Distribution System Restoration Research Articles

Restoration of a Multi-Energy Distribution System With Joint District Network Reconfiguration via Distributed Stochastic Programming

Restoration of a Multi-Energy Distribution System With Joint District Network Reconfiguration via Distributed Stochastic Programming

Transactive Energy-Based Sequential Load Restoration of Distribution Systems With Networked Microgrids Under Uncertainty

Transactive Energy-Based Sequential Load Restoration of Distribution Systems With Networked Microgrids Under Uncertainty

Transportable energy storage assisted post-disaster restoration of distribution networks with renewable generations

More frequent extreme weather events due to climate change impose significant challenges on the post-disaster restoration of distribution systems with significant penetration of renewable generations. To address this challenge, this paper investigates a restoration scheme for distribution networks integrated with renewable generations, and transportable energy storage systems moving along a transportation network, such as railway or road network, are used to support the network restoration after the fault event. To achieve this, for given fault conditions, topological reconfiguration is first facilitated via the soft open point (SOP) technology, and local renewable generators and loads are rapidly regrouped to form microgrids for post-disaster restoration. Then, assisted with transportable energy storage, the resources within the microgrids can be dispatched more efficiently, facilitating a more efficient post-disaster recovery process. To enable a cost-effective restoration of the distribution network, an economic model is formulated with the aim of minimizing the economic losses during the post-disaster restoration process. Case studies considering a significant number of failure scenarios show that the proposed transportable energy storage-assisted restoration scheme can effectively minimize costs during the post-disaster period considering various load types across different areas. This scheme not only achieves cost-effective post-disaster restoration but also ensures compliance with the constraints of the electrical networks, achieving up to 93.3% recovery cost reduction and 74.25% increase in critical load restoration.

Decentralized restoration of distribution systems with coupling neighboring microgrids

In this study, a multi-agent system (MAS) is incorporated in a decentralized strategy to restore distribution systems while taking into account coupling neighboring microgrids (CNMGs). This provides modeling for renewable energy sources (RESs), electric vehicles (EVs), battery storage systems (BSS) and load. The desired and most favorable restoration path is found by the MAS, in which zone agents are dispersed across the distribution system. The MAS can also manage microgrids (MGs) overloaded as the unbalance operation of RESs, BSS, EVs, and load. This is realized by making a bridge between MGs and neighboring non-overloaded MGs. The suggested method adheres to voltage and power flow restrictions while operating according to expert system standards. The recommended approach is put to the test using a 33-bus radial distribution system. MATLAB calculations on agents and power flow are carried out in order to verify the validity of the choices made by agents. The proposed restoration plan is able to obtain the best power supply path with a low number of switching in the event of a fault so that the voltage magnitude is higher than 0.9 p.u. and free capacity is available for the distribution lines. The smart charging strategy of EVs reduces 93% of their turn off compared to the non-smart charging strategy. However, if the CNMG plan is established, all vehicles can be powered.

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.

Acceleration Framework and Solution Algorithm for Distribution System Restoration Based on End-to-End Optimization Strategy

The distribution system restoration (DSR) problem is traditionally modeled as a mixed-integer linear programming (MILP) model. However, a significant number of integer variables are introduced to describe the DSR process, which introduce additional complexities in both time and space dimensions. Moreover, an enormous number of constraints are constructed to establish a rational DSR decision, while some of them may not be considered tight in practice. The enormous number of binary variables and inactive constraints could make the DSR problem very hard to solve and apply in real-time. The DSR computation burden would be reduced significantly if binary variables and binding constraints are pre-determined. This paper proposes an acceleration framework and solution algorithm based on the end-to-end optimization, which applies deep neural network (DNN) and gradient boosting decision tree (GBDT) methods to DSR. The DSR problem, which is solved in offline and online stages, will accordingly be reduced to a linear programming problem which can be solved more efficiently and reliably. Case studies are carried out on the modified IEEE 33-bus and 123-bus systems and a practical 1069-bus system. The proposed results indicate that the DSR problem with the proposed end-to-end acceleration framework is solved more than tenfold faster than those of traditional solvers.

Joint Scheme for Electric Locomotive Evacuation and Distribution System Restoration with Faults of External Power Supply Systems

Joint Scheme for Electric Locomotive Evacuation and Distribution System Restoration with Faults of External Power Supply Systems

Distributed Coordinated Restoration of Transmission and Distribution Systems with Repair Crews and Mobile Emergency Generators

Distributed Coordinated Restoration of Transmission and Distribution Systems with Repair Crews and Mobile Emergency Generators

Enhancing Distribution System Restoration With Coordination of Repair Crew, Electric Vehicle and Renewable Energy

Enhancing Distribution System Restoration With Coordination of Repair Crew, Electric Vehicle and Renewable Energy

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.

Adaptive ADMM-based entire-process distributed restoration of transmission and distribution systems

In the event of a major power outage in the power systems, there is an urgent need to investigate entire-process coordinated restoration strategies for the transmission systems (TSs) and distribution systems (DSs), aiming to accelerate the restoration speed of generating units, network reconfiguration, and load restoration. Furthermore, it is imperative to address the multiple uncertainties that arise during the restoration process to mitigate potential security risks associated with the restoration. Hence, an adaptive ADMM-based entire-process distributed restoration method of TSs and DSs considering CVaR is proposed in this paper. Firstly, an entire-process distributed restoration model of TSs and DSs considering CVaR is proposed to maximize the total restoration benefits of TSs and DSs. Then, an adaptive ADMM-based distributed solving algorithm for the coordinated restoration model of the TSs and DSs is introduced, which incorporates adaptive penalty parameter adjustments, leading to faster convergence compared to the standard ADMM. Finally, case studies on an improved 179-bus transmission system are employed to verify that the proposed restoration method can achieve higher restoration benefits and faster convergence speed compared to existing restoration models.

Equity‐aware power distribution system restoration

AbstractThe efficient, reliable, and resilient supply of electricity has become essential for social and economic well‐being of the modern society. However, more frequent occurrence of extreme weather events has exposed inequity in the planning and operation practices of power distribution systems, evidenced in higher vulnerability and longer power interruptions for some parts of the grid as compared to others. This paper proposes an equity‐aware power distribution system restoration model in an effort to ensure a more equitable yet resilient power distribution operation after outages. To this end, the proposed equity‐aware distribution system restoration model balances the efficiency of the restoration operation and the equitable allocation of distributed energy resources among affected customers after an outage, while prioritizing the critical infrastructure (e.g. hospitals). The results demonstrate the effectiveness of the proposed framework to ensure a more equitable restoration process as measured by the proposed fairness and restoration performance indices.

Sequential Load Restoration With Soft Open Points and Time-Dependent Cold Load Pickup for Resilient Distribution Systems

Global changes in climate conditions and increasing number of weather-related disasters in the world have elevated the quest for better load restorations in power distribution systems. This paper proposes a distribution system restoration (DSR) model where non-exponential time-dependent cold load pickup (CLPU) characteristics and soft open point operations are simultaneously addressed. We develop a time-dependent CLPU model for attaining a more realistic and secure DSR in which we demonstrate that the traditional delayed exponential model (DEM) for a long outage duration is a special case of the adopted CLPU model. The current soft open point (SOP) models would typically relax the operation constraints without devising any tightening procedures, which might lead to inaccurate or even infeasible SOP states. In this paper, we analyze the reasons traditional SOP constraints would lead to unsecure operations and present a set of novel SOP operation constraints for recovering bindings. Also, we adopt the sequential second order cone programming (SSOCP) algorithm to establish the sequential framework of DSR and solve the proposed model effectively. Our presented case studies discuss the rationality of the proposed DSR model and algorithm.

A Self-Healing Strategy for Modern Distribution Networks

Electrical distribution companies have been investing in modernizing their structures, especially operation automation. The integration of information technologies and communications makes fast power restoration during fault events, providing better profit to companies and a more reliable and safe distribution network for customers. A self-healing strategy can be implemented for protection and control devices to work cooperatively, achieving the global purpose of automatic distribution system restoration. Thus, this work proposes a methodology for short-circuit fault detection, isolation of the faulted section, and restoration of downstream sections using neighbor feeders. The protection devices use standardized IEC and ANSI/IEEE functions to sensitize faults in the system and to promote adequate isolation, allowing the consequent restorative process. A genetic algorithm optimizes the devices’ parameters used in the protection scheme, making fastest the isolation process and ensuring the protection system coordination and selectivity. Results obtained using Simulink® allows for verifying the proposed methodology’s behavior and efficiency.

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.

Optimal allocation of distributed renewable generations in low-carbon distribution system considering impact of natural disasters

With global climate change, increasingly frequent natural disasters have brought great challenges to the safe and reliable power supply and low-carbon transition of power distribution systems. Most of the existing researches on the distribution system under the impact of natural disasters only focus on the improvement of power supply reliability, but have not consider the impact of disaster severity and disaster response measures on carbon emissions. In order to juggle the load restoration and carbon emission mitigation of distribution system under natural disasters, this paper proposes an optimal allocation method of distributed renewable generations (DRGs) considering carbon emission for multi-scenario natural disasters based on the framework of cyber-physical-social system in energy (CPSSE), and establishes a three-stage optimization model of pre-disaster prevention-disaster attack-post-disaster restoration. For the purpose of ensuring the practicability and robustness of the allocation results, the disaster scenario is modeled and the selection method of the worst fault scenario under disaster is proposed. The progressive hedging algorithm (PHA) is adopted to solve the proposed multi-scenario optimization problem. Finally, the simulation results indicate that the proposed method can restore more lost load at a lower cost of carbon emissions.

Presenting a new scheme for developing a distribution system restoration using online islanding method in the presence of dispatchable and non‐dispatchable DGRs and energy storages

AbstractThis paper proposes a two‐layer method for restoring service. Firstly, a new strategy is used to obtain binary variables relating to closed switches and tie switches. In addition, after the fault occurs it is also possible to obtain optimal islands online. Secondly, a non‐linear model is solved to optimally determine the islands created in the first step, in which distributed generation resources (DGRs), energy storages (ESs) and load shedding (LSH) are reprogrammed. When LSH is performed, the sensitive loads must experience the least amount of outage possible. Therefore, the loads are categorized into residential, industrial, and sensitive categories. In order to demonstrate the validity and effectiveness of the proposed method, a modified IEEE 33‐bus system is simulated. In simulations, the proposed strategy has been compared with one of the important existing methods. Based on these results, the proposed method can reduce the cost function by modifying the critical load less than other methods. In this method, the critical LSH is 220 kW, which is 105 kW less than the compared method. By reducing the amount of load, 46,000 dollars have been saved in system expenses. The results confirm that the proposed strategy is considerably superior to the existing method.

A distributionally robust resilience enhancement model for transmission and distribution coordinated system using mobile energy storage and unmanned aerial vehicle

Extreme natural disasters can seriously disrupt power systems. The increased damage intensity of natural disasters also leads to synchronous failures in communication systems. Mobile energy storage and unmanned aerial vehicles have high economy and flexibility, so they can provide various services including power support and temporary information transmission when disasters occur and disable the whole system. To address the unavailability of the communication system and improve the collaborative restoration of transmission and distribution systems, this paper proposes a multi-period distributionally robust resilience enhancement model (MPDRRM) for the transmission and distribution coordinated system, and a large quantity of mobile energy storage and unmanned aerial vehicles are rented before natural disasters and dispatched to support critical loads and maintain power balance during restoration. The MPDRRM is divided into three kinds of sub-problems: i) a communication system sub-problem, ii) a transmission system sub-problem, iii) several distribution system sub-problems, and solved by a modified three-level analytical target cascading algorithm. Case studies demonstrate the benefits of mobile energy storage and unmanned aerial vehicles in improving load restoration and increasing the resilience of a TDCS against natural disasters.

Preventive allocation and post-disaster cooperative dispatch of emergency mobile resources for improved distribution system resilience

Repair crews (RCs) and mobile power sources (MPSs) are emergency mobile resources (EMRs) for distribution system repair and restoration. In this paper, an outage management strategy (OMS) is proposed to improve the distribution system resilience against extreme weather events in both preventive and post-disaster stages. In the preventive stage, considering the uncertainty of fault location, the resource allocation problem is formulated as a two-stage stochastic programming to minimize the expected load shedding cost shortly after the disaster. The post-disaster stage is a cooperative dispatch of EMRs, accompanied by dynamic microgrid formation. With the progress of line repair, the MPSs are dispatched to different locations to support microgrids. The optimal repair sequence of faulted lines and scheduling of MPSs are determined in a multi-timeslot dispatch model. The proposed algorithm is validated by IEEE 123-bus test system and a 118-bus real-world system. The simulation results demonstrate that OMS can efficiently restore critical loads via an optimal utilization of mobile resources.

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.