Outages In Distribution Systems Research Articles

Day-ahead resilience-economic energy management and feeder reconfiguration of a CCHP-based microgrid, considering flexibility of supply

Many challenges have emerged due to the intense integration of renewables in the distribution system and the associated uncertainties in power generation. Consequently, local management strategies are developed at the distribution level, leading to the emergence of concepts such as microgrids. Microgrids include a variety of heating, cooling, and electrical resources and loads, and the operators' aim is to minimize operation and outage costs. Since significant distribution system outages are typically caused by events such as earthquakes, floods, and hurricanes, microgrid operators are compelled to improve resilience to ensure uninterrupted service during such conditions. A mixed-integer linear programming model is designed in this paper to optimize the energy management and structural configuration of microgrids. This optimization aims to enhance resilience cost, minimizing operation and capital costs as well as power loss and pollution. To achieve these goals, several tools are implemented including reconfiguration, storages, combined cooling, heat and power units, wind turbines, photovoltaic panels, as well as capacitors. Four case studies are defined to prove the developed model efficiency. The first case study focuses on energy management in the microgrid for operation cost minimization. The second case study emphasizes the improvement of resilience alongside energy management, aiming at minimizing costs and enhance resilience. In the third case, the microgrid's reconfiguration capability is also added to the second case. Therefore, this case aims to optimize both energy and structural management within the microgrid to simultaneously enhance resilience and minimize operational costs. Finally, in the fourth case, the problem is studied in a multi-objective approach. By comparing the results, the resilience impact on the operation of microgrids is elucidated. By considering the resilience concept in microgrid operation and based on the results of case 2, it is found that the operating costs are increased by an average of 10.38 %. However, because of reducing resilience costs by an average of 13.91 %, the total cost is reduced by an average of 5.93 % in case 2 compared to case 1. Furthermore, when comparing cases 2 and 3, the reconfiguration effect can be determined. It can be observed that the operating costs are decreased by an average of 4.5 %. Moreover, the resilience cost is decreased by an average of 1.61 %, resulting in an overall reduction of the total objective function by an average of 2.43 % in case 3 compared to case 2.

Stochastic pre‐disaster planning and post‐disaster restoration to enhance distribution system resilience during typhoons

AbstractIn recent years, extreme weather events, such as typhoons, have led to large‐scale power outages in distribution systems. As a result, developing strategies to bolster distribution system resilience has become imperative. This paper proposes a two‐stage stochastic programming model aimed at enhancing this resilience. Prior to a typhoon, the first stage establishes a comprehensive wind field model based on extreme value distribution for accurate wind speed predictions. Simultaneously, a refined stress–strength interference model is used to determine the likelihood of distribution line failures. Taking into account the uncertainty of line damage, repair crews and mobile emergency generators are then strategically positioned at staging depots. Following the typhoon, the second stage coordinates network reconfiguration, dispatches repair crews, and mobilizes mobile emergency generators to minimize load shedding and expedite repairs. This model was validated on the IEEE 33‐bus distribution system, coupled with a corresponding transportation network, utilizing data from the 2018 super typhoon Mangkhut'' in China. Simulations indicate that our approach can effectively reduce load shedding and power outage durations, thereby enhancing the resilience of distribution systems.

Power Grid Infrastructural Resilience against Extreme Events

Extreme weather events are one of the main causes of large-scale power outages in distribution systems. The changing climate has led to an increase in the frequency and severity of these events, which, if not mitigated, are expected to lead to more instances of widespread outages and the severe societal and economic damages that ensue. Protecting the power grid against such events, which are high impact yet low frequency, requires a paradigm shift in grid design practices. In recent years, many researchers have focused on the resilience of the power grid against extreme weather events by proposing various grid hardening and/or redundancy solutions. The goal of this paper is to provide a survey of the literature related to the infrastructural resilience of the power grid against extreme events. Currently, no standard definitions or metrics exist for power grid resilience, and researchers adopt various models for quantifying and assessing it. Hence, a review of the most commonly used definitions and metrics for resilience is provided first, with a discussion of their advantages and disadvantages. Next, the paper presents an extensive and critical review of the solution methodologies proposed in the literature for improving the infrastructural resilience of the power grid. The shortcomings of the current solution methods and gaps in research are identified, followed by a discussion of the future directions in research.

Multi-target prediction model of urban distribution system rainfall-caused outage based on spatiotemporal fusion

Improving the prediction accuracy of weather-related outages in the distribution system is crucial for disaster prevention and mitigation. Due to the vulnerability of the distribution system in rainfall, we propose a multi-target prediction model of the rainfall-caused outages of the distribution system based on the fusion of spatiotemporal attributes. In this article, the ground subdivision model based on element reconstruction is firstly proposed to establish a spatial grid. Then the rainfall waterlogging model considering seepage, drainage and confluence is established to provide a targeted reference for distribution system outage prediction. Then the multi-target random forest based on superposition features is proposed to deal with the meteorological data, geographic environment data, power grid data and waterlogging data. The model outputs the rainfall outage probability and visualization results of ground and overhead equipment branded with time and space labels. Finally, the model is tested on the instance data and compared with other models.

Hierarchical Distribution System Adaptive Restoration With Diverse Distributed Energy Resources

Distributed energy resources (DERs) can be utilized as alternative power sources for fast service recovery in cases of distribution system outages. However, the diverse characteristics and stochastic nature of DERs impose challenges on the optimal operation of diverse resources during the restoration phase. In this article, we introduce a hierarchical structure to coordinate distribution system entities and propose an adaptive restoration strategy to sustain a reliable power supply to outage loads in an unbalanced distribution system utilizing DERs. An efficient system reconfiguration model is developed to guarantee the radiality of restoration islands. The flexibility of DERs is evaluated by a novel method that focuses on the cumulative energy consumption, the superiority of which is also proven. Model predictive control (MPC) is adopted to enable the adjustment of system topology and DER operation strategies based on up-to-date data. The performance of the proposed restoration model is validated on a modified IEEE 123-bus test system. Moreover, the effectiveness of the proposed novel flexibility assessment and the MPC-based adaptive restoration strategy are verified through comparative studies.

Power Distribution System Outage Management With Co-Optimization of Repairs, Reconfiguration, and DG Dispatch

This paper proposes a two-stage method for the outage management of power distribution systems. The first stage is to cluster repair tasks of damaged power generation and delivery components based on their distances from the depots (central crew stations) and the availability of resources, to improve the computational efficiency in solving outage management problems for large distribution systems. The second stage is to co-optimize the repair, reconfiguration, and distributed generation dispatch to maximize the picked-up loads and minimize the repair time. The distribution system repair and restoration problem is formulated as a mixed integer linear program, considering constraints of system operation and routing repair crews. Crews are dispatched considering equipment resources, traveling time, and repair time. The proposed method is tested on modified IEEE 34 and 123-bus distribution test systems with multiple damages. The results demonstrate the advantages of co-optimizing repair and restoration.

Bayesian Network Model With Monte Carlo Simulations for Analysis of Animal-Related Outages in Overhead Distribution Systems

This paper extends previous research on using a Bayesian network model to investigate impacts of time (month) and weather (number of fair weather days in a week) on animal-related outages in distribution systems. Outage history (outages in the previous week) is included as an additional input to the model, and inputs and outputs are classified systematically to reduce errors in estimates of outputs. Conditional probability table obtained from the historical data are used to estimate weekly animal-related outages which is followed by a Monte Carlo simulation to find estimates of mean and confidence limits for monthly animal-related outages. Comparison of results obtained for four cities of different sizes in Kansas with those obtained using a hybrid wavelet/neural network model shows consistency between the two models. The methodology presented in this paper is simple to implement and useful for the utilities for year-end analysis of the outage data to identify specific reliability-related concerns.

Storm modeling for prediction of power distribution system outages

Utility companies spend tremendous amounts of money on storm outage restoration. A storm model which can accurately predict time-varying outages for approaching or ongoing storms will help with outage management. With such a model both customer down time and outage costs can be reduced. This paper proposes a two-stage prediction method to forecast storm related outages. In the first stage, historical outage and weather data are employed to create empirical models for different types of storms. In the second stage, observers are employed for tracking storm outages in real time. The effectiveness of the prediction model is evaluated with simulations applying actual storm data. Lightning is a primary cause of outages during summer storms. Results are presented on a possible relationship between flash density and outages.

A Classification Approach for Power Distribution Systems Fault Cause Identification

Power distribution systems play an important role in modern society. When distribution system outages occur, fast and proper restorations are crucial to improve the quality of services and customer satisfaction. Proper usages of outage root cause identification tools are often essential for effective outage restorations. This paper reports on the investigation and results of two popular classification methods: logistic regression (LR) and artificial neural network (ANN) applied on power distribution fault cause identification. LR is seldom used in power distribution fault diagnosis, while ANN has been extensively used in power system reliability researches. This paper discusses the practical application problems, including data insufficiency, imbalanced data constitution, and threshold setting that are often faced in power distribution fault cause identification problems. Two major distribution fault types, tree and animal contact, are used to illustrate the characteristics and effectiveness of the investigated techniques.

Knowledge-based system for distribution system outage locating using comprehensive information

This paper presents a knowledge-based system (KBS) designed to locate distribution system outages using comprehensive data from customer trouble calls, a wireless automated meter reading (AMR) system, and distribution supervisory control and data acquisition (SCADA). The algorithm uses the on-demand read capability of a wireless AMR system, which allows the utility to communicate directly with the meter controllers. The algorithm of the developed KBS has two major parts: an outage escalation procedure and meter-polling procedure. The escalation procedure involves searching for the outage region according to the comprehensive outage information. The meter-polling procedure includes designing the meter-polling schema and confirming the outage locations based on the meter polling results. The algorithm was designed to handle both single outage and multiple outage situations. The expert system shell G2 was used as the development tool.

Knowldege-Based System for Distribution System Outage Locating Using Comprehensive Information

This paper presents a knowledge-based system (KBS) designed to locate distribution system outages using comprehensive data from customer trouble calls, a wireless automated meter reading (AMR) system, and distribution supervisory control and data acquisition (SCADA). The algorithm uses the on-demand read capability of a wireless AMR system, which allows the utility to communicate directly with the meter controllers. The algorithm of the developed KBS has two major parts: an outage escalation procedure and meter-polling procedure. The escalation procedure involves searching for the outage region according to the comprehensive outage information. The meter-polling procedure includes designing the meter-polling schema and confirming the outage locations based on the meter polling results. The algorithm was designed to handle both single outage and multiple outage situations. The expert system shell, G2, was used as the development tool.

Reliability Impacts of Subtransmission and Radial Configurations on the Distribution System

Analysis of customer failure statistics indicates that distribution systems make a significant individual contribution to overall customer supply unavailability. Most of these outages originate in the radially connected segments of the distribution system. The contribution from the subtransmission segments, however, can also be significant for certain systems; therefore, there is a need to conduct quantitative reliability evaluation of the overall distribution system in its entirety, i.e., to be able to assess quantitatively the contributions of failures in the radial and subtransmission segments of the system on the customer supply reliability. This paper pertains to evaluating the reliability impact on customer load points of component outages in distribution systems. The effects of changes in component failure rates, adverse weather-related failures, etc. on the individual customer load point reliabilities are presented. Furthermore, the impact of such outages on the various customer sectors, such as residential, small industrial, and commercial are also determined, presented, and illustrated using a distribution test system.

A MODIFIED LEAST-MEAN-SQUARE-ERROR ALGORITHM FOR LOCATION OF OUTAGES IN DISTRIBUTION SYSTEMS

ABSTRACT This paper presents an algorithm based on the Least-Mean-Square Error criterion modified appropriately for location of outages in distribution systems. Under this scheme, analysis of information obtained remotely can be carried out at the control center before dispatching repair crews to the field. Time-of-outage measurements obtained at different customer-ends are used as input. These measurements are considered to be imperfect due to measurement errors and noise in the communication medium. The most attractive feature of this method is its ability to identify the faulted location and its type without assuming a specific mathematical distribution for modeling the noise. The validity of this claim was tested for different noise characteristics at different noise levels. Limiting noise levels that ensure correct fault classification and location under each noise scenario studied are also presented as a guide for selecting suitable timing devices for implementation of this scheme in practice.

Location of outages in distribution systems based on statistical hypotheses testing

Quick restoration of power following an outage has been a challenge for power distribution engineers. Searchers have drawn attention to this problem by proposing methods for location of outages in distribution systems. The method presented in this paper for outage location is based on statistical hypotheses testing. The input data consists of time-of-outage measurements obtained from customer-ends. The effect of various levels of noise in the measurements on performance of the method is studied. A procedure for discarding outliers in the measurements is included. A theoretical procedure to determine the maximum noise allowed in the measurements is presented. Performance of the method at different locations in a test system is also studied.

A computer assisted intelligent storm outage evaluator for power distribution systems

A knowledge-based approach for evaluating storm-related outages in distribution systems is proposed. At the outset, binary voltage sensors capable of transmitting the real-time voltage on/off symptoms are recommended to be installed at strategic locations in the distribution system. A semantic network model has been developed to represent the knowledge of the distribution system faults and their associated symptoms. The real-time voltage symptoms, transmitted by the sensors, serve as the basic input to the semantic network model in generating fault hypotheses. A rule base, incorporating temporal and topological knowledge of symptoms and system operators' heuristics, is used by the inference mechanism to determine the actual faults from the hypotheses set. The results suggest that the fault types and their exact sections can be diagnosed accurately. >

A distribution system outage dispatch by data base method with real-time revision

A distribution system emergency dispatch technique based on outage conditions is proposed that is based on searching from a database containing precalculated solutions. Load flow solutions for possible outage cases under various conditions are simulated and calculated and kept in the database. At the occurrence of fault, a suitable solution is retrieved from this database and use to restore the nonfaulted out-or-service area. The method, which is implemented on an AT&T 3B2/300 supermicrocomputer, finds the appropriate solution in a very efficient way. A real-time computation for load-flow solutions is also suggested to improve the database for better system operation. Tests to support the technique are discussed. >

Influence of Rate-of-Rise on Distribution Arrester Protective Characteristics

Lightning causes 30% to 50% of all distribution system outages, and accounts for a similar percentage of distribution equipment failures, according to studies reported by several Utility Engineers. Equipment failures, distribution transformer failures in particular, have been difficult to explain because the published protective levels of distribution surge arresters are so low compared to the equipment Basic Impulse Level (BIL). The object of this paper is to show that distribution arresters may exhibit voltages high enough to endanger transformer insulation if reasonable assumptions are made regarding the magnitudes and rates of rise of currents through arresters.

Quantitative evaluation of permanent outages in distribution systems

This paper is primarily concerned with the formulation of a group of simple equations which can be used to provide numerical estimates of the rates and durations of system load point failures. The reliability indices obtained by an existing approximate technique2and by the equations developed in this paper are compared with those obtained by a Markov approach using a two state weather model. An equivalent component formation method suggested in the literature5is investigated. The paper shows that this method gives results which are no better than those obtained by the approximate technique2. The modified equations presented in this paper provide reliability indices which compare very closely with those calculated by the Markov approach. The concepts developed are extended to more complex configurations in which loadpoint reliability parameters are obtained by the technique of failure modes and effects analysis and therefore eliminate the necessity of forming equivalent components.