysis methodologies, two case studies are employed—the IEEE 30-bus and 57-bus systems. It is found that the inclusion of wind power and load uncertainty affects the vulnerability of the system. While the impact on cascading indices is negligible for small networks, it becomes notable in the case of larger networks.

Analysis of contingency scenarios towards a suitable transmission pathway in the southern interconnected grid (SIG) of Cameroon

In order to improve on system reliability, utilities conduct contingency scenarios to determine the impact of component outages to the network. It is usually challenging to conduct this analysis due to the complex grid structure. However, the huge consequences of an equipment outage on the grid and the connected loads are compelling enough for researchers to find ways to mitigate these events. Several blackouts have occurred in various parts of the Southern Interconnected Grid (SIG) of Cameroon due to the transient state of the power system. The most recent was the blackout that kept a greater part of the country's political capital, Yaounde, for several days with no electricity. The paper analyses 44 single line (N-1) contingency scenarios in the SIG using ETAP in order to evaluate the impact of line outage events on grid stability. A combined performance index was calculated from the bus voltage security index (PIV/Vsp), real power flow index (PIΔP), reactive power flow index (PIΔQ) and the branch overloading security index (PIS/Ssp). The results showed that the Mangombe - Oyomabang line outage (scenario 22) was the most severe with the load flow failing to converge, followed by the Oyomabang - Ngousso line 1 (scenario 36) with combined performance indices of 992.6191 and 79.74415 respectively. Other scenarios showed overloaded lines, overloaded transformers, high power losses and under/over bus voltages. This study has identified lines with high vulnerability and should be used by utility operators to enhance the reliability of the transmission network and minimize its susceptibility to cascaded power outages.

Conjoint Enhancement of VSG Dynamic Output Responses by Disturbance-Oriented Adaptive Parameters

Virtual synchronous generator(VSG) is an encouraging concept to ensure provision of inertia, following large integration of renewable sources in system. However, apart from VSG contribution in improving system's frequency stability, it is also important to ensure long term robust and stable operation of VSG itself. In this regard, active power loop(APL) of VSG constituting variable control parameters is analyzed subsequent to contingency scenarios. It is observed that dynamics of APL outputs (virtual frequency and active power) is critically sensitive and prone to intrinsic oscillations. Further, during disturbance in system, utilization of constant control parameters of APL illustrates contradictory effects in dynamics of its outputs. As a solution to above issues, novel adaptive inertia and adaptive damping coefficient is proposed. The work provides distinct approach in following manner- 1) judicious selection of different operation range of natural undamped frequency( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ω_{n}$</tex-math></inline-formula> ) and damping ratio(ζ) of APL as compared to existing strategies, along with 2) reduced computational complexity and improved accuracy of controller, 3) thereby reduction in over(-under)shoots and intrinsic oscillations in virtual frequency and output active power of APL during transient period. This strengthens classical VSG design and contributes in its robust long term industry/real-time application. The efficacy of proposed strategy is showcased by real-time simulations along with validation at PHIL level.

Ecological network analysis and optimization of resilience and efficiency for electric power systems design

The simultaneous increase in natural disasters and human dependence on critical infrastructures for essential services such as water, electricity, etc., places ever-increasing demands on the reliable, safe, resilient design and operation of these infrastructures, with a trade-off between continuity of supply (safety and resilience) and quality of supply (reliability and efficiency) at limited cost. With this in mind, a new methodology for the analysis of electric power systems inspired by natural ecosystems is proposed here and applied to representative systems from literature. Information theory is used to quantify the results of the ecological network analysis (ENA) performed. The analysis shows that electric power systems are more efficient than reliable and vulnerable to disasters. A flow matrix is constructed from the available IEEE systems data, quantified and analyzed using information theory, and finally validated by contingency analysis and SCOPF analysis. The original network configurations are compared to random generated topologies. Comparisons are also made with ENA-inspired configurations. The latter show significantly fewer violations in each contingency scenario compared to the original configurations, further supporting the use of ENA to balance power system efficiency and resilience. Thus, ENA can be used to develop power systems with balanced efficiency and resilience.

N − k Static Security Assessment for Power Transmission System Planning Using Machine Learning

This paper presents a methodology for static security assessment of transmission network planning using machine learning (ML). The objective is to accelerate the probabilistic risk assessment of the Hydro-Quebec (HQ) TransÉnergie transmission grid. The model takes the expected power supply and the status of the elements in a N−k contingency scenario as inputs. The output is the reliability metric Expecting Load Shedding Cost (ELSC). To train and test the regression model, stochastic data are performed, resulting in a set of N−k and k=1,2,3 contingency scenarios used as inputs. Subsequently, the output is computed for each scenario by performing load shedding using an optimal power flow algorithm, with the objective function of minimizing ELSC. Experimental results on the well-known IEEE-39 bus test system and PEGASE-1354 system demonstrate the potential of the proposed methodology in generalizing ELSC during an N−k contingency. For up to k=3 the coefficient of determination R2 obtained was close to 98% for both case studies, achieving a speed-up of over four orders of magnitude with the use of a Multilayer Perceptron (MLP). This approach and its results have not been addressed in the literature, making this methodology a contribution to the state of the art.

Transformer Outage Contingency in the Southern Interconnected Grid of Cameroon Based on Dynamic Power Flow Simulation

The power system is a network of components having specific operational limits such that any violation of these limits during their operation could lead to more damage in the grid. The grid in Cameroon has been facing several undesired loading events at some sections of the network and the utility company has over the years, employed measures to mitigate these issues. This study has used the ETAP software to conduct a contingency analysis on the largest independent grid in Cameroon, supplying 6 regions in Cameroon. Seventeen transformer outage events in the network were used to conduct the N‐1 scenario‐based contingency, so that the vulnerability of the network can be evaluated. The fast decoupled power flow method was continuously applied on the network, and the different contingency scenarios were ranked using a combination of 4 performance indices. These four performance indices gave information on the impact of the various transformer outage scenarios on the grid. The four indices were added to give a combined value, and the outages were ranked in order of severity based on this combined index. A major contribution of this paper is the use of four scalar indices to classify the critical elements in the grid of a developing country. The initial load flow simulation of the network showed that 12 nodes had under voltages, while 3 transformers and one transmission line were overloaded. The Ngousso corridor suffered one of the worst under voltage situation during the initial power flow study. This is an indication that the utility company needs to undertake intensive network upgrade and implore measures to increase power generation to support the growing loads. It was observed that all the contingency scenarios experienced convergence. The Oyomabang transformer 1 had the highest combined index of 71.20, showing a huge negative impact on the network as a result of the outage of the transformer. This was followed by the Oyomabang transformer 2 with a combined index of 48.63. The results will be very useful to utility operators in prioritizing transformers in the network with high risk of causing huge impact on the network in case of an outage.

Nonpilot directional protection of a microgrid

AbstractProtection of an islanded inverter‐based microgrid is challenging because of variable and small fault current contribution of inverter‐based resources (IBR) and the absence of sequence currents. This paper proposes a fast and robust nonpilot directional protection scheme to address this challenge. This scheme relies on support vector machines (SVM) and the harmonic current injection capability of IBRs. Examining the harmonic currents measured by a relay during a fault shows that harmonic currents have similar magnitudes but different orientation under forward and reverse faults. Additionally, harmonic currents have similar orientation but different magnitudes under forward faults at different locations along the protected line. Using this, six SVMs are trained for each relay, given that there are three main types of faults (three‐phase‐to‐ground, line‐to‐line, and line‐to‐ground): three as directional elements and three as zone detection elements. A fault is detected and classified by the undervoltage element of a relay. Then, the measured harmonic currents are routed to the appropriate directionality and zone detection SVMs to facilitate proper relay coordination. The performance of the proposed method is evaluated on the CIGRE North American MV distribution benchmark system under various types of contingency scenarios using PSCAD/EMTDC software.

Fueling the seaport of the future: Investments in low‐carbon energy technologies for operational resilience in seaport multi‐energy systems

AbstractThe ability to withstand and recover from disruptions is essential for seaport energy systems, and in light of the growing push for decarbonization, incorporating clean energy sources has become increasingly imperative to ensure resilience. This paper proposes a resilience enhancement planning strategy for a seaport multi‐energy system that integrates various energy modalities and sources, including heating, cooling, hydrogen, solar, and wind power. The planning strategy aims to ensure the reliable operation of the system during contingency events, such as power outages, equipment failures, or extreme weather incidents. The proposed optimization model is designed as a mixed‐integer nonlinear programming formulation, in which McCormick inequalities and other linearization techniques are utilized to tackle the model nonlinearities. The model allocates fuel cell electric trucks (FCETs), renewable energy sources, hydrogen refueling stations, and remote control switches such that the system resilience is enhanced while incorporating natural‐gas‐powered combined cooling, heating, and power system to minimize the operation and unserved demand costs. The model considers various factors such as the availability of renewable energy sources, the demand for heating, cooling, electricity, and hydrogen, the operation of remote control switches to help system reconfiguration, the travel behaviour of FCETs, and the power output of FCETs via vehicle‐to‐grid interface. The numerical results demonstrate that the proposed strategy can significantly improve the resilience of the seaport multi‐energy system and reduce the risk of service disruptions during contingency scenarios.

Security-constrained Transmission Expansion Planning with N-k Security Criterion and Transient Stability

The integration of large-scale wind power into the power system is a global trend in response to climate change, however, the intermittent nature of wind power presents new challenges for planners. Conventional transmission expansion planning (TEP) methods, which only consider N-1 static security, may not ensure the security of multiple contingencies and transient stability in a large-scale wind power integration scenario. Incorporating N-k-based static constraints and dynamic constraints, such as differential-algebraic-equations-(DAEs)-based transient stability constraints, into TEP can result in a heavy computational burden and render the TEP model intractable. This paper proposes a tractable mathematical model (S&DSTEP) for joint consideration of static and dynamic security in TEP with wind power. The uncertain wind power generation is captured through selected scenarios and the model is divided into a TEP master problem and multiple sub-problems for evaluating static security and transient stability. Static security is determined by the use of fault chain theory (FCT), avoiding the need for enumerating all N-k contingencies, thus reducing the computational burden. Transient stability is analyzed through the use of stabilizing cuts generated from the extended equal-area criteria (EEAC) rather than dynamic constraints expressed as DAEs. The proposed model is verified on the modified New England 39-bus system and IEEE 118-bus system, demonstrating its ability to consider transient stability while being more computationally efficient compared to the robust method that directly incorporates all contingency scenarios.

Generation Rescheduling Based Contingency Constrained Optimal Power Flow Considering Uncertainties Through Stochastic Modeling

The generation rescheduling is described as the power generation shifting from one or more generators to one or more other generators as a preventive action to improve and maintain the security of the power system. Since, there is a direct link between security improvement and the lines overload under contingencies, by rescheduling generation, the transmission lines become more flexible and thus, the overload can be relieved. In this paper, contingency constrained optimal power flow (CCOPF) problem based on generation rescheduling by considering the uncertainty of photovoltaic (PV), wind turbine (WT), and plug-in hybrid electric vehicle (PHEV) have been addressed. Water cycle algorithm (WCA) using its potential in finding optimal solution has been used in order to reschedule the generators and optimize the total fuel cost, power losses under contingency scenario, and system security. Moreover, stochastic approach has been proposed for taking into account the uncertainty of PV, WT, and PHEV. Overall performance index including the power and the voltage severity indices have been provided for determining overloaded transmission lines due to the lines’ outage and consequently elimination of overloaded lines. The efficiency of the proposed algorithm has been evaluated on two IEEE-30 and IEEE 118-bus systems. The results are compared with the results of other classical and metaheuristic optimization algorithms. The simulations reveal that the WCA outperforms the other reported optimizers, and is more efficient and effective in improving security for power systems. In addition, the obtained numerical results show that renewable energy sources can significantly reduce fuel costs.

Monitoring Transient Stability in Electric Power Systems Using a Hybrid Model of Convolutional Neural Network and Random Forest

Real-time monitoring of transient stability has gained significant importance in the power system. This paper proposes an advanced hybrid Transient Stability Assessment (TSA) monitoring system, composed of a Convolutional Neural Network (CNN) and Random Forest (RF) classifier which captures the PMU measurements in real-time. CNN utilizes heatmaps for detecting the transient stability status which is hereby used as a feature extractor for the RF classifier. The IEEE-14 and the IEEE-39 bus systems are used as case studies in MATLAB. A wind turbine with a doubly-fed induction generator is also installed to inspect its impact on the power system stability. The efficiency of the system is tested under different contingency scenarios by implementing various faults on distinct locations under dynamic loading conditions. The test results verified that the proposed hybrid CNN-RF (Convolutional Neural Network-Random Forest) model exhibited excellent results in terms of monitoring accuracy, higher precision, and performance in the presence of noise.

Optimized Escape Path Planning for Commercial Aircraft Formations

There is growing interest in commercial aircraft formation flight as a means of reducing both airspace congestion and the carbon footprint of air transportation. Wake vortex surfing has been proven to have significant fuel-saving benefits; however, commercial air transportation has yet to take advantage of these formation benefits due to understandable safety concerns. The realization of these formations requires serious consideration of safety contingencies during closer-in maneuvering of large commercial aircraft. Formation contingency scenarios are much more complex than those of individual aircraft and have not yet been studied in depth. This paper investigates the utility of an optimization modeling approach in generating maneuvering and escape paths for commercial formation aircraft by comparing model-generated paths with pilot-generated escape plans. Three high-altitude commercial aircraft formation scenarios are presented: formation join, formation emergency exit, and formation escape. The model-generated paths are evaluated and compared with pilot-generated escape plans using the author’s pilot expertise. Model results compare well with pilot intuition and are useful in presenting solutions not previously considered, in evaluating separation requirements for improvement of escape path planning, and in confirming the viability of the pilot-generated plans. The novel optimization model formulation presented in this paper is the first model capable of generating exit and escape paths comparable to pilot-generated plans. It is also the first model to incorporate path avoidance of persistent and drifting wake turbulence.