Outage Events Research Articles

Risk‐neutral and risk‐averse transmission switching for load shed recovery with uncertain renewable generation and demand

One of the main desired capabilities of the smart grid is ‘self-healing’, which is the ability to quickly restore power after a disturbance. Due to critical outage events, customer demand or load is at times disconnected or shed temporarily. While deterministic optimisation models have been devised to help operators expedite load shed recovery by harnessing the flexibility of the grid's topology (i.e. transmission line switching), an important issue that remains unaddressed is how to cope with the uncertainty in generation and demand encountered during the recovery process. This study introduces two-stage stochastic models to deal with these uncertain parameters, and one of them incorporates conditional value-at-risk to measure the risk level of unrecovered load shed. The models are implemented using a scenario-based approach where the objective is to maximise load shed recovery in the bulk transmission network by switching transmission lines and performing other corrective actions (e.g. generator re-dispatch) after the topology is modified. The benefits of the proposed stochastic models are compared with a deterministic mean-value model, using the IEEE 118- and 14-bus test cases. Experiments highlight how the proposed approach can serve as an offline contingency analysis tool, and how this method aids self-healing by recovering more load shedding.

Leveraging social media data to study the community resilience of New York City to 2019 power outage

Power outages across the world have severe social impacts. The public's responses to power outages provide valuable insights into their capacities of adapting crisis and an invaluable perspective to demonstrate community resilience. As social media has connected people in the community, the discussion on social media can reflect their responses as a criterion of resilience throughout power outages in a timely and effective manner. In the field of power outages, the potentials of social media data have only been investigated with recent advancements of big data techniques. Nonetheless, studies focusing on community resilience using social media data are quite limited. We filled this gap by introducing a novel and quantitative method to study the community resilience throughout power outages, based on a case study on the Manhattan blackout occurred in July 2019. The study examined community resilience from both mental and behavioral perspectives via sentiment analysis and behavior analysis. The sentiment analysis was used to track people's mental outlook and shape the overall mental status during and after this emergency. On the other side, six major patterns of behaviors were identified, and the behavioral index was defined to learn how the community responded to power outages amid the response and recovery periods. Both the mental and behavioral results reveal that New York City recovered at approximately one and a half hours after the blackout occurred, implying a strong community resilience to such short and emergent power outage events.

A Risk-Based Approach to Assess the Operational Resilience of Transmission Grids

Modern risk analysis studies of the power system increasingly rely on big datasets, either synthesized, simulated, or real utility data. Particularly in the transmission system, outage events have a strong influence on the reliability, resilience, and security of the overall energy delivery infrastructure. In this paper we analyze historical outage data for transmission system components and discuss the implications of nearby overlapping outages with respect to resilience of the power system. We carry out a risk-based assessment using North American Electric Reliability Corporation (NERC) Transmission Availability Data System (TADS) for the North American bulk power system (BPS). We found that the quantification of nearby unscheduled outage clusters would improve the response times for operators to readjust the system and provide better resilience still under the standard definition of N-1 security. Finally, we propose future steps to investigate the relationship between clusters of outages and their electrical proximity, in order to improve operator actions in the operation horizon.

Effect of thermal ballast loading on temperature stability of domestic refrigerators used for vaccine storage.

Vaccine temperature control failures represent a significant public and private healthcare cost. Vaccines damaged by excessive heat or freezing lose their effectiveness, putting public health at risk. Some vaccine administration programs recommend placing water bottles inside domestic refrigerators used for vaccine storage as a thermal ballast, to mitigate temperature excursion risks. However, the effect of variable thermal ballast loading on refrigerator performance has not been thoroughly quantified or documented, and generalized programmatic recommendations are subject to end-user interpretation. Here we show that a thermal ballast load comprising ten to fifteen percent of the total refrigerator storage volume provides a measurable effect on domestic refrigerator temperature stability during power outage events, maintaining vaccine temperatures between 2 °C and 8 °C for 4 to 6 hours without power. Thermal ballast usage does not reliably reduce the frequency or severity of temperature excursions caused by repeated door opening, accidental "door left open" events, or refrigerator defrost cycle activation. Use of a moderate thermal ballast load is a practical strategy for mitigating temperature excursions risks in areas with frequent or protracted power outages, but the practice has limited benefit in other adverse scenarios. Empowering providers to make informed decisions about the use of thermal ballast materials supports better, safer vaccine management.

Outage Detection in Partially Observable Distribution Systems Using Smart Meters and Generative Adversarial Networks

In this paper, we present a novel data-driven approach to detect outage events in partially observable distribution systems by capturing the changes in smart meters' (SMs) data distribution. To achieve this, first, a breadth-first search (BFS)-based mechanism is proposed to decompose the network into a set of zones that maximize outage location information in partially observable systems. Then, using SM data in each zone, a generative adversarial network (GAN) is designed to implicitly extract the temporal-spatial behavior in normal conditions in an unsupervised fashion. After training, an anomaly scoring technique is leveraged to determine if real-time measurements indicate an outage event in the zone. Finally, to infer the location of the outage events in a multi-zone network, a zone coordination process is proposed to take into account the interdependencies of intersecting zones. We have provided analytical guarantees of performance for our algorithm using the concept of entropy, which is leveraged to quantify outage location information in multi-zone grids. The proposed method has been tested and verified on distribution feeder models with real SM data.

Cooperative Spectrum Sensing with Distributed/Centralized Relay Selection

In this paper, we suggest a new Cooperative Spectrum Sensing (CSS) algorithm using Distributed Relay Selection. Spectrum sensing is performed at Fusion Node (FN) using only the signal from Primary User (PU) when its Signal to Noise Ration (SNR) is larger than threshold $$\gamma _{th}$$ . If SNR of the link between PU and FN is lower than $$\gamma _{th}$$ , a relay is activated using distributed relay selection. All relays compare their SNRs to threshold $$\alpha$$ and transmit only if the SNR exceeds $$\alpha$$ . If the SNR of all relays are less than $$\alpha$$ , there is an outage event, no relay is selected and only the signal from PU will be used for spectrum sensing. If SNRs of more relays than two relays are larger than $$\alpha$$ , a collision occurs and spectrum sensing uses only the signal from PU. Threshold $$\alpha$$ is optimized using the Gradient algorithm to yield the largest detection probability. We also compare our results to spectrum sensing with centralized relay selection using opportunistic Amplify and Forward (OAF) or Partial Relay Selection (PRS). In OAF, the end-to-end SNR between PU, relays and FN are sent to the FN to be compared and the relay with largest SNR is activated. In PRS, the SNR at the relays are sent to the FN in order to be compared. Then, the relay with largest SNR of first hop, between PU and relays, is activated. CSS with distributed relay selection is novel and no yet studied.

A Throughput and Energy Efficiency Scheme for Unlicensed Massive Machine Type Communications †.

In this paper, the throughput and energy efficiency of an unlicensed machine type communications network is studied. If an outage event happens in the network, there is a possibility for packet retransmission in order to obtain a lower error probability. The concept of spectrum sharing is used here for modeling the network, which allows the two types of licensed and unlicensed users to share the same uplink channel allocated to the licensed users. However, it is done in a way that no harm is done to the licensed nodes’ transmission for sharing the same channel with the unlicensed users, while licensed nodes’ transmission causes interference on the unlicensed network. Poisson point process is used here to model the location of the nodes and the effect of interference on the network. We study how different factors such as the number of retransmissions, SIR threshold and outage can affect the throughput and energy efficiency of the network. Throughput and energy efficiency are also both studied in constrained optimization problems where the constraints are the SIR threshold and the number of retransmission attempts. We also show why it is important to use limited transmissions and what are the benefits.

Hierarchical energy management scheme for residential communities under grid outage event

The ever-increasing energy demand and extreme weather days lead to more frequent power outage events. This paper proposes a hierarchical energy management scheme for residential communities, aiming to facilitate energy sharing among houses and minimize the impact on the grid outage on the whole community. In the proposed model, the complexity of scheduling residential energy resources of multiple houses is decomposed into a bi-level structure, in which the Home Energy Management System (HEMS) of each house iteratively interacts with the Community Energy Management System (CEMS). In the upper layer, the CEMS receives the information of the planned outage; it then solves a social warfare optimization model to determine: (1) charging/discharging power of a community battery energy storage system, and (2) load re-shaping instructions of each house. In the lower layer, the HEMS of each house performs appliance-level autonomous scheduling to try to satisfy the load re-shaping instructions received from the CEMS. The autonomous scheduling result of individual HEMSs are sent back to the CEMS, and the latter updates the upper-layer result based on the received result. This process continues until the convergence criteria is achieved. Extensive simulations are conducted to validate the proposed solution

Quantifying impacts of automation on resilience of distribution systems

Automating the process of restoring service to customers after a large-scale outage event have significant impacts on the agility and speed of recovery in distribution systems. This study develops a set of probabilistic metrics to assess the impact of automation in enhancing the resilience of power distribution systems. The proposed metrics capture the features and detailed process of automatically locating and isolating faults and restoring the service to customers in distribution systems. In addition, this study develops a model to evaluate the spatio–temporal impacts of hurricane on power distribution systems, which is used to generate hurricane-induced outage scenarios to calculate the resilience metrics given different automation schemes. The proposed model is utilised to evaluate the resilience of bus number four of Roy Billinton Test System in the face of a passing hurricane. The metrics are calculated to evaluate the impact of different levels of automation throughout the network, the intensity of the hurricane, and line hardening on the resilience of the system.

Enhancing Power Grid Resilience Through an IEC61850-Based EV-Assisted Load Restoration

Contrary to reliability analysis in power systems with the main mission on safely and securely withstanding credible contingencies in day-to-day operations, resilience assessments are centered on high-impact low probability (HILP) events in the grid. This paper proposes an autonomous load restoration architecture founded on IEC 61850-8-1 GOOSE communication protocol to engender an enhanced feeder-level resilience in active power distribution grids. Different from the past research on outage management solutions, most of which 1) are not resilience-driven; 2) are reactive solutions to local single-fault events; and 3) do not address both network built-in flexibilities and flexible resources. The proposed solution harnesses 1) the imported power and flexibility from the neighboring networks; 2) distributed energy resources; and 3) vehicle to grid capacity of electric vehicles aggregations to enhance the feeder-level resourcefulness for agile response and recovery. Through real-time self-reconfiguration strategies, the suggested solution is capable of coping both single and subsequent outage events, and will engender a heightened resilience before and during the contingency period. Moreover, a resilience evaluation framework, which quantifies the contribution of all resources involved in service restoration, is developed. Real-time performance of the designed architecture is evaluated on a real-world power distribution grid using a real-time hardware-in-the-loop platform. Numerical case studies through a number of diverse scenarios demonstrate the efficacy of the proposed restoration solution in practicing an enhanced resilience in power distribution systems in response to HILP scenarios.

μPMU‐based intelligent island detection – the first crucial step toward enhancing grid resilience with MG

With the increased climatic change and modern grid complexity, extreme grid power outage events caused by natural calamity and human interruptions have led to an urgency to enhance the grid resiliency. Microgrids (MGs) have proved to be a concrete solution to these situations. However, these events are quite uncertain, leading to the unintentional island of MGs that has adverse effects. Thus, as a first step toward increasing grid resiliency with MG, informing the distributed generations about the unintentional island is a critical task. Hence, there is a need to develop a quick and reliable unintentional island detection scheme. Micro phasor measurement units (μPMUs) are becoming popular in MG. Given this, this study proposes an inadvertent island detection scheme in an MG using an intelligent μPMU. With the μPMU, the voltage at solar generator bus is measured, three features are extracted through spectral kurtosis and random forest classifier is employed for island detection. After island detection, a control methodology is proposed to circumvent the post-effects. The method has zero non-detection zone, 99.83% accuracy and a detection time of 20 ms. The reliability of the algorithm is ascertained using the analytical hierarchical approach and software fault-tree analysis.

Power Outage Estimation: The Study of Revenue-Led Top Affected States of U.S

The electric power systems are becoming smart as well as complex with every passing year, especially in response to the changing environmental conditions. Resilience of power generation and transmission infrastructure is important to avoid power outages, ensure robust service, and to achieve sustained economic benefits. In this study, we employ a two-stage model to estimate the power outage in terms of its intensity as well as the duration. We identify the top three potentially critical states of United States of America, not merely based on duration of the power outage, but by also incorporating outage related revenue loss. In the proposed model, the first stage classifies the intensity of the outage event while the second stage predicts the duration of the outage itself. Moreover, the key predictors are characterized and their association with outage duration is illustrated. We use a comprehensive and publicly available dataset, which provides the information related to historical power outage events, such as electricity usage patterns, climatological annotations, socio-economic indicators, and land-use data. Our rigorous analysis and results suggest that the power outage interval is the function of several parameters, such as climatological condition, economic indicators as well as the time of the year. The proposed study can help the regulatory authorities taking appropriate decisions for long term economic paybacks. It can also help disaster management authorities to take risk-informed resilient decisions for system safety.

Static and dynamic evaluation of different architectures for an actual HVDC link project

The employment of High-Voltage Direct Current (HVDC) transmission in modern power systems is mainly driven by requirements of increasing power transfer over high distances in a cost-efficient and controllable way. HVDC control can ensure fast and accurate power flow control to improve the whole system performance as well as increased flexibility to manage power flows from remote renewable generation to load centers, especially in a zonal market configuration. However, in the network planning phase the implications of a new HVDC connection, both on the steady-state and on the dynamic operation of a power system, must be carefully evaluated with particular reference to possible outage events, depending on the operating conditions and on the exploited control schemes. In this paper, a real HVDC project within the Italian transmission network is considered in both the Voltage Source Converter (VSC) and Line Commutated Converter (LCC) technologies. The study implies a methodology to analyze the impact on steady-state and dynamic performances in the presence of faults, with a focus on grid security and market efficiency aspects. The behavior of the interconnected Italian power system is simulated in a development planning viewpoint by means of software tools exploited by the System Operator.

Downlink Cooperative MIMO in LEO Satellites

We consider a communication scheme in which two low earth orbit (LEO) satellites jointly transmit (at the same time and frequency) to a multi antenna land terminal (LT). This scheme can increase the achievable terminal throughput by up to a factor of 2, depending on the channel matrices. The implementation aspects of this scheme are well-known but the usefulness of this scheme for LEO satellites has yet to be studied. Because the satellite channel is dominated by its line-of-sight (LOS) component, the terminal’s ability to separate the two streams depends critically on the system’s instantaneous-configuration; i.e., the relative location of the satellites and the terminal antennas. Since the relative locations of LEO satellites vary rapidly, the throughput characterization is not straightforward. To characterize the network performance we consider two satellites having one antenna, transmitting to a single LT having multiple antennas. We introduce a novel stochastic framework that assumes the terminal orientation as random. Using the proposed framework, we show that if the terminal antennas are close, the network throughput is nearly independent of the terminal orientation. When the terminal antennas are sufficiently separated, the result is completely different. For this case, we define an outage event and calculate the outage probability. The definition of outage in our novel stochastic framework allows us to prove that dual satellite transmission can indeed increase the downlink throughput with high probability.

Outage prediction models for snow and ice storms

Abstract This paper describes the development of two Outage Prediction Models (OPMs) for power outages caused by snow and ice storms on electric distribution networks, and their performance evaluation in the Northeastern United States. The first is a Machine Learning (ML) based model that is set up to predict power outages on a regular grid (4-km grid spacing). The second is a Generalized Linear Model (GLM) that is set up to predict total power outages at the town level. Both models are fed with Numerical Weather Prediction (NWP) outputs, satellite-derived leaf area index (LAI) and land cover data, and utility specified infrastructure and historical outage data. For both models the most important variables are the amount of assets on the territory, LAI, snow density, and – for the ice model – the amount of freezing rain. Results from cross-validation experiments based on 54 outage events spanning three orders of magnitude show median absolute percentage errors around 70% for both models. The GLM is able to predict extreme events (such as the devastating October 2011 nor’easter) better than ML models do, while ML models have better performance for lower impact events and present better characterization of the spatial distribution of power outages.

SINR Analysis and Interference Management of Macrocell Cellular Networks in Dense Urban Environments

The increasing growth of wireless applications leads to the congestion of radio spectrum below 10 GHz. This has slowed down the growth of high data rate applications due to the limited bandwidth of currently used frequency bands. This prompted wireless service providers to look for 30–300 GHz frequency band for mobile Broadband and enhanced mobile Broadband services. In this paper, the performance of 5G cellular networks at 30 GHz frequency by suppressing the interference caused by multiple transmissions from potential interferers sharing the same physical medium has been studied. Here initially the appropriate interference model that can predict the outage events in 5G cellular networks in any scenario has been investigated. Next using a cellular network site in dense urban environment with high traffic loads, user density and pedestrian and vehicular movement was created. The signal-to-interference-plus-noise ratio (SINR) is visualized for different antenna systems on a map using real geospatial information. The SINR maps of single antenna element and antenna array are plotted and compared. Both Free-space propagation model and Close-in-Propagation model are used to compare the results. Also using a uniform planar array of antennas on network-side the directionality increases hence interference reduces and higher values of SINR are achieved.

Interactive Energy Management for Enhancing Power Balances in Multi-Microgrids

This paper proposes a bi-layer game-theoretic framework for the interactive energy management (IEM) of multi-microgrids (MMGs) under the presence of uncertainties imposed by both supply and demand sides. The higher-layer of the framework manages the energy trading and the consumption behavior for each microgrid (MG) within a slow timescale, where its operation cost model is designed in terms of economic factors and users’ willingness. The lower-layer runs at higher frequency that relies on a model predictive control approach, and adjusts the operations of MGs to minimize the difference between the planned strategies generated by the higher-layer and the real ones. In doing so, the supply and demand uncertainties as well as the outage events can be handled properly. As the internal prices are adjusted with system net load, the power balance levels in MMG would be enhanced by minimizing operation cost. On this basis, the energy trading among MGs can be achieved directly without an intermediary. We design a decentralized interactive algorithm to implement the bilayer IEM framework by using the Nash equilibrium concept. Furthermore, the emergency situation is covered to enhance the resilience of the system. Numerical simulations on a practical MMG verify the effectiveness of the proposed models.

Using data from connected thermostats to track large power outages in the United States

The detection of power outages is an essential activity for electric utilities. A large, national dataset of Internet-connected thermostats was used to explore and illustrate the ability of Internet-connected devices to geospatially track outages caused by hurricanes and other major weather events. The method was applied to nine major outage events, including hurricanes and windstorms. In one event, Hurricane Irma, a network of about 1000 thermostats provided quantitatively similar results to detailed utility data with respect to the number of homes without power and identification of the most severely affected regions. The method generated regionally uniform outage data that would give emergency authorities additional visibility into the scope and magnitude of outages. The network of thermostat-sensors also made it possible to calculate a higher resolution version of outage duration (or SAIDI) at a level of customer-level visibility that was not previously available.

Tradeoffs Between Weak-Noise Estimation Performance and Outage Exponents in Nonlinear Modulation

We focus on the problem of modulating a parameter onto a power-limited signal transmitted over a discrete-time Gaussian channel and estimating this parameter at the receiver. Considering the well-known threshold effect in the non-linear modulation systems, our approach is the following: instead of deriving upper and lower bounds on the total estimation error, which weighs both weak-noise errors and anomalous errors beyond the threshold, we separate the two kinds of errors. In particular, we derive upper and lower bounds on the best achievable tradeoff between the exponential decay rate of the weak-noise expected error cost and the exponential decay rate of the probability of the anomalous error event, also referred to as the outage event . This outage event is left to be defined as a part of the communication system design problem. Our achievability scheme, which is based on lattice codes, meets the lower bound at the high signal-to-noise limit and for a certain range of tradeoffs between the weak-noise error cost and the outage exponent.

Impact of fixed power allocation in wireless energy harvesting NOMA networks

SummaryThe time switching‐based relaying (TSR) scheme is considered in energy harvesting protocol to implement with its advantage to nonorthogonal multiple access (NOMA) system. In particular, decode‐and‐forward (DF) mode is proposed to employ in relay to forward signal to serve two far NOMA users. There are two main metrics including outage probability and ergodic rate, which are derived in exact expressions with respect to varying performance under impacts of energy harvesting fractions. To evaluate system performance, outage event and related capacity are illustrated, and we tailor performance gap among two NOMA users and such gap can be controlled by selecting of appropriate power allocation factors assigned for each user to obtain optimal performance. By examining node arrangement, target rates and varying transmit signal to noise ratio (SNR), it can be further achieved performance in several situations of such NOMA. As important result, the considered NOMA system outperforms than the conventional multiple access scheme, and this expected result is confirmed in numerical result and theoretical results. We also explore impacts of transmit power at source, noise power, the other key parameters of energy harvesting scheme to exhibit outage, and ergodic performance. Simulation results are presented to corroborate the proposed methodology.