Distribution System Restoration Research Articles

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.

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.

Layered Coordination Architecture for Resilient Restoration of Power Distribution Systems

The current practices for restoring critical services in the distribution system during a disaster align with the traditional centralized ideology of distribution systems operations. A central processor evaluates the distribution system after a disruption and attains a restoration plan. However, the centralized operational paradigm is to single-point failures, requires full situational awareness of the distribution system, and poses scalability challenges for large multi-feeder distribution systems. This motivates a distributed decision-making paradigm where multiple agents solve smaller sub-problems and jointly coordinate their individual decisions to achieve the global/network-level objective. Towards this goal, we propose a Layered Architecture for Distributed Algorithms for Resilience (LADAR) and a two-stage distributed algorithm for distribution system restoration. The proposed distributed decision-making framework enables the bottom-up restoration of the distribution system using all available resources, including distributed generation (DGs), while only requiring local awareness and limited communications with neighboring connected regions. The proposed framework is robust to single-point failures, enables autonomy using distributed algorithms, and had reduced computational cost compared to centralized optimization solutions.

Utilizing Aggregated Distributed Renewable Energy Sources With Control Coordination for Resilient Distribution System Restoration

Due to the energy transition process, distribution systems will feature a high penetration of distributed renewable energy sources (RESs). The multiple distributed generation can provide emergency power supply to critical loads against blackouts caused by natural disasters and malicious attacks. However, the uncertainty of RESs, the control mode variation of RESs together with energy storage systems (ESSs), and the interaction among distribution system operator (DSO) and RESs add increasing difficulties to load restoration decisions. This paper applies the coordination control strategy to enable RESs to regulate the frequency and voltage during restoration process. Then, distributed energy resource management systems (DERMSs) and its aggregation characteristic are studied in load restoration. Finally, a two-step scenario-based stochastic optimization with the DSO-DERMS interaction framework is formulated to utilize RESs for resilient distribution systems. At last, the proposed critical load restoration optimization method is validated on a modified IEEE 37 and 123 node test feeder to verify the effectiveness.

Hierarchical Combination of Deep Reinforcement Learning and Quadratic Programming for Distribution System Restoration

This paper proposes a model for hierarchical combination of deep reinforcement learning (DRL) with quadratic programming for distribution system restoration after major outages. In the proposed model, optimal power dispatch of a collection of distributed energy resources, called integrated hybrid resources (IHRs), is determined by a DRL-trained controller, while a grid-level quadratic programming problem checks grid constraints and performs critical restoration operation. DRL is implemented using Soft Actor-Critic (SAC) algorithm, which is shown to outperform the common Deep Deterministic Policy Gradient in continuous action spaces. The numerical studies, performed on the 123-bus test distribution system, demonstrates that the hierarchical combination of DRL and quadratic programming not only speeds up the local operation of multiple IHRs, but also ensures that the network constraints are satisfied during the restoration operation.

Boost Distribution System Restoration With Emergency Communication Vehicles Considering Cyber-Physical Interdependence

Enhancing restoration capabilities of distribution systems is one of the main strategies for resilient power systems to cope with extreme events. However, most of the existing studies assume the communication infrastructures are intact for distribution automation, which is unrealistic. Motivated by the applications of the emergency communication vehicles (ECVs) in quickly setting up wireless communication networks after disasters, in this paper, we propose an integrated distribution system restoration (DSR) framework and optimization models, which can coordinate the repair crews, the distribution system (physical sectors), and the emergency communication (cyber sectors) to pick up unserved power loads as quickly as possible. Case studies validated the effectiveness of the proposed models and proved the benefit of considering ECVs and cyber-physical interdependencies in DSR.

Resilient Distribution System Restoration With Communication Recovery by Drone Small Cells

Distribution system (DS) restoration after natural disasters often faces the challenge of communication failures to feeder automation (FA) facilities, resulting in prolonged load pick-up process. This letter discusses the utilization of drone small cells for wireless communication recovery of FA, and proposes an integrated DS restoration strategy with communication recovery. Demonstrative case studies are conducted to validate the proposed model, and its advantages are illustrated by comparing to benchmark strategies.

Analytical Derivation of Frequency Response Rate Constraint in Distribution System Restoration With Single Master Diesel-Based DG

Transient frequency deviation resulting from load pick-up operations is a barrier to DG-based service restoration. The prime mover dynamics and control-parameter configuration have a significant effect on the frequency response rate (FRR) in the restoration process, but have not been sufficiently considered in previous works. To address this issue, this letter analyzes the FRR based on the transfer function of the speed-regulator of a single master diesel generation unit, and simplifies the model through variable substitution and order reduction. A succinct FRR constraint is derived according to the characteristics of the standard Type 2 system. The simulation of the restoration process verifies the accuracy of the derivation.

Outage management of hybrid AC/DC distribution systems: Co-optimize service restoration with repair crew and mobile energy storage system dispatch

To achieve the most efficient restoration of hybrid AC/DC distribution system, this paper proposes an outage management through co-optimizing service restoration with repair crew (RC) and mobile energy storage system (MESS) dispatch. Firstly, this paper proposes a hybrid AC/DC distribution system restoration (DSR) model considering network reconfiguration, distributed generation (DG) islanded operation and voltage source converter (VSC) based DSR, which means adjusting VSC control mode to realize uninterrupted power supply of loads. In the DSR model, a restoration coordination model that can coordinate network reconfiguration and the control modes of VSCs, DGs and MESS discharge stations is developed to ensure the radial configuration of the system. Moreover, to accurately calculate the power flow of the system during a failure of VSCs, a new power flow model considering VSC’s operation status is proposed in the DSR model. Then, a bi-level co-optimization method is developed based on the DSR model and improved dispatch models of RC and MESS. The upper level co-optimizes hybrid AC/DC distribution system restoration with RC and MESS dispatch to maximize the restored loads. The lower level further optimizes the power outputs of VSCs and DGs to minimize the voltage deviation of the restored loads. Finally, simulations studies are performed to verify the effectiveness and superiority of the proposed method.

Enhanced Situational Awareness for DER-Rich Distribution Systems Under Pre- and Post-Outage

Situational awareness (SA) is critical to properly operating active power distribution systems during normal and outage conditions. Appropriate SA tools should provide an accurate estimate of system voltage and current variables, the operational network topology, and the power injections from distributed energy sources (DERs), including behind-the-meter (BTM) photovoltaics (PVs). Obtaining an accurate SA, especially by estimating network topology and gross load demand, is increasingly challenging with the proliferation of BTM DERs with intermittent power generation. Moreover, the SA required for distribution system restoration is even more challenging after a medium to a prolonged outage due to inaccurate estimates of cold load pickup (CLPU) and switch statuses. This paper proposes an integrated real-time model update (RTMU) module for SA enhancement to help distribution system operators (DSOs) understand the power system conditions in dynamic and DER-rich environments. The proposed RTMU consists of several modules to obtain the required level of SA for operational decision-making. It includes estimators for a) BTM PV power, b) network topology, and c) CLPU. The proposed approaches leverage multiple data resources, deep learning approaches, and domain knowledge of the power system to provide the required level of SA to DSOs. The dependencies among these modules are actively leveraged to enhance SA under normal conditions and during power outages. We demonstrate and analyze the performance of the proposed RTMU on a modified IEEE 123-node feeder and a utility distribution system from the Western United States.

Presenting a new IGDT-based scheme for risk-based developing of distribution system restoration using online islanding method

Optimal service restoration in emergency cases is considered one of the most essential features of a smart distribution system. In the present study, a new IGDT-based scheme for risk-based system restoration in the form of a two-layer method is proposed. In the first layer, the binary variables related to the status of the closed switches and tie switches are obtained by a new strategy. Also, the optimal islands are obtained online after the fault. In the second step, the continuous variables, including reprogramming the distributed generation resources (DGR), storage, and amount of load shedding in the optimal island or islands that have been created in the first layer, are solved by a nonlinear model. In a fault event, to establish the power equilibrium, reprogramming the generation resources (GRs) and storage must be associated with the load shedding in the system. The load shedding must be done so that the sensitive load undergoes minimum outage. Thus the loads are prioritized by placing the loads into three residential, industrial, and sensitive categories. The risk-based problem is formulated in two operation modes: risk-averse and risk-seeker. In the risk-averse mode, the robustness function is used under high market prices. Moreover, in the risk seeker mode, an opportunity function is used under lower market prices towards lower-cost results. The simulation process is performed on an IEEE 33-bus system to evaluate the proposed model. To validate the proposed method, it is compared with one of the existing methods, and the proposed method is considerably better.

Restoration of a new age power distribution system

The new age transformed distribution system became the most popular content in the literature. In that regard a simulation is attempted for the load flow with dynamically changing system data. Any topic of interest like electrical vehicle, distributed generators, FACTS devices were generalized and considered as an element and placed at different possible locations to observe their effect. The outcomes of the work were generalized to re-consider on site. As the power industry is undergoing a drastic change throughout the world. Restoration of a distribution system along with switching sequence has become a challenging task. In the last half of the twentieth century, analysis of transmission system was the prime concern and faced many challenges to power engineers. The chosen equivalent IEEE 33-bus electric power distribution system has been considered to analyze the system properties such as its maximum capacity, operating limits, restoration issues. The placement of static var compensator (SVC) was experimented for each and every bus. Considering the minimization of total power loss as a prime objective, the best location was found. This analysis also suggested a ranking of different buses and can be helpful in deciding the switching sequence, provided in case of fault, weak area clustering is done. So, congestion is managed in this distribution system.