Outage Events Research Articles

Legacy and shockwaves: A spatial analysis of strengthening resilience of the power grid in Connecticut

Grid resilience and reliability are pivotal in the transition to low and zero carbon energy systems. Tree-trimming operations (TTOs) have become a pivotal tool for increasing the resilience power grids, especially in highly forested regions. Building on recent literature, we aim at assessing the temporal and spatial extents of the benefits that TTOs produce on the grid from three perspectives: the frequency, extent, and duration of outages. We use a unique dataset provided by Eversource Energy, New England's largest utility company, with outage events from 2009 to 2015. We employ spatial econometrics to investigate both the legacy and spatial extent of TTOs. Our results show TTOs benefits occur for all three metrics for at least 4 years, and benefits spillover to up to 2 km throughout the treated areas, with significant spatial spillovers across the state greater than direct effects. Implications lead to supporting TTOs as part of the hardening policies for utility companies, especially as home-based activities increase in importance in a post-COVID19 world.

Multi‐stage stochastic wind‐thermal generation expansion planning with probabilistic reliability criteria

Due to the growing proportion of uncertain and fluctuating renewable energy sources in the power system, reasonable generation expansion considering the uncertainties is desperately needed. This paper proposes a novel three-stage wind-thermal generation expansion planning model based on representative day unit commitment that incorporates the uncertainty of wind power and load and the probabilistic reliability criteria. The probabilistic reliability criteria are considered by simulating random outage events of thermal units using a robust approach. The consequent robust reliability model is reformulated as mixed-integer linear programming models and integrated into the multi-stage generation expansion planning model. A decomposed strategy based on the Benders decomposition is adopted to tackle with the large-scale original model. Case studies are performed on both the revised IEEE 14-bus and IEEE 118-bus test systems to validate the effectiveness of proposed model and method. Numerical results show that the proposed generation planning method balances the economy and reliability of the system.

Event-Based Under-Frequency Load Shedding Scheme in a Standalone Power System

Under-frequency load shedding (UFLS) prevents a power grid from a blackout when a severe contingency occurs. UFLS schemes can be classified into two categories—event-based and response-driven. A response-driven scheme utilizes 81L relays with pre-determined settings while an event-based scheme develops a pre-specified look-up table. In this work, an event-based UFLS scheme is presented for use in an offshore standalone power grid with renewables to avoid cascading outages due to low frequency protection of wind power generators and photovoltaic arrays. Possible “N-1” and “N-2” forced outages for peak and off-peak load scenarios in summer and winter are investigated. For each forced outage event, the total shed load is minimized and the frequency nadir is maximized using particle swarm optimization (PSO). In order to reduce the computation time, initialization and parallel computing are implemented using MATLAB/Simulink because all forced outage events and all particles in PSO are mutually independent. A standalone 38-bus power grid with two wind turbines of 2 × 2 MW and photovoltaics of 7.563 MW was studied. For each event, the proposed method generally obtains a result with a smaller shed load and a smaller overshoot frequency than the utility and existing methods. These simulation results verify that the proposed method is practically applicable in a standalone power system with penetration of renewables.

The Effect of Lead-Time Weather Forecast Uncertainty on Outage Prediction Modeling

Weather-related power outages affect millions of utility customers every year. Predicting storm outages with lead times of up to five days could help utilities to allocate crews and resources and devise cost-effective restoration plans that meet the strict time and efficiency requirements imposed by regulatory authorities. In this study, we construct a numerical experiment to evaluate how weather parameter uncertainty, based on weather forecasts with one to five days of lead time, propagates into outage prediction error. We apply a machine-learning-based outage prediction model on storm-caused outage events that occurred between 2016 and 2019 in the northeastern United States. The model predictions, fed by weather analysis and other environmental parameters including land cover, tree canopy, vegetation characteristics, and utility infrastructure variables exhibited a mean absolute percentage error of 38%, Nash–Sutcliffe efficiency of 0.54, and normalized centered root mean square error of 68%. Our numerical experiment demonstrated that uncertainties of precipitation and wind-gust variables play a significant role in the outage prediction uncertainty while sustained wind and temperature parameters play a less important role. We showed that, while the overall weather forecast uncertainty increases gradually with lead time, the corresponding outage prediction uncertainty exhibited a lower dependence on lead times up to 3 days and a stepwise increase in the four- and five-day lead times.

Down for failure: Active power status monitoring

Despite society’s strong dependence on electricity, power outages remain prevalent. Standard methods for directly measuring power availability are complex, often inaccurate, and prone to attack. This paper explores an alternative approach to identifying power outages through intelligent monitoring of IP address availability. In finding these outages, we explore the trade-off between the accuracy of detection and false alarms.We begin by experimentally demonstrating that static, residential Internet connections serve as good indicators of power, as they are mostly active unless power fails and rarely have battery backups. We construct metrics that dynamically score the reliability of each residential IP, where a higher score indicates a higher correlation between the IP’s availability and its regional power. We then select and monitor subsets of residential IPs and evaluate the exactitude with which they indicate current county power status.Using data gathered during the power outages caused by Hurricane Florence, we demonstrate that we can track power outages at different granularities (state and county), in both sparse and dense regions. By comparing our detections with the reports gathered from power utility companies, we achieve an average detection accuracy of 90%, where we also show some of our false alarms and missed outage events could be due to imperfect ground truth data. Therefore, our method can be used as a complementary technique for power outage detection.

Reliability Evaluation of Renewable Power Systems through Distribution Network Power Outage Modelling

Intermittent power interruptions and blackouts with long outage durations are very common, especially on weak distribution grids such as in developing countries. This paper proposes a hybrid photovoltaic (PV)-battery-system sizing optimization through a genetic algorithm to address the reliability in fragile grids measured by the loss of power supply probability (LPSP) index. Recorded historical outage data from a real stochastic grid in Ethiopia and measured customer load is used. The resulting hybrid-system Pareto solutions give the flexibility for customers/power utilities to choose appropriate sizes based on the required reliability level. To evaluate the sizing solutions’ robustness, this work considers and compares grid outage modeling through two different approaches. The first is a Markov model, developed to be minimally implemented with limited outage data available. The second is a Weibull model, commonly used to describe extreme phenomena and failure analysis. It is more faithful in reproducing the dispersion of outage events. Using these models, the effectiveness and performance of the PV-battery system is verified on a large number of simulated outage scenarios, to estimate the real performance of the optimized design. It leads to a more accurate evaluation of the behavior of a renewable power system to a weak and unreliable electrical grid.

Resilient residential energy management with vehicle-to-home and photovoltaic uncertainty

Resilient operation of residential buildings is an emerging topic. This paper proposes a resilient home energy management strategy to enable residential houses to implement self-power supply during a planned grid outage period. The proposed strategy utilizes the energy backup capability of plug-in hybrid electric vehicles (PHEV) to accommodate residential solar photovoltaic (PV) source and power the house in the presence of grid outage event. The proposed method also harnesses the load shifting flexibility of the smart home through a user disutility model. A stochastic programming approach based on Wasserstein distance metric and K-medoids based is applied to handle the residential photovoltaic solar power uncertainty. Simulation results show the proposed method can effectively enhance the residential resilience.

Are electricity system outages and the generation mix related? Evidence from NSW, Australia

This paper examines the patterns of electricity system outages and their association with the generation mix. Using hourly outage and generation data from the state of New South Wales, Australia between 2013 and 2019 we investigate the relationship between different generation types, electricity demand, weather conditions and the onset and severity of power outages in the following hour. We find that higher levels of wind generation prior to an outage event are associated with an increase in the extent of outages measured in customer-minutes, while more black coal generation is associated with a lower extent of outages. High wind and storm conditions are related to more blackouts. Our results also indicate that wind generation appears to be the most vulnerable under stormy weather conditions. In recent years solar generation has been associated with a lower expected value of outage events, due to its ability to supply electricity on hot summer days during peak demand. Electricity demand, partly driven by high temperatures is also significant in explaining outages. Overall, our findings indicate that a successful clean energy transition in the electricity sector will have to focus on addressing reliability challenges connected to the changing generation mix.

Tracing spinning reserve inadequacy risk via hybrid importance sampling with an optimised partially collapsed Gibbs sampler

From the simulation perspective, spinning reserve risk evaluation of power system is commonly a rare-event assessing issue, for which importance sampling is an appealing solution technique. This paper proposes a hybrid importance sampling method for tracing spinning reserve inadequacy risk of power system integrating with renewables. The proposed method is oriented towards a generic high-dimensional integral calculus, which allows for continuous and discrete random variables mixed together for casting kinds of risk indices. To improve the robustness in high-dimensional applications, a partially collapsed Gibbs sampler is devised to help in exploring adaptive training samples which feature typical rare outage events. Interestingly, the originators engendering extreme spinning reserve inadequacy events can be traced using the sampling information of the proposed method. The performance of the proposed method is tested in an updated RTS-79 system vis-à-vis several existing methods. Spinning reserve inadequacy risk tracing is explained by uncovering a scenario frequency distribution concerning the short-term expected energy not supplied.

Inertia estimation of diesel generators based on modified loading rejection tests

An inertia identification approach has been proposed for diesel generators by using the modified load rejection procedures. Large frequency deviation problems often occur in the Kinmen island power system where many diesel generators are used. In order to avoid erroneous evaluation of frequency deviation caused by generator tripping, one of the critical issues in system planning is to accurately estimate the maximum frequency deviation during system outage events. The main factors that influence the frequency deviation include the generator inertia (H), damping constant (D), and governor controller parameters. In order to improve the accuracy of the transient stability analysis to obtain more actual frequency deviation, it is critical to have more accurate inertia values of these diesel generators. This paper proposes a useful approach to evaluate reasonable inertia values. With the identified inertia values of diesel generators, the frequency responses of the Kinmen power system obtained from PSS/E simulation are more consistent with the field measurement results.

Studying information recurrence, gatekeeping, and the role of communities during internet outages in Venezuela

Many authoritarian regimes have taken to censoring internet access in order to stop the spread of misinformation, restrict citizens from discussing certain topics, and prevent mobilization, among other reasons. There are several theories about the effectiveness of censorship. Some suggest that censorship will effectively limit the flow of information, whereas others predict that a backlash will form, resulting in ultimately more discussion about the topic. In this work, we analyze the role of communities and gatekeepers during multiple internet outages in Venezuela in January 2019. First, we measure how critical information (e.g., entities and hashtags) spreads during outages focusing on information recurrence and burstiness within and across language and location communities. We discover that information bursts tend to cross both language and location community boundaries rather than being limited to a single community during several outages. Then we identify users who play central roles and propose a novel method to detect gatekeepers—users who prevent critical information from spreading across communities during outages. We show that bilingual and English-speaking users play more central roles compared to Spanish-speaking users, but users inside and outside Venezuela have similar distribution of centrality. Finally, we measure the differences in social network structure before and after each outage event and discuss its effect on how information spreads. We find that with each outage event social connections tend to get less connected with higher mean shortest path, indicating that the effect of censorship makes it harder for information to spread.

Modeling Queuing Delay of 5G NR With NOMA Under SINR Outage Constraint

In this paper, we present an analytical model to characterize the average queuing delay for non-orthogonal multiple access (NOMA) downlink system by taking the outage event into account such that the user fails either decoding its own signal or performing SIC for the signals of other users at the receiver when the SINR is lower than a predefined outage threshold. The departure process of the queuing model is characterized by obtaining the first and second moment statistics of the service time that depends on the resource allocation strategy, the packet size and channel distributions. The proposed model is utilized to obtain the optimum power allocation that minimizes the maximum of the average queuing delay (MAQD) for a two-user network scenario. The Monte Carlo simulation experiments are performed to numerically validate the model by providing MAQD results for both NOMA and orthogonal multiple access (OMA) schemes. The results demonstrate that the NOMA achieves lower latency for low SINR outage thresholds while its performance is degraded faster than OMA as the SINR outage threshold increases such that OMA outperforms NOMA beyond a certain threshold. Another important result is that the latency performance of NOMA is significantly degraded when the 5G NR frame types having wider bandwidth are utilized. The results provide powerful insights for queuing delay analysis of 5G services.

A novel methodology for optimal sizing photovoltaic-battery systems in smart homes considering grid outages and demand response

–This paper deals with the optimal sizing of a hybrid photovoltaic-battery storage system for home energy management considering reliability against grid outages and demand response. To that end, a novel optimization framework is developed which aims at minimizing the electricity bill while the reliability of the system is ensured for certain common outages. In order to ensure the accuracy of the results, a large amount of characteristic outages along with demand, solar irradiance and temperature profiles are generated from real data. Clustering techniques are used for reducing this data to those most characteristics profiles and manage with the unpredictable behaviour of the outage events. Demand response is incorporated via different incentives like tariffs based on time of use and real time pricing, along with the optimal scheduling of different typical deferrable appliances. A case study on a smart-prosumer environment serves to illustrate the capabilities of the developed approach as providing sufficient guidelines for its universal applicability. Different cases studies are simulated considering different battery technologies and electricity tariffs for comparison. Various aspects related with the reliability against grid outages are also analysed like its impact on the project cost or the influence of demand response strategies.

Efficient Prediction of Link Outage in Mobile Optical Wireless Communications

Optical wireless networks, especially those relying on visible light communications, suffer from severe deterioration in signal's quality when the line-of-sight (LOS) link is absent due to user's mobility. In order to enable efficient resource management within such networks, reliable prediction of LOS link outage is essential. Towards this objective, this article proposes a data-driven approach based on deep machine learning techniques to predict the outage events in an LOS link. First, we present a framework to generate sufficient data representing the channel gain in mobile optical wireless networks that consist of visible light communications in the downlink and infrared communications in the uplink. Using the developed dataset, we propose a channel predictor that forecasts the burst outages or signal recoveries in the upcoming frames using a deep recurrent neural network that implements long-short-term-memory (LSTM) units. To achieve this goal, we propose a low-complexity approach to reduce the data sparsity due to the user's mobility by abstracting and densifying the channel state sequence. For a one second prediction interval, the proposed prediction framework achieves an event hit rate of 91.55% for abrupt outages with an average event timing error of 79 ms, and 83.19% for recoveries from outages with 145 ms timing error. This timing error is on the same order of magnitude with the coherence time of the optical wireless channel. Therefore, this predictor is very useful in developing efficient resource management strategies in such optical networks.

The Use of Digital Technology Platforms in the Context of Cybersecurity in the Industrial Sector

Abstract This study discusses the use of digital technology platforms (DTPs) in the context of cyber-security in the industrial sector, with a focus on digital industry (industrial) platforms (DIPs). A definition of DTPs is presented, including the author's interpretation, as well as the scope of DTP application in the industrial sector, which includes, in particular, European Digital Platforms (EDPs) and Polish Digital Platforms (PDPs), such as non-ferrous metals PDP or intelligent transport systems PDP. This is followed by a section covering the theoretical basis of the study that highlights the key challenges and risks associated with the use of DTPs as well as the methods for their neutralization in the form of specific concepts and systems that can be employed in the industrial sector. The subsequent section of the study is based on results of the author's own survey which collected information from a total of 120 companies operating in Poland, which were granted subsidies under the Operational Program Innovative Economy for investments involving the implementation and development of DTPs. The survey was carried out using a questionnaire developed by the author, which consisted of 23 questions. In this respect, as shown by the author's own studies, of greatest relevance are hardware failures and Internet outage events. Most importantly, concerns about such risks are some of the major factors underlying the negative attitudes of management staff of industrial companies toward DTPs, and therefore, it is so important to ensure that any such risks can be effectively addressed. They can be avoided through the use of certain concepts and systems such as STOE or CVSS. A typical company may know the model of DTPs in the context of challenges in the field of cybersecurity through this study; in particular, it can improve IT security.

SINR and Delay Analyses in Two-Way Full-Duplex SWIPT-Enabled Relaying Systems

We first investigate the accumulated loopback self-interference (ALSI) under amplify-and-forward (AF) protocol in two-way full-duplex (TWFD) relaying systems with simultaneous wireless information and power transfer (SWIPT). We analyze how SWIPT affects ALSI and establish the condition for ALSI to converge when there is no energy buffer. Unlike ALSI in TWFD relaying without SWIPT (Chang et al., 2019), ALSI in TWFD relaying with SWIPT always converges and does not grow unbounded. In addition, we study the delay performance in such a system under AF and decode-and-forward (DF) protocols. In AF protocol, the condition for successful signal transmissions is provided. When that condition is met, the received signals at the relay can always get through the channels but experience delays. Otherwise, the outage event occurs. In DF protocol, the best-effort scenario (having no energy buffer) and the QoS scenario (having energy buffer) are discussed. The probability of successful transmissions and the distribution of the delay in both scenarios are derived. Simulation results of DF protocol show that the best-effort scenario is suitable for the delay-tolerant service and the QoS scenario is suitable for the delay-sensitive service. Furthermore, AF protocol is more suitable for the service with less QoS demanding.

Major impacts of weather events on the electrical power delivery system in the United States

Weather events are a major source of disruptions to the electrical power grid. This study examines the occurrence and variability of major weather-related electrical power disruptions by utilizing multiple datasets. Our results show that weather is responsible for 50% of all events and 83% of customers affected by them. Thunderstorms are responsible for 47% of weather-related events, while winter storms and tropical cyclones contribute 31.5% and 19.5%, respectively. The median restoration cost for utilities during major storms was ∼$12 million, with a median restoration time of 117.5 h (∼5 days). Tropical storms had the highest median number of customers affected, followed by thunderstorms and winter storms. Fewer mutual aid crews are utilized for thunderstorms than tropical or winter storms. The unique challenges that electrical utilities face from weather events are highlighted with case studies of Hurricane Harvey in Texas (2017), a 2011 snowstorm in the Mid-Atlantic, and severe thunderstorms in Alabama in 2011. We conclude by demonstrating that variations in the number of outage events (r = 0.79) and customers affected (r = 0.65) are primarily controlled by the number of storms. Weather-related power outages continue to be an important cause of power disruptions despite investments in storm outage prediction models and system hardening.

Power outage prediction for natural hazards using synthetic power distribution systems

Power outage prediction for natural hazards usually relies on one of two approaches, statistical models or fragility-based methods. Statistical models have provided strong predictive accuracy, but only in an area-aggregated manner. Fragility-based approaches have not offered strong prediction accuracy and have been limited to systems for which system topology or performance models are available. In this paper, we create an algorithm that (1) generates a synthetic power system layout for any U.S. city based only on public data and then (2) simulates power outages at the level of individual buildings under hazard loading using fragility functions. This approach provides much more localized, building-level estimates of the likelihood of losing power due to a natural hazard. We validate our model by comparing the network properties and power outage events based on our approach with data from a real power system in Ohio. We find that our model relies on less input data comparing to statistical learning approaches yet can make accurate predictions, provided accurate fragility curves are available.

Bayesian Stochastic Neural Network Model for Turbomachinery Damage Prediction

Turbomachinery often suffers various defects such as impeller cracking, resulting in forced outage, increased maintenance costs, and reduced productivity. Condition monitoring and damage prognostics has been widely used as an increasingly important and powerful tool to improve the system availability, reliability, performance, and maintainability, but still very challenging due to multiple sources of data uncertainties and the complexity of analytics modeling. This paper presents an intelligent probabilistic methodology for anomaly prediction of high-fidelity turbomachine, considering multiple data imperfections and multivariate correlation. The proposed method adeptly integrates several advanced state-of-the-art signal processing and artificial intelligence techniques: wavelet multi-resolution decomposition, Bayesian hypothesis testing, probabilistic principal component analysis, and fuzzy stochastic neural network modeling. The advanced signal processing is employed to reduce dimensionality and to address multivariate correlation and data uncertainty for damage prediction. Instead of conventionally using raw time series data, principal components are utilized in the establishment of stochastic neural network model and anomaly prediction. Bayesian interval hypothesis testing metric is then presented to quantitatively compare the predicted and measured data for model validation and anomaly evaluation, thus providing a confidence indicator to judge the model quality and evaluate the equipment status. Bayesian method is developed in this study for denoising the raw data with multiresolution wavelet decomposition, quantifying the model accuracy, and assessing the equipment status. The dynamic stochastic neural network model is established via the nonlinear autoregressive moving average with exogenous inputs approach. It seamlessly integrates the fuzzy clustering and independent Bernoulli random function into radial basis function neural network. A natural gradient method based on Kullback-Leibler distance criterion is employed to maximize the log-likelihood loss function. The effectiveness of the proposed methodology and procedure is demonstrated with the 11-variable time series data and the forced outage event of a real-world centrifugal compressor.

A Unified Optimisation Framework for QoS Management and Congestion Control in VHTS Systems

The design of Very High Throughput Satellite (VHTS) systems will be an important milestone for future telecommunications systems in the light of the ongoing integration between satellite and 5G technologies as well as with the upcoming 6 G evolution. To this end, satellite systems will be operating at EHF frequency bands, hence requiring a special attention to the overall network design in terms of ground and space segment optimisation. In particular, the mitigation of feeder link outage events through advanced prediction techniques and the exploitation of smart gateway diversity concepts will be combined with an overarching network resources’ orchestration, jointly conceived to meet the QoS requirements of the end users. In this respect, this paper proposes a novel optimisation framework able to achieve very high throughput system figures, building on an adaptive QoS management concept. The proposed framework has been compared with other existing solutions from the literature and validated by means of extensive simulation campaigns, whose collected results show significant performance improvements.