Resilience Of Distribution Networks Research Articles

Design of ant colony planning algorithms for multi-stage grids of distribution networks considering network resilience

Network Destruction Resilience is the ability of a system to maintain good operation in the event of an external attack or internal failure. The resilience of distribution network is crucial to guarantee the reliability of power supply. In this paper, we design a planning algorithm considering network destructibility for the multi-stage ant colony planning problem of distribution networks. The method establishes the multi-stage network planning objective function of distribution network from the perspectives of total investment cost and annual operation cost of multi-node network frame, destruction resistance, active power and reactive network loss of distribution network, etc. Then based on the constraints of the model, the improved ant colony algorithm is used to solve the multi-stage network planning objective function of distribution network, and the results of the anti-termite colony planning of multi-stage network frame of distribution network are obtained. In order to verify the effectiveness of the algorithm, simulation experiments are carried out on real distribution network data. The results show that the proposed ant colony planning algorithm for multi-stage grid frames can effectively improve the destruction resistance of distribution networks, and reduce the total investment cost and annual operation cost of multi-stage grid frames, and reduce the network loss rate data of multi-stage grid frames of distribution networks after application. It provides an effective method for planning the distribution network.

Resilience enhancement strategies for power distribution network based on hydrogen storage and hydrogen vehicle

AbstractIn light of the increasing hydrogen permeability in distribution networks as a means to cope with extreme events and improve network resilience, this paper introduces a novel strategy for enhancing power distribution network resilience. It outlines a comprehensive approach that focuses on dispatching hydrogen storage (HS) and hydrogen vehicle (HV) within hydrogen penetrated distribution systems (HPDS), segmenting the strategy into pre‐disaster and post‐disaster stages. Firstly, in the pre‐disaster stage, models for HS and HVs are established to gather operational data and facilitate rapid post‐disaster response, alongside a coupled electric grid and road network model for optimising HV routing and dispatch. Subsequently, the post‐disaster stage focuses on a scheduling model that aims to minimise load power losses and economic costs, balancing immediate power support with cost‐effectiveness through detailed analysis of HS and HV dispatch strategies. Finally, this paper demonstrates the effectiveness of this strategy via a case study, highlighting significant improvements in network resilience and recovery and underscoring the potential of hydrogen technologies in enhancing infrastructure resilience.

Pre-disaster allocation and post-disaster dispatch strategies of power emergency resources for resilience enhancement of distribution networks

With the frequent occurrence of large-scale power outages caused by extreme disasters, coordinated dispatch of power emergency resources (PERs) and repair crews (RCs) has gradually become an essential method to enhance the resilience of distribution networks (DNs). Previous works solve this coordinated dispatch problem in a risk-neutral approach with the assumption that the supply of power repair materials (PRMs) is still fully available after the disaster. However, growing evidence indicates that certain extreme disasters may lead to limited availability of PERs in the DN, rendering risk-neutral decision making impractical. To fill this gap, this paper proposes a resilience-oriented PERs pre-disaster allocation and post-disaster dispatch strategy in a risk-averse manner. In the pre-disaster prevention process, a two-stage distributionally robust optimization (DRO) model based on mean-CVaR is developed, considering the uncertainties of both system damage and PERs demands, along with the decision maker's preference for risk. In the post-disaster restoration process, a collaborative spatio-temporal dispatching model for PERs and RCs with the goal of minimizing outage losses is established. To tackle the computational challenges associated with PRMs allocation based on DRO, robust counterpart transformation and dual theory are utilized. Case studies are conducted using a real world modified 118-bus distribution network. In comparative analysis, five control groups are established to validate the necessity to consider the limited supply of PRMs and the risk-averse attitude of decision makers in enhancing DNs resilience.

Optimal Scheduling Strategy for Urban Distribution Grid Resilience Enhancement Considering Renewable-to-Ammonia Coordination

The integration of numerous distributed energy sources into the power system offers exciting opportunities to enhance the resilience of distribution networks. It is worth noting that the renewable-to-ammonia system has the potential to alleviate the multi-temporal and spatial imbalance of the power system. Therefore, this paper proposes a mathematical model for a renewable-to-ammonia system, taking into account the material balance and power balance of each unit. Based on this, this paper further explores the optimization scheduling method for flexible ammonia loads in distribution networks. A relaxation method for branch flow models in distribution networks based on second-order cone programming is proposed. An optimization scheduling model for flexible ammonia loads in distribution networks is constructed to minimize network loss. Moreover, considering the environmental advantages of the renewable-to-ammonia system, this paper compares the changes in hydrogen production technologies under different carbon emission constraints. Finally, a case study of the IEEE 33-node system is adopted to verify the effectiveness of the proposed model and method. It indicates that the renewable-to-ammonia system has environmental benefits and can reduce network loss to a certain extent.

Cloud-edge collaborative high-frequency acquisition data processing for distribution network resilience improvement

To realize transparent monitoring and resilience improvement of low-voltage distribution network, both the data acquisition scope and frequency have been greatly expanded. Cloud-edge collaboration leverages the edge server’s real-time response capabilities and the cloud server’s robust data processing power to enhance the performance of high-frequency data acquisition processing. Nonetheless, it continues to confront challenges such as the entanglement of optimization variables, the presence of uncertain information, and a lack of awareness regarding acquisition frequencies. In this paper, we propose a machine learning-based cloud-edge collaborative data processing optimization algorithm to minimize the weighted sum of data processing delay and device energy consumption for distribution network resilience improvement. The joint optimization problem is decoupled into device-edge data offloading subproblem and edge-cloud data splitting subproblem, which are solved by the proposed upper confidence bound (UCB) based frequency-aware device-edge data offloading optimization algorithm and the exponential-weight algorithm for exploration and exploitation (EXP3) based edge-cloud data splitting optimization algorithm, respectively. Simulation results show that the proposed algorithm is superior to existing algorithms in performances of energy consumption and total processing delay.

A three-level robust scenario-based model for resilience-oriented placement of remote-controlled switches in distribution networks

This paper introduces an innovative strategy to improve the resilience of Distribution Networks (DN) following natural disasters. The approach involves a multi-stage model for the strategic placement of Remote-Controlled Switches (RCS) to expedite fault isolation and service restoration. The model integrates various resilience enhancement techniques, such as network reconfiguration and microgrid formation, throughout the pre-disaster, disaster progression, and post-disaster phases. To mitigate uncertainties related to line failures and ensure planning robustness, a novel three-level Robust Scenario-based Model (RSM) is introduced. The RSM optimizes the strategic RCS placement to enhance System Performance Criteria (SPC) in response to anticipated line failure scenarios, considering individual line vulnerabilities and disaster severity. Subsequently, the model identifies the most critical failures from each predicted scenario based on RCS placements and then recalibrates the placements to enhance DN resilience against both anticipated and critical failures. The RSM is formulated as a mixed-integer linear programming problem to maximize SPC by allocating resources optimally to serve maximum weighted loads while adhering to structural and operational constraints. The effectiveness and validity of the proposed model are demonstrated through case studies conducted on a modified IEEE 33-bus test system and a modified real DN.

Dynamic restoration electricity price optimization method to enhance the resilience of distribution networks with multiple‐microgrids

AbstractResilience is one of the main features of smart distribution networks, and a microgrid (MG) access to the distribution network provides an effective way to improve resilience. MG and distribution network belong to different interests, so it is necessary that MGs and flexible resources are actively guided through price leverage. In this way, MGs take part in the post‐disaster restoration and enhance its resilience. Firstly, this paper proposes a dynamic restoration electricity price response mechanism after extreme disasters and constructs a power response model for loads and electric vehicles within the MGs. Secondly, the optimal scheduling model of the distribution network with multiple‐microgrids (MMG) is proposed to improve the restoration rate of critical loads (RRCL). Single microgrid achieves the largest microgrid revenue and restoration contribution, and MMG uses the power headroom index to optimize the dynamic restoration electricity price to achieve the smallest power purchase cost of distribution network. Finally, the optimal scheduling method for resilience enhancement of distribution networks with MMG considering dynamic restoration electricity price response mechanism is validated by dual microgrid access to an IEEE 33‐node distribution system. The simulation results show that the proposed optimization method effectively improves the RRCL of distribution network.

A resilience evaluation index system and evaluation method for regional distribution networks facing typhoon disasters

Abstract The occurrence of natural climate disasters such as typhoons may lead to large-scale power outages in the distribution network, and a quantitative assessment of the resilience of the power grid is a key step in preventing and resisting the harm caused by natural disasters to the power grid. This article establishes a regional distribution network resilience evaluation index system for typhoon disasters from the perspectives of reliability, safety, and economic resilience of the power grid after a typhoon occurs. Based on the above indicator system, the fuzzy comprehensive evaluation technology is adopted to evaluate the resilience level of the power grid under typhoon disasters. Taking a regional distribution network in Guangzhou, China, as an example, a numerical analysis was conducted, and the results showed that the proposed resilience evaluation indicators and methods can reasonably and quantitatively evaluate the resilience level of urban distribution networks under typhoon disasters.

A peer‐to‐peer joint energy and reserve market considering renewable generation uncertainty: A generalized Nash equilibrium approach

AbstractPeer‐to‐peer (P2P) energy trading enhances distribution network resilience by reducing energy demand from central power plants and enabling distributed energy resources to support critical loads after extreme events. However, adequate reserves from main grids are still required to ensure real‐time energy balance in distribution networks due to the uncertainty in renewable generation. This paper introduces a novel two‐stage joint energy and reserve market for prosumers, wherein local flexible resources are fully utilized to manage renewable generation uncertainty. In contrast to cooperative optimization methods, the interactions between prosumers are modelled as a generalized Nash game (GNG), considering that prosumers are self‐interested and should follow distribution network constraints. Then, linear decision rules are employed to ensure a feasible market equilibrium and develop a privacy‐preserving algorithm to guide prosumers toward the market equilibrium with a proven convergence. Finally, the numerical study on a modified IEEE 33‐power system demonstrates that the designed market effectively manages renewable generation uncertainty, and that the algorithm converges to the market equilibrium.

Enhancing reclaimed water distribution network resilience with cost-effective meshing

Water Distribution Networks (WDNs) are critical infrastructures that ensure a continuous supply of safe water to homes. In the face of challenges, like water scarcity, establishing resilient networks is imperative, especially in regions vulnerable to water crises. This study evaluates the resilience of network designs through graph theory, including its hydraulic feasibility using EPANET software, an aspect often overlooked. Novel mathematical algorithms, including Resilience by Design (RbD) and Resilience-strengthening (RS) algorithms, provide cost-effective and resilient network designs, even with budget constraints. A novel metric, Water Availability (WA), is introduced to offer a comprehensive measure of network resilience, thereby addressing ongoing discrepancies in resilience evaluation methods. Practical benefits are illustrated through a case study in which a resilient-by-design reclaimed water network is created, and an existing equivalent non-resilient network is improved. The resilient-by-design network demonstrates remarkably better results compared to the equivalent non-resilient design, including up to a 36 % reduction in the probability of service disruptions and a nearly 65 % decrease in the annual average unserved water due to service disruptions. These findings underscore the enormous advantages of a resilience-focused network design approach. When compared to the equivalent non-resilient design, the resilient-by-design network generated effectively safeguards up to a significant 91,700m3 of water from the impacts of water disruption events over a 50-year operational period. In addition, the resilient-by-design WDN solution incurs a subtle decrease in overall costs compared to consuming tap water from the drinking WDN baseline over a 50-year operational period. These findings highlight the cost-effectiveness of the approach, even offering financial benefits. This paper builds on our previous research by expanding its scope to include resilience considerations, providing algorithms that can be easily adapted from reclaimed to drinking WDNs. Ultimately, we contribute to the enhancement of water resource management and infrastructure planning in ever-evolving urban environments.

Hybrid genetic algorithm-simulated annealing based electric vehicle charging station placement for optimizing distribution network resilience

Rapid placement of electric vehicle charging stations (EVCSs) is essential for the transportation industry in response to the growing electric vehicle (EV) fleet. The widespread usage of EVs is an essential strategy for reducing greenhouse gas emissions from traditional vehicles. The focus of this study is the challenge of smoothly integrating Plug-in EV Charging Stations (PEVCS) into distribution networks, especially when distributed photovoltaic (PV) systems are involved. A hybrid Genetic Algorithm and Simulated Annealing method (GA-SAA) are used in the research to strategically find the optimal locations for PEVCS in order to overcome this integration difficulty. This paper investigates PV system situations, presenting the problem as a multicriteria task with two primary objectives: reducing power losses and maintaining acceptable voltage levels. By optimizing the placement of EVCS and balancing their integration with distributed generation, this approach enhances the sustainability and reliability of distribution networks.

Cascading failure and resilience optimization of unmanned vehicle distribution networks in IoT

With the development of science and technology, unmanned vehicles have become one of the main ways of distribution, and the appearance of unmanned vehicles undoubtedly brings great convenience to people. However, few researchers consider the cascading failure times for different situations in unmanned vehicle distribution networks. Especially, few researchers have integrated them into maintenance strategies to real-time path changes to increase the reliability of accomplishing distribution tasks and improve resilience after congestion. One of the key issues is how to improve the resilience of distribution network and what maintenance strategies should be developed. In this paper, cascading failures are analyzed and resilience is optimized in unmanned vehicle distribution network. First, cascading failures for different situations are analyzed, and two importance-based diversion methods are proposed to describe the three processes of congestion formation, persistence, and evacuation. Second, failure and performance degradation processes are investigated. Third, the Floyd time-varying resilience optimization algorithm is proposed, and the calculation method of resilience under different strategies is given. At last, simulation results show that the proposed strategy 3 improves the original resilience by 101 % compared with the traditional waiting path strategy.

A tri-level Typhoon-DAD robust optimization framework to enhance distribution network resilience

Extreme natural disasters such as typhoon often cause failures in distribution networks within a short time, and even lead to large area blackouts. We propose a tri-level robust optimization framework that combines pre-disaster, in-disaster and post-disaster strategy comprehensively to enhance distribution network resilience. The typhoons are regarded as attackers, and a tri-level Defender-Attacker-Defender model for typhoon disaster is used to integrate multiple resilience resources. In the first level, line hardening and distributed generation unit commitment are used to improve resilience in pre-disaster. The second level contains attack budget, attack time, hardening budget, repair time and load loss. It couples with the third level iteratively to generate the worst failure scenario under typhoon. In the third level, the Nested Column-and-Constraint Generation algorithm is used to solve distribution network reconfiguration and intentional islanding. Simulation time is spanned to the entire 24-hour disaster day. In the end, the proposed framework is tested through a case study using real data from super typhoon “Mangkhut” (2018) in Yangjiang, China. The result shows that it can effectively reduce load loss under the worst typhoon scenario and enhance distribution network resilience with limited resources.

A stochastic two-stage microgrid formation strategy for enhancing distribution network resilience against earthquake event incorporating distributed energy resources, parking lots and responsive loads

The sustainability of energy systems has been severely affected in recent years by the quantity and intensity of high-impact, low-probability events. As a result, the resilience concept has been introduced to analyze the operation of networks against such events. This paper proposes a stochastic two-stage microgrid formation formulation to maximize the restored loads and enhance the resilience index of electrical distribution systems in the face of earthquake events. In this regard, an earthquake incident is modeled in the first step utilizing geographical data, peak ground acceleration and fragility curves. Then, a two-stage linear programming and graph theory is applied to form microgrids in response to the mentioned disturbances, where the variables in the planning and operating states are optimized in the first and second stages, respectively. The influence of different components including diesel generators, renewable energies, storage units and electric vehicles is investigated. In addition, the role of responsive loads and uncertainties related to line failure, load variation, renewable resources availability and electric vehicles are considered during the analysis. The results for various scenarios validate that the presented approach increases load supply capability and resiliency index by 38.7% and 38.8%, respectively.

Coordinated Distributionally Robust Optimal Allocation of Energy Storage System for HV-MV Distribution Network Resilience Enhancement

Coordinated Distributionally Robust Optimal Allocation of Energy Storage System for HV-MV Distribution Network Resilience Enhancement

A Review on Black-Start Service Restoration of Active Distribution Systems and Microgrids

There has been a notable increase in the frequency and severity of extreme events, such as hurricanes, floods, wildfires, and storms. These events, although infrequent, have a significant disruptive effect, causing prolonged outages and compromising essential services, thereby severely affecting customers’ safety. As a result, there is an urgent requirement to enhance the resilience of distribution networks by quickly restoring the loads during and after a disaster. In this regard, this paper reviews the existing studies on black-start service restoration in active distribution systems and microgrids. A comprehensive review is conducted for each aspect of the restoration problem, encompassing various proposed methods and modeling techniques found in the existing literature. The aim of this review is to consolidate the knowledge and findings from previous studies, providing a valuable resource for researchers and practitioners in the field. Also, some key research directions for the future work in this field are recommended for developing more practical and reliable methods.

A two‐stage scheduling model for urban distribution network resilience enhancement in ice storms

AbstractThis paper proposes a two‐stage stochastic scheduling model for urban distribution network resilience enhancement against ice storms, which coordinates mobile deicing equipment routing and distributed energy resources dispatching. An improved line ice thickness prediction model and a photovoltaic power generation prediction method in accordance with conditional generative adversarial networks are proposed to provide data boundaries for scheduling strategy. Facing the uncertainty of line failure, a two‐stage scenario‐based distribution network optimization model is established. At first stage, the mobile deicing equipment routing strategy is decided to mitigate the impact caused by ice storms. The Monte‐Carlo simulation method is introduced to describe the uncertainty of line failure due to ice acceleration. For the second stage, based on the results of photovoltaic forecasting and possible distribution line failure scenario generated by Monte‐Carlo simulation method, the optimal distributed energy resources dispatching strategy can be obtained through the mixed integer programming. The proposed model is simplified to a mixed integer linear programming model that can be solved by a commercial solver. The test results on the modified IEEE 33‐node system and modified 69‐node system demonstrate that the proposed method can effectively improve the resilience performance of urban distribution network under ice storms.

Mobile Energy Storage System Scheduling Strategy for Improving the Resilience of Distribution Networks under Ice Disasters

The distribution system is easily affected by extreme weather, leading to an increase in the probability of critical equipment failures and economic losses. Actively scheduling various resources to provide emergency power support can effectively reduce power outage losses caused by extreme weather. This paper proposes a mobile energy storage system (MESS) scheduling strategy for improving the resilience of distribution networks under ice disasters. First, the influence of wind and ice loads on power transmission lines is analyzed, and a detailed fault statistical model of transmission lines under an ice disaster is established. Then, the MESS scheduling problem considering the coupling of transportation-distribution networks is transformed into a two-stage optimization problem. The first stage determines the optimal configuration scheme for MESS, and in the second stage, the optimal path selection for MESS can be obtained. Finally, the effectiveness and feasibility of the algorithm proposed in this paper are verified through an improved IEEE-33 node testing system.

Reconfiguration of the real distribution network and providing a new resilience index, considering the uncertainty of renewable energy sources and digital protection devices in order to improve the resilience

Electric energy, as the most widely used form of energy, has always captured human attention to itself; however, with the climate change and the increase of natural phenomena, it is now necessary to provide solutions to improve the resilience of the network. Resilience is defined as the capability of the system to maintain an acceptable level of performance against a severe disturbance and return to its original condition in an appropriate period of time, which demonstrates the degree of endurance, vulnerability, and reversibility of an infrastructure system. The occurrence of adverse weather conditions and natural disasters causes extensive losses and power outages in distribution networks; and the number and severity of such incidents have seen an increase in recent years. Therefore, it is of great significance to evaluate the resilience of the network and its reversibility capability in dealing with adverse weather conditions. In this paper, after defining the concept of resilience in distribution networks, its characteristics are determined; and then, modeling common natural events such as floods and storms, and introducing a new index to evaluate resilience in distribution networks with digital directional overcurrent relays (DOCR) and Digital dual-setting directional overcurrent relays (DS-DOCR) in the presence of renewable energy sources (RES), the proposed scheme is presented.

Presenting a new mathematical modeling for distribution network resilience based on the optimal construction of tie-lines considering importance of critical electrical loads

Presenting a new mathematical modeling for distribution network resilience based on the optimal construction of tie-lines considering importance of critical electrical loads