Resilience Of Distribution Systems Research Articles

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

Enhancing Distribution System Resilience with Peer-to-Peer Transactions

Enhancing Distribution System Resilience with Peer-to-Peer Transactions

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.

Stochastic pre‐disaster planning and post‐disaster restoration to enhance distribution system resilience during typhoons

AbstractIn recent years, extreme weather events, such as typhoons, have led to large‐scale power outages in distribution systems. As a result, developing strategies to bolster distribution system resilience has become imperative. This paper proposes a two‐stage stochastic programming model aimed at enhancing this resilience. Prior to a typhoon, the first stage establishes a comprehensive wind field model based on extreme value distribution for accurate wind speed predictions. Simultaneously, a refined stress–strength interference model is used to determine the likelihood of distribution line failures. Taking into account the uncertainty of line damage, repair crews and mobile emergency generators are then strategically positioned at staging depots. Following the typhoon, the second stage coordinates network reconfiguration, dispatches repair crews, and mobilizes mobile emergency generators to minimize load shedding and expedite repairs. This model was validated on the IEEE 33‐bus distribution system, coupled with a corresponding transportation network, utilizing data from the 2018 super typhoon Mangkhut'' in China. Simulations indicate that our approach can effectively reduce load shedding and power outage durations, thereby enhancing the resilience of distribution systems.

Enhancing Service Restoration in Tanzanian Power Grid using Internet of Things Sensors and Renewable Energy Sources

The increased dependence on the electric power grid, coupled with the increasing number of new customers, motivates the need to improve the reliability and resilience of the electric distribution systems. Modern distribution systems are becoming more resilient against power outages due to the increasing integration of Distributed Energy Resources (DERs) and the availability of efficient mechanisms for load shedding. Several studies considered fault detection and Service Restoration (SR) as separate problems without considering the implementation feasibility of integrating both solutions. To ensure the resiliency in the power system, it is important to have proper mechanisms which integrate sensing, detection, and SR as one problem while considering the load shedding and DER for improved capacity. Hence, this study proposed an IoT-based-sensor network framework with an enhanced algorithm coupled with a Binary Bat metaheuristic algorithm for SR integrating sensing, detection, and restoration. The proposed algorithms have been tested in Tanzania's electrical distribution network, considering the inclusion of DERs and load shedding. The results showed that DERs' size and locations significantly impact restoration schemes' performance with a power loss reduction of 74%. Therefore, efficient SR schemes should consider optimal DERs placement and a combination of load shedding and DERs integration for improved performance.

Assessment of topological boundaries for microgrids in an active power distribution network during emergencies

Microgrid (MG) operation has become an intriguing possibility to enhance the sustainability, reliability, and resilience of electricity distribution systems in emergencies. However, identifying suitable physical boundaries of MGs from an active power delivery infrastructure is a highly challenging task. This study describes a novel approach for determining the topological boundary of multiple MGs to restore power supply in a power distribution system during the unavailability of grid power supply. This method tries to enhance the self-adequacy of the MGs by increasing power flow within the MG boundaries. In this context, possible demand at the nodes of the power delivery infrastructure during an emergency is anticipated by an autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) network-based time series forecast models. A modified backward-forward sweep (MBFS) power flow analysis, tailored for islanded mode, is employed to assess the power flow status in the MGs during emergencies. The MGs are supplied with locally available dispatchable distributed generators (DDGs). A particle swarm optimization (PSO) algorithm intertwined with the MBFS power flow method has been employed to determine the boundary of MGs by identifying minimum power flow line in the shortest path between the consecutive DDGs. Further, this study demonstrates the evaluation of the shortest path between paths between the consecutive DDGs with the help of a breadth-first search (BFS) algorithm. The proposed strategy has been tested on a modified 33-bus, 69-bus and 118-bus power distribution test systems. The experimented results are verified with the results obtained from similar existing approaches to explore the opportunities in the proposed methodology.

Online evaluation of the distribution system resiliency criteria using Choquet‐based graph technique

AbstractNowadays, due to network expansions, system operators are dealing with natural events including system failures. Evaluating the system readiness to maintain the network interconnections during severe faults are known as an important challenge of today's distribution networks. To do this, this paper presents an online non‐model‐based scheme of evaluating the system resiliency and supplying critical loads within severe natural conditions. In this case, by developing system resiliency as multi‐metric decision making problem and proposing the Choquet integral and graph theoretic methodology, the system resiliency is evaluated. The proposed resiliency scheme is an online non‐model‐based approach can be used for all distribution system configurations with the potential of natural event failures. In this case, by proposing online resiliency criteria based on the system critical loads and branches and developing the proposed criteria on Choquet integral scheme in the form of optimization procedure, the system resiliency is evaluated. The validity of the proposed approach is carried out on two different systems including the IEEE 123‐bus test system and the Consortium for Electric Reliability Technology Solutions (CERTS) microgrid. Simulation results present effectiveness of the proposed scheme to provide proper protective switching strategy and design the system resiliency criteria for future distribution systems.

A stochastic investment decision making method for distribution system resilience enhancement considering automation, hardening and distributed energy resources

Extreme weather events have always posed as a serious threat to the power system. In recent years the frequent occurrences of extreme events highlight the requirement for a resilient distribution system capable of maximizing load restoration under the most adverse situations. Because the requirement to reinforce the distribution system appears to be an expensive approach, it is crucial to make the proper investment decisions. In this regard, this article introduces a single-stage stochastic multi-criteria optimization method for maximizing network resilience over a series of investments while minimizing overall costs. This method conducts different categories of investments including the installation of sectionalizing switches, distributed energy resources, and hardening of overhead distribution lines. The optimization method is based on a genetic algorithm and can be used to address a large-scale network. Several numerical studies are tested on the IEEE 69-bus system to verify the effectiveness of the proposed method. The results of the investment decision-making method show the significance of combining planning and operational preparedness in distribution systems to respond to extreme weather events.

A CVaR-based stochastic framework for storm-resilient grid, including bus charging stations

This paper proposes a two-stage stochastic framework for improving distribution system resilience against storms, in which the uncertainties associated with load demands, solar irradiance after a storm event, and maximum gust wind speed are considered. In the first stage, the available idle electric buses (EBs) are optimally allocated to the charging stations prior to storm arrival. In the second stage, critical loads are restored through island formation after the storm. In this framework, the solar-powered charging stations of EBs and the distributed generators (DGs) are part of the electric energy resources. The solar charging stations contribute to supplying the critical loads in two ways: 1- The electricity generation through rooftop-installed photovoltaic modules 2- By discharging the batteries of the pre-allocated EBs. The objective of this optimization model is to minimize the expected priority-weighted curtailed energy. At the same time, the risk imposed by the uncertain parameters is taken into account through the conditional value-at-risk (CVaR) index. The proposed framework is expressed in terms of a stochastic mixed-integer linear programming (MILP) optimization problem. This framework is implemented on the 33-node distribution test system, and its superior resilient performance is demonstrated through four case studies.

A graph theory and coalitional game theory-based pre-positioning of movable energy resources for enhanced distribution system resilience

This paper proposes an approach based on graph theory and coalitional game theory for pre-positioning of movable energy resources (MERs) to improve the resilience of the electric power supply. By utilizing the weather forecasting and monitoring data, the proposed approach determines staggering locations of MERs in order to ensure the quickest possible response following an extreme event. The proposed approach starts by generating multiple line outage scenarios based on fragility curves of distribution lines, where the fuzzy k-means method is used to create a set of reduced line outage scenarios. The distribution network is modeled as a graph and distribution network reconfiguration is performed for each reduced line outage scenario. The expected load curtailment (ELC) corresponding to each location is calculated using the amount of curtailed load and probability of each reduced scenario. The optimal route to reach each location and its distance is determined using Dijkstra’s shortest path algorithm. The MER deployment cost function associated to each location is determined based on the ELC and the optimal distance. The MER deployment cost functions are used to determine candidate locations for MER pre-positioning. Finally, the Shapley value, a solution concept of coalitional game theory, is used to determine the sizes of MERs at each candidate location. The proposed approach for pre-positioning of MERs is validated through case studies performed on a 33-node and a modified IEEE 123-node distribution test systems.