Resilience Of Distribution Systems Research Articles

Post-event electric taxis dispatch for enhancing resilience of distribution systems

With the frequent occurrences of high-impact-low-probability (HILP) events such as typhoons, lightning strikes, and cyber attacks, people have the increasingly need of raising distribution network’s (DN) resilience. Deploying backup sources into the system can effectively improve the resilience of DNs. With the development of transportation electrification, the number of electric taxis (ETs) in urban transit is now growing. Besides, Vehicle-to-Grid (V2G) technology has also developed vigorously. Therefore, ETs can be dispatched to charging stations under unified command to feed power back to the grid when HILP events happen. To this end, we establish an optimization model for the post-event recovery of the DN using ETs. In this model, we combine the energy provided by V2G stations with the existing distributed generators and energy storage in the grid to restore the outage areas jointly. Topology reconfiguration technique and power flow balance of DN have also been fully considered in the modeling process. The whole grid is divided into several microgrids and powered by different sources. The multi-source multi-region power restoration method improves the stability of the DN restoration. It is easier to control the voltage and power quality. The method we proposed is tested on the modified IEEE 33-bus system. The simulation results verify the effectiveness and efficiency of our work.

Active distribution system resilience quantification and enhancement through multi-microgrid and mobile energy storage

The functional capability of the active distribution network is continually challenged by extreme weather and unforeseen events. A complete resilience quantification framework is required to assess the resilience of a distribution system. With this objective, a framework for demonstrating resilience enhancement through the utilization of multi-microgrids (MMGs) and mobile energy storage in extreme operating conditions is developed in this paper. In the proposed framework, four resilience indices, that is, withstand, recovery, adapt, and prevent (WRAP), are introduced. Withstand index signifies the coping capability after the event, where the MG plays a vital role. The recovery index measures the restoration after the event ends through the system reconfiguration using MGs, tie-lines, and mobile energy storage. The adapt index shows the stability of the system before and during the events. Finally, the prevent index suggests how different resources are important and responsible for fast recovery and minimizing consequences. WRAP, as a resilience quantification framework, is formulated in this study, and indices are quantified and enhanced through the MMG and mobile energy storages. The IEEE 33-bus system is considered for this study, and simulation is performed with different scenarios and measured resilience indices. It is found that appropriate reconfiguration through the use of MMG, tie-lines, and mobile storages can remarkably enhance the resilience of a distribution system.

Developing a framework to enhance the seismic resilience of the electricity distribution system feeding the healthcare system

Delivering electricity to hospitals is vital for maintaining hospitals' functionality. Given the seismic vulnerability of electrical substations, providing effective rehabilitation plans for repairing these systems seems necessary for a resilient healthcare system. Studies concerning interdependent systems' functionality have not thoroughly addressed the interdependency of healthcare and electrical distribution systems. A framework to identify the effectiveness of different post-disaster rehabilitation plans for electrical substations to enhance the resilience of the healthcare system electricity is developed in this study, which increases the recoverability of the healthcare system after an earthquake. The vulnerability analysis for electrical facilities is done using modified fragility functions, considering the effects of aging as well. The geographical information system is used for assigning patients to hospitals given several patients' decision criteria. The framework uses a stochastic discrete event simulation to determine the effectiveness of different rehabilitation scenarios for five days after an earthquake occurs while using patients’ waiting time as the performance measure. Finally, the proposed framework is applied to Ghazvin city in the north-west of Iran. The results show that the best scenario would increase system resilience by 85%, highlighting the importance of identifying and implementing the best scenario.

Resilient scheduling of MESSs and RCs for distribution system restoration considering the forced cut-off of wind power

Mobile energy storage systems (MESSs), wind power and repair crews (RCs) are usually coordinated to restore distribution systems damaged by hurricanes. However, the forced cut-off of wind power occurs as wind speed exceeds the cut-off value, which can lead to sudden power shortage. To address this challenge, this paper proposes a restoration approach considering wind power cut-off to enhance distribution system resilience. The resilient coordination approach takes advantage of MESSs, RCs, network reconfiguration and distributed generators (DGs) to restore the outage distribution system. Considering the uncertainty of wind power cut-off, the proposed method is formulated as a two-stage model on two timescales. On the long timescale, MESSs, RCs, DGs and line switches are robustly optimized to reduce critical load loss considering wind power cut-off. On the short timescale, the output decisions of MESSs and DGs are re-dispatched to compensate for the first-stage strategies after uncertainties realization. In addition, the nonlinear output curve of wind power is linearized in mixed integer linear programming form to facilitate the solution of the robust model. Finally, the modified IEEE-69 distribution system is employed to verify the proposed method. Simulation results show that the proposed method can increase the amount of load restoration by 24.1%.

Distribution system resilience enhancement through resilience-oriented optimal scheduling of multi-microgrids considering normal and emergency conditions interlink utilizing multi-objective programming

One of the major concerns of power systems is dealing with extreme power outages during severe events and consequently, its repercussions on the lives of people in society and social aspects. Therefore, the optimal application of microgrids that can be identified as a smart city which is equipped with renewable energies and distributed energy generation is considered with the aim of distribution system resilience improvement. In this study, an innovative modeling approach is formulated as resilience-oriented multi-objective two-stage scenario-based stochastic modeling based on mixed-integer-linear-programming. Meanwhile, innovative modeling is executed based on disaster effect on human society response (HSR) according to the presence of plug-in-electric-vehicles as clean transportation systems. Using a sensitivity analysis on the maximum allowable energy that can be stored by electric-vehicle owners, according to a conservative decision, it is possible to charge electric vehicles up to 40% in a specific emergency situation, without significantly jeopardizing the resilience of microgrids and distribution systems. In this regard, on average, the resilience metric of MG1, MG2, and MG3 improves by 3.94%, 11.3%, and 4.4%, respectively, compared to the situation where electric vehicles are allowed to charge up to 100% of their battery capacity. More comprehensive studies are also investigated in results.

AWR: Anticipate, Withstand, and Recover Resilience Metric for Operational and Planning Decision Support in Electric Distribution System

With the increasing number of catastrophic weather events and resulting disruption in the energy supply to essential loads, the distribution grid operators’ focus has shifted from reliability to resiliency against high impact, low-frequency events. Given the enhanced automation to enable the smarter grid, there are several assets/resources at the disposal of electric utilities to enhances resiliency. However, with a lack of comprehensive resilience tools for informed operational decisions and planning, utilities face a challenge in investing and prioritizing operational control actions for resiliency. The distribution system resilience is also highly dependent on system attributes, including network, control, generating resources, location of loads and resources, as well as the progression of an extreme event. In this work, we present a novel multi-stage resilience measure called the Anticipate-Withstand-Recover (AWR) metrics. The AWR metrics are based on integrating relevant ‘system characteristics based factors’, before, during, and after the extreme event. The developed methodology utilizes a pragmatic and flexible approach by adopting concepts from the national emergency preparedness paradigm, proactive and reactive controls of grid assets, graph theory with system and component constraints, and multi-criteria decision-making process. The proposed metrics are applied to provide decision support for a) the operational resilience and b) planning investments, and validated for a real system in Alaska during the entirety of the event progression.

Leveraging a micro synchrophasor for fault detection in a renewable based smart grid—A machine learned sustainable solution with cyber-attack resiliency

The advent of renewable distributed generation has led to the rethinking of the conventional protection systems, especially during fault. A sustainable fault detection algorithm is needed to enhance the distribution system's resiliency and safety. Keeping this point of view, the paper presents a micro synchrophasor or µPMU-based fault detection algorithm with fewer probabilities of cyber-attack. In the µPMU, after obtaining the current signals, the three-phase sequence components are evaluated and the angular sum of the positive and zero sequences is recorded. The maximum angular sum is fed to a trained machine learning (random forest) classifier for fault detection. The algorithm has a high resistance to cyber-attacks and is strongly immune to noise. The method has 98.91% accuracy, 99.89% precision and exhibits a detection time of 8.5 ms. The method is cost effective as it leverages a µPMU for fault detection algorithm, reducing the need of additional hardware and software. The method also proves to be superior than other fault detection methods in terms of accuracy, precision, detection time and the capability to handle noise and cyber-attacks. All the simulations are done in MATLAB/SIMULINK for a renewable based IEEE 13 node distribution test feeder.

Enhance the resilience of distribution system against direct and indirect effects of extreme winds using battery energy storage systems

The primary purpose of this paper is to enhance the resilience of the distribution system (DS) against the direct and indirect effects of extreme winds (EWs), considering the variety of batteries. Besides, the second objective is to use fines to prioritize the supply of critical loads in post-disaster conditions. This study attempts to use fragility curves for the failure probability of the electric pole (EP) against EWs and locate the batteries using the battery placement index. The optimal amount of power and energy of batteries for various technologies is achieved through the curtailed critical loads' reduction objective function. The two-objective function has been used in terms of resilience enhancement and penalty cost reduction of the curtailed priority loads. For better illustration, the DS of Urmia city is conducted as a case study. Finally, the analysis on a case study is carried out and results, are obtained. The results show an increase in the resilience index while the cost of curtailed critical loads penalties falls. Furthermore, a resilient DS is obtained by using different battery technologies against the destructive effects of EWs. This resilient system benefits from the load prioritization method in order to supply loads based on their importance in system improvement after the disaster.

Microgrids and Resilience: A Review

The occurrence of large-scale disturbances is increasing at an alarming rate throughout the world. As a consequence of this trend, a primary concern of today’s power system is to enhance its resilience against low-probability, high-impact events. In this regard, microgrids, as the smart grid’s building blocks, offer promising approaches toward achieving higher levels of distribution system resilience by accommodating and integrating various distributed energy resources. Accordingly, microgrid-based techniques have been the focus of a growing body of research seeking a more resilient power system. These methods mainly rely on the stand-alone operation of microgrids to supply loads locally in case of extreme events. The objective of this paper is to present an updated comprehensive review of the literature on two main categories of microgrid-based resilience enhancement approaches in distribution systems: 1) optimal microgrid formation and 2) optimal microgrid scheduling and energy management. Distinctive from other review papers, this article systematically surveys the research studies under multiple well-sectionalized features, such as various technologies, techniques, models, constraints, and concepts for each of the above two categories. These features include but are not limited to networked microgrids, demand response programs and electric vehicles scheduling, multi-energy microgrids, dynamic optimization schemes, control schemes, communication resilience, hybrid microgrids, and mobile energy resources. Additionally, a comprehensive introduction to resilience definitions and assessment methods, microgrid components, architectures, and control schemes, as well as, sources of uncertainty is provided.

A Risk-Based Framework to Improve Distribution System Resilience Against Earthquakes

A Risk-Based Framework to Improve Distribution System Resilience Against Earthquakes

Pre-Event Two-Stage Proactive Control for Enhanced Distribution System Resiliency

Recent advancements in prediction technologies have motivated the power distribution utilities to actively utilize forecasts to minimize the impact of high-impact low frequency (HILF) events. Accurate forecasting coupled with the past operational experiences potentially enables pre-event control, aiming to minimize the impact on the system in a network with finitely large numbers of automatic and manually operated control devices. Impacts to the critical loads (CLs) are minimized when the network is appropriately reconfigured before the event strikes. However, predictions are not perfectly accurate, and given limited accuracy with ever-changing weather forecasts, control actions are required to be constantly updated until the event has passed through. Once the events are predicted with high enough confidence and the necessary reconfiguration strategy has been identified, the manually operable switches must be configured significantly ahead of event landfall to ensure the operational crews’ safe return from potentially hazardous zones. Given limited resource availability and requisite manual switching operations, some loads would also have to be deenergized, which could be alleviated if those loads remain connected through remotely operable switches until minutes before the event makes the landfall. The proposed two-stage control framework with a necessary mathematical formulation facilitates the same. Furthermore, the proposed framework facilitates continuous corrective action based on available lead time. The developed proactive control framework is demonstrated using a modified IEEE 123-Node system model and a real-world isolated μ-grid based on the Cordova Electric Cooperative system with superior performance results.

A Novel Pre-Storm Island Formation Framework to Improve Distribution System Resilience Considering Tree-Caused Failures

This paper presents a new framework for island formation prior to windstorms, which considers tree-caused failures of distribution networks. In the proposed framework, both direct and indirect effects of windstorms on distribution lines are quantified. Thus, a novel discrete Markov chain model is proposed for representing the failure modes of trees in each time interval of windstorm duration. This model categorizes “healthy”, “uprooted”, “stem breakage”, and “branch breakage” states of a tree. In addition, a new line-tree interaction model is presented for calculating tree-caused failure probability of overhead lines. The results of the proposed Markov model are taken as inputs by the developed line-tree interaction model. In these models, the different characteristics of windstorms are taken into account. Tree vulnerability to windstorms is characterized by different factors such as their species, height, and critical wind speeds. Windstorm duration is sectionalized into multiple time intervals, and the proposed models are applied to trees and distribution system components in each interval. Moreover, the interdependency between the intervals is captured by the Markov model. The results of the models are used by an optimization model, thereby dividing a distribution system into multiple islands before storm onset. Subsequently, the framework is extended as a two-stage stochastic optimization problem to address the uncertainties of loads. In addition, this framework considers the allocation of mobile emergency resources. The proposed models are implemented on the IEEE 33- and 123-bus test systems, as well as a practical distribution feeder, and their effectiveness is demonstrated through several case studies.

Scheduling of Separable Mobile Energy Storage Systems With Mobile Generators and Fuel Tankers to Boost Distribution System Resilience

Mobile energy resources (MERs) have been shown to boost DS resilience effectively in recent years. In this paper, we propose a novel idea, the separable mobile energy storage system (SMESS), as an attempt to further extend the flexibility of MER applications. “Separable” denotes that the carrier and the energy storage modules are treated as independent parts, which allows the carrier to carry multiple modules and scatter them independently throughout the DS. The constraints for scheduling SMESSs involving carriers and modules are derived based upon the interactive behavior among them and the DS. In addition, the fuel delivery issue of feeding mobile emergency generators (MEGs), which was usually bypassed in previous studies involving the scheduling of MEGs, is also considered and modeled. SMESSs, MEGs, and fuel tankers (FTs) are then jointly routed and scheduled, along with the dynamic DS reconfiguration, for DS service restoration by integrating them in a mixed-integer linear programming (MILP) model. Finally, the test is conducted on the modified IEEE test systems, and results verify the effectiveness of the model in boosting DS resilience.

Improving Distribution System Resilience by Undergrounding Lines and Deploying Mobile Generators

Improving Distribution System Resilience by Undergrounding Lines and Deploying Mobile Generators

Coordinating multiple resources for enhancing distribution system resilience against extreme weather events considering multi-stage coupling

Under extreme weather events with a certain warning time, the prevention and restoration of distribution systems (DSs) involve multiple processes, including pre-event prevention, DS degradation, and post-event fault isolation and service restoration. Multiple resources are coupled and interact with each other in the above processes, which makes it a complicated task to develop resilience enhancement strategies for DSs. In this paper, a multi-resource DS resilience enhancement method is proposed to assist DS operators in making decisions against extreme weather events. On one hand, in the proposed method, the prevention and restoration of DSs are formulated as multiple stages based on the physical process of DSs when affected by extreme weather events. On the other hand, distributed generations (DGs), mobile emergency generators (MEGs), remote-controlled switches (RCSs), manual switches (MSs) and operation crew teams are fully coordinated to enhance the resilience of DSs against extreme weather events. And multiple coupling relationships are considered, including the topology coupling relationships among each stage, the spatiotemporal coupling relationships of mobile resources, and the switching coupling relationships between MSs and operation crew teams. The proposed method is mathematically formulated as a mixed-integer linear programming (MILP) model. Several cases are introduced to verify the effectiveness of the proposed method. The results demonstrate that the degradation of the DS can be effectively reduced by the coordination of network pre-reconfiguration and resources pre-positioning before events. The recovery process can be significantly speeded up by the coordination of the network reconfiguration and the real-time dispatching of MEGs and operation crew teams after events.

Enhancing distribution system resilience against extreme weather events: Concept review, algorithm summary, and future vision

Distribution system infrastructures are vulnerable to extreme weather events, such as hurricane, ice coating, flood, and wildfires. Resilience is a measure of the system's ability to prevent the damage during extreme events and to recover the system function after such events. With the economic development, it becomes increasingly important for power utilities to maintain critical loads always in service and to reduce the unserved energy of all loads. If many distribution system equipments are damaged, the utility companies dispatch static or mobile distributed energy resources, reconfigure the network topology in order to restore the islanded sections of the distribution system. In recent years, a large number of studies have been done on operation and planning strategies to enhance the distribution system resilience. This review paper introduces the background of resilient distribution system. Then, it makes a comprehensive summary of the resources for resilience enhancement, the mathematical model of operation and planning algorithms. In particular, the objective function, mathematical formulation, decision variables, and solution algorithm of each study are compared. Finally, the roadmap of resilient distribution system is extracted and the future research direction on this topic is proposed.

Optimal planning of mobile emergency generators of resilient distribution system

Mobile emergency generators(MEGs) can accelerate disaster recovery and enhance the resiliency of the distribution system (SD). However, the resilience-oriented planning of MEGs has not been fully investigated. This paper proposes a method to achieve the resilience-oriented optimal plan of different kinds of MEGs via a two-stage stochastic optimization model. In the first stage, DS operators invest in various types of MEGs and allocate them in the second stage according to the line damage situation. A distributionally robust model based on confidence sets is used to reduce the number of scenario samples. The proposed model is transformed into a second-order cone program that commercial solvers can solve after convex relaxation. The case studies of a 33-bus test system demonstrate the effectiveness of the proposed method for scenario reduction and optimization of MEGs investment and allocation strategy.

AC/DC hybrid distribution network reconfiguration with microgrid formation using multi-agent soft actor-critic

Recent extreme events trigger tremendous concerns on distribution system resilience. Meanwhile, high penetration of inverter-interfaced distributed generators (DGs) and diversified source and load mix facilitate the development and implementation of hybrid AC and DC distribution networks (HDNs). This paper proposes a deep reinforcement learning-based (DRL) approach for distribution network reconfiguration with microgrid formation in face of extreme events. The proposed optimization model facilitates critical service restoration by forming isolated sections nested inside the HDNs when severe power outages occur (e.g., disconnection from the main grid). The operational characteristics of isolated HDNs (e.g., droop-controlled nodes in AC and DC sections, lack of slack buses in autonomous operation, etc.) are considered. To reduce the computational burden, a multi-agent soft actor-critic (MA-SAC) approach is developed to solve the proposed reconfiguration problem, where multiple agents coordinately control circuit breakers to sectionalize the HDNs and can cater for different system states and scales. Simulation tests are conducted in two test systems to verify the validity of the proposed approach.

Segment-based resilience response and intervention evaluation of water distribution systems

Abstract The consideration of isolation valves and segments is essential for evaluating the water service and resilience of water distribution systems when shutdowns are required under pipe failure. However, little work has been done on assessing the resilience response and intervention based on segments. This study investigates the impact of intervention (valve density and protection of critical segment) and response (recovery time and recovery sequence) on system resilience taking valve layout into consideration. An algorithm to identify segments based on the graph theory is proposed. Resilience is quantified using the satisfactory rate of the water supply demand. Critical segments are ranked based on resilience analysis. The resilience evaluation method is applied to a case study network. It is found that valve optimization can significantly reduce the number of valves without considerably decreasing the resilience performance. Valve density and the protection of critical segment can reduce the severity of pipe failure, while efficient recovery response can reduce the severity and shorten the duration of pipe failure simultaneously. The criticality of segments depends on the segment location and hydraulic interdependency among segments.

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.