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This paper develops an optimization model for determining the placement of switches, tie lines, and underground cables in order to enhance the reliability of an electric power distribution system. A central novelty in the model is the inclusion of nodal reliability constraints, which consider network topology and are important in practice. The model can be reformulated either as a mixed-integer exponential conic optimization problem or as a mixed-integer linear program. We demonstrate both theoretically and empirically that the judicious application of partial linearization is key to rendering a practically tractable formulation. Computational studies indicate that realistic instances can indeed be solved in a reasonable amount of time on standard hardware. [ABSTRACT FROM AUTHOR]

The analysis of harmonic distortions and their propagation in electric power systems is crucial for power quality assessment. Harmonic power flow (HPF) is a widely used technique for this purpose. However, most existing research on HPF focuses on deterministic algorithms, and only a few studies have considered uncertainties associated with harmonic sources connected to microgrids. Due to the lack of recent approaches for evaluating the impact of uncertain data on HPF results, this paper proposes a new methodology for calculating harmonic voltages and distortions in microgrids by assuming uncertain parameters for both loads and harmonic sources. To obtain interval results for harmonic voltages, the load flow equations are expanded up to the second-order terms using Taylor series, considering all partial derivatives of the system variables with respect to a percentage uncertainty value. The proposed method is evaluated using simulations on a 33-bus distribution system, considering the microgrid in both islanded mode and connected to a main power substation. As demonstrated, the interval results are similar to those obtained from Monte Carlo simulations, proving the efficiency of the proposed method with robust solution and reduced computational time. [ABSTRACT FROM AUTHOR]

This research uses data analysis and mining techniques to determine the technological expansion of measurement systems in a public service company. It integrates technical, economic, geographic, and social variables into the analysis using machine learning techniques to discover patterns and relationships in large data sets. The fuzzy logic methodology is applied using the MATLAB programming tool "Fuzzy Logic" to build algorithms that allow for the correct selection of measurement, achieving greater efficiency and precision in the assignment of meter types. The results show that 98% of the metering systems in the significant part are electronic meters, with the "Residential BT" rate being the most extensive data set. Implementing the "fuzzy logic" technique recognizes that more than 60% of the meters are electronic, with the registration of active energy, reactive energy, and demand, allowing for greater control over the marketing variables of the distribution system operator. This research suggests that a future restructuring of electrical metering systems benefits the company and its users. By applying the analysis, a portfolio of viable projects for the replacement of measurement systems is obtained, and they are grouped into two clusters based on the total cost of the technological change. [ABSTRACT FROM AUTHOR]

In the conditions of a long-term threat of the destruction of the energy structure, the distribution networks of our country need to change the principles of their construction. For this purpose, a multi-level structure of the construction of the distribution system is proposed. Such a structure, due to the use of modern hardware solutions and extensive involvement of local energy resources, makes it possible to ensure a high level of their flexibility. In order to maintain power supply to the maximum number of consumers in the event of damage of varying severity and duration that may occur in the centralized power supply system. [ABSTRACT FROM AUTHOR]

Globally, grid systems are facing substantial challenges due to the rapid growth in power demand. New technologies equipped by means of smart energy resources are one promising solution to cope with this challenge, leading to microgrid systems. The growing demand to develop the power sector by utilizing alternative energy resources plays an influential role in the advancement of low-voltage DC (LVDC) systems. With the prompt development of LVDC distribution grids, bipolar LVDC distribution topology is becoming a leading and promising solution. Therefore, this review paper represents a comprehensive overview as well as a comparative and critical analysis for the development of novel methods on LVDC grid analysis, control strategy, and protection schemes to solve energy management problems that lead to a futuristic novel solution in the green revolution. [ABSTRACT FROM AUTHOR]

The integration of renewable energy is transforming traditional energy systems, blurring the distinction between producers and consumers and shifting towards a distributed grid network. This change demands innovative approaches to optimize Energy Storage Systems (ESS) and manage grid incidents efficiently, all without significant infrastructural changes. While optimization algorithms like Swarm Intelligence are gaining traction, critical aspects, such as worst-case scenario analysis in distribution networks, remain underexplored. This study addresses this gap by applying stochastic optimization techniques to determine the optimal placement and capacity of ESS in a medium voltage radial distribution system, using the IEEE 33-bus model. The findings highlight the importance of considering worst-case scenarios, offering a balanced evaluation of current methodologies. This research provides valuable insights for improving system flexibility and resilience, contributing to more effective and practical energy optimization strategies in real-world applications.

The rapid growth of renewable energy is integrated into the distribution system. The creation of both new market mechanisms and investment models has critical effects on the economics and security of the distribution market. Mobile energy storage has been used to increase the resilience of distribution grids due to their advantages in mobility and flexibility, which offer electricity arbitrage options for merchant investments. This paper presents a bi-level optimization framework based on location marginal pricing settlement of mobile energy storage financial rights revenue in active distribution systems. In the developed framework, the upper-level problem determines the optimal capacity, routing, and dispatching of mobile energy storage to maximize revenue in the liberalized electricity market. The lower-level problem performs the joint optimization of energy and reserve market clearing as well as renewable energy capacity optimization based on the alternating current optimal power flow model. The non-convexity of the line flow and location marginal pricing is linearized via second-order cone relaxation and Karush-Kuhn-Tucker. The bi-level programming is reformatted as mathematical programming with equilibrium constraints. Further, the revenue risk due to renewable energy uncertainty is measured via conditional value-at-risk. Finally, the effectiveness of the optimization framework and solution method is verified using the modified IEEE-33 test system. The results show that mobile energy storage promotes renewable energy and reduces distribution network costs by 2.3%.

This study explores the feasibility of using impedance relays in electrical distribution systems, a context where their application is not as common as in transmission systems. Given the dynamic nature and complex topology of medium-voltage distribution systems, this work proposes an innovative methodology integrating clustering algorithms and metaheuristic techniques to optimize the placement of impedance relays and enhance system reliability and resilience. Using CYME simulation and the Ant Colony Optimization (ACO) method, case studies were designed to validate the effectiveness of the proposed methodology. The results demonstrated that strategic placement of impedance relays reduces failure frequency and significantly improves the system's response to such failures. This approach allows for a more efficient configuration and quicker response, which is crucial for maintaining continuity and quality of power supply. A detailed analysis of system behavior under various fault scenarios illustrates the robustness and adaptability of the proposed solution, marking a significant advancement in the protection and optimization of electrical distribution systems. [ABSTRACT FROM AUTHOR]

Installing a battery energy storage system (BESS) and renewable energy sources can significantly improve distribution network performance in several aspects, especially in electric vehicle (EV)-integrated systems because of high load demands. With the high costs of the BESS and PV, optimal placement and capacity of them must be carefully considered. This work proposes a solution for determining the optimal placement and capacity of a BESS and photovoltaic (PV) in a distribution system by considering EV penetrations. The objective function is to reduce system costs, comprising installation, replacement, and operation and maintenance costs of the BESS and PV. The replacement cost is considered over 20 years, and the maintenance and operation costs incurred in the distribution system include transmission line loss, voltage regulation, and peak demand costs. To solve the problem, two metaheuristic algorithms consisting of particle swarm optimization (PSO) and the African vulture optimization algorithm (AVOA) are utilized. The tenth feeder of Phitsanulok substation 1 (PLA10), Thailand, which is a 91-bus distribution network, is tested to evaluate the performance of the proposed approach. The results obtained from the considered algorithms are compared based on distribution system performance enhancement, payback period, and statistical analysis. It is found from the simulation results that the installation of the BESS and PV could significantly minimize system cost, improve the voltage profile, reduce transmission line loss, and decrease peak demand. The voltage deviation could be reduced by 86%, line loss was reduced by 0.78 MW, and peak demand could be decreased by 5.706 MW compared to the case without BESS and PV installations. [ABSTRACT FROM AUTHOR]

Renewable energy resources have increasingly been integrated into electric power grids. Moreover, new challenges constantly arise, showing the need for modernization in monitoring, control, and protection. Transmission and distribution systems have explored the use of phasor measurement units (PMUs) in an attempt to meet these requirements. The decreasing cost of hardware components of PMUs, similar to what occurred with digital relays, is an important factor in terms of their worldwide use in different applications. Low-cost variations of PMUs focused on distribution systems, such as micro- and D-PMUs, are growing in number. Thus, this survey article presents significant and recent research conducted in the field of power systems using PMUs. Firstly, it briefly describes the characteristics of PMUs concerning hardware and phasor estimation algorithms. Afterward, recent studies related to transmission and distribution system protection based on PMUs are presented, such as fault detection and location, stability, and wide-area protection. Finally, the article also provided important guidelines, which certainly can be useful for future research in the years to come. [ABSTRACT FROM AUTHOR]

Biofilms within drinking water distribution systems serve as a habitat for drinking water microorganisms. However, biofilms can negatively impact drinking water quality by causing water discoloration and deterioration and can be a reservoir for unwanted microorganisms. In this study, we investigated whether indicator organisms for drinking water quality, such as coliforms, can settle in mature drinking water biofilms. Therefore, a biofilm monitor consisting of glass rings was used to grow and sample drinking water biofilms. Two mature drinking water biofilms were characterized by flow cytometry, ATP measurements, confocal laser scanning microscopy, and 16S rRNA sequencing. Biofilms developed under treated chlorinated surface water supply exhibited lower cell densities in comparison with biofilms resulting from treated groundwater. Overall, the phenotypic as well as the genotypic characteristics were significantly different between both biofilms. In addition, the response of the biofilm microbiome and possible biofilm detachment after minor water quality changes were investigated. Limited changes in pH and free chlorine addition, to simulate operational changes that are relevant for practice, were evaluated. It was shown that both biofilms remained resilient. Finally, mature biofilms were prone to invasion of the coliform, Serratia fonticola. After spiking low concentrations (i.e., ±100 cells/100 mL) of the coliform to the corresponding bulk water samples, the coliforms were able to attach and get established within the mature biofilms. These outcomes emphasize the need for continued research on biofilm detachment and its implications for water contamination in distribution networks. [ABSTRACT FROM AUTHOR]

In contrast to "frank" pathogens, like Salmonella entrocolitica, Shigella dysenteriae, and Vibrio cholerae, that always have a probability of disease, "opportunistic" pathogens are organisms that cause an infectious disease in a host with a weakened immune system and rarely in a healthy host. Historically, drinking water treatment has focused on control of frank pathogens, particularly those from human or animal sources (like Giardia lamblia, Cryptosporidium parvum, or Hepatitis A virus), but in recent years outbreaks from drinking water have increasingly been due to opportunistic pathogens. Characteristics of opportunistic pathogens that make them problematic for water treatment include: (1) they are normally present in aquatic environments, (2) they grow in biofilms that protect the bacteria from disinfectants, and (3) under appropriate conditions in drinking water systems (e.g., warm water, stagnation, low disinfectant levels, etc.), these bacteria can amplify to levels that can pose a public health risk. The three most common opportunistic pathogens in drinking water systems are Legionella pneumophila, Mycobacterium avium, and Pseudomonas aeruginosa. This report focuses on these organisms to provide information on their public health risk, occurrence in drinking water systems, susceptibility to various disinfectants, and other operational practices (like flushing and cleaning of pipes and storage tanks). In addition, information is provided on a group of nine other opportunistic pathogens that are less commonly found in drinking water systems, including Aeromonas hydrophila, Klebsiella pneumoniae, Serratia marcescens, Burkholderia pseudomallei, Acinetobacter baumannii, Stenotrophomonas maltophilia, Arcobacter butzleri, and several free-living amoebae including Naegleria fowleri and species of Acanthamoeba. The public health risk for these microbes in drinking water is still unclear, but in most cases, efforts to manage Legionella, mycobacteria, and Pseudomonas risks will also be effective for these other opportunistic pathogens. The approach to managing opportunistic pathogens in drinking water supplies focuses on controlling the growth of these organisms. Many of these microbes are normal inhabitants in biofilms in water, so the attention is less on eliminating these organisms from entering the system and more on managing their occurrence and concentrations in the pipe network. With anticipated warming trends associated with climate change, the factors that drive the growth of opportunistic pathogens in drinking water systems will likely increase. It is important, therefore, to evaluate treatment barriers and management activities for control of opportunistic pathogen risks. Controls for primary treatment, particularly for turbidity management and disinfection, should be reviewed to ensure adequacy for opportunistic pathogen control. However, the major focus for the utility's opportunistic pathogen risk reduction plan is the management of biological activity and biofilms in the distribution system. Factors that influence the growth of microbes (primarily in biofilms) in the distribution system include, temperature, disinfectant type and concentration, nutrient levels (measured as AOC or BDOC), stagnation, flushing of pipes and cleaning of storage tank sediments, and corrosion control. Pressure management and distribution system integrity are also important to the microbial quality of water but are related more to the intrusion of contaminants into the distribution system rather than directly related to microbial growth. Summarizing the identified risk from drinking water, the availability and quality of disinfection data for treatment, and guidelines or standards for control showed that adequate information is best available for management of L. pneumophila. For L. pneumophila, the risk for this organism has been clearly established from drinking water, cases have increased worldwide, and it is one of the most identified causes of drinking water outbreaks. Water management best practices (e.g., maintenance of a disinfectant residual throughout the distribution system, flushing and cleaning of sediments in pipelines and storage tanks, among others) have been shown to be effective for control of L. pneumophila in water supplies. In addition, there are well documented management guidelines available for the control of the organism in drinking water distribution systems. By comparison, management of risks for Mycobacteria from water are less clear than for L. pneumophila. Treatment of M. avium is difficult due to its resistance to disinfection, the tendency to form clumps, and attachment to surfaces in biofilms. Additionally, there are no guidelines for management of M. avium in drinking water, and one risk assessment study suggested a low risk of infection. The role of tap water in the transmission of the other opportunistic pathogens is less clear and, in many cases, actions to manage L. pneumophila (e.g., maintenance of a disinfectant residual, flushing, cleaning of storage tanks, etc.) will also be beneficial in helping to manage these organisms as well. [ABSTRACT FROM AUTHOR]

This article discusses the optimal placement of distributed generation (DG) in power distribution systems and the evaluation of losses and voltage using machine learning algorithms. The proposed algorithm accurately predicts DG positions using Support Vector Machine, Random Forest, and Radial Neural Networks. The research highlights the effectiveness of these algorithms in enhancing system performance and providing valuable insights for decision-making in the field of distributed generation. The proposed algorithm effectively addresses voltage imbalance, line loading, and power losses in the distribution system, offering advantages over other algorithms. The performance of the algorithm is evaluated using metrics such as R-squared and Mean Absolute Percentage Error, and it consistently outperforms existing methods in terms of prediction accuracy. The findings suggest that the proposed model can effectively address the challenge of predicting distribution systems. The given text is a list of references to various research papers and articles related to distributed generation in power systems, providing valuable insights for researchers and library patrons interested in understanding and exploring the field. [Extracted from the article]

Non-technical losses (NTL) have been a growing problem over the years, causing significant financial losses for electric utilities. Among the methods for detecting this type of loss, those based on Artificial Intelligence (AI) have been the most popular. Many works use the electricity consumption profile as an input for AI models, which may not be sufficient to develop a model that achieves a high detection rate for various types of energy fraud that may occur. In this paper, using actual electricity consumption data, additional statistical and temporal features based on these data are used to improve the detection rate of various types of NTL. Furthermore, a model that combines both the electricity consumption data and these features is developed, achieving a better detection rate for all types of fraud considered. [ABSTRACT FROM AUTHOR]

The fast-paced growth in digitization of smart grid components enhances system observability and remote-control capabilities through efficient communication. However, enhanced connectivity results in heightened system vulnerability towards cybersecurity risks in the cyber-physical power system. Coordinated cyber-attacks (CCA), when undetected, lead to system-wide impact in terms of large disturbances or widespread outages. Detecting CCA in the cyber layer is critical to thwart cyber-attacks in real-time before the attack impacts the physical system. The challenge of locating CCA stems from the complex grid dynamics, making it difficult to distinguish between normal operational variations and cyber-attack impact. CCA often employs multiple attack vectors targeting geographically distributed components, further complicating CCA identification. Existing research in intrusion detection is primarily focused on the transmission network and limited to detecting individual attacks. In this paper, a novel proactive DCA strategy is proposed for early detection of CCA by establishing correlations among distinct attack events through model-based reinforcement learning that utilizes abductive reasoning to conclude the attacker goal. The solution includes understanding the system model, learning the system dynamics, and correlating individual cyber-attacks to extract the attacker’s objective. The developed learning algorithm identifies the most probable attack path to reach the attacker’s objective by predicting the next attack steps. A DNP3-based cyber-physical co-simulation testbed is developed to test the proposed algorithm using the IEEE 13-node test feeder.

Distribution system state estimation (DSSE) has been developed for real-time monitoring of distribution systems. As a weighted least square (WLS) based method, DSSE relies on measurement variances to properly weigh the reduction of the measurement residuals. However, traditional DSSE considers an approximated modeling of measurement uncertainty/variance, which can limit the accuracy and quality of state estimates. The main contribution of this paper is the adoption of a new approach to represent uncertainties in loads and measurements more accurately. The paper shows this approach improves the quality of state estimates using DSSE. Several statistical metrics, like bias, quality, and error rate are used to define the quality and accuracy of the state estimates. Comparative Monte Carlo analysis using an IEEE test distribution feeder and an example distribution feeder based on a real feeder is provided to illustrate the improvement from modeling measurement uncertainty more accurately.

The present paper proposes a new methodology to solve a problem in the literature in which a consumer becomes eligible for reactive power excess charging (RPEC) after installing a photovoltaic (PV) system, even if its reactive demand has not changed. This problem mainly affects this kind of consumer, which is known as PV prosumers, in countries where the power factor at the point of common coupling (PCC) is used as the main parameter in calculating reactive power excess charges instead of directly pricing the surplus reactive energy delivered from the distribution network. The classical approaches to reducing or eliminating RPEC are based on providing reactive power within the consumer’s installations from their PV inverters and their capacitor banks. However, the solutions presented require optimization studies since the costs of producing reactive energy can be even higher than the penalties involved. In this context, we developed a new hourly-based optimization model for the RPEC problem based on the variable neighborhood search meta-heuristic and the Fibonacci search method. In addition, we apply the proposed methodology to two medium voltage prosumers in Santo André, Brazil. The results showed the proposal’s effectiveness, mitigating RPEC and minimizing the prosumers’ electricity bills without installing new equipment to supply their reactive demand. The best results were found when reactive power was delivered by the capacitor bank and PV inverters simultaneously rather than applying these solutions separately. The proposed hourly-based correction method can help in day-ahead energy management, avoiding premature wear from switching the consumer’s reactive power compensation equipment switches. Also, the methodology proposal improved the power factor at the utility substation, which benefits the power distribution systems.

This paper aims to investigate the impact of non-Gaussian measurement noise on state estimation (SE) results in distribution systems. To this end, the measurement noise is assumed to be distributed according to Gaussian or one of the following non-Gaussian probability distribution functions: Uniform, Laplace, Weibull and Gaussian mixture of two Gaussian components. The influence is investigated on three different state-of-the-art SE methods: weighted least squares (WLS) based static SE method, and two Kalman filter based forecasting-aided SE methods, namely extended Kalman filter (EKF) and unscented Kalman filter (UKF). Analyses are conducted on modified IEEE 37-bus system under different operating conditions, including quasi-steady state, sudden state changes and bad data. Performance of the methods in the presence of non-Gaussian measurement noise is compared against their performance when measurement noise is Gaussian distributed. The main conclusions were drawn, summarizing the impacts non-Gaussian measurement noise has on SE and proposing the solutions for overcoming some of the negative impacts.

Line parameters play an important role in the control and management of distribution systems. Currently, phasor measurement unit (PMU) systems and supervisory control and data acquisition (SCADA) systems coexist in distribution systems. Unfortunately, SCADA and PMU measurements usually do not match each other, resulting in inaccurate detection and identification of line parameters based on measurements. To solve this problem, a data-driven method is proposed. SCADA measurements are taken as samples and PMU measurements as the population. A probability parameter identification index (PPII) is derived to detect the whole line parameter based on the probability density function (PDF) parameters of the measurements. For parameter identification, a power-loss PDF with the PMU time stamps and a power-loss chronological PDF are derived via kernel density estimation (KDE) and a conditional PDF. Then, the power-loss samples with the PMU time stamps and chronological correlations are generated by the two PDFs of the power loss via the Metropolis-Hastings (MH) algorithm. Finally, using the power-loss samples and PMU current measurements, the line parameters are identified using the total least squares (TLS) algorithm. Hardware simulations demonstrate the effectiveness of the proposed method for distribution network line parameter detection and identification.

The integration of photovoltaic (PV) panels has allowed power distribution systems (PDSs) to regulate their voltage through the injection/absorption of reactive power. The deployment of information and communication technologies (ICTs), which is required for this scheme, has made the PDS prone to various cyber threats, e.g., false data injection (FDI) attacks. To counter these attacks, this paper proposes a data-driven framework to detect FDI attacks against voltage regulation of PV-integrated PDS. Initially, an attack-free system is modeled along with its voltage regulation scheme, where the grid measurements are sent to a centralized controller and the control signals are transmitted back to PVs to be used by their local controllers. Then, a convolutional neural network (CNN) framework is proposed to detect FDI attacks. To train this framework—which should be able to distinguish between normal grid behaviors and attacks—a complete and realistic dataset is formed to cover all normal conditions and unpredictable changes of a PDS during a year. Since normal variations and fluctuations in power consumption lead to changes in the voltage profile, this dataset is enriched using features such as season, weekdays, weekends, load conditions, and PV generation power. The performance of the trained framework has been compared with other supervised Machine Learning-based and deep-learning techniques for FDI attacks against modified IEEE 33- and 141-bus PDSs. Simulation results demonstrate the superior performance of the proposed framework in detecting FDI attacks.

A protocol for responding to Legionella pneumophila detections in distribution system samples is presented and justified. The protocol was developed using existing protocols for building water systems, vetted in workshops, and provided to utilities participating in a research project. The protocol provides action levels and actions that utilities can take when L. pneumophila is detected in distribution system samples. Action levels are based on the best available science and expert judgment and are similar to those in protocols for assessing building water system monitoring results. Action levels should be reassessed as additional data and knowledge on the occurrence and growth potential of L. pneumophila in distribution systems become available. All positive detections trigger assessments that focus first on conditions local to the sample location and then extend further into the distribution system. Higher concentration or more frequent detections initiate more assertive responses and communication than lower concentration and frequency detections. The protocol provides a starting point for the development of protocols for purposes such as L. pneumophila operational surveillance or within an outbreak investigation. [ABSTRACT FROM AUTHOR]

The introduction of distributed generation (DG) into distribution systems is expanding due to carbon neutral policies. DG with intermittent output characteristics brings frequent voltage variations in distribution systems. Generally, a step voltage regulator (SVR) is installed in the long distribution line and controls its voltage, which has limits of stable voltage regulations due to the slow tap-changing rate, switching losses, and short life span. To solve those problems, this paper proposes an IGBT-based SVR using zero-voltage switching. It is verified that the stable operation can be obtained through simulation by PSCAD/EMTDC 4.6 software and empirical experiments. [ABSTRACT FROM AUTHOR]

Conventional protection systems in distribution systems are capable of identifying low-impedance faults since such faults produce high fault current levels. But in a high impedance fault case protection schemes fail to detect a fault due to the fact that such faults do not generate enough fault current to operate conventional protection devices and fuses. This paper presents a novel algorithm for high impedance fault detection in medium voltage distribution systems. The proposed algorithm leverages multi-resolution analysis using discrete wavelet transform analysis for improved reliability and accuracy, extracting d5 coefficients at the transformer secondary terminal of all phases and the neutral, calculating their energies and integrating and calculating the average slope and comparing them to a threshold slope value in order to take the right decision for high impedance fault detection. The research explores the algorithm's effectiveness through extensive testing using MATLAB/Simulink software, demonstrating its potential as a valuable tool for enhancing the reliability of medium voltage distribution systems. [ABSTRACT FROM AUTHOR]

Natural Organic Matter (NOM) in water arises from decomposed plant and animal matter and is ubiquitous in drinking water sources. The variation in NOM concentrations and characteristics, influenced by events like floods and droughts, plays a crucial role in water treatment efficiency and water quality received by the public. For example, increased NOM concentrations necessitate higher levels of coagulants and disinfectants, leads to the formation of disinfection by-products (DBPs), and plays a key role in biofilm development. When considering impacts of NOM, it is not only the presence or concentration but the makeup or proportion of varying sub-groups which can impact water quality. Formation of DBPs, corrosion and scaling, pollutant transport, aesthetic deterioration, and biofilm growth are dependent on the relative composition of NOM within the distribution system. Although the role of NOM concentration and characteristics is well studied during treatment, the impacts of residual NOM in water distribution systems have received less attention. In particular, it is clear, due to the varying roles of NOM sub-groups, that greater consideration of NOM characteristics in distribution systems is needed. This paper reviews the broad implications of NOM characteristics for water distribution systems and explores challenges and opportunities in NOM characterization within distribution systems. Furthermore, the influence of NOM characteristics in premise plumbing is examined. The review highlights the necessity for precise NOM characterization and real-time monitoring, aiming to strengthen water distribution system resilience. [ABSTRACT FROM AUTHOR]

The reduction in GHG emissions has become one of the most important objectives in Europe. One of these clean technologies in the quest for large-scale decarbonisation is the blending of hydrogen and natural gas (H2NG). The significance of this article lies in conducting a technical–economic analysis that relies on simulating annual consumption patterns. This study focuses on the utilization of H2NG at different proportions within a distribution network in Bucharest, Romania's largest city. The paper is a fallow up of another paper and presents a case study for the injection through all four entry points in the city of different percentages of hydrogen and the dynamic of the mix through the gas distribution network. The study found that the variation in H2 in the network is high and depends on the entry point levels and consumption. An economic analysis was conducted on seasonal real consumption, which confirmed the investment's profitability. [ABSTRACT FROM AUTHOR]

A DC microgrid is an efficient way to combine diverse sources; conventional droop control is unable to achieve both accurate current sharing and required voltage regulation. This paper provides a new adaptive control approach for DC microgrid applications that satisfies both accurate current sharing and appropriate voltage regulation depending on the loading state. As the load increases in parallel, so do the output currents of the distributed generating units, and correct current sharing is necessary under severe load conditions. The suggested control approach raises the equivalent droop gains as the load level increases in parallel and provides accurate current sharing. The droop parameters were checked online and changed using the principal current sharing loops to reduce the variation in load current sharing, and the second loop also transferred the droop lines to eliminate DC microgrid bus voltage fluctuation in the adaptive droop controller, which is different and inventive. The proposed algorithm is tested using a variety of input voltages and load resistances. This work assesses the performance and stability of the suggested method using a linearized model and verifies the results using an acceptable model created in MATLAB/SIMULINK Software Version 9.3 and using Real-Time Simulation Fundamentals and hardware-based experimentation. [ABSTRACT FROM AUTHOR]

In this paper, an optimization approach based on an arithmetic optimization algorithm (AOA) is proposed for specifying the optimal allocation of distribution generations/generators (DGs) and capacitor banks (CBs) in radial distribution systems. The AOA is a new population-based meta-heuristic algorithm that is essentially based on using basic arithmetic operators in mathematics. The proposed approach is employed to specify the optimum placement, capacity, and power factor of DGs and CBs to decrease the distribution systems' total power loss and voltage deviation. To state the performance of the proposed approach, DGs and CBs are placed in IEEE 33-bus and 69-bus systems separately or together. When only DGs are used and the parameters of location, capacity, and power factor of DGs are determined simultaneously, the total active power loss reductions in the IEEE 33-bus and 69-bus systems are achieved at 94.42% and 98.03%, respectively. When the results of other optimization algorithms are examined, it is seen that better results are obtained with AOA. [ABSTRACT FROM AUTHOR]

The aim of this study is to provide minimum operating cost by using the energy storage system at the feeder scale of the electricity distribution network with renewable and distributed energy sources. The operating optimization of the network is handled with the two-stage stochastic programming problem developed in the study. The problem was formulated with Mixed Integer Linear Programming (MILP), a linear model, through the General Algebraic Modeling System (GAMS) and solved with the CPLEX solver. In order to deal with the uncertainties in the modeling, scenario generation and reduction were carried out through Monte Carlo Simulation. Simulation studies carried out to verify the effectiveness of the proposed model were applied on IEEE-33 test busbars. Operating costs were calculated under possible grid conditions and compared among themselves according to the use of energy storage. According to the results, in cases where the energy storage system is integrated into the grid, a decrease of more than 200 dollars was observed in the operating cost in only one day's average time period compared to the cases where the storage system is not available at all. Thus, the optimum programming of energy storage with the proposed system; It has been proven to be an effective method in reducing operating costs and thus providing economic optimization, which is one of the most critical issues of power systems. [ABSTRACT FROM AUTHOR]

The Distribution System of the Zona Especial de Desarrollo Mariel (ZDEM) is deficient today because it was designed for a much higher load level than the one it works with. The indices of electrical losses in this scheme exceeded the limits established by the Unión Eléctrica de Cuba (UNE). In order to reduce these losses, five proposals for the reconfiguration of the distribution circuits were analyzed (transformer connections with the system), which were aimed at reducing the number of transformers connected in the substations, which contributed considerably to the increase in electrical losses as they were very underloaded concerning their rated power. After completing the analysis of the proposals, the electrical scheme improved reliability, the installed capacity was reduced by half, and total losses decreased. [ABSTRACT FROM AUTHOR]

Key Takeaways: Digital twins of water systems can give utility staff the real‐time, detailed analyses they need to manage the complexities of pumping and distribution operations. Among others, the benefits yielded by investing in digital twins include cost savings, improved staff efficiency, optimal equipment operation, and the ability to quickly and accurately identify system issues. Five utility case studies illustrate how real‐time data analysis effortlessly produced information that helped solve significant problems, some of which might not have been discovered otherwise. [ABSTRACT FROM AUTHOR]

As the modern power system continues to grow in size, complexity, and uncertainty, traditional methods may occasionally prove insufficient in addressing the associated challenges. The improper location of distributed generation varies the voltage profile, increases losses and compromises network capacity. Machine learning algorithms predict accurate site positions, and network reconfiguration improves the capacity of the power system. The proposed algorithm is a hybrid of machine learning and deep learning algorithms. It cascades Support Vector Machine as the main model and uses Random Forest and Radial Neural Networks as classification algorithms for accurately predicting DG position. The non-linearity characteristics of the DG problem are directly mapped to the proposed algorithms. The proposed algorithm is employed on familiar test setups like the IEEE 33-bus and 69-bus distribution systems using MATLAB R2017 as simulation software. The R-squared (R2) values for all parameters yield a value of 1, while the MAPE values are minimal for the proposed cascaded algorithm in contrast to other algorithms of LSTM, CNN, RNN and DQL.

The number of Electric Vehicles (EVs) on the roads is expected to dramatically rise within the next few years. This is poised to substantially increase the total electricity demand due to EV charging. The key question is whether today’s distributions systems can handle this increased charging demand. Our central hypothesis is that coordinated charging is a must to enable wide scale deployment of EV chargers. Coordinated charging refers to scheduling, and possibly optimizing, the charging action of EVs so that charging is more focused during grid off-peak hours. Due to the importance of charging incentives, the objective of this paper is to take a step back and obtain real data that help gauge EV drivers’ level of acceptance to charging scheduling incentives. Using New York City as a living lab, a case study was carried out to analyze the effectiveness of those incentives. The results of a survey, taken by 119 New Yorkers, shed light on people’s response to charging incentives. For instance, 85% of the survey respondents chose to travel longer for a cheaper EVSE instead of heading to a near EVSE at a higher cost.

Various electric vehicle charging and discharging strategies (EVs) and V2G technologies are discussed in this article as their impacts on energy distribution networks. The V2G application that can be used on vehicles offers many benefits, as demonstrated. Features such as active power regulation, reactive power support, load balancing, and current harmonic filtering are incorporated into this technology. Although V2G technology has many benefits, there are also several challenges. These challenges include reduced battery life, communication overhead between EVs and grids, and changes in distribution network infrastructure. The article briefly discusses the effects of electric vehicle penetration levels, charging profiles, and various other aspects of controlled charging and discharging from a performance perspective. This includes overloading, deteriorating power quality, and power loss. A comprehensive analysis of controlled and uncontrolled charging–discharging methods, delayed charging–discharging methods, indirect controlled discharging methods, bidirectional charging–discharging methods, and intelligent scheduling is presented in this study. Several challenges and issues regarding electric vehicle applications are discussed from an aggregator’s perspective. Analysis shows that Li-ion batteries can be recharged 2000–4000 times, and a mass-produced Li-ion battery costs $200–$500 per kWh. Degradation costs of batteries at 80% discharge depth are estimated to be $130 per MWh at 300 kWh investment cost. 10% of peak capacity could come from PEVs in the 20% range. Around 87.5% of PEVs are properly charged.

This research uses data analysis and mining techniques to determine the technological expansion of measurement systems in a public service company. It integrates technical, economic, geographic, and social variables into the analysis using machine learning techniques to discover patterns and relationships in large data sets. The fuzzy logic methodology is applied using the MATLAB programming tool “Fuzzy Logic” to build algorithms that allow for the correct selection of measurement, achieving greater efficiency and precision in the assignment of meter types. The results show that 98% of the metering systems in the significant part are electronic meters, with the “Residential BT” rate being the most extensive data set. Implementing the “fuzzy logic” technique recognizes that more than 60% of the meters are electronic, with the registration of active energy, reactive energy, and demand, allowing for greater control over the marketing variables of the distribution system operator. This research suggests that a future restructuring of electrical metering systems benefits the company and its users. By applying the analysis, a portfolio of viable projects for the replacement of measurement systems is obtained, and they are grouped into two clusters based on the total cost of the technological change.

The continuity of power supply is affected by many factors; the weather conditions to which power system components are exposed are considered the most effect. A new methodology to assess the reliability of distribution system considering the impact of different weather conditions is presented in this paper. The proposed method considers possible future changes in the weather patterns that might occur differently from what it was in the past. Modified sets of reliability indices are presented for mathematically predicting reliability performance based on historical reliability data under specific past weather conditions combined with forecasted future weather conditions. The proposed method is applied to the distribution system components of RBTS; the results demonstrate the significance of considering the forecast of weather conditions in reliability assessment.