Explore the vast range of titles available.

Because of the development of smart grid technology, today's power grid infrastructures are increasingly and heavily coupled with communication networks for many new and existing power applications. The interdependent relationship between the two systems, in which power control relies on the communication system to deliver control and monitoring messages and network devices require power supplies from the electrical grid, brings challenges in the effort to build a highly resilient integrated infrastructure. In this work, the authors summarise existing research on power grid resilience enhancement with the consideration of the interdependencies between power systems and communication networks. They categorise these works according to stages of resilience enhancement (i.e. failure analysis, vulnerability analysis, failure mitigation, and failure recovery) and methodologies (i.e. analytical solutions, co‐simulation, and empirical studies). They also identify the limitations of existing works and propose potential research opportunities in this demanding area.

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.

In the past few decades, the arcing‐high‐impedance fault (arcing‐HIF) detection problems have become an important issue in the effectively grounded distribution network. Many solutions have been proposed to address this problem. The most attractive way is artificial intelligence (AI) method. The paper gives a comprehensive review of arcing‐HIF detection in distribution network‐based AI. First, characteristics and models of arcing‐HIF are analysed, the arcing‐HIF database construction method is also explained; this part is a foundation work for arcing‐HIF detection. Next, arcing‐HIF detection methods based AI are summarised in details including data acquisition, feature extraction and classifier selection. Then, a set of criteria are proposed to evaluate the reliability of arcing‐HIF detection algorithm. Finally, the future trends and challenges to arcing‐HIF detection are also fully accounted. This review can be a valuable guide for researchers who are interested in arcing‐HIF detection‐based AI.

Generation maintenance scheduling (GMS) is one of the most important scheduling problems in the restructured power systems. The maintenance time interval of generation units is the crucial factor of GMS for an operation lifespan of generation units, particularly within the smart grid which needs high reliability. Accordingly, this study proposes a multi‐objective‐GMS (MO‐GMS) optimisation model for maintenance scheduling of generation units based on the global criterion approach, adopting a suitable compromise function. The proposed MO‐GMS model determines the maintenance intervals, aims to maximise both the generation company's (GenCo's) financial returns from selling electricity and the system reserve at every time interval from the independent system operator (ISO) standpoint. This method searches the optimal maintenance weeks for each generation unit, considering the objectives of both GenCo and ISO, simultaneously. The proposed MO‐GMS model is formulated as a mixed‐integer non‐linear programming problem and examined on the IEEE 24‐bus and IEEE 118‐bus test systems. The success of the proposed multi‐objective model is validated by comparing the obtained results with intelligent optimisation algorithms.

Renewable resource based microgrids provide reliable and cost‐effective electricity with low carbon emissions. The flexibility of operating in grid‐connected or islanded modes enables a microgrid to serve loads reliably. In the case of unexpected events happening to the main grid, the microgrid will isolate itself and operate in islanded mode to prevent any adversary impacts. The availability of renewable generation in the microgrid has significant impacts on the islanding strategy and different scenarios need to be considered. This study proposes a comprehensive microgrid self‐healing strategy under different circumstances. The proposed strategy encompasses generation re‐dispatch, network reconfiguration, and load shedding. The microgrid self‐healing problem is formulated as a mixed‐integer quadratic programming problem, which provides a globally optimal solution to facilitate smooth islanding of the microgrid. A modified Consortium for Electric Reliability Technology Solutions microgrid is used to conduct simulation under various scenarios. The simulation results demonstrate the efficiency of the proposed self‐healing strategy in minimising costs of load shedding and generation with optimal switching actions.

Photovoltaic (PV) systems are weather‐dependent. A solar eclipse causes significant changes in these parameters, thereby impacting PV generation profile, performance, and power quality of larger grid, where they connect to. This study presents a case study to evaluate the impacts of the solar eclipse of 21 August 2017, on two real‐world grid‐tied PV systems (1.4 MW and 355 kW) in Miami and Daytona, Florida, the feeders they are connected to, and the management areas they belong to. Four types of analyses are conducted to obtain a comprehensive picture of the impacts using 1 min PV generation data, hourly weather data, real feeder parameters, and daily reliability data. These analyses include: individual PV system performance measurement using power performance index; power quality analysis at the point of interconnection; a study on the operation of voltage regulating devices on the feeders during eclipse peak using an IEEE 8500 test case distribution feeder; and reliability study involving a multilayer perceptron framework for forecasting system reliability of the management areas. Results from this study provide a unique insight into how solar eclipses impact the behaviour of PV systems and the grid, which would be of concern to electric utilities in future high penetration scenarios.

The purpose of this research is to present an innovative load frequency control in the presence of wind turbines in islanded micro‐grid (MG). As islanded MG suffers from low inertia and insufficient primary frequency response (PFR), utilising the variable wind turbines in de‐loaded area can be considered as an alternative solution to deal with frequency control problems. In this context, the de‐load area is referred to a region where wind turbines release their stored kinetic energy in rotational masses following frequency disturbances. For effective utilisation of wind turbines, a self‐tuning, adaptive fuzzy droop is proposed, whose membership function parameters are optimised through artificial bee colony algorithm based on a multi‐objective decision making process. A comparison is made between the obtained results of the self‐tuning, adaptive fuzzy droop with conventional proportional integral derivative droop control in order to assess the proposed method performance in different disturbances.

An approach is demonstrated to visualise overhead line failure rates and estimated wind power output during extreme wind events on transmission networks. Reanalysis data is combined with network data and line failure models to illustrate spatially resolved line failure probability with data corrected for asset altitude and exposure. Wind output is estimated using a corrected power curve to account for high speed shutdown with wind speed corrected for altitude. Case studies demonstrate these methods' application on representations of real networks of different scales. The proposed methods allow users to determine at‐risk regions of overhead line networks and to estimate the impact on wind power output. Such techniques could equally be applied to forecasted weather conditions to aid in resilience planning. The methods are shown to be particularly sensitive to the weather data used, especially when modelling risk on overhead lines, but are still shown to be useful as an indicative representation of system risk. The techniques also provide a more robust method of representing weather‐related failure rates on lines considerate of the altitude, voltage level, and their varying exposure to weather conditions than current techniques typically provide, which can be used to usefully represent failure probability of lines during storms.

Smart city adoption and deployment has taken the centre stage worldwide with its realisation clearly hinged on energy efficiency, but its planning is threatened by the vulnerability of smart grids (SGs). Adversaries launch attacks with various motives, but the rampaging electricity theft menace is causing major concerns to SGs deployments and consequently, energy efficiency. Smart electricity meters deployments via the advanced metering infrastructure present promising solutions and even greater potential as it provides adequate data for analytical inferences for achieving proactive measures against various cyberattacks. This study suggests the sources of threats as the first step of such proactive measures of curbing electricity thefts. It provides a framework for monitoring, identifying and curbing the threats based on factors indicative of electricity thefts in a smart utility network. The proposed framework basically focuses on these symptoms of the identified threats indicative of possible electricity theft occurrence to decide on preventing thefts. This study gives a useful background to smart city planners in realising a more reliable, robust and secured energy management scheme required for a sustainable city.

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.