Resilience Of Power Distribution Systems Research Articles

A Novel Machine Learning-Based Approach for Fault Detection and Location in Low-Voltage DC Microgrids

DC microgrids have gained significant attention in recent years due to their potential to enhance energy efficiency, integrate renewable energy sources, and improve the resilience of power distribution systems. However, the reliable operation of DC microgrids relies on the early detection and location of faults to ensure an uninterrupted power supply. This paper aims to develop fast and reliable fault detection and location mechanisms for DC microgrids, thereby enhancing operational efficiency, minimizing environmental impact, and contributing to resource conservation and sustainability goals. The fault detection method is based on compressed sensing (CS) and Regression Tree (RT) techniques. Besides, an accurate fault location method using the feature matrix and long short-term memory (LSTM) model combination has been provided. To implement the proposed fault detection and location method, a DC microgrid equipped with photovoltaic (PV) panels, the vehicle-to-grid (V2G) charging station, and a hybrid energy storage system (ESS) are used. The simulation results represent the proposed methods’ superiority over the recent studies. The fault occurrence in the studied DC microgrid is detected in 1 ms, and the proposed fault location method locates the fault with an accuracy of more than 93%. The presented techniques enhance DC microgrid reliability while conserving renewable resources, vital to promoting a greener and more sustainable power grid.

A robust optimization approach for resiliency improvement in power distribution system

AbstractThe occurrence of natural disasters has led to an alarming increase in power interruptions, with severe impacts. Compounding this problem is the uncertain nature of data, which presents significant challenges in enhancing the resiliency of power distribution systems following such events. To tackle these issues, this paper introduces a robust optimization approach for improving the resiliency of power distribution systems. The approach encompasses crew teams for switching actions as part of the restoration process, along with demand response programs and mobile generators (MGs). By simultaneously leveraging these elements and considering the uncertainty associated with electrical load and electrical price, the resiliency of the system is enhanced. The objective function is tri‐level, comprising minimum, maximum, and minimum functions. At the first level, the approach minimizes the cost of commitment of combined heat and power plants (CHPs) by taking into account the location of MGs and the reconfiguration structure in power distribution systems. The second level aims to identify the worst‐case scenario for the uncertainty variables. Finally, the third level focuses on minimizing the total operation cost under the worst‐case scenario using demand response programs. The proposed algorithm is implemented on an IEEE 33‐bus test distribution system, with four different cases investigated.

Evaluating the planning and operational resilience of electrical distribution systems with distributed energy resources using complex network theory

Electrical Distribution Systems (EDS) are extensively penetrated with Distributed Energy Resources (DERs) to cater the energy demands with the general perception that it enhances the system’s resilience. However, integration of DERs may adversely affect the grid operation and affect the system resilience due to various factors like their intermittent availability, dynamics of weather conditions, non-linearity, complexity, number of malicious threats, and improved reliability requirements of consumers. This paper proposes a methodology to evaluate the planning and operational resilience of power distribution systems under extreme events and determines the withstand capability of the electrical network. The proposed framework is developed by effectively employing the complex network theory. Correlated networks for undesirable configurations are developed from the time-series data of active power monitored at nodes of the electrical network. For these correlated networks, compute the network parameters such as clustering coefficient, assortative coefficient, average degree, and power law exponent for the anticipation; and percolation threshold for the determination of the network’s withstand capability under extreme conditions. The proposed methodology is also suitable for identifying the hosting capacity of solar panels in the system while maintaining resilience under different unfavorable conditions and identifying the most critical nodes of the system that could drive the system into non-resilience. This framework is demonstrated on IEEE 123 node test feeder by generating active power time-series data for a variety of electrical conditions using the simulation software, GridLAB-D. The percolation threshold resulted as an effective metric for the determination of the planning and operational resilience of the power distribution system.

Risk-Based Active Distribution System Planning for Resilience Against Extreme Weather Events

Enhancing the resilience of power distribution systems to extreme weather events is of critical concern. Upgrading the distribution system infrastructure by system hardening and investing in smart grid technologies effectively enhances grid resilience. Existing distribution system planning methods primarily consider the persistent cost of the expected events (such as faults and outages likely to occur) and aim at improving system reliability. The resilience to extreme weather events requires reducing the impacts of the high impact low probability (HILP) events that are characterized by the tail probability of the event impact distribution. Thus, the resilience-oriented system upgrades solutions need to be driven by the risks imposed by extreme weather events on the power grid infrastructure rather than persistent costs. This paper aims to develop a risk-based approach for the long-term resilience planning of active power distribution systems against extreme weather events. The proposed approach explicitly models (1) the impacts of HILP events using a two-stage risk-averse stochastic optimization framework, thus, explicitly incorporating the risks of HILP events in long-term planning decisions, and (2) the advanced distribution grid operations (in the aftermath of the event) such as intentional islanding into infrastructure planning problem. The inclusion of risk in the planning objective provides additional flexibility to the grid planners to analyze the trade-off between risk-neutral and risk-averse planning strategies.

Modeling the Resilience of Power Distribution Systems Subjected to Extreme Winds Considering Tree Failures: An Integrated Framework

Overhead electrical power distribution systems (PDS) are very susceptible to extreme wind hazards. Power outages can cause catastrophic consequences, including economic losses, loss of critical services, and disruption to daily life. Therefore, it is very important to model the resilience of PDS against extreme winds to support disaster planning. While several frameworks currently exist to assess the resilience of PDS subjected to extreme winds, these frameworks do not systematically consider the tree-failure risk. In other words, there is no integrated framework that can simultaneously consider tree failures, PDS component failures induced by falling trees, resilience assessment, and evaluation of resilience enhancement with vegetation management. Therefore, this study proposed an integrated simulation framework to model the resilience of PDS against extreme winds, which includes tree fragility modeling, PDS fragility modeling, PDS component failure estimation, system performance evaluation, system restoration modeling, and resilience enhancement evaluation. The framework is demonstrated with a power distribution network in Oklahoma. The results show that the estimated system resilience will reduce if tree failures are considered. Crown thinning can effectively enhance the system’s resilience, but the effectiveness is affected by both wind speed and direction.

Resilience assessment and enhancement evaluation of power distribution systems subjected to ice storms

• A probabilistic framework for assessing and evaluating the enhancements of the ice storm resilience of power distribution systems is developed; • Fragility models of power distribution components subjected to ice storms are developed; • Three failure modes of power distribution components induced by trees, ice, and wind, are investigated for the first time; • The impact of tree failures on the resilience of power distribution systems is thoroughly investigated. Overhead power distribution systems are very susceptible to ice storms. Each year power outages due to ice storms result in extensive economical loss and restoration costs all around the world. Climate change and aging further highlight the need for resilient power distribution systems against ice storms. This paper proposes a probabilistic framework for assessing and evaluating the enhancements of the ice storm resilience of power distribution systems, with a focus on fragility modeling of power distribution components (i.e., power poles and wires). The framework is able to assess the impact of ice storms on the resilience of power distribution systems and evaluate the cost-effectiveness of resilience enhancement strategies such as upgrading poles and vegetation management. Specifically, the limitations of tree-induced risk assessment in previous studies are overcome by developing fragility models of tree-induced component failures and using an empirical tree damage fragility function. The fragility of distribution components subjected to ice storms is thoroughly investigated by considering four different failure modes and the effect of wind attack angle. The proposed framework is demonstrated with a power distribution network in Oklahoma.

A full time scale resilience improvement strategy of distribution network under extreme weather

To improve the resilience of power distribution systems to fight natural disasters caused by extreme weather, A framework for enhancing disaster resilience at the full time scale is proposed. First, the influence of extreme weather events on power system components is analyzed, the failure probability of system components during extreme weather is evaluated. Then the vulnerability model of the components is established and the multi-stage performance response curve is used to quantify the resilience level of the network in each period. On this basis, framework of disaster resilience enhancement at full time scale before, during and after the occurrence of disasters is proposed. In the pre-failure phase, preventive strategies can be used to avert serious losses. Planning model of distributed power supply and tie line in distribution network based on active resilience lifting strategy during disaster. This paper presents the problem of flexible distribution system planning with resource redistribution and minimizing the cost of post-failure operation and load reduction as objective functions. After a fault occurs, maintenance measures for the faulty components could be optimized to restore the resilience of the distribution network as effectively as possible. Finally, the proposed strategy was applied to an ice and snow disaster in northern China in 2008 to study the effectiveness of the strategy in practical application.

Multisource Data Fusion Outage Location in Distribution Systems via Probabilistic Graphical Models

Efficient outage location is critical to enhancing the resilience of power distribution systems. However, accurate outage location requires combining massive <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">evidence</i> received from diverse data sources, including smart meter (SM) last gasp signals, customer trouble calls, social media messages, weather data, vegetation information, and physical parameters of the network. This is a computationally complex task due to the high dimensionality of data in distribution grids. In this paper, we propose a multi-source data fusion approach to locate outage events in partially observable distribution systems using Bayesian networks (BNs). A novel aspect of the proposed approach is that it takes multi-source evidence and the complex structure of distribution systems into account using a probabilistic graphical method. Our method can radically reduce the computational complexity of outage location inference in high-dimensional spaces. The graphical structure of the proposed BN is established based on the network’s topology and the causal relationship between random variables, such as the states of branches/customers and evidence. Utilizing this graphical model, accurate outage locations are obtained by leveraging a Gibbs sampling (GS) method, to infer the probabilities of de-energization for all branches. Compared with commonly-used exact inference methods that have exponential complexity in the size of the BN, GS quantifies the target conditional probability distributions in a timely manner. A case study of several real-world distribution systems is presented to validate the proposed method.

Stochastic pre-event preparation for enhancing resilience of distribution systems

Extreme weather events are the common causes for power supply interruptions and power outages in electrical distribution systems. Improving the distribution system and enhancing its resilience is becoming crucial due to the increased frequency of extreme weather events. Preparation and allocation of multiple flexible resources, such as mobile resources, fuel resources, and labor resources before extreme weather events can mitigate the effects of extreme weather events and enhance the resilience of power distribution systems. In this paper, a two-stage stochastic mixed-integer linear programming (SMILP) is proposed to optimize the preparation and resource allocation process for upcoming extreme weather events, which leads to faster and more efficient post-event restoration. The objective of the proposed two-stage SMILP is to maximize the served load and minimize the operating cost of flexible resources. The first stage in the optimization problem selects the amounts and locations of different resources. The second stage considers the operational constraints of the distribution system and repair crew scheduling constraints. The proposed stochastic pre-event preparation model is solved by a scenario decomposition method, Progressive Hedging (PH), to ease the computational complexity introduced by a large number of scenarios. Furthermore, to show the impact of solar photovoltaic (PV) generation on system resilience, three types of PV systems are considered during a power outage and the resilience improvements with different PV penetration levels are compared. Numerical results from simulations on a large-scale (more than 10,000 nodes) distribution feeder have been used to validate the effectiveness and scalability of the proposed method.

A resilience-oriented two-stage recovery method for power distribution system considering transportation network

In recent years, extreme weather events have resulted in a number of severe power outages and economic losses. It is becoming urgent to study approaches to enhance power distribution system resilience. This paper presents a two-stage optimization method for improving resilience of power distribution systems. The two-stage method is influenced by a transportation network, which considers the interdependence between traffic roads and overhead distribution lines. In the first stage, based on the Bayesian network and the Monte Carlo method, the locations of emergency stations, e.g., charging stations and repair stations are pre-positioned to improve the recovery speed of interrupted loads after disasters. Then the second stage is to co-optimize the dispatch of mobile energy storage systems and repair crews on transportation network to minimize the shedding of critical loads while considering operation constraints of power, voltage and power flow for power distribution system. The dispatch of mobile energy storage systems and repair crews are affected by the locations of emergency stations in the first stage. The route of mobile energy storage systems and repair crews on transportation network can be obtained from the results in the second stage. The proposed method is tested on a modified IEEE 33-bus distribution test system with a corresponding transportation network. Simulation results demonstrate the effectiveness of the proposed method in enhancing power distribution system resilience by pre-positioning emergency stations and co-dispatching mobile energy storage systems and repair crews.

Life cycle resilience quantification and enhancement of power distribution systems: A risk-based approach

Distribution systems in the US are commonly supported by wood utility poles. Since wood poles may experience substantial decay rates, current standards specify a strength-based maintenance program for pole replacement regardless of the poles’ vulnerability and importance in the system. While state-of-the-art methods have developed risk-based metrics to guide system hardening decisions, such metrics are analyzed for the current conditions of the system. In this context, the potential of a stochastic series of hazards over extended horizons and the subsequent effects on the resilience of systems have been largely neglected. To address these limitations, a risk-based methodology is proposed for quantifying the life cycle resilience of power distribution systems. To project pole vulnerability, a recursive approach is developed that captures the stochasticity of precedent failures and subsequent corrective actions over extended horizons. Furthermore, a risk-based replacement index called Expected Outage Reduction (EOR) is introduced that estimates the expected power outage reduction if an existing pole is replaced by a new pole. The application of the proposed method for life cycle resilience analysis and management of a realistic distribution system subjected to stochastic hurricanes indicates that EOR can improve the cumulative life cycle resilience by up to 22.3% over 70 years.

Artificial intelligence for resilience enhancement of power distribution systems

The threat of high impact low probability (HILP) events on power distribution system is substantial but quite unpredictable. Enhancing the resilience of power distribution grids against such events requires solving combinatorial planning and operational problems in stochastic spaces, as well as classifying system conditions based on high-dimensional input data. Since traditional mathematical solutions struggle with both uncertainty and the curse of dimensionality, data-driven techniques based on artificial intelligence (AI) are gaining momentum for solving those problems. This paper reviews AI capabilities for decision making in uncertain and high-dimensional spaces in general, and their particular application in resilient enhancement problems such as damage detection and estimation, cyber-physical anomaly detection, stochastic operation, and cyber security enhancement. Efficient data structures and AI approaches are suggested for each problem, which depend on the type of input signals, search-based or game-based structure of the problem, as well as the uncertainty sources involved. In particular, potential applications of supervised and unsupervised deep learning combined with Monte Carlo Tree Search and ε-greedy search is explored to find near optimal operational decisions that help enhance the resilience of power distribution systems.

Distribution System Resilience Enhancement via Mobile Emergency Generators

Natural calamities always have been a serious threat to energy systems. In this regard, this paper constitutes a stochastic mixed integer linear programming (SMILP) model to enhance the resilience of power distribution systems to deal with disastrous events. In particular, the proposed model is developed to enhance both survivability and restoration capability of distribution systems. In this regard, to increase the preparedness of the power distribution system, the system operator reconfigures the network by utilizing remote-control switches (RCSs), manual switches (MSs), and distributed generations (DGs) before the natural calamity hits. The proposed model contemplates the traveling time of crew teams to the MS sites in the transportation system in order to achieve the best switching sequence. In addition, it pre-positions the crews and mobile emergency generators (MEGs) in staging locations to hasten the likely post-disturbance operations. To do so, likely post-event operations are included in the model using the scenario generation technique. To validate the performance of the developed model a distribution system is employed. The results of simulations confirm the effectiveness of the proposed approach in declining the interruption of electric energy for customers.

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.

The sensitivity of electric power infrastructure resilience to the spatial distribution of disaster impacts

Credibly assessing the resilience of energy infrastructure in the face of natural disasters is a salient concern facing researchers, government officials, and community members. Here, we explore the influence of the spatial distribution of disruptions due to hurricanes and other natural hazards on the resilience of power distribution systems. We find that incorporating information about the spatial distribution of disaster impacts has significant implications for estimating infrastructure resilience. Specifically, the uncertainty associated with estimated infrastructure resilience metrics to spatially distributed disaster-induced disruptions is much higher than determined by previous methods. We present a case study of an electric power distribution grid impacted by a major landfalling hurricane. We show that improved characterizations of disaster disruption drastically change the way in which the grid recovers, including changes in emergent system properties such as antifragility. Our work demonstrates that previous methods for estimating critical infrastructure resilience may be overstating the confidence associated with estimated network recoveries due to the lack of consideration of the spatial structure of disruptions.

Quantifying Power Distribution System Resiliency Using Code-Based Metric

It is essential to improve the resiliency of power distribution systems (PDS) given the increase in extreme weather events, number of malicious threats, and consumers’ need for higher reliability. This paper provides a formal approach to evaluate the operational resiliency of PDS and quantify the resiliency of a system using a code-based metric. A combination of steady state and dynamic simulation tools is used to determine the resiliency metric. Dynamic simulation tools help analyzing the impact of short-term events, which might affect operational resiliency in the long term. A dynamic optimization algorithm for changing operating criteria to increase the sustainability of the most critical loads has been proposed. The proposed theoretical approach is validated using a simple PDS model and simulation results demonstrate the ability to quantify the resiliency using the proposed code-based metric. The time-dependent quantification of resiliency has been demonstrated on a test system of two connected Consortium of Electric Reliability Technology Solutions (CERTS) microgrids.

Reliability-based assessment of climatic adaptation for the increased resiliency of power distribution systems subjected to hurricanes

Power distribution systems are vulnerable to hurricanes as has been documented in recent years. Hurricane intensity or/and frequency may change as a result of increased CO2 emissions. This paper proposes six climatic adaptation strategies for timber distribution poles that may aid in mitigating the hurricane damage costs that may be expected to increase because of global climate change. The effectiveness of adaptation is assessed through a life-cycle cost analysis, which includes direct cost (e.g. cost of pole replacement, maintenance, and adaptation) and indirect cost (e.g. cost of power outage to customers). The viability of the adaptation strategies is examined considering three CO2 emission scenarios. Furthermore, the scenario of no climate change is considered in this paper to show the applicability the proposed framework for hurricane risk mitigation under current conditions (i.e. wind speeds remain stationary). This paper finds that certain adaptation measures can effectively reduce costs, resulting in more resilient power distribution systems.

Distribution Systems: Reliable But Not Resilient? [In My View

Examines the topic of resiliency of power distribution systems in the face of catastrophic climate disturbances.

Factors in the resilience of electrical power distribution infrastructures

One measure of the resilience of any dynamical system is the speed of return to equilibrium following perturbation. In electrical power distribution systems this may be approximated by the duration of unscheduled outages due to failure of the distribution system (i.e., excluding outages due to failure of the generation or transmission systems). We hypothesize that the resilience of power distribution systems depends on two main factors. One is the power distribution infrastructure, the biophysical environment within which it operates, and interactions between the two. The other is the priority given to restoration by the power company, and the effectiveness of the power company’s response. To test this we modeled outage duration in the residential electrical power distribution system in part of the City of Phoenix, Arizona between 2002 and 2005. We found that while the type of infrastructure did not have a significant effect on outage duration, the interaction between infrastructure (overhead lines) and the biophysical environment (vegetation) did. We also found strong evidence that proximity to particular high priority emergency assets (i.e., hospitals) confers resilience on residential distribution systems. More generally, residential outage duration was found to be most spatially dependent up to around 1000 feet from an outage location. Overall, a spatial outage duration model provided a better fit to the data than a non-spatial model.