Service Restoration In Distribution Systems Research Articles

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.

Resilience-oriented comparative study of SOP-based service restoration in distribution systems

Soft open points (SOPs) can perform load shifting and voltage improvement, and are critical to improving distribution service restoration (DSR) during power outages. Although multiple DSR strategies utilizing local resources have been reported, their effectiveness has not been sufficiently studied. To solve this problem and utilize the flexibility of SOP, a comparative study of different strategies for SOP-based DSR is presented in this paper. First, various SOP-based DSR models, including single-source, single-island model (SSM), islanding partition model (IPM) and multi-source collaborative model (MCM), are proposed and formulated as mixed-integer nonlinear programming (MINLP) problems, incorporating static voltage characteristics of loads. Second, to enhance the solution efficiency, mixed-integer second order cone programming is introduced to convert the MINLP problems into easily solvable convex optimization problems. A restorative index is defined to evaluate the effectiveness of various SOP-based DSR strategies. Finally, a comparative simulation analysis of the proposed models on the IEEE 33-bus system illustrates that SOP-based MCM achieves an 94 % power output for distributed energy resources, while exhibiting the lowest RI for the total loads, standing at just 9.5 %. Furthermore, the RI for critical loads consistently remains at 0, demonstrating the superiority of SOP-based MCM in comparison to other strategies.

Optimizing service restoration in distribution systems based on scheduling of crews considering priorities of loads

AbstractThe service restoration to sound sections within a short period as much as possible, performed after the permanent fault occurs in a distribution system, is significant for maintaining power supply reliability. This paper proposes an efficient method to find service restoration procedures based on fault searching and the optimal work scheduling of crews, considering the power supply priorities of loads and the probability of occurrence of faults. The proposed method is composed of two‐stage problems to reduce the outage sections efficiently: (i) the problem to find a series of searching procedures for unknown fault locations in the first stage and (ii) the optimization problem of work schedules of crews in the second stage. In the first stage, the order of operation of switches opened for searching for the fault direction is determined, constituting the searching tree. After that, the proposed optimization method can efficiently solve the work scheduling problem by formulating it as a mixed‐integer linear programming problem in the second stage. The computational experiments using a large‐scale distribution system model with many remote and manual switches show that the proposed method can provide efficient service restoration procedures within a reasonable computational time.

Modelling spatiotemporal impact of flash floods on power distribution system and dynamic service restoration with renewable generators considering interdependent critical loads

AbstractIn recent decades, flash floods have become more common because of climate change and are considered a substantial risk for many cities worldwide. This catastrophic natural hazard presents a significant threat to critical infrastructure in urban areas, particularly the power distribution system. As modern societies are much more dependent on electrical energy these days, it is essential and imperative to make existing distribution systems resilient against flash flooding. Although researchers in this area have proposed various algorithms to impart resilience to a distribution system, however, the focus in these works is on wind‐related events such as hurricanes, cyclones, and windstorms. Therefore, here, the spatiotemporal effects of a flash flood on the distribution system are modelled using a grid‐based hydrodynamic model. The evolving line faults are then included in the proposed resilience‐oriented time horizon‐based service restoration model that also considers dynamic load demand, heavy uncertainties related to renewable generation, and interdependence among critical loads. Finally, the resilience of the distribution system's response is assessed using an operational resilience metric. The efficacy of the proposed framework is evaluated on IEEE 33‐bus and 69‐bus systems and the results show that the model provides an efficient restoration solution despite increased complexity caused by varying conditions.

Robust service restoration of distribution systems towards coordinated parallel network reconstruction and cold load pick-up

This paper proposed a robust service restoration method coordinating parallel network reconstruction and load restoration decisions considering cold load pick-up (CLPU) effects. Firstly, a novel parallel network reconstruction model is proposed, which can simultaneously guarantee the radiality of each subsystem and the feasibility of the restoration sequence. The load behaviors during restoration are modeled considering CLPU effects over multiple steps. A two-stage service restoration model is developed to address the uncertainties arising from renewable DGs and loads. The first stage is to minimize total load losses by optimizing the operational decisions of DGs, switches and loads ahead of restoration, while the second state aims to optimize power flow after the decisions are implemented. The model is further formulated as a two-stage robust optimization model with infeasibility penalties in the second stage, which can be solved by the column-and-constraint generation algorithm. Numerical simulation results on a 33-bus distribution system demonstrate the high efficiency and robustness of the proposed method.

Coordinated Repair Crew Dispatch Problem for Cyber–Physical Distribution System

Reasonable repair crew dispatch can accelerate the service restoration in distribution systems after some components are damaged. It is worthy noting that as traditional distribution systems are involving into cyber-physical distribution systems composed of physical distribution networks and cyber networks, damaged components in these two networks should be coordinately repaired to achieve maximal restoration performance. However, repair crews in physical distribution networks are scheduled without considering the impact of the corresponding cyber networks in existing repair crew dispatch strategies. In this paper, a coordinated model is proposed to co-optimize the repair crew schedule of the two networks. Besides the cyber-physical interdependence between the two networks, backup power systems, topology reconfiguration and intentional distribution generator (DG) island are also modelled to improve the repair performance. The proposed model is formulated as a mixed-integer linear programming model, and its effectiveness is verified on the modified IEEE 33-node and 123-node test systems. Numerical results highlight the necessity of co-optimizing repair crew dispatch in both physical distribution networks and cyber networks.

Cooperative Repair Scheduling and Service Restoration for Distribution Systems With Soft Open Points

Fault management is one of the crucial functions of Smart Grid to bring system back to normal operation from fault events. In this paper, we propose a multi-period cooperative solution for the jointly repair and restoration problem in distribution systems (DSs) to minimize the de-energized loads. In the proposed scheme, the novel fully-controlled power-electronic device SOP (soft open point) and fault isolation are included to accelerate system restoration. As the repair and restoration process goes forward, system topology changes frequently. How to determine the control mode of SOPs considering their characteristics during the entire restoration process is rarely studied in the literature. To this end, we present a new virtual power flow based automatic solution to adaptively perform control mode switching for the SOPs in faulted DS. Furthermore, a co-optimization model is formulated that coordinates SOP control, repair scheduling and DS restoration to obtain optimal repair and switch action sequences matching SOP control mode for DS resilience enhancement. The fault isolation is also conducted, since it has great impacts on repair and switch operations and can guarantee the safety of repair crews in practice. Numerical simulations on a modified IEEE 123-node test system and a large-scale actual distribution system verify the effectiveness of the proposed model.

An Optimization-Based Intentional Islanding Scheme for Service Restoration in Distribution Systems Considering Anti-Parallel Operation of Distributed Generations

An islanding operation of distributed generations (DGs) in emergencies due to a fault in distribution systems can be a means of power supply for important loads in outage areas by facilitating the self-sufficient capability of DGs forming microgrids. This paper presents an optimization-based intentional islanding scheme to derive a near-optimal service restoration (SR) plan. The anti-parallel operation of DGs is considered a new constraint that avoids more than two DGs in an island thereby, enabling simpler control and operation of the distribution system in an emergency. Each island is created by an island partitioning scheme based on the tree representation of the network and fast searching scheme for the tree structure considering load importance, and a genetic algorithm (GA) is utilized to explore possible SR solutions. Case studies on IEEE 69-bus distribution system according to various fault locations are conducted, and the simulation results show that the proposed scheme can restore more loads with higher priority in outage areas by the intentional islanding of DGs. Furthermore, the time for deriving the optimal solution can be reduced since the evaluations for infeasible solutions are not performed.

Service Restoration in Distribution Systems based on Optimal Scheduling of Search for Fault Location and Switching Operation by Crews

The service restoration within a short period, performed when the permanent fault occurs in a distribution system, is essential for high supply reliability. The service restoration can be achieved by searching the fault location and controlling switches to isolate the fault area. However, the service restoration faces difficulties in the uncertainty of the fault location and the multiple crews' scheduling of the switching operations. It is important to search for the unknown fault location efficiently and optimize the crews' scheduling for their service restoration procedures. This paper proposes an optimization method of the service restoration procedures performed by crews considering the search of the unknown fault location by crews to handle the difficulties. The proposed method combines an efficient fault location search with scheduling for multiple crews. We formulate the scheduling problem as a mixed-integer linear programming problem which allows us to schedule the procedures efficiently using optimization solvers. The proposed method is verified by the computational experiments using the large-scale distribution system model with many manual and remote switches.

Service restoration for resilient distribution system with coordination of battery swapping stations and distributed generations

Due to sufficient energy storage resources and central dispatch, the battery swapping station (BSS) offers a promising solution for the service restoration and resilience enhancement of the distribution system (DS). This paper proposes a joint service restoration strategy for the DS by coordinating BSSs and distributed generations (DGs) with distribution network reconfiguration. Firstly, the spatio-temporal distribution of battery swapping demand for electric vehicles (EVs) is analyzed. On this basis, the participation of the BSS in the DS service restoration is investigated, and the model of restoration capacity for the BSS is established, which can describe the power supply ability of the BSS to support the DS restoration. Secondly, a mathematical model for the resilient DS restoration with coordination between BSSs and DGs is established, aiming to minimize the total system cost by optimally allocating BSSs and DGs during the restoration stage. The second-order cone programming (SOCP) relaxation is also adopted to transform the proposed restoration model into a mixed-integer convex formulation, which can be tractably solved. Finally, numerical simulations are conducted based on the modified IEEE 33-bus distribution test system. The results demonstrate the effectiveness of the proposed strategy for the service restoration and resilience enhancement of the DS.

Service Restoration for Resilient Distribution Systems Coordinated With Damage Assessment

The time required to restore distribution systems following an extreme event is highly dependent on damage assessment. Waiting for field assessors patrolling the feeders to identify fault locations is a bottleneck in improving restoration efficiency. This paper proposes an optimal service restoration model for resilient distribution systems considering the coordination with damage assessment, as a contribution to earlier studies. The restoration actions such as fault isolation, network reconfiguration, crew mobilization and fault repair are brought forward to the damage assessment stage and the restoration schedules are dynamically updated with the reveal of the damage status. The relationship between fault location, switch status and node status is established to optimize the network topology and guarantee crew operation safety under the condition that the network has multiple faults or unchecked potential faulted areas. Moreover, the crew routing formulations are modified to enable fault isolation and load island reconnection by manual switches during the restoration process. Case studies validate the effectiveness of the proposed model in reducing load shedding and restoration duration.

Multi-population evolutionary computing based multi-agent smart distribution system service restoration

Multi-population evolutionary computing based multi-agent smart distribution system service restoration

Resilience-Motivated Distribution System Restoration Considering Electricity-Water-Gas Interdependency

A major outage in the electricity distribution system may affect the operation of water and natural gas supply systems, leading to an interruption of multiple services to critical customers. Therefore, enhancing resilience of critical infrastructures requires joint efforts of multiple sectors. In this paper, a distribution system service restoration method considering the electricity-water-gas interdependency is proposed. The objective is to provide electricity, water, and natural gas supplies to critical customers in the desired ratio according to their needs after an extreme event. The operational constraints of electricity, water, and natural gas networks are considered. The characteristics of electricity-driven coupling components, including water pumps and gas compressors, are also modeled. Relaxation techniques are applied to nonconvex constraints posed by physical laws of those networks. Consequently, the restoration problem is formulated as a mixed-integer second-order cone program, which can readily be solved by the off-the-shelf solvers. The proposed method is validated by numerical simulations on electricity-water-gas integrated systems, developed based on benchmark models of the subsystems. The results indicate that considering the interdependency refines the allocation of limited generation resources and demonstrate the exactness of the proposed convex relaxation.

Robust coordination of repair and dispatch resources for post-disaster service restoration of the distribution system

Since extreme weather events usually severely damage the power grid and lead to widespread power outages, coordinating repair and dispatch resources, such as repair crews (RCs), mobile power sources (MPSs), renewable energy sources (RESs) and energy storage systems (ESSs), is an efficient method to restore the power supply for outage loads. However, the uncertainties existing in RES output and load demand pose significant challenges to the service restoration of distribution systems in terms of security constraints. To address these uncertainties, this paper proposes a robust restoration approach to maximize the restored load in a distribution system by coordinating RCs, MPSs, RESs, ESSs and network reconfiguration. The coordination problem is formulated as a two-stage robust optimization model to generate optimal dispatching and operation schemes of multiple resources with guaranteed robustness against uncertainties. Involving some unexpected changes in topologies and distributed sources during post-disaster repair, the robust coordination model is re-optimized to guarantee the efficiency of repair and operation scheme. Simulation results show that the repair and dispatch resources can be coordinated effectively to accommodate the RES output and load demand uncertainties while maximizing the amount of restored load for service restoration of the distribution system.

Resilient service restoration for distribution systems with mobile resources using Floyd‐based network simplification method

The large-scale blackouts of distribution systems (DS) caused by extreme natural disasters have aroused a lot of attention. To enhance the resilience of DSs, a novel service restoration and recovery model to minimize the system-wide load loss is proposed here, where reconfiguration systems and two kinds of moveable resources, that is, mobile sources (MS) and repair crews (RC), are considered. Then, time-space network (TSN) is applied to model the movement of MSs and RCs over the real-world transportation networks (TN). However, the introduction of TSN makes solving the model become a time-consuming task as plenty of variables are involved by it. To tackle this challenge which may lead to the impracticability of the model, a Floyd-algorithm based TN simplification method is proposed to reduce the variables without sacrificing the equivalence of the TNs before and after the transformation. Finally, the applicability and effectiveness of the proposed model are verified on a 33-bus test DS and a real-world DS with complicated TNs.

Mobility‐As‐A‐Service for Resilience Delivery in Power Distribution Systems

Inspired by the opportunities provided by the Industry 4.0 technologies for smarter, risk‐informed, safer, and resilient operation, control, and management of the lifeline critical networks, this study investigates mobility‐as‐a‐service for resilience delivery during natural disasters. Focusing on effective service restoration in power distribution systems, we introduce mobile power sources (MPSs) as the restoration technology of the future, the mobility of which can be harnessed for spatiotemporal flexibility exchange and effective response and recovery during disasters. We present automated decision‐making solutions that coordinate the MPSs utilization with repair crew (RC) schedules taking into account constraints in both energy and transportation networks. When integrated, the suggested technology aided by the proposed optimization models will have the potential to disrupt the current practice in boosting the resilience and operational endurance of the mission‐critical systems and services during disasters, ultimately resulting in an enriched social welfare and national security.

Robust coordination of multiple power sources for sequential service restoration of distribution systems

When a distribution system encounters a major outage due to extreme events, an efficient sequential service restoration (SSR) strategy is required to restore the outage system. However, uncertainties associated with renewable energy generation and load demands pose significant and complex challenges for the optimization of SSR strategies. This paper proposes a robust multiple power source co-optimization approach for the SSR of an outage distribution system. First, network configuration and sequential switching actions are jointly optimized to sequentially form microgrids (MGs). Second, multiple types of power sources, such as distributed generators and energy storage units, are re-energized and coordinated to pick up critical loads in parallel restored MGs. In addition, uncertainties associated with renewable energy generation and load demands are considered during the restoration process, thereby requiring a robust SSR scheme to increase the reliability of MG operations, The mathematical problem is modelled as a mixed-integer tri-level programming problem with inner-level binary variables. Thus, the extended column-and-constraint generation (EC&CG) algorithm is employed to solve the proposed robust model. The numerical results demonstrate the effective performance of the proposed approach and its robustness and advantages compared to existing models that do not consider uncertainties.

Service restoration in modern distribution systems addressing grid-connected and islanded operations

In modern Distribution Systems (DSs), multiple decentralized generators can be connected to the network and, in some scenarios, can operate isolated from the substation. The island operation affects the Service Restoration (SR) process and may benefit the Distribution Companies (DICOs). However, most of the studies involving SR take into account only the system’s static behavior, ignoring the transient characteristics. While these studies have been validated for decades on classic passive DSs, they cannot be extended for SR with load insertion into islands. This paper studies the dynamic aspects of the SR process, verifying the protective devices’ performance due to the island’s formation and operation, and loads reconnection through Distributed Generators (DGs). Based on the test results for a 405-bus DS, the paper points out the possibility of infeasible SR plans due to disregarding the transient behavior of DGs. The paper also proposes a novel set of constraints that aggregates practical dynamic and static aspects for the SR problem. Finally, a computationally efficient Multi-Objective Evolutionary Algorithm (MOEA) based on subpopulation tables is employed to solve the SR problem considering the newly proposed constraints.

Distributed generator-based distribution system service restoration strategy and model-free control methods

The rapid growth of distributed generator (DG) capacities has introduced additional controllable assets to improve the performance of distribution systems in terms of service restoration. Renewable DGs are of particular interest to utility companies, but the stochastic nature of intermittent renewable DGs could have a negative impact on the electric grid if they are not properly handled. In this study, we investigate distribution system service restoration using DGs as the primary power source, and we develop an effective approach to handle the uncertainty of renewable DGs under extreme conditions. The distribution system service restoration problem can be described as a mixed-integer second-order cone programming model by modifying the radial topology constraints and power flow equations. The uncertainty of renewable DGs will be modeled using a chance-constrained approach. Furthermore, the forecast errors and noises in real-time operation are solved using a novel model-free control algorithm that can automatically track the trajectory of real-time DG output. The proposed service restoration strategy and model-free control algorithm are validated using an IEEE 123-bus test system.

Integrating Protection Constraints to a MEAN-Based Method for Service Restoration in Radial Distribution Systems

The distribution system service restoration (SR) process implies to change the network topology configuration via a real-time maneuver plan, which in turn results in increased load current and changed short-circuit levels in some adjacent feeders. As a consequence, some protective devices (PD) can operate undesirably. In addition, some network branches may itself become totally unprotected. Therefore, it is essential to consider phase and neutral overcurrent protection in the SR problem, in order to maintain service continuity during emergency situations. A very efficiently approach, named MEAN (Multi-Objective Evolutionary Algorithm with NDE), for solving the SR problem is based on Multi-objective Evolutionary Algorithm (MOEA) that combines subpopulation tables with the Node-Depth Encoding (NDE) technique. This paper proposes to incorporate the phase and neutral overcurrent protection constraints into the MEAN approach for service restoration in radial distribution systems. The approach proposed in this paper, named MEAN-PC (MEAN with Protection Constraints), uses additional subpopulation tables to store the solutions that consider protection constraints. The objective is to show that the overcurrent protection constraints on service restoration problems can be considered in an effective way, correctly indicating feasible solutions of maneuver plan. The proposed overcurrent protection constraints in service restoration are tested and verified on the 135-bus test distribution system.