A Survey on Power System Blackout and Cascading Events: Research Motivations and Challenges

Hidden high-risk states identification from routine urban traffic

One of the core risk management tasks is to identify hidden high-risk states that may lead to system breakdown, which can provide valuable early warning knowledge. However, due to the high dimensionality and nonlinear interactions embedded in large-scale complex systems like urban traffic, it remains challenging to identify hidden high-risk states from huge system state space where over 99% of possible system states are not yet visited in empirical data. Based on the maximum entropy model, we infer the underlying interaction network from complicated dynamical processes of urban traffic and construct the system energy landscape. In this way, we can locate hidden high-risk states that may have never been observed from real data. These states can serve as risk signals with a high probability of entering hazardous minima in the energy landscape, which lead to huge recovery cost. Our findings might provide insights for complex system risk management.

Impacts of heatwaves on electricity reliability: Evidence from power outage data in China

Heatwaves, driven by climate change, have increasingly challenged energy systems with increased demand and reduced supply, leading to power outages. This study empirically examines the impact of heatwaves on power outages, employing fixed-effects models and using high-frequency outage data from China (2019-2021). The results indicate that heatwaves increase the frequency of outages by 3.9%-4.0% and extend their duration by 7.9%-8.3%. Additionally, each degree of temperature rise increases outages by 0.1%, and an additional heatwave day raises outages by 0.5%. We also observed heterogeneity in outage impacts across different socio-demographic groups. Furthermore, projections under RCP2.6, RCP4.5, and RCP8.5 show that outages will increase by 5.2%-12.5% in 2030 and 7.4%-20.3% in 2050. These findings underscore the urgency of grid upgrades and provide insights for resource allocation to adaptation to climate change.

A blockchain consensus mechanism for real-time regulation of renewable energy power systems

With the ongoing development of renewable energy sources, information technologies and physical energy systems are further integrated, which leads to challenges in ensuring the secure and stable operation of renewable energy power systems in the face of potential cyber threats. The strengths of blockchain in cybersecurity make it a promising solution to these challenges. However, existing blockchains are not well-suited for control tasks due to their low real-time performance. Here, we present a consensus mechanism that enables real-time security control of systems, called Proof of Task. Instead of solving meaningless hash puzzles in Proof of Work, Proof of Task addresses problems closely related to the stable operation and control performance of these systems. With the proposed verification mechanism, Proof of Task significantly enhances the real-time performance of blockchain while mines its computational resources for tasks of interest. To demonstrate the effectiveness and necessity of Proof of Task, it is deployed across three renewable energy power systems. The results show that Proof of Task markedly fortifies the security and computing capability of these systems, ensuring their reliable and stable operation. This work highlights the promise of blockchain to facilitate security control and trusted computing of large-scale, complex-dynamic systems.

Next generation power inverter for grid resilience: Technology review

Distributed generation (DG) systems are becoming more popular due to several benefits such as clean energy, decentralization, and cost effectiveness. Because the majority of renewable energy sources provide DC power, power electronic inverters are necessary for their conversion from DC to AC power. To fulfill this demand, the next generation power inverter employs innovative technologies while simultaneously assuring stability and resilience. This paper highlights the limitations of current inverter technology and points the way forward to the next generation of inverters that overcome those limitations. A more efficient, trustworthy, and system-resilient inverter employs new technology such as the internet of things (IoT). However, these new technologies expose the system to cyber-physical threats. This problem is being overcome through the application of artificial intelligence and machine learning. Initially, the present state of the inverter technology with its current challenges against grid resilience has been investigated in this paper. After that, the necessity of smart inverter and their impact on the power system has been reviewed to enhance grid resilience, stability, and adaptability. Finally, a directional pathway to the next generation inverter has been proposed by addressing the features, components requirement integration challenges, and possible solutions in this paper.

An adaptive load shedding methodology for renewable integrated power systems

System stability issues regarding frequency and voltage in modern power systems are growing in importance as they incorporate more and more complex components. To ensure a sustainable, pollution-free power generation, modern power systems are designed to incorporate more renewable generation sources than traditional ones. Therefore, in the event of a large-scale disruption event, conventional load-shedding strategies are unable to keep the voltage and frequency limit below the threshold value. The suggested approach takes into account this issue by rating load buses in relation to relevant frequency changes, their voltage stability, system load damping coefficients, and the introduction of green energy sources in place of fossil fuel-based ones. Battery Energy Storage Systems (BESS) are used in the proposed method to minimize load shedding amount required for conventional schemes. After determining the amount, the scheme dynamically chooses feeders as per relative weightage of the stability components (voltage, frequency) to ensure that the overall load shed amount is near to the calculated value. To verify this, the scheme is tested on IEEE 39 bus with python scripted simulation. There are four scenarios considering 250 MW, 500 MW and 1500 MW injection of PV based power generation sources with conventional generation loss of 800 MW and 1000 MW. The threshold frequency is considered 49.10 Hz. The total amount of BESS is 300 MW. For every scenario, it has been found that the methodology successfully maintains the system frequency above 49.10 Hz with a minimal amount of load shedding. Hence, the proposed methodology is able to maintain frequency stability for a modern power system with large-scale PV generation through adaptive feeder selection for load shedding.

Study on the configuration causal factors of electric power generation safety incidents based on grounded theory and fsQCA

Electric power generation safety incidents can lead to severe consequences, including casualties and widespread power outages. Previous research has mainly focused on the mechanisms and causal relationships of accidents. However, these incidents result from multiple factors working together, lacking systematic analysis. This study examines 161 electric power generation safety incidents from 2015 to 2022, utilizing Grounded Theory for coding to construct a causal model. The derived model is used as a conditional variable for fuzzy set qualitative comparative analysis (fsQCA), with accident severity as the outcome variable. Forty-five cases are selected for assigning values, and R language and fsQCA software are integrated for univariate necessary condition analysis, followed by configurational analysis. Results show the Grounded Theory-derived causal model includes six factors: human unsafe behavior, equipment factors, enterprise safety management, on-site safety management, safety qualifications of personnel, and environmental factors. Necessary condition analysis indicates incidents result from multiple conditions. Configurational analysis identifies seven paths condensed into three types: management deficiency, low safety qualifications, and unsafe behavior. Recommendations are proposed for each type, discussing intrinsic connections between variables based on conditional variables in configurational paths. The aim is to reduce electric power generation safety incidents, ensure personnel safety, and guarantee continuous electricity supply.

Machine Learning Model Development to Predict Power Outage Duration (POD): A Case Study for Electric Utilities

In the face of increasing climate variability and the complexities of modern power grids, managing power outages in electric utilities has emerged as a critical challenge. This paper introduces a novel predictive model employing machine learning algorithms, including decision tree (DT), random forest (RF), k-nearest neighbors (KNN), and extreme gradient boosting (XGBoost). Leveraging historical sensors-based and non-sensors-based outage data from a Turkish electric utility company, the model demonstrates adaptability to diverse grid structures, considers meteorological and non-meteorological outage causes, and provides real-time feedback to customers to effectively address the problem of power outage duration. Using the XGBoost algorithm with the minimum redundancy maximum relevance (MRMR) feature selection attained 98.433% accuracy in predicting outage durations, better than the state-of-the-art methods showing 85.511% accuracy on average over various datasets, a 12.922% improvement. This paper contributes a practical solution to enhance outage management and customer communication, showcasing the potential of machine learning to transform electric utility responses and improve grid resilience and reliability.

Multi-agent Continuous Control with Generative Flow Networks

Generative Flow Networks (GFlowNets) aim to generate diverse trajectories from a distribution in which the final states of the trajectories are proportional to the reward, serving as a powerful alternative to reinforcement learning for exploratory control tasks. However, the individual-flow matching constraint in GFlowNets limits their applications for multi-agent systems, especially continuous joint-control problems. In this paper, we propose a novel Multi-Agent generative Continuous Flow Networks (MACFN) method to enable multiple agents to perform cooperative exploration for various compositional continuous objects. Technically, MACFN trains decentralized individual-flow-based policies in a centralized global-flow-based matching fashion. During centralized training, MACFN introduces a continuous flow decomposition network to deduce the flow contributions of each agent in the presence of only global rewards. Then agents can deliver actions solely based on their assigned local flow in a decentralized way, forming a joint policy distribution proportional to the rewards. To guarantee the expressiveness of continuous flow decomposition, we theoretically derive a consistency condition on the decomposition network. Experimental results demonstrate that the proposed method yields results superior to the state-of-the-art counterparts and better exploration capability. Our code is available at https://github.com/isluoshuang/MACFN.

Multi-objective optimal allocation of multiple capacitors and distributed generators considering different load models using Lichtenberg and thermal exchange optimization techniques

Integrating distributed generations (DGs) into the radial distribution system (RDS) are becoming more crucial to capture the benefits of these DGs. However, the non-optimal integration of renewable DGs and shunt capacitors may lead to several operational challenges in distribution systems, including high energy losses, poor voltage quality, reverse power flow, and lower voltage stability. Therefore, in this paper, the multi-objective optimization problem is expressed with precisely selected three conflicting goals, incorporating the reduction in both power loss and voltage deviation and improvement of voltage stability. A new index for voltage deviation called root mean square voltage is suggested. The proposed multi-objective problems are addressed using two freshly metaheuristic techniques for optimal sitting and sizing multiple SCs and renewable DGs with unity and optimally power factors into RDS, presuming several voltage-dependent load models. These optimization techniques are the multi-objective thermal exchange optimization (MOTEO) and the multi-objective Lichtenberg algorithm (MOLA), which are regarded as being physics-inspired techniques. The MOLA is inspired by the physical phenomena of lightning storms and Lichtenberg figures (LF), while the MOTEO is developed based on the concept of Newtonian cooling law. The MOLA as a hybrid algorithm differs from many in the literature since it combines the population and trajectory-based search approaches. Further, the developed methodology is implemented on the IEEE 69-bus distribution network during several optimization scenarios, such as bi- and tri-objective problems. The fetched simulation outcomes confirmed the superiority of the MOTEO algorithm in achieving accurate non-dominated solutions with fewer outliers and standard deviation among all studied metrics.

Accurate prediction of electricity consumption using a hybrid CNN-LSTM model based on multivariable data

The stress placed on global power supply systems by the growing demand for electricity has been steadily increasing in recent years. Thus, accurate forecasting of energy demand and consumption is essential to maintain the lifestyle and economic standards of nations sustainably. However, multiple factors, including climate change, affect the energy demands of local, national, and global power grids. Therefore, effective analysis of multivariable data is required for the accurate estimation of energy demand and consumption. In this context, some studies have suggested that LSTM and CNN models can be used to model electricity demand accurately. However, existing works have utilized training based on either electricity loads and weather observations or national metrics e.g., gross domestic product, imports, and exports. This binary segregation has degraded forecasting performance. To resolve this shortcoming, we propose a CNN-LSTM model based on a multivariable augmentation approach. Based on previous studies, we adopt 1D convolution and pooling to extract undiscovered features from temporal sequences. LSTM outperforms RNN on vanishing gradient problems while retaining its benefits regarding time-series variables. The proposed model exhibits near-perfect forecasting of electricity consumption, outperforming existing models. Further, state-level analysis and training are performed, demonstrating the utility of the proposed methodology in forecasting regional energy consumption. The proposed model outperforms other models in most areas.

Construction and Evaluation of an Optical Medium Voltage Transducer Module Aimed at a 132 kV Optical Voltage Sensor for WAMPAC Systems

This paper reports on the construction and characterization of an optical voltage transducer module for applications in the field of wide-area monitoring, protection, and control (WAMPAC). The optical medium voltage transducer (MVT) module was designed to be combined with a capacitive voltage divider (CVD) to form a voltage sensor intended for 132 kV high voltage (HV) networks. The MVT module comprises a combination of a piezoelectric transducer (PZT) and a fiber Bragg grating (FBG) as a core optical sensing element. Changes in the input voltage across the PZT translate into strain being detected by the FBG. The resultant FBG peak wavelength can be calibrated in terms of the input voltage to obtain a precise voltage measurement. The module was experimentally evaluated in the laboratory, and its performance was assessed based on the requirements specified by the IEC standards for electronic voltage transformers and low power voltage transformers. The results of accuracy tests demonstrate that the MVT module is free from hysteresis, within the experimental error, and is capable of simultaneously meeting the requirements for 0.1 metering and 1P protection classes specified by the IEC 60044-7 and IEC 61869-11 standards.

Application of IIA Method and Virtual Bus Theory for Backup Protection of a Zone Using PMU Data in a WAMPAC System

Many wide area monitoring, protection, and control (WAMPAC) systems are being deployed by grid operators to deal with critical operational conditions that may occur in power systems. Thanks to the real-time measurements provided by a set of distributed phasor measurement units (PMUs), different protection algorithms can be run in a central location. In this context, this article presents and validates a novel method that can be used as a backup protection for a selected area in a power system. It merges the integrated impedance angle (IIA) protection method with the theory of virtual buses in wide area electrical power systems. The backup protection works this way: once a fault is detected (pickup time), another delay (added to the pickup time) is defined in order to wait for the primary protection to act. If this does not happen, the algorithm generates its backup trip. The proposed method has been called the zone integrated impedance angle (Zone IIA). A real-time PMU laboratory has been used to test the proposed algorithm using a real-time digital simulator (RTDS). The algorithm has been programmed in a real-time automation controller (RTAC). It has been tested in two different simulated setups: first, a 400 kV transmission system, with and without the use of renewable energy sources (RES); second, a 150 kV submarine line between the Greece mainland and an island, which is currently the longest submarine alternating current connection in the world. The results obtained during the tests have yielded tripping times for area protection in the order of 48 ms, if no time delay is used between the fault detection and the trip order. According to the test results, the proposed method is stable, reliable, obedient, and secure, also with RES installed in the power system. Additionally, the method is selective, i.e., during the tests no trip was executed for external faults, no trip was executed in no-fault condition, and all the applied internal faults were detected and tripped correctly. Finally, the protection method is easy to implement. The method is also applicable to protection against short circuits in distribution systems. According to the trip times observed during the tests, it is clear that these algorithms are well suited to implement backup protections in transmission grids, even in scenarios with high penetration of renewable energies. Considering that backup trip times in transmission grids are usually set between 400 and 1000 ms, and that the actuation times obtained by the proposed algorithm are under 100 ms, the method is suitable for its use as a backup protection.

Synchronizing Torque-Based Transient Stability Index of a Multimachine Interconnected Power System

Newly developed tools and techniques are continuously established to analyze and monitor power systems’ transient stability limits. In this paper, a model-based transient stability index for each generator is proposed from the synchronizing torque contributions of all other connected generators in a multi-machine interconnected power system. It is a new interpretation of the generator’s synchronizing torque coefficient (STC) in terms of electromechanical oscillation modes to consider the synchronizing torque interactions among generators. Thus, the system operator can continuously monitor the system’s available secured transient stability limit in terms of synchronizing torque more accurately, which is helpful for planning and operation studies due to the modal based index. Furthermore, the popular transient stability indicator critical clearing time (CCT), and the traditionally determined synchronizing torque values without other generator contributions, are calculated to verify and compare the performance of the proposed transient stability index. The simulations and test result discussions are performed over a western system coordinating council (WSCC) 9-bus and an extensive New England 68-bus large power test system cases. The open-source power system analysis toolbox (PSAT) on the MATLAB/Simulink environment is used to develop, simulate, validate and compare the proposed transient stability index.

Managing Heterogeneous Datasets for Dynamic Risk Analysis of Large-Scale Infrastructures

Risk assessment and management are some of the major tasks of urban power-grid management. The growing amount of data from, e.g., prediction systems, sensors, and satellites has enabled access to numerous datasets originating from a diversity of heterogeneous data sources. While these advancements are of great importance for more accurate and trustable risk analyses, there is no guidance on selecting the best information available for power-grid risk analysis. This paper addresses this gap on the basis of existing standards in risk assessment. The key contributions of this research are twofold. First, it proposes a method for reinforcing data-related risk analysis steps. The use of this method ensures that risk analysts will methodically identify and assess the available data for informing the risk analysis key parameters. Second, it develops a method (named the three-phases method) based on metrology for selecting the best datasets according to their informative potential. The method, thus, formalizes, in a traceable and reproducible manner, the process for choosing one dataset to inform a parameter in detriment of another, which can lead to more accurate risk analyses. The method is applied to a case study of vegetation-related risk analysis in power grids, a common challenge faced by power-grid operators. The application demonstrates that a dataset originating from an initially less valued data source may be preferred to a dataset originating from a higher-ranked data source, the content of which is outdated or of too low quality. The results confirm that the method enables a dynamic optimization of dataset selection upfront of any risk analysis, supporting the application of dynamic risk analyses in real-case scenarios.

Significance of SMES Devices for Power System Frequency Regulation Scheme Considering Distributed Energy Resources in a Deregulated Environment

Nowadays, the restructuring of power systems is extremely urgent due to the depletion of fossil fuels on the one hand and the environmental impact on the other. In the restructured environment, the incorporation of renewable energy sources and storage devices is key as they have helped achieve a milestone in the form of microgrid technology. As the restructuring of the power system increases, there are several types of generation sources, and distribution companies express their interest in trading in a deregulated environment to operate economically. When considering the power system deregulation, the contract value deviates in some situations, resulting in an imbalance between the generation and the energy consumption, which can bring the system into a power outage condition. In particular, load frequency control has been a great challenge over the past few decades to ensure the stable operation of power systems. This study considers two generation sources: mini-hydro in GENCO-1 and 3 and microgrid (combination of wind, fuel cell, battery storage, and diesel engine) in GENCO-2 and 4. It is two equal-area networks; in area-1, GENCO-1 and 2, and in area-2, GENCO-3 and 4 are considered, respectively. In addition, a FOPID controller and two ancillary devices, such as a unified power flow controller and a superconducting magnetic energy storage system, have been incorporated. Three different test networks have been formed according to the contract value, such as unilateral, bilateral, and agreement violations. The simulation results show that ancillary devices and controller participation significantly enhance the system response by reducing the frequency and tie-line power fluctuation. To validate the efficacy of the proposed method, respective performance indices and percentages of improvement have been obtained. Finally, this study demonstrated the effectiveness of the proposed restructured power system in a deregulated environment.

A Review of Improvements in Power System Flexibility: Implementation, Operation and Economics

This study presents a literature review on the concept of power system flexibility in terms of its definition, indices, algorithms, implementation, economic impacts, operational impacts, and security. Although there are tremendous reviews on this subject in the literature, each paper discusses specific aspects of flexibility. Moreover, the literature is devoid of a comprehensive review of the latest improvements in terms of implementation, operation, and economics, which are addressed by the collections presented in this study. This paper, therefore, surveys some improvements that have been made in recent decades. Furthermore, we highlight the impact of the high penetration of renewable energy and energy storage systems towards enhancing the improvement of power system flexibility.

Random Forest Regressor-Based Approach for Detecting Fault Location and Duration in Power Systems

Power system failures or outages due to short-circuits or "faults" can result in long service interruptions leading to significant socio-economic consequences. It is critical for electrical utilities to quickly ascertain fault characteristics, including location, type, and duration, to reduce the service time of an outage. Existing fault detection mechanisms (relays and digital fault recorders) are slow to communicate the fault characteristics upstream to the substations and control centers for action to be taken quickly. Fortunately, due to availability of high-resolution phasor measurement units (PMUs), more event-driven solutions can be captured in real time. In this paper, we propose a data-driven approach for determining fault characteristics using samples of fault trajectories. A random forest regressor (RFR)-based model is used to detect real-time fault location and its duration simultaneously. This model is based on combining multiple uncorrelated trees with state-of-the-art boosting and aggregating techniques in order to obtain robust generalizations and greater accuracy without overfitting or underfitting. Four cases were studied to evaluate the performance of RFR: 1. Detecting fault location (case 1), 2. Predicting fault duration (case 2), 3. Handling missing data (case 3), and 4. Identifying fault location and length in a real-time streaming environment (case 4). A comparative analysis was conducted between the RFR algorithm and state-of-the-art models, including deep neural network, Hoeffding tree, neural network, support vector machine, decision tree, naive Bayesian, and K-nearest neighborhood. Experiments revealed that RFR consistently outperformed the other models in detection accuracy, prediction error, and processing time.

Analysis of Synchronous Generators’ Local Mode Eigenvalues in Modern Power Systems

New energy sources, storage facilities, power electronics devices, advanced and complex control concepts, economic operating doctrines, and cost-optimized construction and production of machines and equipment in power systems adversely affect small-signal stability associated with local oscillations. The objective of the article is to analyze local oscillations and the causes that affect them in order to reduce their negative impact. There are no recognized analyses of the oscillations of modern operating synchronous generators exposed to new conditions in power systems. The basic idea is to perform a numerical analysis of local oscillations of a large number of synchronous generators in the power system. The paper represents the local mode data obtained from a systematic analysis of synchronous generators in the Slovenian power system. Analyzed were 74 synchronous generators of the Slovenian power system, plus many additional synchronous generators for which data were accessible in references. The mathematical models convenient for the study of local oscillations are described first in the paper. Next, the influences of transmission lines, size of the synchronous generators, operating conditions, and control systems were investigated. The paper’s merit is the applicable rules that have been defined to help power plant operators avoid stability-problematic situations. Consequently, boundaries were estimated of the eigenvalues of local modes. Finally, experiments were performed with a laboratory-size synchronous generator to assess the regularity of the numerically obtained conclusions. The obtained results enable the prediction of local oscillations’ frequencies and dampings and will be useful in PSS planning.

Readiness of the Polish Crisis Management System to Respond to Long-Term, Large-Scale Power Shortages and Failures (Blackouts)

Large-scale failures of electric power systems (blackouts) have been the subject of intensive research in most countries for several years. This research aims primarily at seeking solutions to improve the reliability of the operation of power systems and the development of effective strategies to protect critical infrastructure from the effects of energy shortages and power cuts. In contrast, systematic research on crisis management and civil protection under conditions of prolonged blackout has been undertaken in Europe only recently, and these extremely important aspects of energy security have been delayed by the COVID-19 crisis. The ability of the Polish crisis management system to cope with the consequences of long-term, large-scale shortages and interruptions in the supply of electricity, as well as the consequences of possible failures in this field, has not been systematically examined to date. This issue is of growing strategic importance, not only from the point of view of security and defence policy, but also economic cooperation in Central and Eastern Europe. Poland’s infrastructural security must be considered in a broad regional and supra-regional context. A long-term lack of electricity in a large area of Poland would undermine the stability of the entire national security system, destabilising the region and supranational security systems. Apart from objective reasons, intentional attacks on the links of such a chain cannot be ruled out. Poland is the leader of this region, a frontline country in the NATO-Russia conflict, as well as a liaison state that provides the Baltic states—being EU and NATO members—with a land connection to Western Europe. In view of the growing risk of blackout, the importance of the problem and the existence of a cognitive gap in this field, we evaluated the Polish crisis management system in terms of its ability to respond to the effects of a sudden, long-term, large-scale blackout. Methodologically, we adopted a systems approach to security management. In order to estimate the consequences of a blackout, we used analogue forecasting tools and scenario analysis. By analysing previous crisis situations caused by blackouts and local conditions of vulnerability to such events, we formulated basic preparedness requirements that a modern crisis management system should meet in the face of the growing risk of blackouts. A review of strategic documents and crisis planning processes in public administration allowed us to identify deficits and weaknesses in the Polish crisis management system. On this basis we formulated recommendations whose implementation shall improve the ability of the national security system to face such challenges in the future.

EV Charging in Case of Limited Power Resource

In the case of the widespread adoption of electric vehicles (EV), it is well known that their use and charging could affect the network distribution system, with possible repercussions including line overload and transformer saturation. In consequence, during periods of peak energy demand, the number of EVs that can be simultaneously charged, or their individual power consumption, should be controlled, particularly if the production of energy relies solely on renewable sources. This requires the adoption of adaptive and/or intelligent charging strategies. This paper focuses on public charging stations and proposes methods of attribution of charging priority based on the level of charge required and premiums. The proposed solution is based on model predictive control (MPC), which maintains total current/power within limits (which can change with time) and imparts real-time priority charge scheduling of multiple charging bays. The priority is defined in the diagonal entry of the quadratic form matrix of the cost function. In all simulations, the order of EV charging operation matched the attributed priorities for the cases of ten cars within the available power. If two or more EVs possess similar or equal diagonal entry values, then the car with the smallest battery capacitance starts to charge its battery first. The method is also shown to readily allow participation in Demand Side Response (DSR) schemes by reducing the current temporarily during the charging operation.

Dynamic Modeling of HVDC for Power System Stability Assessment: A Review, Issues, and Recommendations

High-voltage direct current (HVDC) has received considerable attention due to several advantageous features such as minimum transmission losses, enhanced stability, and control operation. An appropriate model of HVDC is necessary to assess the operating conditions as well as to analyze the transient and steady-state stabilities integrated with the AC networks. Nevertheless, the construction of an HVDC model is challenging due to the high computational cost, which needs huge ranges of modeling experience. Therefore, advanced dynamic modeling of HVDC is necessary to improve stability with minimum power loss. This paper presents a comprehensive review of the various dynamic modeling of the HVDC transmission system. In line with this matter, an in-depth investigation of various HVDC mathematical models is carried out including average-value modeling (AVM), voltage source converter (VSC), and line-commutated converter (LCC). Moreover, numerous stability assessment models of HVDC are outlined with regard to stability improvement models, current-source system stability, HVDC link stability, and steady-state rotor angle stability. In addition, the various control schemes of LCC-HVDC systems and modular multilevel converter- multi-terminal direct current (MMC-MTDC) are highlighted. This paper also identifies the key issues, the problems of the existing HVDC models as well as providing some selective suggestions for future improvement. All the highlighted insights in this review will hopefully lead to increased efforts toward the enhancement of the modeling for the HVDC system.

Insights and decision support for home health care services in times of disasters

Home health care (HHC) services are of vital importance for the health care system of many countries. Further increases in their demand must be expected and with it grows the need to sustain these services in times of disasters. Existing risk assessment tools and guides support HHC service providers to secure their services. However, they do not provide insights on interdependencies of complex systems like HHC. Causal-Loop-Diagrams (CLDs) are generated to visualize the impacts of epidemics, blackouts, heatwaves, and floods on the HHC system. CLDs help to understand the system design as well as cascading effects. Additionally, they simplify the process of identifying points of action in order to mitigate the impacts of disasters. In a case study, the course of the COVID-19 pandemic and its effects on HHC in Austria in spring 2020 are shown. A decision support system (DSS) to support the daily scheduling of HHC nurses is presented and applied to numerically analyze the impacts of the COVID-19 pandemic, using real-world data from a HHC service provider in Vienna. The DSS is based on a Tabu Search metaheuristic that specifically aims to deal with the peculiarities of urban regions. Various transport modes are considered, including time-dependent public transport.

Frequency Stability Issues and Research Opportunities in Converter Dominated Power System

Stable power supply has become a crucial thing in the current era of technology and automation. Although the power system has multiple stability issues and causes, frequency fluctuation plays a vital role in normal operation, whereby a system with significant frequency deviation can lead to the needless blackouts of the whole power system. With the rapid growth in power electronic converter (PEC)-based technologies and the huge penetration of nonsynchronous generators, the modern power system is becoming more complex by the day. This paper provides a comprehensive study on the stability issues that occur in modern power systems, mainly due to PEC-based technology integration. The in-depth reasons and the impacts of unstable power systems, along with their controlling techniques, are discussed to generate a clear understanding. Furthermore, the importance of frequency stability in a power system is discussed with respect to some important events that occurred in the past. This paper also discusses some potential techniques that could be performed to overcome the existing and/or upcoming challenges in the upgrading power system.

Identifying and Analyzing Dependencies in and among Complex Cyber Physical Systems

Contemporary Critical Infrastructures (CIs), such as the power grid, comprise cyber physical systems that are tightly coupled, to form a complex system of interconnected components with interacting dependencies. Modelling methodologies have been suggested as proper tools to provide better insight into the dependencies and behavioural characteristics of these complex systems. In order to facilitate the study of interconnections in and among critical infrastructures, and to provide a clear view of the interdependencies among their cyber and physical components, this paper proposes a novel method, based on a graphical model called Modified Dependency Structure Matrix (MDSM). The MDSM provides a compact perspective of both inter-dependency and intra-dependency between subsystems of one complex system or two distinct systems. Additionally, we propose four parameters that allow the quantitative assessment of the characteristics of dependencies, including multi-order dependencies in large scale CIs. We illustrate the workings of the proposed method by applying it to a micro-distribution network based on the G2ELAB 14-Bus model. The results provide valuable insight into the dependencies among the network components and substantiate the applicability of the proposed method for analyzing large scale cyber physical systems.

Power Outages and Community Health: a Narrative Review

PURPOSE OF REVIEW

Power outages, a common and underappreciated consequence of natural disasters, are increasing in number and severity due to climate change and aging electricity grids. This narrative review synthesizes the literature on power outages and health in communities.

RECENT FINDINGS

We searched Google Scholar and PubMed for English language studies with titles or abstracts containing "power outage" or "blackout." We limited papers to those that explicitly mentioned power outages or blackouts as the exposure of interest for health outcomes among individuals living in the community. We also used the reference list of these studies to identify additional studies. The final sample included 50 articles published between 2004 and 2020, with 17 (34%) appearing between 2016 and 2020. Exposure assessment remains basic and inconsistent, with 43 (86%) of studies evaluating single, large-scale power outages. Few studies used spatial and temporal control groups to assess changes in health outcomes attributable to power outages. Recent research linked data from electricity providers on power outages in space and time and included factors such as number of customers affected and duration to estimate exposure. The existing literature suggests that power outages have important health consequences ranging from carbon monoxide poisoning, temperature-related illness, gastrointestinal illness, and mortality to all-cause, cardiovascular, respiratory, and renal disease hospitalizations, especially for individuals relying on electricity-dependent medical equipment. Nonetheless the studies are limited, and more work is needed to better define and capture the relevant exposures and outcomes. Studies should consider modifying factors such as socioeconomic and other vulnerabilities as well as how community resiliency can minimize the adverse impacts of widespread major power outages.

The Power System and Microgrid Protection—A Review

In recent years, power grid infrastructures have been changing from a centralized power generation model to a paradigm where the generation capability is spread over an increasing number of small power stations relying on renewable energy sources. A microgrid is a local network including renewable and non-renewable energy sources as well as distributed loads. Microgrids can be operated in both grid-connected and islanded modes to fill the gap between the significant increase in demand and storage of electricity and transmission issues. Power electronics play an important role in microgrids due to the penetration of renewable energy sources. While microgrids have many benefits for power systems, they cause many challenges, especially in protection systems. This paper presents a comprehensive review of protection systems with the penetration of microgrids in the distribution network. The expansion of a microgrid affects the coordination and protection by a change in the current direction in the distribution network. Various solutions have been suggested in the literature to resolve the microgrid protection issues. The conventional coordination of the protection system is based on the time delays between relays as the primary and backup protection. The system protection scheme has to be changed in the presence of a microgrid, so several protection schemes have been proposed to improve the protection system. Microgrids are classified into different types based on the DC/AC system, communication infrastructure, rotating synchronous machine or inverter-based distributed generation (DG), etc. Finally, we discuss the trend of future protection schemes and compare the conventional power systems.

A Multitiered Solution for Anomaly Detection in Edge Computing for Smart Meters

In systems connected to smart grids, smart meters with fast and efficient responses are very helpful in detecting anomalies in realtime. However, sending data with a frequency of a minute or less is not normal with today's technology because of the bottleneck of the communication network and storage media. Because mitigation cannot be done in realtime, we propose prediction techniques using Deep Neural Network (DNN), Support Vector Regression (SVR), and k-Nearest Neighbors (KNN). In addition to these techniques, the prediction timestep is chosen per day and wrapped in sliding windows, and clustering using Kmeans and intersection Kmeans and HDBSCAN is also evaluated. The predictive ability applied here is to predict whether anomalies in electricity usage will occur in the next few weeks. The aim is to give the user time to check their usage and from the utility side, whether it is necessary to prepare a sufficient supply. We also propose the latency reduction to counter higher latency as in the traditional centralized system by adding layer Edge Meter Data Management System (MDMS) and Cloud-MDMS as the inference and training model. Based on the experiments when running in the Raspberry Pi, the best solution is choosing DNN that has the shortest latency 1.25 ms, 159 kB persistent file size, and at 128 timesteps.

Photonic Voltage Transducer with Lightning Impulse Protection for Distributed Monitoring of MV Networks

The design, construction and characterization of a photonic voltage transducer with a lightning impulse protection for distributed measurements on medium voltage (MV) networks (11 kV) was presented in this paper. The sensor prototype, comprising a combination of a piezoelectric transducer and a fibre Bragg grating (FBG) as a core optical sensing element, and a dedicated lightning protection device comprising a set of reactive components, was evaluated through laboratory testing and its performance was assessed based on the accuracy requirements specified by the relevant industry standards. It was demonstrated that the sensor has the potential to meet the accuracy requirements for the 3P protection and 0.2 metering classes specified by the IEC 60044-7. The device successfully underwent lightning impulse withstand tests, satisfying the safety requirements applicable to 11 kV networks as specified by the standard. The usage of an FBG as a photonic sensing component enables the multiplexing of multiple such sensors to provide the distributed measurement of voltage along a power network.

A Highly Reliable Propulsion System with Onboard Uninterruptible Power Supply for Train Application: Topology and Control

Providing uninterrupted electricity service aboard the urban trains is of vital importance not only for reliable signaling and accurate traffic management but also for ensuring the safety of passengers and supplying emergency equipment such as lighting and signage systems. Hence, to alleviate power shortages caused by power transmission failures while the uninterruptible power supplies installed in the railway stations are not available, this paper suggests an innovative traction drive topology which is equipped by an onboard hybrid energy storage system for railway vehicles. Besides, to limit currents magnitudes and voltages variations of the feeder during train acceleration and to recuperate braking energy during train deceleration, an energy management strategy is presented. Moreover, a new optimal model predictive method is developed to control the currents of converters and storages as well as the speeds of the two open-end-windings permanent-magnet-synchronous-machines in the intended modular drive, under their constraints. Although to improve control dynamic performance, the control laws are designed as a set of piecewise affine functions from the control signals based on an offline procedure, the controller can still withstand real-time non-measurable disturbances. The effectiveness of proposed multifunctional propulsion topology and the feasibility of the designed controller are demonstrated by simulation and experimental results.

A New Guideline for Security Assessment of Power Systems with a High Penetration of Wind Turbines

By the increase of the penetration of power-electronic-based (PE-based) units, such as wind turbines and PV systems, many features of those power systems, such as stability, security, and protection, have been changed. In this paper, the security of electrical grids with high wind turbines penetration is discussed. To do so, first, an overview of the power systems’ security assessment is presented. Based on that, stability and security challenges introduced by increasing the penetration of wind turbines in power systems are studied, and a new guideline for the security assessment of the PE-based power systems is proposed. Simulation results for the IEEE 39-bus test system show that the proposed security guideline is necessary for PE-based power systems, as the conventional security assessments may not be able to indicate its security status properly.

Interaction Graphs for Cascading Failure Analysis in Power Grids: A Survey

Understanding and analyzing cascading failures in power grids have been the focus of many researchers for years. However, the complex interactions among the large number of components in these systems and their contributions to cascading failures are not yet completely understood. Therefore, various techniques have been developed and used to model and analyze the underlying interactions among the components of the power grid with respect to cascading failures. Such methods are important to reveal the essential information that may not be readily available from power system physical models and topologies. In general, the influences and interactions among the components of the system may occur both locally and at distance due to the physics of electricity governing the power flow dynamics as well as other functional and cyber dependencies among the components of the system. To infer and capture such interactions, data-driven approaches or techniques based on the physics of electricity have been used to develop graph-based models of interactions among the components of the power grid. In this survey, various methods of developing interaction graphs as well as studies on the reliability and cascading failure analysis of power grids using these graphs have been reviewed.