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One of the most serious issues that requires special attention is the protection of distribution networks. Using the proper protective equipment improves the safety of the power distribution network during fault conditions. Fault current limiter (FCL) is a type of modern preservation system used to protect power networks and equipment. The voltage restoration of buses during faulty conditions is one of the main concerns of power networks. A group of coordinated DC reactor type fault current limiters are designed and tested in this study to protect the network and restore its bus voltage during the fault period. The proposed FCLs coordination strategy is modelled and simulated in the MATLAB platform, and the results are validated.

The power distribution systems fault root cause classification is an important but challenging problem. Traditional classifiers fail to achieve high accuracy and good generalization performance due to data insufficiency. A large volume of unlabeled data is available, which can be utilized to improve classification performance. This paper proposes a novel classifier called Robust Semi-Supervised Prototypical Network (RSSPN) based on Prototypical Network architecture and semi-supervised learning to address this issue. The proposed method can mine information from unlabeled data to improve the generalization ability and classification accuracy. Furthermore, RSSPN adopts the idea of meta-learning to obtain the few-shot learning" ability for identifying new fault classes using very few samples encountered during the operation and update online. Experiments have been conducted on a dataset consisting of 1152 labeled samples belonging to 12 different classes and 10000 unlabeled samples. The accuracy of the proposed method is significantly better than the traditional classifiers.

This paper introduces a new model for optimal wind distributed generation (WDG) allocation based on mixed integer linear programming (MILP) in todays' smart grids considering on-load tap changing (OLTC) and power factor control (PFC) strategies. The objectives of the proposed optimization model are improving voltage stability and energy loss minimization subject to power flow constraints, line thermal limits, maximum number and size of WDG units, penetration limit, discrete OLTC tap position constraints, and PFC constraints. An efficient voltage phasor-information-based probabilistic voltage stability index (VSI) is extracted to measure the improvement of voltage stability with WDGs. Besides energy losses, reduction index is used to measure the energy loss reduction with the integration of renewable WDGs in the distribution system. An efficient linearized power flow (LPF) model as well as piecewise linearized model of the quadratic terms associated to the PFC constraints are represented to convert the original nonconvex problem into the form of a well-known MILP problem, which guarantees optimal solution and computationally is efficient to be solved using powerful commercial solvers. The effectiveness and validity of proposed model is investigated in compared to the state-of-the-art population-based methods and classical models.

Insulated aerial bundled cables (ABCs) are preferred over conventional bare conductor cables in electrical distribution system as ABCs are more safe, less prone to electricity pilferage and offers higher reliability. However, the degradation phenomenon of ABCs is sudden as compared to conventional cables especially in coastal areas. Sudden failures of ABCs in coastal areas make the maintenance planning challenging. Hence, accurate reliability estimation is required which can enable timely maintenance planning and in turn reducing the chances of failures. A novel reliability model is reported in this work which is derived from historical failure data of particular type of ABCs coupled with the degradation models. The models are based upon the actual environmental conditions experienced by the cables under study. The actual loading data as well as environmental data of two sites of varying distance from Seashore are used to develop the respective reliability models. The reliability prediction from proposed reliability model is then validated using time to failure computation through comparison of historical infrared thermography based Non-destructive testing (NDT) data, acquired at the sites under study, with reference to NDT measurements acquired from the ruptured/failed cable. The validation indicates the efficacy of the proposed reliability model.

Repair scheduling and routing and logistics support are interdependent and critical for post-disaster distribution system restoration (PDSR), which is also influenced by the geographical characteristics of outage area. Hence, we develop a co-optimization model for the PDSR with logistics support and geographical characteristics. A hybrid improved bacterial colony chemotaxis algorithm is proposed to solve the model, in which A* algorithm is employed to route repair crews and material delivery in the transportation network considering geographical characteristics, and an improved bacterial colony chemotaxis algorithm is proposed to determine the repair scheduling and material allocation in the distribution system. Different scale of distribution system instances with different damage levels and different geographical characteristics are used to demonstrate the effectiveness of the proposed methodology.

New metrics and a new methodology for determining system infrastructural–operational resilience are presented for the optimal real-time operation of two critical interdependent infrastructure systems—the water distribution systems (WDS) and the electric power system (EPS) under critical conditions of limited water and/or limited electrical energy resulting from extreme drought or electric grid failure, respectively. Operational resilience (OR) and infrastructural resilience are defined. The integrated resilience computation method is presented and utilizes results from a combined optimization–simulation framework, which involves capturing the interactions and associated dynamics between the EPS and WDS. A realistic example of the WDS and EPS is used to demonstrate the application of the resilience concepts to assess the interdependent systems’ performance.

The speed and robustness of fault section location directly affect the resilience of the distribution network when the latter is exposed to extreme events. This article is proposing a fast and robust fault section location method for distribution system considering the outage reports from smart meters in low-voltage network and notifications from remote fault indicators (RFIs) in medium-voltage network. To make section location faster, a proposed location model moves underreporting variables and misreporting variables to offline recognition. To make section location more robust, the location model proposes new constraints of expected RFI states to remove the limitation of multiple faults on different paths to all sources. Simulation results on a utility feeder with distributed generations demonstrate that the proposed method has faster location and better robustness, which is helpful to the resilience enhancement of the distribution network.

The future distribution systems expose to an unprecedented level of uncertainties due to renewable resources, nontraditional loads, aging infrastructure, etc., posing potential risks to secure operation of the system.This article proposes a new technique to estimate the voltage stability margin of the distribution systems with high penetration of renewables.Its convergence and robustness under complex and stressed working conditions are guaranteed in theory. This technique is handy for the operation as it features self-adaptive step size and is applicable to general system topology. It leverages a newly derived analytical solvability certificate based on the Kantorovich fixed-point theorem. A fast version of the proposed technique is duly proposed to speed up the computation up to 8 times while maintaining high accuracy, which lends itself to online and time-sensitive emergency tasks. Numerical simulations with various IEEE test feeders verify the performance of the techniques. Ministry of Education (MOE) Nanyang Technological University National Research Foundation (NRF) This work was supported in part by NTU SUG, MOE AcRF TIER 1- 2019-T1-001-119 (RG 79/19), and in part by EMA & NRFEMA-EP004-EKJGC-0003.

When a single line-to-ground fault occurs in the ungrounded distribution system, the steady-state fault current is relatively small for fault analysis and the transient fault current is observable, which can be used for faulted feeder identification and location. The principal frequency component retains most of the characteristics of the transient current. The principal frequency is related to the distance from the fault point to the substation and can be used for fault location. This paper analyzes the sequence network model of a single line-to-ground fault in the distribution network, and gives a method for principal frequency calculation. Depending on the characteristics of the maximum amplitude of the principal frequency component of the faulted feeder, the method of faulted feeder identification is given. Based on the complementary characteristics of the phase angle of the principal frequency component of the fault current and the phase angle at the substation bus, the faulted section location is carried out. MATLAB simulation is used to verify the effectiveness of the faulted feeder identification and location method.

Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the project being ongoing. Copyright © 2023 by the authors. Electric distribution grids are seeing an increased penetration of photovoltaic (PV) generation. High PV generation exceeding the grid load demand results in a reverse active power flow in the grid, which raises the voltage level. This paper presents a reactive power controller to overcome the overvoltage problem in the distribution system. A sequentially coordinated and cooperative volt/var control technique is presented. The proposed controller aims to use as low reactive power as possible while mitigating the voltage issues. Accordingly, it reduces the active power loss associated with reactive power flow and reduces the probability for active power curtailment of the PV system. The controller is developed for each lateral and is replicated for all laterals. The lateral controller coordinates the operation of the smart PV inverters in a sequential manner. Cooperative control is proposed between the laterals’ controllers as well and is engaged when the individual laterals’ controllers are unable to solve their overvoltage issues. The performance of the proposed controller is evaluated by comparing it to two other volt/var controllers, and it demonstrates better performance in terms of reactive power requirement. To conduct the simulation study, a modified version of the unbalanced IEEE 13-bus system is utilized, which includes an additional 44 low-voltage bus. The study involves simulating 720 operating points across daily time series. The results indicate that the proposed controller effectively addresses overvoltage problems that occur during periods of high PV generation. Newton Fund Institutional Links grant number 623801791, under the Newton-Katip Çelebi Fund partnership. The grant was funded by the UK Department for Business, Energy and Industrial Strategy and TUBITAK grant number 120N996 and delivered by the British Council.

This research discusses the goods distribution system in an urban area by considering an odd-even vehicle license plate rule. This rule enables the vehicles with the even-number plate to pass particular roads on even-number dates and vice versa during specific time intervals determined by the authority. This rule is potentially rising the company’s logistic cost up to 20%. Therefore, a Mixed-Integer Linear Programming model is developed to solve the problem. The performance of the model is tested on some numerical examples. Computational results show that the model generates optimal solutions with numerous computational times. The instances of customers located randomly and clustered manner (i.e., mix) are arduous to solve. The average computational time of these instances is 1 hour and 35 minutes.

Self-healing capability is one of the most critical factors for a resilient distribution system, which requires intelligent agents to automatically perform restorative actions online, including network reconfiguration and reactive power dispatch. These agents should be equipped with a predesigned decision policy to meet real-time requirements and handle highly complex $N-k$ scenarios. The disturbance randomness hampers the application of exploration-dominant algorithms like traditional reinforcement learning (RL), and the agent training problem under $N-k$ scenarios has not been thoroughly solved. In this paper, we propose the imitation learning (IL) framework to train such policies, where the agent will interact with an expert to learn its optimal policy, and therefore significantly improve the training efficiency compared with the RL methods. To handle tie-line operations and reactive power dispatch simultaneously, we design a hybrid policy network for such a discrete-continuous hybrid action space. We employ the 33-node system under $N-k$ disturbances to verify the proposed framework.

Despite many studies have been published on non-technical loss management, there is a lack of solutions focused in the financial return of utilities actions. This paper aims to contribute to fill up this gap, presenting a new approach that maximizes the financial return of utilities when selecting consumers for field inspections. The predicted return of an inspection is calculated considering its operational cost versus the potential of energy recovery and the tariff value. The potential of energy recovery, in turn, is calculated considering the probability of non-technical loss existence and the forecast of unmeasured energy. The Rotation Forest algorithm is utilized to indicate the non-technical loss existence, while the eXtreme Gradient Boosting algorithm is utilized for energy recovery forecast. The proposed approach was applied in a real database of 261,489 consumers from a Brazilian utility. Then, 338 new field inspections were performed on unlabeled consumers in order to corroborate results in a real application. Achieved results showed an increase of up to 11.5 times in the financial return of field inspections when applying the proposed approach. Results could also be used to determine the optimal number of inspections in the utility, for which the financial return is maximum.

A fault current compensation technique is proposed in this paper for resonant grounded power distribution systems in bushfire prone areas. Arc suppression devices with residual current compensation inverters are used to compensate fault currents due to single line‐to‐ground faults in order to mitigate powerline bushfires. The main contribution of this paper is the design of a compensation technique for the T‐type residual current compensation inverter using a non‐singular terminal sliding mode control scheme. The main objective of the proposed scheme is to reduce the fault current and bring its value to a level so that it cannot ignite fires. The proposed controller is designed based on the selection of a sliding surface in a way the singularity problem can be avoided and chattering effects in existing sliding mode controllers can be eliminated. The desired current injection through the residual current compensation inverter is ensured by enforcing the control law into the terminal sliding surface where the control law is determined by satisfying the Lyapunov stability criteria. The performance of the non‐singular terminal sliding mode controller is compared with an integral sliding mode controller by considering different values of fault currents where these values are varied by changing fault resistances. Results for simulation in the software and processor‐in‐loop simulations are verified against operational standards which are essential for mitigating powerline bushfires. This work focuses to design a non‐singular terminal sliding mode controller for the residual current compensation inverter which is used in an arc suppression device to compensate both active and reactive components of the fault current and keeps its value below 0.5 A within 2 s after activating the residual current compensation inverter which is a requirement as per the operational standard. This controller is designed based on the selection of a terminal sliding surface while satisfying the condition for avoiding the singularity problem.

This paper presents a new approach to the problem of defining an investment policy in battery energy storage systems in active distribution networks, taking into account a diversity of uncertainties. The proposed methodology allows the selection of type, capacity, and location of battery energy storage systems in distribution networks with distributed generation and electric vehicle charging stations. A mixed‐integer stochastic programming problem is cunningly approached with a metaheuristic, where fitness calculation with stochastic scenarios is performed by introducing an approximation to the operation costs in the form of a polynomial neural network, generated according to the Group Method of Data Handling—GMDH method, with strong computing speeding‐up. The quality of this approximation for heavy Monte Carlo simulations is assessed in a first case study using a 33‐bus distribution test system. The optimization planning model is then validated in the same test system using real data collected from solar and wind sources, demand, prices, and charging stations. Four types of batteries are compared considering degradation impact. The results demonstrate the practicality and advantages of this process.

The status of switches in distribution systems might be changed for protection purposes or to achieve the operation objectives. However, the system topology should be known for the efficacious management and control of these systems. In most practical systems, due to the lack of enough measurements, the topology cannot be identified accurately and speedily. The forecast output can be transformed into the joint probability density function (PDF) of uncertain parameters, e.g., power demands. The measured data and this PDF are first conflated to update the PDF to best comply with the measurements. The topology identification problem, i.e., finding the probability of each topology given a set of measured values, is converted to a simpler form, i.e., finding the probability of observing these measured values in this topology. These probabilities are then calculated by tracing how the measurements affect the shape of the new consolidated PDF in each topology compared to the original PDF. The proposed technique employs as much data as available, is able to find the accurate probabilities, and particularly is yet quite fast. The mathematical treatment of extracting such probabilities is presented and the proposed technique is validated in numerical studies in terms of accuracy, speed and, versatility

Converter interfaced distributed generations in a microgrid feed the modulated current of limited magnitude during fault conditions. The protection design and its operation are thus challenging due to limited fault current which is further reduced by Petersen coil grounding in medium‐voltage (MV) level. This paper aims to address this challenge by developing a current‐only directional relay algorithm for the protection of converter dominated MV microgrid with Petersen coil grounding. The relay's operating principle is based on the sign of the change in phase angle of the fault current with respect to the prefault which indicates the direction of fault. The negative and positive changes in current's phase angle determine the fault in forward and reverse direction, respectively. The tripping decision is derived by comparing the binary output of the relay at both ends of the line segment under protection. This requires a simple, flexible and low bandwidth communication channel. Both theoretical analyses and simulation studies have been performed on a typical distribution grid intended to be operated as microgrid. The proposed protection method is suitable for microgrid having the converters with and without reactive power support. Various operating conditions are evaluated, including bidirectional power flow, high resistance fault, different fault types, loading conditions and signals with noise.

Natural disasters threaten the sustainability of electric power supply. This fact highlights the importance of enhancing technological resourcefulness to handle the upcoming events. Once a natural disaster occurs, the most urgent undertaking is to rapidly pinpoint and assess component damages and dispatch the repair crews towards the most critical impaired elements. Practical efforts confirm that utilising a drone, which is an unmanned aerial vehicle, can notably reduce the duration of post‐disaster distribution system damage assessment (DA) and increase the resilience of power systems. This study presents an optimisation model to determine the optimal number and type of drones required for DA. The main goal is to minimise the time duration of the DA mission, which includes scanning the grid and identifying the damaged components. As a matter of fact, the financial resources are limited, so the problem is subject to the available budget constraint. Besides, the technical capacity of purchased drones for inspecting the grid is considered. The optimisation problem is formulated as a mixed‐integer programme with a guaranteed optimum solution. Case studies confirm the applicability of the model and the optimality of the results. As the major conclusion, the best allocation is a compromise between the speed and price of drones.

Accurate photovoltaic (PV) power forecasting is an essential tool that enables the efficient integration of solar electricity and energy trading. This work proposes a novel methodology for the implementation of a forecasting tool that provides aggregated PV production day‐ahead forecasts for the PV systems installed at a distribution level. Specifically, the tool is a cloud‐based platform, comprising of a data quality block, a weather forecasting model, a machine learning power prediction step and an up‐scaling aggregation stage. In this context and in the absence of a fully observable distribution system, the aggregated PV generation was estimated using a clustering approach and up‐scaling the measured generation of reference systems to the aggregated installed capacity. The results demonstrated that the implemented tool exhibited high forecasting accuracies, lower than 10% given by the mean absolute percentage error when applied to the distribution system of Cyprus. Furthermore, the comparative benchmarking of the up‐scaling techniques against the estimated aggregated PV generation demonstrated that the best‐performing approach was the hybrid model, which provided a normalised root mean square error of 10.29% and mean absolute percentage error of 9.11%. Finally, useful information is provided for establishing a robust day‐ahead forecasting methodology that is based on an optimal supervised learning and hybrid up‐scaling approach.

Online and omnichannel retailers are proposing increasingly tight delivery deadlines, moving toward instant on-demand delivery. To operate last-mile distribution systems with such tight delivery deadlines efficiently, defining the right strategic distribution network design is of paramount importance. However, this problem exceeds the complexity of the strategic design of traditional last-mile distribution networks for two main reasons: (1) the reduced time available for order handling and delivery and (2) the absence of a delivery cut-off time that clearly separates order collection and delivery periods. This renders state-of-the-art last-mile distribution network design models inappropriate, as they assume periodic order fulfillment based on a delivery cutoff. In this study, we propose a metamodel simulation-based optimization (SO) approach to strategically design last-mile distribution networks with tight delivery deadlines. Our methodology integrates an in-depth simulator with traditional optimization techniques by extending a traditional black-box SO algorithm with an analytical model that captures the underlying structure of the decision problem. Based on a numerical study inspired by the efforts of a global fashion company to introduce on-demand distribution with tight delivery deadlines in Manhattan, we show that our approach outperforms contemporary SO approaches as well as deterministic and stochastic programming methods. In particular, our method systematically yields network designs with superior expected cost performance. Furthermore, it converges to good solutions with a lower computational budget and is more consistent in finding high-quality solutions. We show how congestion effects in the processing of orders at facilities negatively impact the network performance through late delivery of orders and reduced potential for consolidation. In addition, we show that the sensitivity of the optimal network design to congestion effects in order processing at the facilities increases as delivery deadlines become increasingly tight.

Distributed Generation (DG) integration, especially based on renewable energy resources, has gained great attention by power utilities and frequently utilized in the electrical distribution systems. However, DG integration imposes some risks towards system stability which may lead to system blackouts. This mainly occurs when the grid is decoupled from a portion of the distribution system consisting DGs while the total load demand is greater than total DGs output power. In order to overcome this problem, load shedding technique can be adopted to stabilize the system frequency. However, existing load shedding techniques were unable to accurately estimate the power imbalance due the variation in system loading. This results in excessive/inadequate load shedding to stabilize the system frequency. Moreover, random selection of the loads without load prioritization might cause vital loads to be shed. Therefore, in this paper, a new load shedding strategy for islanded distribution system is proposed. Polynomial regression analysis estimates the power mismatch while MILP optimization estimates optimal load combination for shedding. Furthermore, load priority (i.e., vital, non-vital, and semi-vital) is also considered to avoid disconnecting vital loads. Efficiency of the proposed scheme is evaluated on three different test systems. Validation is performed by modelling the proposed load shedding on PSCAD/EMTDC software for dynamic analysis. From the results, it can be analyzed that the proposed technique is superior compared to other techniques proposed in the literature.

The increased penetration of distributed energy resources (DERs) has enhanced the importance of coordinated operation of transmission system operator (TSO) and distribution system operators (DSOs) in order to better exploit DERs. When TSO and DSOs coordinately operate to optimize overall performance of the system, it does not ensure that the benefit is attained by all participants. To ensure entering TSO and DSOs as subjective and self-interested agents’ coordinated operation, this article introduces a non-cooperative framework for coordinated operation of TSO and DSOs. In the proposed non-cooperative operation approach, TSO and DSOs are considered as subjective agents whose interest is to optimize their own objective functions selfishly while they are interactive and modify their solutions through exchanged boundary signals until the equilibrium is obtained. The effectiveness of the proposed non-cooperative operation approach has been demonstrated through many studies on two integrated test systems. Comparison of simulation results of applying the proposed non-cooperative approach with those of the cooperative ones demonstrates that although the optimal solution is achieved for the whole system in the cooperative operation, some agents cannot obtain a satisfactory solution. Thus, these agents prefer to enter a non-cooperative game with other agents. The proposed non-cooperative game leads to relative benefits for all agents.

The Alpine ski-resorts are conventionally powered by the electrical distribution network to perform the snow-making operations throughout the ski slopes. On one hand, these grids can present a low capacity, and on the other, the line lengths are not negligible due to natural obstacles and altitude changes. As a consequence, the voltage drops are remarkable when several power loads are fed by the low voltage section. Based on this aspect, a novel evolution of ski-resorts power grids toward microgrids is pursued to improve the power system quality of service and to foster the implementation of distributed energy resources (DERs). The article examines the ac electrical distribution system installed in the ski-resort of San Vito di Cadore, Italy. A preliminary power flow analysis identifies bottlenecks on voltage profiles and weights effective solutions to evolve the power grid. Four system configurations are contrasted in terms of voltage drop in order to establish the most convenient redesign. A proper voltage profile along the entire ski-resort is ensured by the last solution, which redistributes the loads powering among the five resort's substations. Finally, a techno-economical-environmental analysis explores the possibility of installing DER and storage to feed the resort loads. The study demonstrates the capability of hydropower plant in enhancing microgrid sustainability and environmental friendliness.

Load growth in electrical distribution networks became naturally occurring due to industrial development and human population demand growth. Consequently, system losses are continually raised while the voltage profile is reduced. This paper presents a novel hybrid method to determine the best locations and sizes of single and multiple of different renewable distributed energy resources (DER). The presented hybrid method is based on Salp Swarm Algorithm (SSA) and combined power loss sensitivity (CPLS). Integration of photovoltaics (PV) and wind turbines (WT) in distribution network is used to enhance the system voltage, minimize system losses and increase the system capacity. The effect of annual load growth in system load and system operating constraints are taken in consideration. IEEE 33-bus and 69-bus radial distribution systems (RDS) are used to validate the presented algorithm for integrating the DERS in distribution system. In addition, the presented algorithm is compared with different recent optimization algorithms in order to prove its effectiveness and superiority.

This paper proposes an application of the recent metaheuristic rider optimization algorithm (ROA) for determining the optimal size and location of renewable energy sources (RES) including wind turbine (WT), photovoltaic (PV), and biomass‐based Distributed Generation (DG) units in distribution systems (DS). The main objective function is to minimize the total power and energy losses. Power loss‐sensitivity factor (PLSF) is used with the ROA to determine the suitable candidate buses and accelerate the solution process. The Weibull and Beta probability distribution functions (PDF) are employed to characterize the variability of wind speed and solar radiation, respectively. The high penetration of intermittent renewable resource together with demand variations has introduced many challenges to distribution systems such as power fluctuations, voltage rise, high losses, and low voltage stability, therefore battery energy storage (BES) and dispatchable Biomass are considered to smooth out the fluctuations and improve supply continuity. The standard 33 and 69‐bus test systems are used to verify the effectiveness of the proposed technique compared with other well‐known optimization techniques. The results show that the developed approach accelerates to the near‐optimal solution seamlessly, and in steady convergence characteristics compared with other techniques.

Z/T/Γ-source circuit breakers used in flexible DC distribution systems have been rapidly developed due to their advantages of high speed and low cost. However, the precise model of the circuit breaker is generally a high-order circuit, which is difficult to solve. The lack of simplified topology and parameter design considering system parameters (line inductance, line resistance and load capacitance) restricts their applications in practical engineering. Therefore, based on the equivalent mathematical model, a simplified topology that can represent Z/T/Γ-source circuit breakers is proposed and their working principles are revealed in this paper. Considering system parameters, the parameters of Z/T/Γ-source circuit breakers are designed to take care of short circuit fault and step changes in load. Finally, simulation and experiment show that parameters designed by the proposed method can ensure the reliable operation for Z/T/Γ-source circuit breakers.

Both frequency and intensity of natural disasters have intensified in recent years. It is, therefore, essential to design effective strategies to minimize their catastrophic consequences. Optimizing recovery tasks, including distribution system reconfiguration (DSR) and repair sequence optimization (RSO), are the key to enhance the agility of disaster recovery. This article aims to develop a resilience-oriented DSR and RSO optimization model and a mechanism to quantify the recovery agility. In doing so, a new metric is developed to quantify the recovery agility and to identify the optimal resilience enhancement strategies. The metric is defined as “the number of recovered customers divided by the average outage time of the interrupted customers.” A Monte-Carlo-based methodology to quantify the recovery agility of different DSR plans is developed. It will be shown that if the total number of interrupted customers over the recovery horizon is minimized, the metric will be maximized. Accordingly, the DSR and RSO optimization models are modified to maximize the introduced metric. The proposed optimization model is formulated as a mixed-integer linear programming model that can be solved via commercial off-the-shelf solvers. Finally, the proposed methodology is applied to several case studies to examine its effectiveness. It will be also shown how the proposed methodology can be utilized for distributed generator (DG) and tie-line placement problems in planning for enhanced structural resilience.

The main technical challenges in the emerging concept of the micro-grid (MG) and integration of sustainable energy resources in the Distribution System (DS) is the low fault current phenomena in the islanded mode of the MG operation and change in the network topologies with frequent DG connection and disconnection. The existing protection schemes require modification as they may fail to operate because of their inability to adapt to the changes due to the integration of Distributed Generator (DG). Various new protection schemes have been introduced by many researchers for this new concept of Microgrid (MG) that is not only adaptive to the changes in the levels of fault current and network topologies in both grid-connected and islanded modes but also intuitive enough to determine and clear the fault with minimum disturbances in the supply. This paper gives a brief review of the challenges associated with Microgrid protection and presents a critical review of the existing Microgrid protection schemes. The limitations of the existing adaptive protection schemes that need to be overcome to make adaptive protection scheme a viable solution are also presented.

This paper presents the results of a vulnerability analysis in different water distribution system (WDS) benchmarks, performed under a framework based on a graph model that integrates topological features and hydraulic characteristics, allowing the comparison between different attack strategies and centrality measures in terms of their ability to predict the shortage of water supply. This vulnerability framework has been previously applied to electric power systems and it employs a vulnerability prediction measure to quantify the amount of damage caused in terms of the physical damage measure. Different attack strategies and centrality measures were applied to four WDS benchmarks: the New York Tunnel, the Hanoi, the Modena, and the Balerma networks. It was determined that removing the most central element and recalculating the centrality for each stage are the most damaging attack strategies. Degree, eigenvector, and Katz centrality measures presented the best performance to predict the elements that are more relevant to the network and can have a larger impact on the water supply. It was demonstrated that the vulnerability framework can be applied to the WDS in the same way it was previously applied to electric power systems. Future work will be oriented to the design of the WDS using optimization techniques to minimize the vulnerability of the network under faults that can be generated by droughts and other extreme weather conditions. HIGHLIGHT This paper presents the results of a vulnerability analysis in different WDS benchmarks, performed under a framework based on a graph model that integrates topological features and hydraulic characteristics, allowing the comparison between different attack strategies and centrality measures in terms of their ability to predict the shortage of water supply.

High-impedance faults in power distribution systems is a lasting problem with decades of steady investigation. Due to the complexity of the problem, the field can also be challenging to navigate. Although there exist surveys of the field in the literature, it is not easy to find a comprehensive contextualization of how and when the field developments unfolded. This paper presents the historical narrative of the progress and developments based on the most cited papers since the inception of the field. The accounts are not limited to archaic and obsolete works. They are all contextualized from the seminal papers to contemporary methods and related technology. Quantitative figures on the survey of the methods and relevant knowledge gaps are also discussed at the closing of the paper.

This article proposes a framework as a remedial action scheme (RAS) for close to real-time system operation when system operator becomes aware of possibility of decentralized outages due to simultaneous attacks on several market retailers. The upper level of the framework is an offline RAS where system operator immediately implements a customized reconfiguration preventing the decentralized outages. Due to immediate implementation of upper-level reaction, it is possible to face decentralized congestions in the new topology having minimum reliance on the attacked market retailers. Since severity of congestions is less than that of outages, the lower level of the framework is implemented as an online RAS. In this level, eligible nonattacked market retailers are invited into a secondary market to compete for generation/consumption redispatch and contribute into alleviation of congestions with the purpose of increasing their income. The effectiveness of the proposed framework is validated on a well-known 136-bus distribution system.

Актуальность обусловлена, с одной стороны, необходимостью активной газификации крупных территориальных образований (строящихся новых жилых агломераций, коттеджных и дачных поселков, а также крупных промышленных комплексов). С другой стороны, продолжается увеличение общей протяженности газораспределительных систем и усложнение конфигурации схем газопроводов. Перечисленные факторы влияют на надежность работы газораспределительных систем и предъявляют высокие требования со стороны потребителей энергии к безаварийной их работе. Цель. Основная исходная надежностная информация, используемая для анализа надежности газопроводов, - это интенсивности отказов его элементов: отдельных участков линейной части, газоперекачивающих агрегатов и т.д., и интенсивности восстановления этих элементов. Эти показатели характеризуют такие свойства надежности как, безотказность и ремонтопригодность. Цель исследований заключается в оценке влияния показателей безотказности и ремонтопригодности оборудования на интегральные показатели надежности газораспределительной системы (ГС). К ним относятся математическое ожидание, дисперсия и среднеквадратическое отклонение (с.к.о.) пропускной способности ГС, а также коэффициент надежности газопровода. Методы: эквивалентирования, аналитический метод на уровне случайных Марковских процессов - схема «гибели и размножения», теоремы сложения и умножения вероятностей, композиция рядов распределения; математическая модель анализа надежности проектируемого магистрального газопровода (МГ). Результаты. На основе математической модели оценки надежности проектируемого магистрального газопровода и разработанной вычислительной программы были выполнены расчеты по определению интегральных показателей надежности условной газораспределительной системы. Исследования показали, что при изменении исходных показателей надежности (интенсивностей отказов и восстановлений) элементов ГС на одинаковую величину их влияние на изменение интегральных показателей надежности ГС имеет разную силу. Выводы. На основе предложенной математической модели анализа надежности ГС показано влияние показателей интенсивности отказов и интенсивности восстановлений элементов, характеризующих безотказность и ремонтопригодность газотранспортных систем, на ее интегральные показатели надежности, в частности, на коэффициент надежности. The Relevance of the research is due to the need for active gasification of large territorial entities (new residential agglomerations under construction, cottage and dacha settlements, as well as large industrial complexes). On the other hand, the increase in the total length of gas distribution systems and the complexity of the configuration of gas pipeline schemes continues. These factors affect the reliability of the gas distribution systems and place high demands on the part of energy consumers for trouble-free operation. Purpose. The main initial reliability information used for analyzing the reliability of gas pipelines is the failure rates of its elements: individual sections of the linear part, gas pumping units, etc., and the recovery rates of these elements. These indicators characterize such properties of reliability as reliability and maintainability. The purpose of the research is to assess the impact of reliability and maintainability of equipment on the integral reliability indicators of the gas distribution system (GDS). These include the expectation, variance and standard deviation of the throughput of the GDS, as well as the reliability coefficient of the pipeline. Methods: equivalent methods, analytical method at the level of random Markov processes - the scheme of "death and reproduction", the theorem of addition and multiplication of probabilities, the composition of distribution series; mathematical model of reliability analysis of the designed main gas pipeline. Results. On the basis of a mathematical model for assessing the reliability of the designed main gas pipeline and the developed computational program, calculations were performed to determine the integral indicators of the reliability of the conventional gas distribution system. So, studies have shown that when the initial reliability indicators (failure rates and recoveries) of the GDS elements are changed by the same amount, their effect on the change in the integral reliability indicators of the GDS has a different effect. Conclusion. On the basis of the proposed mathematical model for analyzing the reliability of the GDS, the influence of the failure rate indicators and the recovery rate of elements characterizing the reliability and maintainability of gas transmission systems on its integral reliability indicators, in particular, on the reliability coefficient, is shown.

Integrated sensors are installed in water distribution systems for the real-time detection of pipe leakage and contamination. We design a novel strategy for sensor placement to monitor leakages and...

Inventory Management in Stochastic Distribution Systems

The increasing calls for decarbonization, and the consequent need for the energy system to integrate ever-increasing shares of renewable energy, place the electric grid at the core of an integrated, decarbonized energy system. This calls for Transmission System Operators (TSOs) and Distribution System Operators (DSOs) to fast-track the integration of clean energy solutions into the electric grid [1]–[3]. In this framework, whatever new management/business models can be envisaged for modern electric power systems, they must be based on the availability of suitable information. Consequently, the continuous evolution of applications in power grids stresses the need to define, develop, implement, and qualify innovative measurement components, devices, and systems for proper, effective, and efficient implementation of enhanced monitoring and control applications in electric transmission [1] and distribution [4] networks.

This article investigates the privacy issues in the distributed energy management system (EMS) of smart distribution systems and microgirds. A novel private collaborative distributed energy management system (P-CoDEMS) is proposed to solve the AC optimal power flow (ACOPF) problem in a distributed and private fashion. The proposed P-CoDEMS algorithm is based on an original primal dual subgradient distributed optimization technique and a state-of-the-art fully homomorphic encryption algorithm. The convergence and optimality of the proposed P-CoDEMS algorithm are evaluated on four representative systems. Simulation results indicate that the proposed P-CoDEMS algorithm can accurately solve the ACOPF problem in a fully distributed way while preserving individual agent privacy.

Phase balancing of American-type three-phase distribution circuits is one of the classical problems faced by distribution system operators and planners. In this paper, a mixed-integer non-linear programming problem is formulated to minimize the number of phase connection changes in existing circuits to reach the desired phase balancing. The proposed algorithm uses geographical information system models of distribution circuits to build a reduced model and realistic load profiles offered by smart meters, or measurements at the distribution transformers to characterize the demand. The formulation is validated in highly unbalanced distribution circuits with thousands of single-phase customers. The results show that it is possible to rebalance large-scale circuits by moving a few single-phase laterals and loads. In addition, the application of the proposed method, based on loading conditions at different hours of the day, pointed out the same places to make the changes.

Generally accepted industry standards, such as National Fire Protection Association (NFPA) 70E-2018, require arc-flash incident energy analysis to be updated when changes occur in the electrical power distribution system, and the analysis shall be reviewed for accuracy at least every five years. NFPA 70E also requires the data used for creating equipment labels, which contain arc-flash energy information, to be reviewed at least every five years, and, if the review identifies a change that renders the arc-flash energy label inaccurate, the equipment label must be updated.

The optimization of the distribution system is one of the most important operations for running an efficient power system. At the distribution stage, a wide variety of development frameworks are ac...

Fundamental changes in economic relations, instability of the market environment in which enterprises operate and increased intensity of competitive influences to obtain better business conditions emphasize the importance of the nature of interaction for all participants of business processes in the distribution channel and the success of the chosen business structure. This circumstance determines the expansion of the content of the ecosystem approach to the organization of enterprise activities and the clarification of the participants' roles in such systems. The purpose of the study is to identify the key features and differences in business models of digital ecosystem participants that are critical to shaping the value of the distribution system.,In this paper, within the framework of the theoretical component, the authors analyze conceptual and empirical articles contained in the Web of Science database and also rely on information obtained from the study of articles by Russian academicians in specialized journals, monographs and conference abstracts. Using the method of content analysis, data on enterprises were collected and generalized into cases, which made it possible to propose possible classification properties of enterprise archetypes within the digital ecosystem and to determine the general properties of the objects under study and their interrelations.,The systematization of results allows us to present a co-competitive activity model for ecosystem participants, depending on their archetype, where they are segmented according to the dominant principle of role and activity within the system. From a practical point of view, the considered classification of the archetypes of recipient enterprises and their economic relations make it possible to structurally visualize a digital ecosystem, which significantly reduces the distance between the consumer of the product/service, the manufacturer and the seller. This reduces the time of delivery and waiting as well as the time to find a suitable option making the market more perfect. The proposed conceptual framework indicates the interdependence of the development of all participants in the product distribution and proves that successful business models take part in the market expansion. Finally, the authors' systematic review of the academic literature results in identifying certain promising directions for future research based on the consideration of open ecosystems with transparent infrastructure.,The results are limited by the authors' sample data and the case study approach. The study does not show the further evolution of the digital ecosystem depending on the set and configurations of ecosystem participants. The authors introduce a possible new classification of archetypes of ecosystem participants and a co-competitive activity model for ecosystem participants depending on these archetypes.,From a practical point of view, the considered classification of the archetypes of recipient enterprises and their economic relations make it possible to structurally visualize a digital ecosystem, which significantly reduces the distance between the consumer of the product/service, the manufacturer and the seller.,From a practical point of view, the considered classification of the archetypes of recipient enterprises and their economic relations makes it possible to structurally visualize a digital ecosystem, which significantly reduces the distance between the consumer of the product or service and the manufacturer or the seller. This reduces the time of delivery and waiting as well as the time to find a suitable option making the market more perfect in this respect.,經濟關係作根本性的變化、企業營運的市場環境日趨不穩定、以及為求達致更好的經營狀況，企業不得不面對日益劇烈的競爭，這都清晰顯示了在分銷渠道內、業務過程中，所有參與者之相互作用的性質是必須重視的。同樣地，被採用的企業結構之成效也不可忽視。這情況就決定了企業在如斯的系統裏，對其經營活動的籌劃及參與者角色的闡明會擴大其生態系統方法的內容。本研究擬確定數位生態系統參與者的各商業模式之主要特點及不同之處；而這些商業模式，對分配系統價值之塑造、至關重要。,在本文中，我們在理論成分的框架裏，分析在Web of Science 資料庫內概念性和實徵性的文章。我們使用的資料，亦包括取自俄國院士在專業期刊、專著和會議摘要內發表的學術論文。研究人員透過內容分析，收集關於企業的數據，並概括為案例；這樣，研究人員就可以在數位生態系統裏提出企業原型可能的分類屬性，以及能夠判斷研究對象的一般屬性及它們之間的相互關聯。,研究結果之系統化，讓我們能夠依賴生態系統參與者的原型，為它們展示一個共同競爭活動模型，而在這模型裏，它們會根據在系統內作用和活動的主導原則而被分割。從實際的角度來看，因受援企業的原型及其經濟關係得到分類，故數位生態系統的結構可被形象化，這可大幅縮短商品或服務的消費者、製造商和銷售者三者之間的距離；這可減少交貨和等候的時間，以及尋找合適選擇所需的時間，使市場更趨完善。被提出之概念性框架顯示了在產物分佈上，所有參與者的發展是相互依賴的；同時，亦證實了成功的商業模式在市場擴展上擔當著一定的角色。最後，我們詳盡而有系統的學術文獻探討，確定了今後研究的某些合適方向，這都是建基於對擁有透明基礎設施的開放生態系統之研究。,研究結果因樣本數據以及採用了個案研究法而有其局限，研究沒有顯示數位生態系統如何依賴生態系統參與者的設置和配置而進一步進化。我們引進一個生態系統參與者原型的可能新分類，以及仗賴這些原型，為生態系統參與者介紹一個共同競爭活動模型。,從實際的角度來看，因受援企業的原型及其經濟關係得到分類，故數位生態系統的結構可被形象化，這可大幅縮短商品或服務的消費者、製造商和銷售者三者之間的距離。,從實際的角度來看，因受援企業的原型及其經濟關係得到分類，故數位生態系統的結構可被形象化，這可大幅縮短商品或服務的消費者、製造商和銷售者三者之間的距離；這可減少交貨和等候的時間，以及尋找合適選擇所需的時間，使市場就此而言更趨完善。

We study a two-echelon, single-product fulfillment system where a regional fulfillment center (FC) replenishes multiple independent local distribution center (LDC) orders within an internal committ...