Risk Of Blackouts Research Articles

North American Blackout Time Series Statistics and Implications for Blackout Risk

We use North American Electric Reliability Corporation historical data to give improved estimates of distributions of blackout size, time correlations, and waiting times for the Eastern and Western interconnections of the North American grid. We then explain and estimate the implications of the power law region (heavy tails) in the empirical distribution of blackout size in the historical data for the Western interconnection. Annual mean blackout size has high variability and the risk of large blackouts exceeds the risk of medium size blackouts. Ways to communicate blackout risk are discussed.

Data-Driven Diagnostics of Mechanismand Source of Sustained Oscillations

Sustained oscillations observed in power systems can damage equipment, degrade the power quality and increase the risks of cascading blackouts. There are several mechanisms that can give rise to oscillations, each requiring different countermeasure to suppress or eliminate the oscillation. This work develops mathematical framework for analysis of sustained oscillations and identifies statistical signatures of each mechanism, based on which a novel oscillation diagnosis method is developed via real-time processing of phasor measurement units (PMUs) data. Case studies show that the proposed method can accurately identify the exact mechanism for sustained oscillation, and meanwhile provide insightful information to locate the oscillation sources.

The Rising Frequency of IT Blackouts Indicates the Increasing Relevance of IT Emergency Concepts to Ensure Patient Safety.

As many medical workflows depend vastly on IT support, great demands are placed on the availability and accuracy of the applications involved. The cases of IT failure through ransomware at the beginning of 2016 are impressive examples of the dependence of clinical processes on IT. Although IT risk management attempts to reduce the risk of IT blackouts, the probability of partial/total data loss, or even worse, data falsification, is not zero. The objective of this paper is to present the state of the art with respect to strategies, processes, and governance to deal with the failure of IT systems. This article is conducted as a narrative review. Worst case scenarios are needed, dealing with methods as to how to survive the downtime of clinical systems, for example through alternative workflows. These workflows have to be trained regularly. We categorize the most important types of IT system failure, assess the usefulness of classic counter measures, and state that most risk management approaches fall short on exactly this matter. To ensure that continuous, evidence-based improvements to the recommendations for IT emergency concepts are made, it is essential that IT blackouts and IT disasters are reported, analyzed, and critically discussed. This requires changing from a culture of shame and blame to one of error and safety in healthcare IT. This change is finding its way into other disciplines in medicine. In addition, systematically planned and analyzed simulations of IT disaster may assist in IT emergency concept development.

Cascading Power Outages Propagate Locally in an Influence Graph that is not the Actual Grid Topology

In a cascading power transmission outage, component outages propagate non-locally, after one component outages, the next failure may be very distant, both topologically and geographically. As a result, simple models of topological contagion do not accurately represent the propagation of cascades in power systems. However, cascading power outages do follow patterns, some of which are useful in understanding and reducing blackout risk. This paper describes a method by which the data from many cascading failure simulations can be transformed into a graph-based model of influences that provides actionable information about the many ways that cascades propagate in a particular system. The resulting "influence graph" model is Markovian, in that component outage probabilities depend only on the outages that occurred in the prior generation. To validate the model we compare the distribution of cascade sizes resulting from $n-2$ contingencies in a $2896$ branch test case to cascade sizes in the influence graph. The two distributions are remarkably similar. In addition, we derive an equation with which one can quickly identify modifications to the proposed system that will substantially reduce cascade propagation. With this equation one can quickly identify critical components that can be improved to substantially reduce the risk of large cascading blackouts.

Research on Electrical Network Blackout Risk under Different Connection Patterns

With the gradual expansion of China's electrical network construction, network scale and transmission capacity, a reasonable electrical network construction scale research is urgent for the optimized power system development. In this paper, a blackout risk assessment index system as well as the risk calculation model under different electrical network scales is established, followed by a series of blackout risk value calculation of constitutive units in an IEEE-14 node standard system under typical patterns, hence the validity of the model is verified via comparison with the actual blackout risk value of the system. The result indicates that that the ranking of blackout risk values under different connection patterns with the same amount of lines is polygon, quadrilateral and triangle.

Assessing Short-Term Voltage Stability of Electric Power Systems by a Hierarchical Intelligent System

In the smart grid paradigm, growing integration of large-scale intermittent renewable energies has introduced significant uncertainties to the operations of an electric power system. This makes real-time dynamic security assessment (DSA) a necessity to enable enhanced situational-awareness against the risk of blackouts. Conventional DSA methods are mainly based on the time-domain simulation, which are insufficiently fast and knowledge-poor. In recent years, the intelligent system (IS) strategy has been identified as a promising approach to facilitate real-time DSA. While previous works mainly concentrate on the rotor angle stability, this paper focuses on another yet increasingly important dynamic insecurity phenomenon-the short-term voltage instability, which involves fast and complex load dynamics. The problem is modeled as a classification subproblem for transient voltage collapse and a prediction subproblem for unacceptable dynamic voltage deviation. A hierarchical IS is developed to address the two subproblems sequentially. The IS is based on ensemble learning of random-weights neural networks and is implemented in an offline training, a real-time application, and an online updating pattern. The simulation results on the New England 39-bus system verify its superiority in both learning speed and accuracy over some state-of-the-art learning algorithms.

A new approach for mitigating blackout risk by blocking minimum critical distance relays

Abstract Electric power systems are complex and commonly run near their operational limits. Power systems are basically designed based on the ( N − 1) criterion. In these systems, total or partial blackouts are unavoidable. Cascading failures following an initial event is recognized as the main mechanism for power system blackouts. Undesirable activation of zone 3 of distance relays due to their local function has been identified as one of the major causes for propagation of cascading failures. In this paper, in order to mitigate the risk of power system blackouts, a novel approach is proposed for recognizing the critical line outages with the highest contribution in cascading failure and preventing their undesirable outage by blocking zone 3 of their distance relay. In this paper, based on the statistical activation of distance relays, a deterministic–probabilistic approach is developed for identifying the critical lines with the highest contribution in blackouts due to activation of zone 3 of their distance relays. In order to avoid interference of relay blocking with the main protection duty of distance relays, the proposed approach recognizes the minimum critical lines with the highest contribution in cascading failure for blocking zone 3 of their distance relays. The proposed approach is demonstrated on the New England 39-bus system and detailed simulation studies carried out to examine the validity and effectiveness of the proposed approach.

Inclusion of Blackouts Risk in Probabilistic Transmission Expansion Planning by a Multi-Objective Framework

Risk of blackouts is more than expected and must be considered in the power system studies. The interest of this paper is to minimize the blackouts risk during minimizing the costs in probabilistic transmission expansion planning (TEP). In this paper, a long-term reliability analysis, which was developed by self-organized criticality theory, is used in a multi-objective optimization to include risk of blackouts in TEP. The objective functions are costs and expected energy not supplied. Also, a new formulation for calculating transmission operating cost as a part of cost function is proposed. The problem is solved by a multi-objective particle swarm optimization to find an optimal Pareto set from which the decision-maker can choose the most desirable one, regarding existing budget. The proposed method is applied to the IEEE 118-bus test system to investigate its applicability and scalability. The results are compared with security-constrained TEP, which uses famous deterministic criterion known as N-1. The proposed approach produces more efficient plans for suppressing cascading blackouts with less network investment. Moreover, the results provide motivation for considering the risk of cascading blackouts in TEP, which afford substantial saving to society.

A hybrid neuro-fuzzy power prediction system for wind energy generation

Abstract Wind energy generation is expected to increase in future electric grids. The generated wind power has an intermittent nature which may affect power system stability and increase the risk of blackouts. Therefore, a prediction system for wind power generation is essential for optimum operation of a power system with a significant share of wind energy conversion systems. In this paper, a hybrid neuro-fuzzy wind power prediction system is proposed. A wireless sensor network (WSN) is used to measure and transmit the required parameters for the prediction model at the operator centre. Those parameters are the major factors affecting wind farm output power, namely air temperature, wind speed, air density and air pressure. Considering all these factors will increase the prediction accuracy of the proposed model. The proposed prediction model is designed and tested using fuzzy rules with adaptive network. To decide the optimal number of fuzzy rules, the clustering of the data using modified Fuzzy C-Means (FCM) is used to implement hybrid optimization method. The prediction model is tested using four subsets of data divided into four seasons of year. The proposed prediction model is implemented using Matlab. Analysis of results shows that the proposed model has good prediction accuracy and provides a useful qualitative description of the prediction system.

Contribution of Water Saving to a Stable Power Supply in Vietnam

In Vietnam, the rapid expansion of cities is exceeding the supply capacity for water and electricity, and restrictions on water supply and blackouts occur on a daily basis. In this study, the authors examined whether water-saving equipment could solve these problems. This paper focused on toilet bowls that consumed a large amount of water, and on showers for which heat consumption was high. In Vietnam, the main heat source for showers is the electric water heater, typically having a power consumption of 2500–4500 W. Although the current diffusion rate of such water heaters is just 13%, their use will spread widely in the future. These heaters have already placed a peak load on electricity consumption in winter when a large amount of energy is consumed for heating water, and they will become a significant factor in blackout risks as their use becomes commonplace nationwide. It is clear that the introduction of water-saving showers will allow not only a more efficient use of water resources, but will also mitigate against the risk of blackouts.

Hybrid multiple attribute group decision-making for power system restoration

Due to deregulated power industry, distributed power generation, aging infrastructure and many other factors, the modern society is exposed to higher blackout risks. Decision-making optimization is indispensable for ensuring fast and secure power supply restoration to end users. As an important stage of power system restoration, backbone-network reconfiguration is necessary to re-establish the skeleton network and restore loads. Backbone-network reconfiguration after a large-scale outage is influenced by many factors about system safety and restoration speed. In order to evaluate candidate restoration schemes, multiple types of attributes including crisp data, fuzzy numbers, interval numbers and linguistic terms are employed. An extended VIKOR method is proposed to provide compromise solutions considering hybrid attributes. The method can reflect the vagueness and uncertainties in practical restoration problems, and avoid too much fuzzification. Different forms of preference relations and maximum deviation model are integrated by the minimum relative entropy to determine combined weights of attributes. Sensitivity analysis on the weights provides efficient guidelines for decision makers. Finally, an actual power system demonstrates the basic features of the developed method. It is more reasonable and creditable to consider multiple types of information and unsatisfactory attributes in decision-making.

Blackout risk prevention in a smart grid based flexible optimal strategy using Grey Wolf-pattern search algorithms

Developing a flexible and reliable power system planning strategy under critical situations is of great importance to experts and industrials to minimize the probability of blackouts occurrence. This paper introduces the first stage of this practical strategy by the application of Grey Wolf Optimizer coordinated with pattern search algorithm for solving the security smart grid power system management under critical situations. The main objective of this proposed planning strategy is to prevent the practical power system against blackout due to the apparition of faults in generating units or important transmission lines. At the first stage the system is pushed to its margin stability limit, the critical loads shedding are selected using voltage stability index. In the second stage the generator control variables, the reactive power of shunt and dynamic compensators are adjusted in coordination with minimization the active and reactive power at critical loads to maintain the system at security state to ensure service continuity. The feasibility and efficiency of the proposed strategy is applied to IEEE 30-Bus test system. Results are promising and prove the practical efficiency of the proposed strategy to ensure system security under critical situations.

Group decision support system for backbone-network reconfiguration

Abstract Modern power system is unprecedentedly vulnerable and the society is exposed to higher blackout risks. Therefore restoration is a key issue to power systems. Reasonable backbone-network reconfiguration is necessary for re-establishing the network and restoring loads quickly. In order to speed up network reconfiguration under security constraints, a group decision support system (GDSS) is developed. A unified data platform based on data warehouse is constructed to resolve inconsistencies of data, information and models. A three-stage restoration strategy and a two-layer restoration path searching method are proposed to establish restoration schemes. Multiple attribute decision-making is implemented to evaluate alternatives and consider various attributes about restoration speed and system security comprehensively. Group decision-making provides an aggregated cardinal ranking of candidate restoration schemes. The GDSS uses expert rules for qualitative analysis and numerical computation programs for quantitative analysis, and it can resolve the semi-structured decision problem of network reconfiguration efficiently. Restoration plan of Shandong power grid is developed with the proposed method and it has been adopted by State Grid Shandong Electric Power Company. Performance results of Shandong power grid of China demonstrate the feasibility and flexibility of the method.

Renewable energy sources for complete optimal power system black‐start restoration

Integration of renewable energy sources (RESs) may increase the risk of power system blackouts because of the uncertainty nature of their output power. In the meantime, RESs have relatively short starting time when compared with non-black start (BS) generating units. For this reason, RES needs to participate in power system restoration after blackouts. For a complete power system restoration, three stages must be completed. These stages are: generation restoration, transmission system restoration and load pick up. To achieve a faster restoration process, an optimal schedule for the BS units to crank the non-BS (NBS) units is required with optimal transmission path selection. In addition, to maintain the stability of the system and satisfy the system operational constraints, an optimal load pick-up sequence is required. In this study, the firefly algorithm (FA) is used to find the optimal final sequence of NBS units restoration, transmission paths and load pick-up sequence with and without the aid of RES in the system. The objective is to minimise the overall restoration time and the unserved load which maximise the energy capability and improve the sustainability of the system. The proposed algorithm is applied successfully to the IEEE 39-bus system.

A variable-frequency electric drive with power supply by two independent inputs

Excessive sensitivity to interruptions of power supply is a disadvantage of variable frequency drives: short-term power cutoffs or voltage depressions. Within the period of integration of the frequency changing circuit at essential units of a thermal power stations, this can result in increasing risk of electrical power unit blackout. To compensate for this disadvantage, variable frequency drives with electrical supply from two independent inputs have been developed. A frequency-changing circuit with two rectifiers connected on the direct current side has been taken as the basic one. To prevent uncontrolled switching over between inputs, priority of one input should be provided. In this case, increasing the voltage on the input that has been taken as primary is not an efficient solution. Continuous operation of the electric drive should be provided with automatic switching over the reserve input at the loss of supply voltage on the primary input. We propose an arrangement based on two groups of identical frequency-changing circuits connected in parallel on the direct current side. The arrangement also contains a voltage adjuster equipped with an automatic control system, which must provide steady operation of the electric drive with primary input supply. The power unit of the adjuster is constructed based on the active field-bus module (ALM) realized on fully controllable switch contacts. A frequency drive with a simplified main circuit and with a diode rectifier and a group of the active field-bus module ALM has been developed. To carry out experimental investigations of its modes, an experimental prototype of an electric drive with a fast response STATCOM model voltage (power) switch converter has been developed and manufactured. This converter acts as a voltage adjuster of ALM. Experimental investigations of the electric drive with simulation of blackout on one of the inputs and gradual voltage load transfer between inputs and other modes have been conducted. The oscillograms of the main coordinates have been presented. It is shown that the required quality of the transient phenomena is provided in all modes. Thus, automatic switching of the frequency changing circuit over the reserved input practically has no effect on the operation of the electric drive. Thus, we can draw the conclusion that the technological mode of the basic unit will not be affected. Generally, experimental investigations have confirmed that steady operation is maintained of the variable frequency drive under conditions of voltage changes or switching off one of the inputs.

Numerical Analysis of High-Performance Lithium-Ion and Lead-Acid Batteries with Capacity Fade for an Off-Grid Residential PV System

AbstractThis paper introduces and integrates effective models to describe the fundamental characteristics of high-performance lithium-ion (graphite-LiFePO4) and lead-acid (VRLA) batteries with capacity fade for use in an off-grid residential photovoltaic (PV) generation system. The lithium iron phosphate (LFP) and VRLA batteries have been simulated using a 1D electrochemical model with thermal and aging components, and using the CIEMAT model, a general analytical model of the lead-acid battery, respectively. In the off-grid PV/battery/demand system, the calculated annual state of charge (SOC) distribution and time history of degradation in the LFP and VRLA batteries describe the internal battery behaviors and the blackout risk; the LFP battery is expected to accomplish a one-third lower annual blackout frequency and a 25% longer degradation time than a VRLA battery with a 24-kWh battery capacity over 15 years of operation. According to the calculated loss and surplus electricity indexes, the LFP battery h...

Imperialistic Competitive Algorithm Based Unit Commitment Considering Risk of Cascading Blackout

—The generation scheduling of different units in the power system is one of the procedures that affects the cascading blackout. Generators are usually scheduled based on the solution of the unit commitment problem, which should minimize the running cost of the power system. In this article, a new objective function is presented to consider the load-shedding effect on the power generation scheduling. The simulations are carried out in MATLAB software (The MathWorks, Natick, Massachusetts, USA), showing that it may lead to high risk of blackout in some conditions if the generation scheduling is done only by considering the cost of generation.

Integration of wind power for optimal power system black-start restoration

Integration of wind farms into power systems may increase the risk of power system blackouts due to the uncertain nature of their output power. In the meantime, wind turbines have relatively short starting time when compared to non-black-start (NBS) generating units. For this reason, wind farms need to participate in power system restoration after blackouts. The decision of restoring a wind farm depends on its output power and the characteristics of the power system. The power system restoration should be accomplished as soon as possible. For complete power system restoration, three stages must be completed: generation restoration, transmission system restoration, and load pick up. To achieve a faster restoration process, an optimal schedule for the black-start units to crank the NBS units is required with optimal transmission path selection. During the restoration process, to maintain the stability of the system and satisfy the system's operational constraints, an optimal load pick up sequence is required. In this paper, the firefly optimization algorithm is used to find the optimal final sequence of NBS unit restoration, the optimal transmission paths, and the optimal load pick up sequence with and without integration of wind farms in the system. The objective is to minimize the overall restoration time and the unserved load, which maximizes the energy capability and improves the sustainability of the system. The proposed algorithm is applied successfully to the IEEE 39-bus system.

Discussion on “Solving Preventive-Corrective SCOPF by a Hybrid Computational Strategy”

Preventive control (PC) and corrective control (CC) are complementary actions in protecting large power systems against the risk of blackout. This paper addresses the optimal coordination between PC and CC via a preventive-corrective security-constrained optimal power flow model. The objective is to minimize the total expected-security-control cost which is the sum of the costs of PC and CC considering the probability of the contingencies. The constraints include system operating limits in pre- and post-contingency states, the existence of the post-contingency short-term equilibrium, and the coupling constraints on the CC actions. To solve the model, a hybrid computational strategy combining evolutionary algorithm and interior-point method is developed. The solution process consists of globally searching the critical feasible region and locally optimizing the operating variables in the found region, the two procedures interact iteratively to progressively tighten the solution region leading to the final solution. The proposed model and computational strategy are demonstrated on the IEEE 14-bus and 118-bus test systems. To speed up the computation, parallel processing of the approach is implemented.

Closure to Discussion on “Solving Preventive-Corrective SCOPF by a Hybrid Computational Strategy”

Preventive control (PC) and corrective control (CC) are complementary actions in protecting large power systems against the risk of blackout. This paper addresses the optimal coordination between PC and CC via a preventive-corrective security-constrained optimal power flow model. The objective is to minimize the total expected-security-control cost which is the sum of the costs of PC and CC considering the probability of the contingencies. The constraints include system operating limits in pre- and post-contingency states, the existence of the post-contingency short-term equilibrium, and the coupling constraints on the CC actions. To solve the model, a hybrid computational strategy combining evolutionary algorithm and interior-point method is developed. The solution process consists of globally searching the critical feasible region and locally optimizing the operating variables in the found region, the two procedures interact iteratively to progressively tighten the solution region leading to the final solution. The proposed model and computational strategy are demonstrated on the IEEE 14-bus and 118-bus test systems. To speed up the computation, parallel processing of the approach is implemented.