Distribution System Restoration Research Articles

On the Radiality Constraints for Distribution System Restoration and Reconfiguration Problems

Radiality constraints are involved in both distribution system restoration and reconfiguration problems. However, a set of widely used radiality constraints, i.e., the spanning tree (ST) constraints, has its limitations which have not been well recognized. In this letter, the limitation of the ST constraints is analyzed and an effective set of constraints, referred to as the single-commodity flow constraints, is presented. Furthermore, a combined set of constraints is proposed and case studies indicate that the combined constraints can gain computational efficiency in the reconfiguration problem. Recommendations on the use of radiality constraints are also provided.

Multiperiod Distribution System Restoration With Routing Repair Crews, Mobile Electric Vehicles, and Soft-Open-Point Networked Microgrids

This paper proposes a distribution system restoration model which is in response to multiple outages caused by natural disasters. The proposed restoration model includes the coordination of routing repair crews (RRCs), mobile battery-carried vehicles (MBCVs), and networked microgrids (NMGs) formed by soft open points (SOPs). The travel and repair time constraints are modeled for each RRC; travel path and charging strategy are modeled for each MBCV; and the network reconfiguration is developed considering the optimal operation of SOP-based NMGs. The proposed model is presented as a mixed-integer linear program which is solved by an auxiliary induce function based algorithm to reduce the computational complexity. The modified IEEE 33-bus and 69-bus distribution systems are tested with multiple outages. The presented results demonstrate the effectiveness of the proposed model.

Warm‐start piecewise linear approximation‐based solution for load pick‐up problem in electrical distribution system

As the core sub-problem of both network reconfiguration and service restoration of the electrical distribution system (EDS), the load pick-up (LPP) problem in EDS searches the optimal configuration of the EDS, aiming to minimise the power loss or provide as much power as possible to load ends. The piecewise linearisation (PWL) approximation method can be used to tackle the network power flow constraints’ non-linearity in the LPP problem model, and transform the LPP model into a mixed-integer linear programming model (LPP-MILP model). The errors in the PWL approximation of the network power flow constraints may affect the feasibility of the solving results of the LPP-MILP model. The single method to reduce the PWL approximation errors by increasing the number of discretisations in PWL function is not stable. Moreover, the solution efficiency of the LPP-MILP model is sacrificed severely. In this study, a warm-start PWL approximation-based solution for the LPP problem is proposed. The variable upper bounds in the PWL approximation functions are renewed dynamically in the warm-start solution procedure to reduce the PWL approximation errors with higher computational efficiency. Modified IEEE 33-bus radial distribution test system and a real and large distribution system, 1066-bus system, are used to test and verify the effectiveness and robustness of the proposed methodology.

Seismic-Resilient Electric Power Distribution Systems: Harnessing the Mobility of Power Sources

Mobile power sources (MPSs), including mobile emergency generators, truck-mounted mobile energy storage systems, and electric vehicles, have great potentials to be employed as grid-support resources during power grid emergency operating conditions to supply the critical loads and enhance the resilience of distribution system (DS) via a swift disaster restoration. We here investigate the MPS dispatch (i.e., routing and scheduling) in coordination with DS dynamic network reconfiguration. We propose a two-stage restoration scheme to facilitate the DS restoration following the high-impact low-probability (HILP) seismic disasters. In the first stage, a seismic hazard is simulated through a Monte Carlo simulation engine to estimate the unavailability of power distribution branches under a suite of seismic force scenarios. In the second stage, a mixed-integer nonlinear programming (MINLP) optimization model is formulated for DS restoration that cooptimizes the routing and scheduling of MPSs and DS dynamic network reconfiguration. The MINLP model is then linearized to a mixed-integer linear programming model to reduce the computation complexity, where the seismic-resilience recovery strategies are generated at different timescales. The efficacy of the proposed method is evaluated on the IEEE 33-node test system and the results verify a significant reduction in the load outages and an improved power system resilience to HILP earthquakes.

Resilient Service Restoration for Unbalanced Distribution Systems With Distributed Energy Resources by Leveraging Mobile Generators

This article proposes an integrated optimization model for unbalanced distribution system restoration after large-scale power outages caused by extreme events. The model can coordinate the control actions of multiple types of distributed energy resources (DERs), including dispatchable distributed generators (DGs), renewable DGs (mainly wind and solar), and energy storage systems (ESSs). The model also considers topology flexibility by forming dynamic islands through reconfiguration. Besides, the optimal dispatch model of repair crews and mobile emergency generators are also proposed to leverage the restoration capabilities of existing DERs installed in the distribution systems. The integrated optimization model is linearized to be a mixed-integer linear programming form, which can be effectively solved by off-the-shelf solvers such as Cplex and Gurobi. Numerical results on IEEE 123 and 8500 node test feeders validated the effectiveness of the proposed model and highlighted the necessity of coordinating various flexible resources.

Advanced FLISR With Intentional Islanding Operations in an ADMS Environment Using GridAPPS-D

An advanced distribution management system (ADMS) is a software platform that supports grid management and decision support applications to address the growing operational challenges faced by power distribution systems to ensure reliable, resilient, and economic operations. In this paper, a fault location, isolation, and restoration (FLISR) application is developed and integrated with an open-source standards-based platform for ADMS application development viz. GridAPPS-D, developed by Pacific Northwest National Laboratory (PNNL). Also, a new distribution system restoration (DSR) algorithm, an integral part of the FLISR application, is proposed that utilizes backup feeders and distributed generators (along with intentional islanding features) to restore critical loads. The proposed application is developed in python and is tested on a modified IEEE 8500-node test system using the GridAPPS-D platform. The successful deployment of the FLISR application on the GridAPPS-D platform provides a proof-of-concept for the adoption of new and advanced ADMS applications to support future power distribution systems.

Electric Vehicle Markov-Based Adequacy Modeling for Electric Microgrids

Microgrids (MGs) offer a new paradigm for the operation of electricity networks, allowing end users to significantly improve the power quality and, more importantly, reliability of their power supply systems. While it is clear that MGs offer a significant advantage by providing electricity to customers who would otherwise be disconnected during outages, there are numerous issues with the safe, secure, and efficient operation of MGs, the most basic being integrated management and coordinated control of all resources in grid-connected and off-grid modes. A valuable component that can provide new opportunities for increased reliability of the system and reduced vulnerability to faults is the electric vehicle (EV). One of the major benefits of utilizing EV energy storage is the mobility feature of EVs, which can add great value to the restoration of the power distribution system. The main aim of this work is to understand and model how EV batteries can be used as an intelligent energy reservoir, utilizing both controllable loads (home-to-vehicle and grid-to-vehicle) and controllable energy storage (vehicle-to-home and vehicle-to-grid). This requires modeling the stochastic behavior of EV driving and charging/discharging as well as quantifying the impact of utilizing EV energy storage to restore service to customers. This paper proposes an EV Markov adequacy model that evaluates the reliability of an MG distribution system utilizing EVs and investigates EV mobility and available capacity modeling and EV system adequacy analysis, including the effects on the system reliability of the EV capacity, driving behavior, recharging mode, and EV penetration.

Optimal islanding for restoration of power distribution system using Prim's MST algorithm

Optimal islanding for restoration of power distribution system using Prim's MST algorithm

Distributed Restoration for Integrated Transmission and Distribution Systems With DERs

A resilient power system is capable of providing fast and efficient recovery from outages caused by natural or man-made attacks. After a major blackout, transmission, and distribution system operators (TSOs and DSOs) coordinate to restore the system in a timely and reliable manner. However, these two systems are usually operated separately without considering capabilities and limitations of each individual network, or their mutual impacts. This could prolong the restoration process and lead to unpractical outcomes. Therefore, a distributed strategy for integrated transmission and distribution systems restoration (ITDSR) has been developed in this paper. Based on the alternating direction method of multipliers (ADMM), the distributed algorithm is able to provide the coordinated restoration for both TSOs and DSOs by sharing limited information of boundary buses in an iterative procedure. The ADMM-ITDSR model includes convex ac power flow model and three-phase unbalanced branch flow model. The ITDSR strategy coordinates the operation of distributed energy resources in terms of serving high priority loads across different networks. The effectiveness and advantage of developed models and algorithms are validated and demonstrated through testing of the integrated IEEE test cases, such as 14-bus transmission with 13-node distribution systems, and 39-bus transmission with 123-node distribution systems.

Distribution System Restoration With Renewable Resources for Reliability Improvement Under System Uncertainties

Integration of renewable distributed generation (DG) units into distribution networks is gaining widespread popularity. However, uncertainties in generation availability associated with renewable DG units pose a major challenge. These uncertainties should be properly addressed to ensure acceptable system performance and improve customer-side reliability. In this article, the reliability assessment of distribution systems embedded with renewable DG sources has been carried out giving emphasis to system uncertainties and optimal restoration strategies. The uncertainties associated with the power output from renewable resources, time-varying load demand, stochastic prediction errors, and random fault events have been accounted for in the restoration optimization formulation for reliability evaluation. In this article, a parameter-free particle swarm optimization (PSO) technique is applied to address the complexity involved in the formulation. Moreover, a problem-specific encoding scheme is also proposed in conjunction with PSO to ensure optimality.

Resilient Disaster Recovery Logistics of Distribution Systems: Co-Optimize Service Restoration With Repair Crew and Mobile Power Source Dispatch

Repair crews (RCs) and mobile power sources (MPSs) are critical resources for distribution system (DS) outage management after a natural disaster. However, their logistics is not well investigated. We propose a resilient scheme for disaster recovery logistics to co-optimize DS restoration with dispatch of RCs and MPSs. A novel co-optimization model is formulated to route RCs and MPSs in the transportation network, schedule them in the DS, and reconfigure the DS for microgrid formation coordinately, etc. The model incorporates different timescales of DS restoration and RC/MPS dispatch, the coupling of transportation and power networks, etc. To ensure radiality of the DS with variable physical structure and MPS allocation, we also model topology constraints based on the concept of spanning forest. The model is convexified equivalently and linearized into a mixed-integer linear programming. To reduce its computation time, preprocessing methods are proposed to pre-assign a minimal set of repair tasks to depots and reduce the number of candidate nodes for MPS connection. Resilient recovery strategies thus are generated to enhance service restoration, especially by dynamic formation of microgrids that are powered by MPSs and topologized by repair actions of RCs and network reconfiguration of the DS. Case studies demonstrate the proposed methodology.

Fuzzy System Methodology for Restoration of Distribution System by Distributed Generation

Blackout is the severe thing in the whole power system. Restore the power system is a complex thing for the system operators. In Power system restoration process pickup the critical load is the important task in the restoration process. In this distributed generated using to pick up the critical load. DG placements are done by fuzzy logic system using Virtual instrumentation. In this paper this methodology was tested in two load locations with two distributed generation system. DG placement depends upon severity of the damage of power system which restore with less restoration time.

Distributed Secondary Control Strategy for Microgrid Operation with Dynamic Boundaries

Microgrids (MGs) with dynamic boundaries, also known as dynamic MGs, are able to support critical loads without energization from utility and allow system topology variation upon request. Utilization of dynamic MGs can provide more flexible solutions toward distribution system restoration from natural disasters. This paper proposes a distributed secondary control strategy for dynamic MG operation under both static topology and topology variation. The proposed control strategy aims to guarantee seamless transitions during dynamic MG reconfiguration and proper power management among distributed generators (DGs) that are grouped dynamically. Smart switches (SSWs) are utilized to identify, process, and implement the reconfiguration request. The proposed control strategy provides a framework to coordinate the operation of multiple DGs in neighboring autonomous MGs and determine the operation status of SSWs, so that no transient over-shoots are introduced as SSWs operate and DGs are able to support the system power demand proportionally. Detailed controller designs are provided. Sufficient conditions under which the proposed controllers are exponentially stable are derived and the dynamic performance of the proposed controller are validated by comprehensive case studies in MATLAB/Simulink.

Distributed Load Restoration in Unbalanced Active Distribution Systems

The resilience of modern power systems has been constantly threatened by various natural disasters and man-made attacks. Under those threats, fast and automatic restoration actions are critical for restoring impacted systems to normal operating conditions and preventing from additional disastrous consequences. Smart grid technologies, such as distributed energy resources (DERs) and microgrids, provide both opportunities and challenges for distribution system restoration. In this paper, distributed load restoration (DLR) in unbalanced active distribution networks is developed using the alternating direction method of multipliers (ADMMs) algorithm. The step-by-step restoration actions are provided to schedule the output of DERs and distribution system control devices, as well as the power consumption from transmission system restoration. The nonlinearities from three-phase unbalanced power flow and distribution components modeling are relaxed into a convex quadratic programming model. Then, the problem is decomposed into subproblems for each node by applying ADMM-DLR; and solves through each agent by exchanging limited information with neighboring nodes in an iterative procedure. The developed models and algorithms are validated and demonstrated through testing of the IEEE 13-node and 123-node test feeders. Simulation results demonstrate the effectiveness of restoring unbalanced distribution system with the hybrid bottom-up and top-down restoration strategies.

Repair and Resource Scheduling in Unbalanced Distribution Systems Using Neighborhood Search

This paper proposes an optimization strategy to assist utility operators to recover power distribution systems after large outages. Specifically, a mixed-integer linear programming (MILP) model is developed for co-optimizing crews, resources, and network operations. The MILP model coordinates damage isolation, network reconfiguration, distributed generator re-dispatch, and crew/resource logistics. In addition, a framework for integrating different types of photovoltaic (PV) systems in the restoration process is developed. We consider two different types of crews, namely, line crews for damage repair and tree crews for obstacle removal. We also model the repair resource logistic constraints. Furthermore, a new algorithm is developed for solving the distribution system repair and restoration problem (DSRRP). The algorithm starts by solving DSRRP using an assignment based method, then a neighborhood search method is designed to iteratively improve the solution. The proposed method is validated on modified IEEE 123and 8500-bus distribution test systems.

Service restoration of active distribution systems with increasing penetration of renewable distributed generation

The increasing penetration of renewable generation poses several challenges to the operation of distribution networks under emergency conditions. This study proposes a service restoration method that exploits active management of the distribution network. The proposed active network management (ANM) method considers the coordinated control of the available switches, distributed generation, and the operation of on-load tap changer (OLTC) to determine the optimal service restoration plan. The objectives of the proposed model are the minimisation of (i) the out of service area considering customer priorities, (ii) the number of switch operations, (iii) the number of tap changes of the OLTC, and (iv) the injected power from the substations considering the variation of load demand and renewable generation. The service restoration problem is formulated as a mixed-integer second-order cone programming problem, which is efficiently solved by commercial branch and bound solvers. Results on a 135-bus distribution system and a 540-bus distribution system highlight the importance and the benefits of incorporating ANM into the solution of the service restoration problem.

Enhancement of Distribution System Restoration Capability in Single/Multiple Faults by Using Microgrids as a Resiliency Resource

Natural disasters such as hurricanes and major floods result in extensive interruptions and lack of service to end-use customers in electrical distribution systems. Robust systems immune to extreme abnormal events are uneconomical and hard to achieve, so, fast and reliable solutions to restore the service after major outages are preferable. Rerouting the supply to critical loads and deploying microgrids are common solutions. In this paper, a sophisticated solution based on a combination of reconfiguration and application of microgrids is proposed to enhance the restoration capability of the distribution system using spanning tree search strategy. The coordinated plan would maximally restore the critical loads by using microgrids as emergency sources even when utility power is unavailable. The objectives are minimizing the number of switching operations, total system losses and out-of-service loads. A heuristic approach is proposed to simplify the graph of the distribution network in order to reduce the computational complexity. Instead of removing all degree one/two vertices, a threshold for simplification is applied to avoid establishment of zones with high loads that could not be restored by microgrids. The proposed method is simulated on a four-feeder, 1069-bus unbalanced test system with four microgrids to demonstrate its effectiveness.

A Heuristic Method of Restoring Distribution Systems Using Field Measurements

The restoration of a distribution system (DS) is a complex process that must be completed in a few minutes. This paper presents a practical and simple method for restoring DSs after the occurrence of faults using information reported by equipment installed in the DS and its topological structure. The operating principle of the proposed method is based on the identification of sections that border the faulted section, which is then isolated. Next, a two-level search is executed in a reduced space to achieve restoration. To demonstrate its effectiveness, the proposed method is applied in two real systems, one from the Taiwan Power Company and other from a Brazilian Utility System. The results are compared with other approaches and demonstrate that the proposed method can provide an efficient DS restoration plan even under different load levels and equipment failures. A practical test in laboratory is also present. The method can be used in any DS.

Automatic fault isolation and restoration of distribution system using JADE based Multi-Agents

This paper proposes a solution for automatic service restoration along with automatic fault location and isolation of the faulty sections in feeder in a power distribution system. A Java agent development environment-based multiagent system (MAS) is proposed to solve the problem of automatic service restoration in smart grid distribution systems. The agent-based solution development is discussed in detail and the MAS application to solve power restoration problem is elaborated in this paper. A study is done on a modified IEEE 33 bus system and the solution is implemented in the Velachery substation of the Tamilnadu electricity board. The results prove that fast and effective fault location, isolation and service restoration is achieved using the proposed solution.

Toward a Synthetic Model for Distribution System Restoration and Crew Dispatch

Distribution service restoration (DSR) is critical for improving the resilience and reliability of modern distribution systems by strategically and sequentially energizing the system components and customer loads. Restoring electricity service to affected customers also requires multiple crews with different skill sets to perform multiple tasks that are procedurally interdependent with safety guaranteed. However, in existing DSR practices, switch operations and crew dispatch are scheduled separately, and their interdependence is not fully considered. As advanced technologies are enabling remote communication, control, and dispatch, utilities now desire an integrated DSR framework to achieve seamless coordination among multiple DSR tasks such as switch operation, crew dispatch, and component repair. In this paper, we introduce a synthetic model that integrates the service restoration model and the crew dispatch model based on a universal routing model. The proposed model can provide the estimated time of restoration for each load, the switching sequence for safely operating remotely/manually operated switches, and dispatch solutions for crews with different skill sets. The proposed synthetic model is formulated as a mixed-integer linear programming model, and its effectiveness is evaluated via the IEEE 123 bus test feeder and several large-scale test feeders (EPRI Ckt5, Ckt7, Ckt24, and IEEE 8500 node test feeder.