Resilience Of Distribution Systems Research Articles

Power distribution system resilience to extreme dust storms: probabilistic impact assessment and hardening planning

ABSTRACT Extreme dust storms (EDSs) are high-impact low-probability natural disasters, and their occurrence in humid climates can damage the power distribution systems (PDSs) as a critical infrastructure. In this paper, proposed a bi-level stochastic framework for simultaneously hardening substations and distribution lines. In the first level, total capital cost is addressed for PDS hardening under the financial constraints, while in the second level, the expected operating costs are minimized in the case of an EDS under the operating constraints. In the proposed model, the location of remote-controled switches (RCSs) is determined based on the PDS hardening planning results, and the decisions at each level depend on the planning results of the other level. The simulation results at different budget levels show that simultaneous hardening planning of distribution lines and substations considering network reconfiguration can not only reduce expected operating costs, but also can reducing total capital cost to PDS resilience enhancement.

Reconceptualizing Reliability Indices as Metrics to Quantify Power Distribution System Resilience

Reconceptualizing Reliability Indices as Metrics to Quantify Power Distribution System Resilience

Strengthening distribution systems after earthquakes with a new analytical model

This study highlights that earthquakes can lead to short-circuit faults, power outages, and equipment damage in distribution systems. Based on the observation of the effects of two major earthquakes that occurred in Turkey (February 6, 2023), a new analytical model for strengthening distribution systems against natural disasters is proposed in this study. In this model, the distribution system is analyzed, and the fragility index of the buses is determined. The technical condition of the distribution system is assessed using fragility indexes, and integration and sizing of Distributed Generation (DG) and energy storage units are performed to ensure energy continuity after an earthquake. This fragility index takes into account both the operation of the distribution system in island (microgrid) mode and its operation connected to the main grid. In addition, with the fault index recommended within the scope of the analytical model, the vulnerable points of the distribution system against short circuit faults are analyzed and short circuit fault currents are limited at these points. Thus, the resilience of distribution systems against natural disasters such as earthquakes can be enhanced with the proposed analytical model.

Resilience-oriented planning for active distribution systems: A probabilistic approach considering regional weather profiles

Recent experiences with increasing climatic variability highlighted the importance of enhanced power distribution system resilience. Conventional methods of power distribution system planning usually focus on persistentinvestments associated with predicted faulty events, aiming to enhance the overall system reliability. Improving distribution system resilience in response to extreme weather events involves reduction of impact of the events having varying probability of occurrence. The probability of an extreme event varies significantly by location due to a combination of geographical, climatological, and meteorological factors. Thus, instead of requiring persistent investments, the solutions for resilience-oriented system upgrades should be guided by the potential effects and the probability of occurrence of extreme weather events in a specific area. To achieve this objective, the present paper proposes a two-stage stochastic mixed-integer linear programming (MILP) model based on regional weather profiles for the long-term resilience planning of distribution systems against extreme weather events. In the planning stage, investment decisions are made regarding overhead distribution line hardening and the distributed generation (DG) installation. During restoration process in the operational stage, dynamic microgrid formation is carried out for advanced distribution grid operations to minimize the cost corresponding to loss of load. The inclusion of regional weather profiles into planning objectives provide added flexibility to the network operators for analysing the trade-off between investment cost and load loss cost in resilient distribution system planning strategies.

An Improved Secondary Control Strategy for Dynamic Boundary Microgrids toward Resilient Distribution Systems

In order to achieve the flexible and efficient utilization of distributed energy resources, microgrids (MGs) can enhance the self-healing capability of distribution systems. Conventional primary droop control in microgrids exhibits deviations in voltage and frequency and lacks research on voltage–frequency control during network reconfiguration. Therefore, this paper investigates the control strategy of secondary control for voltage and frequency during the process of reconstructing distribution networks to operate in the form of microgrids after faults. Firstly, the mathematical model of three-phase voltage-source inverters with droop control is analyzed. Then, inspired by the generator’s secondary frequency control strategy, an improved secondary control method based on droop control is proposed, and the effectiveness of the improved droop control strategy is verified. Finally, simulation examples of distribution networks with various dynamic boundary topology changes are designed to simulate a relatively stable control result, validating the proposed strategy.

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.

Dynamic microgrid formation for resilient distribution systems considering large-scale deployment of mobile energy resources

Microgrids (MGs) are promising solutions to improve power distribution system (PDS) resilience against natural disasters. However, the existing MG formation approaches based on the linearized Distflow (LinDistflow) model always demand MG roots and their corresponding topologies. This can result in an increased number of variables and constraints in the optimization problem, and deteriorate their computational performance. To this end, an adaptive LinDistflow model is proposed based on the single commodity flow model in graph theory in this paper. Specifically, we show that active and reactive powers can be represented as commodities, which are sent from one node to each of its adjacent nodes in the graph. Then, the power flow and nodal voltage calculation based on the commodity flow only requires adjacent node information of the original topology rather than various MG topologies caused by the dynamic deployment of mobile energy resources (MERs). Furthermore, by incorporating the adaptive LinDistflow model as constraints, a dynamic MG formation approach is proposed for resilient load restoration considering large-scale MER deployment. The problem is formulated as a mixed-integer nonlinear programming problem (MINLP). A linearization technique is proposed based on the propositional logic constraints. It employs the propositional logic that partitions the solution space into two separated regions. Accordingly, the region that the solution lies in can be selected linearly. The effectiveness of the proposed approach is demonstrated based on the IEEE 37-Node, 123-Node and 8500-Node Test Feeders.

Optimizing Distribution System Resilience in Extreme Weather Using Prosumer-Centric Microgrids with Integrated Distributed Energy Resources and Battery Electric Vehicles

Electric power networks face vulnerabilities from various hazards, including extreme weather and natural disasters, resulting in prolonged outages and service disruptions. This paper proposes prosumer-centric networked electrical microgrids as a solution. EMGs integrate DERs, like SPV panels, WTs, BESSs, and BEVs, to form autonomous microgrids capable of operating independently during grid disruptions. The SMA was used to identify the appropriate allocation of DERs and BEVs to improve the resilience of the system. Prosumers, acting as both producers and consumers, play a crucial role by generating and sharing electricity within the microgrid. BEVs act as mobile energy storage units during emergencies. Load management and demand response strategies prioritize the energy needs for essential facilities, ensuring uninterrupted operation during adverse weather. Robust communication and control systems improve the emergency coordination and response. The resilience analysis focused on two case studies: moderate and severe damage, both under varying weather conditions. Simulations and experiments assessed the microgrid performance with different levels of DERs and demand. By testing on the IEEE 69-bus RDS, evaluated the EMGs’ strengths and limitations, demonstrating their potential to enhance distribution grid resilience against natural disasters.

Distribution system resilience improvement against hurricanes: optimal line hardening according to reconfiguration potentials

AbstractIn recent years, there has been a notable increase in the frequency and intensity of natural disasters related to global warming. Large‐scale blackouts caused by natural disasters demonstrate the extreme vulnerability of power systems to these devastations. Resilience planning is the key to preparing power systems for natural disasters by enhancing the vital infrastructure's robustness. This paper proposes a new resilience improvement framework based on resiliency assessment and optimal hardening to improve the distribution system's resiliency against hurricanes. The main goal is to find the optimal distribution system line hardening solution according to reconfiguration potentials after the hurricane to minimize the distribution system cost of energy not supplied. Fragility curves and Monte Carlo simulations are used for the distribution system resilience assessment. Poles and conductors are vulnerable components of distribution system lines; therefore, two hardening strategies have been outlined using measures like replacing old poles with new poles, upgrading pole classes, and vegetation management. This method is modelled as an optimization program considering budget limitations and load priorities and implemented by a genetic algorithm on the IEEE 33‐bus standard network. The results show that optimal line hardening according to network reconfiguration potentials significantly increased the distribution system's resilience.

Postdisaster Routing of Movable Energy Resources for Enhanced Distribution System Resilience: A Deep Reinforcement Learning-Based Approach

Postdisaster Routing of Movable Energy Resources for Enhanced Distribution System Resilience: A Deep Reinforcement Learning-Based Approach

Resilience-Oriented Coordination of Networked Microgrids: A Shapley Q-Value Learning Approach

High-impact and low-probability extreme events have occurred more frequently than before because of rapid climate change, which can seriously damage distribution systems. However, conventional distribution management can be dysfunctional after an event, destroying its centralized supervision towards resilience enhancement. In this context, networked microgrids (NMGs) with distributed energy resources provide a viable solution for the resilience enhancement of distribution systems. Existing literature tends to employ model-based optimization approaches for resilient operations of NMGs, which require complete system models and can be time-consuming. To address these challenges, this paper suggests a decentralized framework for resilience-oriented coordination of NMGs and proposes a novel multi-agent reinforcement learning (MARL) method to solve it. Specifically, the proposed MARL method develops an efficient credit assignment scheme for NMGs to learn their contributions to the distribution system resilience via the Shapley Q-value technique with more efficient resilience enhancement. Case studies based on two modified IEEE 15- and 69-bus distribution networks are conducted to validate the effectiveness of the proposed MARL method in enabling effective coordination among NMGs and providing a high resilience level.

Two-stage decision-dependent demand response driven by TCLs for distribution system resilience enhancement

Service restoration is a critical function of the self-healing distribution system to cope with extreme events. Cold load pickup (CLPU) caused by thermostatically controlled loads (TCLs) requires extra generation power to retore loads, slowing down the distribution system restoration process. Meanwhile, increasing renewable energy brings the supply-demand imbalance because of the power variability and may cause voltage or current violations. Accordingly, a two-stage decision-dependent demand response (DR) methodology is proposed to address the two barriers to distribution system resilience enhancement. Specifically, two load control strategies are developed in the first-stage DR to regulate TCLs for CLPU mitigation, which reduces the additional power requirement when re-energized. In the second-stage DR, the restored TCLs that can be controlled to provide flexibility are characterized as virtual energy storage (VES) for managing renewable energy variability. In this way, the required power generation is reduced, and the power fluctuation problem caused by renewable energy can be well addressed. It helps to speed up the load restoration and keep the distribution system operating securely. Moreover, the load profiles with CLPU mitigation control, VES parameters and VES availability time, depend on the re-energization time. In other words, load restoration decisions determine the two-stage DR. This is the most important finding of this paper and explicitly formulated in the load restoration model for accurate load characterization. The load restoration model is described by a mixed integer second-order cone programming (MISOCP) problem and solved using MATLAB 2021 with Gurobi solver. Case studies show that the proposed methodology can significantly mitigate CLPU and restore more loads. The decision-dependent behaviors of the two-stage DR are also validated.

State–Space Modelling and Stability Analysis of Solid-State Transformers for Resilient Distribution Systems

Power grids are currently undergoing a significant transition to enhance operational resilience and elevate power quality issues, aiming to achieve universal access to electricity. In the last few decades, the energy sector has witnessed substantial shifts toward modernizing distribution systems by integrating innovative technologies. Among the innovations, the solid-state transformer (SST) is referred to as a promising technology due to its flexible power control (better reliability) and high efficacy (by decreasing losses) compared with traditional transformers. The design of SST has combined three-stage converters, i.e., the input, isolation, and output stages. The key objective of this design is to implement a modern power distribution system to make it a more intelligent and reliable device in practice. As the power converters are used in SST, they exhibit non-linear behavior and can introduce high-frequency components, making stability more challenging for the system. Besides, the stability issue can be even more complicated by integrating the distributed energy resources into the distribution system. Thus, the stability of SST must be measured prior to /during the design. To determine stability, state-space modeling, and its controller design are important, which this paper explains in detail. Indeed, the system’s stability is measured through the controllability and observability test. Further, the stability analysis is performed using frequency and time-domain diagrams: the Bode plot, Nyquist plot, Nichols chart, Root locus, pole-zero plot, and Eigen plot. Finally, the SST Simulink model is tested and validated through real-time digital simulation using the OPALRT simulator to show its effectiveness and applicability. The stability performance of the proposed SST is evaluated and shows the effectiveness of the controller design of each converter circuit.

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.

Flexible resource optimal allocation strategy for distribution system resilience improvement under failure uncertainty

Extreme disasters have led to huge economical losses to the power system and posed a huge threat to the stable supply of electric energy. Especially for traditional passive distribution network systems, failures of transmission lines, towers, and other equipment will cause large-scale power outages and huge economic losses. In order to improve the ability of the power system to deal with extreme weather disasters, this paper proposes an optimal configuration strategy for the distribution network backup power resource oriented to resilience improvement, considering the uncertainty of equipment failure caused by disasters in the case of a known disaster range. The location and capacity of backup resources can be properly configured to reduce the load shedding of the power distribution network during the disaster process, thereby improving the resilience of the distribution network. The method proposed in this paper is modeled as a two-stage stochastic programming, which includes mix-integer constraints. In the first stage, the optimization problem confirms the capacity and location of the flexible resources. In the second stage, the operational constraints and costs of load shedding are considered. The resilience enhancement method is verified on the improved IEEE-33 node system, and the result shows that the method proposed in this paper can effectively enhance the resilience of the urban power network.

Stochastic Planning for Power Distribution System Resilience Enhancement Against Earthquakes Considering Mobile Energy Resources

Stochastic Planning for Power Distribution System Resilience Enhancement Against Earthquakes Considering Mobile Energy Resources

Incentive Control Signals to Support Resilient Distribution System Operations

Incentive Control Signals to Support Resilient Distribution System Operations

Evaluating the impact of coordinated multiple mobile emergency resources on distribution system resilience improvement

Extreme weather events in recent years have resulted in numerous incidents of major power outages and significant economic losses. Therefore, it is crucial to find potential solutions that can improve the resilience of the distribution system (DS). In this regard, this paper proposes a two-stage resilient restoration model for distribution systems utilizing Electric Vehicles (EVs) and Mobile Energy Resources (MERs) to address the issue. The impact of a transportation network on the two-stage model is represented by the consideration of interdependence between vehicle routes and distribution lines. Initially, the charging and repair stations are strategically placed on the transportation network before an extreme event by considering the distribution lines that have a high probability of failure during the event. An integer nonlinear programming model is used to determine the optimal number and staging locations of the charging and repair stations during the development of the first stage. The second stage involves the design of a mixed integer linear programming model to restore loads and recover the DS via optimizing the dispatch of EVs with MERs on the transportation network. The proposed method is tested on modified IEEE 123-Bus system. The utilization of EVs alongwith MERs under post-disaster conditions helps to reduce the ENS by 42.1038 %. The ENS found to be 7.9150 MWh utilising the proposed method is much less compared to the ENS obtained by existing method and hence this demonstrates the effectiveness of the proposed method.

Efficient Resource Utilization in Kubernetes: A Review of Load Balancing Solutions

Modern distributed systems face the challenge of efficiently distributing workloads across nodes to ensure optimal resource utilization, high avail-ability, and performance. In this context, Kubernetes, an open-source container orchestration engine, plays a pivotal role in automating deployment, scaling, and management of containerized applications. This paper explores the landscape of load balancing strategies within Kubernetes, aiming to provide a comprehensive overview of existing techniques, challenges, and best practices. The paper delves into the dynamic nature of Kubernetes environments, where applications scale dynamically, and demand for resources fluctuates. We review various load balancing approaches, including those based on traffic, resource-aware algorithms, and affinity policies. Special attention is given to the unique characteristics of containerized workloads and their impact on load balancing decisions. In this paper the implications of load balancing on the scalability and performance of applications deployed in Kubernetes clusters. It explores the trade-offs between different strategies, considering factors such as response time, throughput, and the adapt-ability to varying workloads. As cloud-native architectures continue to evolve, understanding and addressing the intricacies of load balancing in dynamic con-tainer orchestration environments become increasingly crucial. In this paper we had consolidated the current state of knowledge on load balancing in Kubernetes, providing researchers and practitioners with valuable insights and a foundation for further advancements in the quest for efficient, scalable, and resilient distrib-uted systems.

Enhancing distribution system resilience against extreme disasters considering sequential action of multi-type switches

With the increasing frequency and intensity of extreme disasters, enhancing distribution system (DS) resilience has been an urgent need for smart grid. Among various resilience enhancement measures, network reconfiguration is the most commonly used one. Multiple switches are involved in distribution network reconfiguration, e.g., circuit breakers (CBs), fuses (FUs), remote-controlled switches (RCSs), and manual switches (MSs). According to the characteristics of various switches, it is vital to establish an analytical switch action model for generating feasible and effective DS resilience enhancement strategies. In this paper, an analytical model is developed to characterize the automatic tripping process of switches under multiple faults caused by extreme disasters. A novel DS resilience enhancement method is further proposed, which considers the sequential actions of multiple types of switches to enhance the prevention and rapid restoration capability of DSs against extreme disasters. Before the strike of a disaster, the topology of DS is reconfigured based on RCSs, MSs, and CBs to improve the survivability of loads. The automatic tripping process of CBs and FUs is simulated when faults occur. After the disaster, the DS is reconfigured based on RCSs and CBs after events to isolate faults and restore critical loads. The proposed method is formulated as a mixed-integer linear programming (MILP) problem, which can be efficiently solved using commercial solvers. Case studies conducted on the modified IEEE 123-bus test systems demonstrate the effectiveness of the proposed method for DS resilience enhancement.