Distribution Network Restoration Research Articles

Dynamic Service Restoration of Distribution Networks With Volt-Var Devices, Distributed Energy Resources, and Energy Storage Systems

Dynamic Service Restoration of Distribution Networks With Volt-Var Devices, Distributed Energy Resources, and Energy Storage Systems

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.

Dynamic Restoration of Active Distribution Networks by Coordinated Repair Crew Dispatch and Cold Load Pickup

Dynamic Restoration of Active Distribution Networks by Coordinated Repair Crew Dispatch and Cold Load Pickup

Impact on Long Duration of Overvoltage Due to Back-Feed Restoration of Distribution Network

In power system, power outages can be critical; if the environment and public safety within risk situation. Therefore, restoration power is needed to respond on system distribution to reduce the power restoration time and making the system reliable and efficient. Backfeeding using a nearby feeder in a power system is proposed in this research. When a power outage occurred, the faulted area is isolated from the system and searching available nearby feeders that can restore the outage area without any violation. However, analysing the effects of using a nearby feeder for back feeding is needed to avoid voltage violations. This paper analyses the voltage during the healthy and faulted condition, the impact of using a nearby feeder for backfeeding for a full load, half load and during no-load conditions are examine and also determine the maximum increment limitation of a load during back-feed if a qualified feeder is selected. Newton Raphson method is applied to calculate the power flow in the system and observe the stability of backfeed configuration. The results show the highest voltage supply occur during no load followed by half load and lastly during full load. The maximum voltage increment of load on backfeed is determined to be 0.95pu as an ideal voltage value.

Optimal Coordination of Transportable Power Sources and Repair Crews for Service Restoration of Distribution Networks Considering Uncertainty of Traffic Congestion

Optimal Coordination of Transportable Power Sources and Repair Crews for Service Restoration of Distribution Networks Considering Uncertainty of Traffic Congestion

Evaluation of fixed assets of electric power enterprises-operators of the distribution system under the conditions of the post-war reconstruction of Ukraine

The article is devoted to the assessment of the fixed assets of electric power companies-operators of the distribution system under the conditions of the post-war reconstruction of Ukraine. Attention is focused on areas of criticism of the concept of historical costs. Considerable attention has been paid to the advantages and disadvantages of the approaches of researchers to fair value asset valuations and cost asset valuations. The advantages and disadvantages of using fair value when making management decisions are highlighted. The sectoral features of the assessment were considered for the purpose of forming investment projects in the renewal of fixed assets of electric power enterprises-operators of the distribution system and the development of the distribution system. The majority of electric power enterprises-operators of the distribution system choose a revaluation model for classes of fixed assets and carry out revaluations of fixed assets. The algorithm for determining the asset valuation base with the aim of transitioning to stimulating regulation (RAB-regulation) was studied. It was found that the basis of asset valuation can be the market value or the residual value of replacement (reproduction). The expediency of applying the concept of asset impairment assessment in electric power enterprises-operators of the distribution system has been established. A method of testing for the reduction of utility of fixed assets of electric power companies-operators of the distribution system is proposed, with the help of which internal users will make decisions regarding their further safe operation and the need to find sources of investment in the restoration of distribution networks.

Distribution network restoration supply method considers 5G base station energy storage participation

This paper proposes a distribution network fault emergency power supply recovery strategy based on 5G base station energy storage. This strategy introduces Theil's entropy and modified Gini coefficient to quantify the impact of power supply reliability in different regions on base station backup time, thereby establishing a more accurate base station's backup energy storage capacity model that can fully utilize the base station's energy storage resources to participate in the emergency power supply of distribution network faults. First, the optimal Copula function in each cycle is determined through the Akaike information criterion and squared Euclidean distance to establish a wind power-photovoltaic output scenario. Secondly, the traditional Gini coefficient is modified using the weighted average node degree and influence rate indicators. A comprehensive vulnerability model is established through the modified Gini coefficient and Theil's entropy indicators. Based on the comprehensive vulnerability model, a backup energy storage time model and a modified backup energy storage capacity model of the base station affected by power supply reliability are established. Thus, the callable energy storage capacity of base stations in different areas is obtained. Finally, a two-stage robust optimization model is introduced to minimize system operating costs to solve the volatility of 5G base station communications and wind-solar output, thereby establishing an emergency power supply recovery model for base station energy storage and wind-solar output. Simulated with the improved IEEE-33 node model, the results show that the proposed base station's energy storage model improves the utilization of the base station energy storage resources and, at the same time, effectively reduces the loss of load during the fault phase of the distribution network and improves the absorption of the PV output.

Distributed Optimization for Autonomous Restoration in DER-Rich Distribution System

Autonomous service restoration (ASR) of active distribution network (ADN) reduces service restoration time with the help of measurement devices, smart switches and distributed energy resources (DERs) considering the system's operational and radiality constraints. Restoration scheme can be deployed in both centralized and distributed manner. However, with increasing data points, the requirement for measurement availability at the control center for the decision support makes centralized optimization challenging. The decomposition and coordination scheme of distributed algorithms enhances its ability to solve large scale optimization problems. Moreover, distributed optimization enables robustness, scalability and resiliency compared to centralized optimization. In this work, a) the distributed ASR problem is solved using a novel penalty-driven distributed alternating direction method of multipliers algorithm (PD-ADMM), b) a switch level decomposition and coordination technique is proposed, c) load restoration, DER utilization are considered directly and radiality of ADN is enforced using commodity flow model. The applicability of the algorithm is validated using modified IEEE 33-bus, IEEE 123-bus and 1069-bus test systems for contingency cases, communication failure, scalability, accuracy and sensitivity to number of partitions, starting points, change in DER / solar generation.

Stochastic restoration of distribution networks with high penetration of solar PV systems through a generic switching order mechanism

The process of fault isolation and service restoration (FISR) is an essential component of the implementation of distribution automation. By using FISR to determine the switching order, whether manually or remotely, the network's reliability is significantly enhanced in comparison to that of conventional switching. On the other hand, it is impossible to refute the exponential development of distributed energy resources such as photovoltaic (PV) generators. This paper introduces a general FISR for distribution networks with high penetration of solar PV systems. The proposed FISR strategy is an optimum switching sequence with the goal of reaching the best restoration hour before any likely fault occurrence. The proposed framework takes into account the inherent uncertainty in network demand and renewable generation in addition to the innovative mathematical programming model for the switching order. In addition, this stochastic programming handles the radiality constraint in the restoration process by creating a fictional layer as a means of reducing the complexity of the modeling. Moreover, the impact of PV penetration level on the optimal switching order for heavy-loaded distribution feeders is addressed and investigated.

Critical loads restoration of distribution networks after blackout by microgrids to improve network resiliency

Critical loads restoration of distribution networks after blackout by microgrids to improve network resiliency

Evaluating stakeholder structure and private actor involvement on the distribution of relief goods considering a partly centralised decision

ABSTRACT Relief distribution involves numerous stakeholders with various roles and different structures regarding stakeholders’ coordination. This paper proposes a model to evaluate various stakeholder structures for distributing relief goods. Unlike previous research, this model evaluates the coordination structure, assuming no single centralised decision-maker exists. The proposed model is developed based on the decomposition-based framework, assuming a partly centralised decision. The proposed model also integrates road network restoration into the model by capturing the disrupted road network. The role of the private sector is elaborated by exploring the impact of private business involvement in food-related distribution and road network restoration. The proposed model is applied to an actual case, namely the Palu earthquake and tsunami in 2018. We found that the multifocal structure performs better in distributing relief goods. Furthermore, the involvement of the private sector succeeds in minimising the unsatisfied demand.

Co-Evolution Framework Between Humans and Simulations: Planning Post-Disaster Restoration of a Water Distribution Network

This paper developed a new framework for better knowledge management through computational simulations, enabling both human knowledge creation and updating simulation models. The framework was developed based on the concepts of the socialization, externalization, combination, and internalization (SECI) model, and it contains a structured workshop that includes the four types of Ba the model requires. To evaluate this framework, the planning of post-disaster restoration of water distribution networks was employed as a case study. In addition, a new optimization method was developed using empirical heuristics obtained from practitioners, aiming for meaningful feedback from the practitioners working in a water management company. Based on our simulation, three workshops were conducted to create new knowledge, and new features were added to the simulation. In these workshops, practitioners performed simulation-based training in planning the restoration and then discussed their decisions. Afterward, it was concluded that the proposed framework was sufficient for updating the simulation. However, it required additional methods to provide practitioners with opportunities to obtain new insights.

Coordination of network reconfiguration and mobile energy storage system fleets to facilitate active distribution network restoration under forecast uncertainty

The active distribution network (ADN) shows great potential for use in network restoration services, given its ability to actively control the network topology, distributed generation (DG) outputs, and demand response (DR) resources. However, its utility may be limited due to the geographical dispersion of DG and DR resources when applied to natural disasters such as windstorms, earthquakes, and floods. In addition, the increasing use of renewable energy creates fluctuations and uncertainties, hindering ADNs from realizing reliable energy scheduling during disasters. Mobile energy storage system (MESS) fleets can be used to economically provide flexible emergency power supply for network restoration services. MESSs can also hedge against load and DG output forecast risks. This article proposes a novel coordinated network reconfiguration and MESS fleets dispatching model considering the uncertainty in DG output and load forecasts to increase the resilience of the ADN after disasters. The MESS traveling strategy is modeled by an extended transit delay model. Then, a novel deterministic network restoration model incorporating the MESS, stationary energy storage system, DG, DR, and network reconfiguration is proposed and programmed using mixed-integer linear programming. Then, an ellipsoidal uncertainty set is employed to describe the uncertainty of load and DG output forecasts, and a robust network restoration model is proposed based on the deterministic one. The proposed deterministic and robust network restoration models are verified on a 59-bus rural distribution system in China.

A Tabu-search-based Algorithm for Distribution Network Restoration to Improve Reliability and Resiliency

Fault restoration techniques have always been crucial for distribution system operators (DSOs). In the last decade, it started to gain more and more importance due to the introduction of output-based regulations where DSO performances are evaluated according to frequency and duration of energy supply interruptions. The paper presents a tabu-search-based algorithm able to assist distribution network operational engineers in identifying solutions to restore the energy supply after permanent faults. According to the network property, two objective functions are considered to optimize either reliability or resiliency. The mathematical formulation includes the traditional feeders, number of switching operation limit, and radiality constraints. Thanks to the DSO of Milan, Unareti, the proposed algorithm has been tested on a real distribution network to investigate its effectiveness.

Reliability Evaluation Method of New Energy Distribution Network Based on Improved Correlation Weight Method

The reliability of the distribution system includes adequacy and security. Adequacy refers to the degree to which the system capacity meets the power and energy demand of the load. Security is the evaluation of the system’s ability to withstand sudden failures. In view of the uncertainty of decision factors and the difficulty to quantify the weights in the process of reliability evaluation of distribution network, an evaluation method for load restoration of a smart distribution network based on the improved relevance weight with index correlation and improved analytic hierarchy process (AHP) is proposed. The paper also proposes an evaluation system including distribution network structure level indexes. The planning scheme of the distribution network is comprehensively evaluated, and the effectiveness of the method is analyzed through the comparison of actual cases combined with the indicators of the grid structure level. Experimental analysis shows that compared with the traditional power supply reliability or system average outage time, the global index considering LRP is more refined in describing the impact of outage, and the evaluation results are more accurate.

Optimal operation method for load restoration of distribution networks under disturbance scenario

Extreme events can cause damages to transmission system equipment, resulting in a major power outage. In disturbance scenario, local distributed energy resources (DERs) and switches can be fully utilized to restore power loads, which is critical for enhancing the resiliency. A service restoration model for distribution network with soft open point (SOP) is proposed, in which the ZIP loads are considered. Traditional tie switch (TS) operations and the power output capabilities of local DERs are coordinated to restore loads in outage area. On this basis, SOP flexibility is explored to further improve the restoration performance. The restoration model includes nonlinear constraints and integer variables, which is usually a mixed-integer nonlinear programming (MINLP) problem, and is difficult to obtain a feasible solution within an acceptable time. In particular, the ZIP load model is not directly amenable with conic solvers. To improve solution efficiency, convex relaxation, linear approximation, and big-M methods are introduced to convert MINLP problem into a mixed-integer second-order cone programming (MISOCP) problem. Simulation tests are studied to verify the effectiveness of the proposed model.

Resilient cooperative control for optimal current sharing and voltage regulation of microgrid-based distribution network under FDI attacks

In this study, the security secondary control problems are considered for optimal current sharing and voltage restoration of a microgrid distribution network under false data injection (FDI) attacks. To solve these problems, a resilient secondary control method is provided. Specifically, a resilient secondary controller is designed by introducing an adaptive parameter based on the adaptive technique. Then, a theoretical analysis method is provided to show that the designed resilient secondary controller can ensure optimal current sharing and voltage regulation under FDI attacks.

A microgrid formation-based restoration model for resilient distribution systems using distributed energy resources and demand response programs

In recent years, resilience enhancement of electricity distribution systems has attracted much attention due to the significant rise in high-impact rare (HR) natural event outages. The performance of the post-event restoration after an HR event is an effective measure for a resilient distribution network. In this paper, a multi-objective restoration model is presented for improving the resilience of an electricity distribution network. In the first objective function, the load shedding in the restoration process is minimized. As the second objective function, the restoration cost is minimized which contradicts the first objective function. Microgrid (MG) formation, distributed energy resources (DERs), and demand response (DR) programs are employed to create the necessary flexibility in distribution network restoration. In the proposed model, DERs include fossil-fueled generators, renewable wind-based and PV units, and energy storage system while demand response programs include transferable, curtailable, and shiftable loads. The proposed multi-objective model is solved using ɛ-constraint method and the optimal solution is selected using the fuzzy satisfying method. Finally, the proposed model was successfully examined on 37-bus and 118-bus distribution networks. Numerical results verified the efficacy of the proposed method as well.

Preallocation of Electric Buses for Resilient Restoration of Distribution Network: A Data-Driven Robust Stochastic Optimization Method

In recent years, severe hurricanes have occurred frequently, posing a huge challenge to the distribution network (DN) operation. Electric buses (EBs) possess large-capacity batteries and are widely used in public transit under normal circumstances. Before a hurricane occurs, some idle EBs can be preallocated to different charging stations equipped with vehicle-to-grid and served as sources for emergency power supply. This article proposes a prehurricane EB preallocation method to assist the resilience enhancement of a fragile DN. A two-stage data-driven robust stochastic programming technique is applied to build this model. Different scenarios are generated, and the corresponding posthurricane restoration processes are considered in the determination of the preallocation strategy. Also, the uncertainties of hurricane-induced physical damages are considered in modeling, which is described as a strengthened confidence set. The established model aims at exploring the optimal preallocation strategy of EBs with minimum load losses under the worst-case distribution. A column-and-constraint generation algorithm is then used to solve this proposed model. The proposed method is tested on a modified IEEE 33-bus system with 50 EBs. The results indicate that preallocating EBs to charging stations can improve the resilience of the posthurricane DN effectively.

Real-Time UAV Routing Strategy for Monitoring and Inspection for Postdisaster Restoration of Distribution Networks

After a natural disaster, a quick inspection of all damaged components is crucial to recover the functionality of distribution networks. Unmanned aerial vehicles (UAVs) can perform inspection tasks, particularly for damages that are difficult to access for human repair crews. Additionally, UAVs can monitor the transmission lines to find potential dangers and early-stage damages, and to monitor the road infrastructure to provide real-time information about traffic conditions so that repair crews can select the best ways to reach damages. Besides, due to unpredictable events during restoration, the UAV routing strategy (UAVRS) needs to be updated in real time. Thus, the proposed UAVRS in this article determines the optimal routes for the UAVs allocated to inspect damages as well as the optimal routes for the UAVs to monitor transmission lines and roads in real time for distribution networks. To tackle the multi-time-scale characteristic of the proposed UAVRS, a two-layer decision-making architecture is proposed. A bilevel programming problem is solved in the first layer for the large-time-scale problem, and a mixed-integer linear programming problem is solved for the small-time-scale problem in the second layer. A case study based on the distribution network in Zaltbommel and its neighbor areas, in The Netherlands, illustrates the effectiveness of our real-time method compared to the offline methods. Furthermore, different solvers are studied and compared in view of the real-time requirement.