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A continuous-review, two-echelon distribution system with one central distribution centre (CDC) and multiple regional distribution centres (RDCs) is studied. The CDC jointly replenishes its inventories of multiple items from an external supplier, while each RDC replenishes its inventories of the items from the CDC. Each RDC faces a Poisson demand for each item, and the inventories of each stock in the system are controlled by a (Q, S) policy. Under this policy, an order is placed by a stock whenever its aggregate demand since the last order reaches a given quantity, and the inventory position of each item is raised up to its order-up-to level after the order placement. The objective is to optimise these (Q, S) policies so that the expected total cost of this system is minimised. We propose a decomposition and coordination method for this optimisation after deriving analytically the cost function of the system. Our extensive numerical experiments demonstrate the effectiveness of the proposed method. Furthermore, a parameter sensitivity analysis is conducted to analyse the impacts of some key system parameters on the performance of the method, and managerial insights are provided for optimising distribution systems with joint replenishment and real applications of the method. [ABSTRACT FROM AUTHOR]

In this paper, a method for determining line drop compensator (LDC) parameters for step voltage regulators and on‐load tap changers is proposed to avoid voltage violations in distribution systems with voltage fluctuations due to photovoltaic (PV) generation and electric vehicle (EV) charging. Distribution system operators need to effectively solve voltage problems caused by the widespread installation of distributed energy resources with the existing voltage regulators to construct an efficient and rational infrastructure. Our focus was on improving the method for determining the LDC parameters for the existing voltage regulators based on LDC control. A mechanism to dynamically change the LDC parameters depending on the situations in the distribution system was developed by using a mixture of experts, one of the machine learning techniques, and real‐time voltage and power flow information from information technology switches. The power flow calculations were performed using a distribution system model constructed based on the topology, loads, and generation characteristics of an actual distribution system in the Hokuriku region. The effectiveness of the proposed method was evaluated in terms of its improved effect on PV and EV hosting capacity, as well as the impact on the frequency of tap operations of voltage regulators. © 2024 The Author(s). IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The increasing penetration of distributed energy resources poses new challenges for operating distribution systems, as high penetration levels are causing adverse grid impacts. In this context, active grid regulation, such as the smart inverters functions recommended in IEEE 1547–2018, has become necessary. In this regard, advanced monitoring techniques, such as state estimation, are promising alternatives to enhance the operation of active distribution networks. However, the lack of reliable information is a technical limitation for applying estimation algorithms to distribution circuits. Therefore, knowledge of the control mode of distributed energy resources may be valuable information to enhance the accuracy of the estimates. This paper proposes to include the volt-var smart inverter function in WLS three-phase state estimation via iterative adjustment. The volt-var control variables are introduced as pseudo-measurements in the first stage, and the second one ensures the control and state variables' compliance with the control model. The validation and benefits of the proposed method are presented via simulation results in the IEEE 34-node test feeder. [ABSTRACT FROM AUTHOR]

Power flow studies are essential for planning and operating microgrids (MGs). However, power flow is generally calculated separately for MGs and medium voltage (MV) systems, which tends to overlook some characteristics of the joint MG-MV system. In this context, the literature proposes methods to simulate MG and MV systems in a unique power flow structure. However, some Volt/Var controls must be considered when solving the MG power flow, which increases the power flow iterations. This paper proposes a method to include the Volt/Var strategy in the three-phase Newton–Raphson current injection (NRCI) power flow. The Volt/Var strategy is included in the Jacobian matrix and solved with each power flow iteration. The CIGRE benchmark MV and LV networks and the European LV test Feeder are used to validate the proposed methodology. The conventional Newton–Raphson is employed for the MV system, and the NRCI for the MG systems. Three cases are considered: the first evaluates the MG with the Volt/Var strategy; the second compares the implementation; and the last evaluates the global power flow with the Volt/Var. The results show that the proposed implementation can converge smoothly with few iterations. [ABSTRACT FROM AUTHOR]

Because of the low voltage level and high current in the distribution system, as well as the high ohmic resistance of the conductors, the majority of power system losses are attributed to the distribution system. As a result, it is necessary to address the issue of reducing distribution system losses. To reduce losses in distribution systems, a wide variety of methods and algorithms have been proposed and are still being developed. The allocation of capacitors in a distribution system is one of the earliest and most effective methods for reducing system losses. With the growth of distributed generations and the diversity of their productive power, the use of capacitors in distribution systems has decreased slightly. Distributed generation resource allocation has replaced this method. Conversely, the reconfiguration of distribution systems has proven to be one of the easiest and cheapest methods for reducing distribution system losses in the past half‐century. Distribution system reconfiguration has been the subject of many studies, each with different objectives. Many studies have also reconfigured the distribution system on a daily and hourly basis. A novel method for dynamic distribution network reconfiguration is presented in this study, which utilizes capacitors and distributed generation resources simultaneously. This method divides 24 h into several periods instead of considering real‐time and hourly data. By reconfiguring and allocating capacitors and distributed generation resources during these periods, the voltage profile is improved and losses are reduced. An IEEE 33 bus network is used to test the simulations. A genetic algorithm was used to reduce losses and improve the voltage profile using MATLAB software. Based on the simulation results, this model provides better results than previous approaches. [ABSTRACT FROM AUTHOR]

Acquiring real-time status information of the distribution system forms the foundation for optimizing the management of power system operations. However, missing measurements, bad data, and inaccurate system models present a formidable challenge for distribution system state estimation (DSSE) in practical applications. This paper proposes a physics-informed graphical learning state estimation approach, to address these limitations by integrating power flow equations and GraphSAGE. The generalization ability of GraphSAGE for unknown nodes is used to perform inductive learning of measurement information. For unseen measurement points in the training set, the simulation proves that the proposed approach can still satisfactorily predict the state quantity. The training process is guided by power flow equations to ensure it has physical significance. Additionally, the possibility of applying the proposed approach to an actual distribution area is explored. Equivalent preprocessing of the three-phase voltage measurement data of the actual distribution area is conducted to improve the estimation accuracy of the transformer measurement points and simplify the computation required for state estimation. [ABSTRACT FROM AUTHOR]

A hybrid approach is proposed for optimizing distribution systems (DSs) by integrating clean energy sources, specifically photovoltaic (PV) and wind power (WT). The proposed technique combines the mud ring algorithm (MRA) and quantum neural network (QNN), referred to as the MRA‐QNN technique. The primary objective is to minimize power loss and enhance voltage stability. The MRA method generates the control signal of the converter, while the QNN method predicts the control signal based on the MRA output. The effectiveness of the approach is revealed through simulations on standard IEEE 33 bus and 69 bus systems. Implementation in MATLAB shows superior performance compared to existing methods, with lower power loss values. There has been a sustained rise in the system voltage profile (In the WT and PV situations, 0.950. and 93 p.u), as well as a considerable reduction in the active power (AP) losses (to 132.39 kW with PV and 81.23 kW with WT from 362.86 kW). With PV, the entire yearly economic loss is lowered from $158932.68 to just $57996.939, and with WT, it is decreased to $56805.479. With three PVs, the yearly economic loss and active power losses are decreased to 30419.871 $ and 69.449, and 4.27 kW and 1875.930 $, respectively. [ABSTRACT FROM AUTHOR]

This article proposes an optimal placement and sizing of photovoltaic (PV) power systems based distributed generation (DG) in radial electrical distribution networks considering the capability of PV inverters to regulate the voltage by optimal injecting and absorbing reactive power at the point of common coupling (PCC) using honey badger algorithm (HBA), as a recent and efficient optimization algorithm to solve the complicated optimal allocation. Several objective functions are achieved for distribution system performance enhancement: minimizing the power loss and the voltage deviation index (VDI) and maximizing the voltage stability index (VSI). Based on historical data and probabilistic models, seasonal hourly solar irradiance, ambient temperature, and load variation curves have been modeled, which simultaneously consider the light, normal, and heavy load demand. The essential components of distribution power systems have been characterized. To investigate the validity of the proposed approach, IEEE 33 and IEEE 69 BUS radial distribution test systems have been considered for power flow (PF) analyses, where Newton's Raphson method has been applied to solve the PF issue. The simulation results of different numerical scenarios have shown the effectiveness and validity of the newly proposed method to solve the optimal allocation problem considering optimal volt‐var regulation control of PV inverters compared to several valid and robust optimization algorithms. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power is the property of Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The integration of new battery technologies has become a focal point for distribution utilities, driven by decreasing costs and the need for fast responsiveness. Transportable battery energy storage systems (TBESSs) offer additional flexibility, allowing connection at multiple substations or grid feed points. However, concerns remain regarding their impact on distribution systems (DSs), particularly on protection devices (PDs). This study addresses these concerns by investigating the influence of TBESSs on the protection systems of a real-world distribution network. Given the lack of studies in the current literature on this topic, this research aims to fill this gap by examining the potential effects of TBESS integration on PDs, such as reclosers and fuses, within a DS. Utilizing a model based on real data from a Brazilian utility, we conducted simulations to analyze the effects of TBESSs in both charging and discharging modes on the protection systems of three feeders. The methodology involved assessing variations in the operation times and coordination of PDs to determine if TBESS integration would necessitate adjustments to existing protection configurations. The results demonstrated that TBESS integration resulted in only minor variations in PD operating times, typically within hundredths of a second, indicating a negligible impact on protection performance. Consequently, no significant modifications to the protection system are required to accommodate TBESSs. These findings suggest that TBESSs can be seamlessly integrated into existing distribution networks, maintaining system reliability and operational integrity. This study provides valuable insights and a robust procedure for utilities to analyze the integration of TBESSs, supporting the effective deployment of modern energy storage solutions in DSs. [ABSTRACT FROM AUTHOR]

Maximizing the net economic benefits of leak management programs provides the most value to water utilities and their customers. Leak management programs with alternative objectives (e.g., to "break even" or to maximize leak reduction) are not necessarily the most efficient use of resources. Combining hydraulic modeling with principles of economics and using a real distribution system to provide a case study, we present a methodology for maximizing the net economic benefits of a leak management program consisting of leak detection and repair activities and pressure management interventions. The optimization results include recommended leak detection and repair intervals for individual district metered areas, optimal siting of both pressure reducing valves and pumps-as-turbines (PATs), and modifications to the distribution system's connectivity to isolate high-pressure areas. The case study results indicate that 10% to 11% of the system's current energy use can be recovered by PATs and that the avoided leakage over the study period is equivalent to 13% to 23% of the system's current customer demand. While leak detection and repair and pressure management are more commonly considered independently, this analysis demonstrates the feasibility of including their interdependencies when optimizing a leak management program, resulting in more useful recommendations. [ABSTRACT FROM AUTHOR]

The paper proposes a new stochastic multiobjective technoeconomic model for integrating photovoltaic (PV) and wind energy resources in electricity price (EP)‐driven distribution systems. The primary goal of this paper is to determine the optimal location and capacity for renewable energy‐based distributed generation, specifically PV and wind resources, while considering weather and system uncertainties. These uncertainties include stochastic variations in PV illumination intensity, wind speed, EP, and load fluctuations. To address these uncertainties, the paper employs scenario modeling techniques named as Latin hypercube sampling with Cholesky decomposition. This technique generates multiple correlated scenarios that represent uncertain variables. Subsequently, a scenario reduction technique is applied to identify the scenario with the highest probability. Later, a mathematical model is developed to minimize an objective function that encompasses various factors like system losses, node voltage deviations, the cost of purchasing power from the grid; and simultaneously maximize the total annual energy savings. The objective is to find optimal solutions that strike a balance between different objectives. To obtain an efficient optimum solution, this paper employs an effective meta‐heuristic technique named as JAYA algorithm. The results obtained by the JAYA algorithm are juxtaposed with those obtained using particle swarm optimization and genetic algorithm techniques. The proposed method is evaluated using Institute of Electrical and Electronics Engineers (IEEE) 33‐node and IEEE 69‐node test feeders to validate its feasibility and effectiveness. However, the effectiveness of the proposed method is not limited to any size of test systems. [ABSTRACT FROM AUTHOR]

Abstract The paper proposes a new stochastic multiobjective technoeconomic model for integrating photovoltaic (PV) and wind energy resources in electricity price (EP)‐driven distribution systems. The primary goal of this paper is to determine the optimal location and capacity for renewable energy‐based distributed generation, specifically PV and wind resources, while considering weather and system uncertainties. These uncertainties include stochastic variations in PV illumination intensity, wind speed, EP, and load fluctuations. To address these uncertainties, the paper employs scenario modeling techniques named as Latin hypercube sampling with Cholesky decomposition. This technique generates multiple correlated scenarios that represent uncertain variables. Subsequently, a scenario reduction technique is applied to identify the scenario with the highest probability. Later, a mathematical model is developed to minimize an objective function that encompasses various factors like system losses, node voltage deviations, the cost of purchasing power from the grid; and simultaneously maximize the total annual energy savings. The objective is to find optimal solutions that strike a balance between different objectives. To obtain an efficient optimum solution, this paper employs an effective meta‐heuristic technique named as JAYA algorithm. The results obtained by the JAYA algorithm are juxtaposed with those obtained using particle swarm optimization and genetic algorithm techniques. The proposed method is evaluated using Institute of Electrical and Electronics Engineers (IEEE) 33‐node and IEEE 69‐node test feeders to validate its feasibility and effectiveness. However, the effectiveness of the proposed method is not limited to any size of test systems.

Abstract Deploying distributed generators (DGs) supplied by renewable energy resources poses a significant challenge for efficient power grid operation. The proper sizing and placement of DGs, specifically photovoltaics (PVs) and wind turbines (WTs), remain crucial due to the uncertain characteristics of renewable energy. To overcome these challenges, this study explores an enhanced version of a meta-heuristic technique called the prairie dog optimizer (PDO). The modified prairie dogs optimizer (mPDO) incorporates a novel exploration phase inspired by the slime mold algorithm (SMA) food approach. The mPDO algorithm is proposed to analyze the substantial effects of different dynamic load characteristics on the performance of the distribution networks and the designing of the PV-based and WT-based DGs. The optimization problem incorporates various operational constraints to mitigate energy loss in the distribution networks. Further, the study addresses uncertainties related to the random characteristics of PV and WT power outputs by employing appropriate probability distributions. The mPDO algorithm is evaluated using cec2020 benchmark suit test functions and rigorous statistical analysis to mathematically measure its success rate and efficacy while considering different type of optimization problems. The developed mPDO algorithm is applied to incorporate both PV and WT units, individually and simultaneously, into the IEEE 69-bus distribution network. This is achieved considering residential, commercial, industrial, and mixed time-varying voltage-dependent load demands. The efficacy of the modified algorithm is demonstrated using the standard benchmark functions, and a comparative analysis is conducted with the original PDO and other well-known algorithms, utilizing various statistical metrics. The numerical findings emphasize the significant influence of load type and time-varying generation in DG planning. Moreover, the mPDO algorithm beats the alternatives and improves distributed generators' technical advantages across all examined scenarios.

Abstract In the forthcoming decades, significant advancements will shape the construction and operations of distribution systems. Particularly, the increasing prominence of photovoltaic (PV) systems in the power industry will impact the security of these systems. This study employs Monte Carlo Simulation (MCS) in conjunction with genetic algorithm (GA) and differential evolution (DE) to address uncertainties. The probability density functions (pdf) for total voltage harmonic distortion (UTHD), individual voltage harmonic distortion (UIHDh), and RMS voltage (URMS) have been determined for utilization in chance constrained framework. In addition, the uncertainty effects of PV systems on grid losses for various solar radiation conditions are also investigated. Specifically, the paper aims to evaluate these impacts within the context of stochastic limits. The PV system sizing problem has been addressed inside the distribution system using a chance-constrained framework. A key contribution is the integration of GA, DE, and MCS into a cohesive approach, and the study evaluates the benefits of this approach through an analysis of outcomes derived from the stochastic method. The simulation results illustrate the advantages of the proposed stochastic GA methodology.

Abstract Economic development relies on access to electrical energy, which is crucial for society’s growth. However, power shortages are challenging due to non-renewable energy depletion, unregulated use, and a lack of new energy sources. Ethiopia’s Debre Markos distribution network experiences over 800 h of power outages annually, causing financial losses and resource waste on diesel generators (DGs) for backup use. To tackle these concerns, the present study suggests a hybrid power generation system, which combines solar and biogas resources, and integrates Superconducting Magnetic Energy Storage (SMES) and Pumped Hydro Energy Storage (PHES) technologies into the system. The study also thoroughly analyzes the current and anticipated demand connected to the distribution network using a backward/forward sweep load flow analysis method. The results indicate that the total power loss has reached its absolute maximum, and the voltage profiles of the networks have dropped below the minimal numerical values recommended by the Institute of Electrical and Electronics Engineers (IEEE) standards (i.e., 0.95–1.025 p.u.). After reviewing the current distribution network’s operation, additional steps were taken to improve its effectiveness, using metaheuristic optimization techniques to account for various objective functions and constraints. In the results section, it is demonstrated that the whale optimization algorithm (WOA) outperforms other metaheuristic optimization techniques across three important objective functions: financial, reliability, and greenhouse gas (GHG) emissions. This comparison is based on the capability of the natural selection whale optimization algorithm (NSWOA) to achieve the best possible values for four significant metrics: Cost of Energy (COE), Net Present Cost (NPC), Loss of Power Supply Probability (LPSP), and GHG Emissions. The NSWOA achieved optimal values for these metrics, namely 0.0812 €/kWh, 3.0017 × 106 €, 0.00875, and 7.3679 × 106 kg reduced, respectively. This is attributable to their thorough economic, reliability, and environmental evaluation. Finally, the forward/backward sweep load flow analysis employed during the proposed system’s integration significantly reduced the impact of new energy resources on the distribution network. This was evident in the reduction of total power losses from 470.78 to 18.54 kW and voltage deviation from 6.95 to 0.35 p.u., as well as the voltage profile of the distribution system being swung between 1 and 1.0234 p.u., which now comply with the standards set by the IEEE. Besides, a comparison of the cost and GHG emission efficiency of the proposed hybrid system with existing (grid + DGs) and alternative (only DGs) scenarios was done. The findings showed that, among the scenarios examined, the proposed system is the most economical and produces the least amount of GHG emissions.

The main objective of this research is to improve the reliability of the electrical system under study by reducing both the frequency of outages (SAIFI) and the downtime of electrical service (SAIDI). These problems are largely affected by adverse weather conditions, vegetation growth, and bird contact. To carry out the analysis, the 5011 urban feeder of Canton La Troncal was selected, since it presents critical values in terms of reliability compared to other feeders. The proposed methodology involves a mathematical model of heuristic optimization based on the Particle Swarm Optimization (PSO) algorithm. Two scenarios are defined for the modeling: the first focuses on relocating the existing recloser and the second on analyzing the impact of adding additional protection equipment. These scenarios are validated and demonstrate their effectiveness, achieving an improvement of 29.05% in the first case and 70.93% in the second case within a real conventional distribution system. [ABSTRACT FROM AUTHOR]

With increasing interest in using stannous chloride without filtration to achieve hexavalent chromium maximum contaminant level compliance in California, there is new urgency to quantify its effects on water quality and risks to public health manifested in distribution and premise plumbing systems. Accordingly, a 12‐week groundwater pilot study was completed using ubiquitous premise plumbing materials under flowing (to waste) and stagnating conditions of hexavalent chromium‐containing groundwater following stannous chloride addition without filtration. Results quantified the accumulation and release of chromium and tin, with total chromium concentrations exceeding regulatory or acceptable levels under stagnating conditions. Sediment with elevated chromium, tin, arsenic, and manganese concentrations was also observed. Given these observations would likely occur and worsen over time in distribution systems that cumulatively serve large populations, the use of stannous chloride without filtration represents an unacceptable risk to public health compared to other, more well‐established, best available technologies. [ABSTRACT FROM AUTHOR]

This article presents the world energy situation, the importance of energy transition, and it further designates hydrogen as a potential replacement for natural gas. Moreover, the paper specifies that the energy transition will be carried out gradually, sustained by conventional resources. Hydrogen can be mixed with natural gasses in current networks, but additional risks must be considered. These risks prove to be minimal in the distribution sector if the hydrogen fraction is kept within acceptable limits, and if optimal operation and safety measures are enforced. The behavior of the hydrogen-natural gas mixture, also called HENG, is studied based on distinguishing the physical and chemical properties of hydrogen and natural gas. In order to analyze the true behavior of the mixture in a distribution network, a numerical simulation was undertaken, whose analysis shows that there is a tight connection between the concentration of hydrogen in the system, consumption, and linepack, and the operational limits of the network must be established based on these data. Knowing the behavior, strategies and regulations can be drafted in order to safely exploit natural gas-hydrogen distribution networks. However, every distribution network has its own particularities, which is why additional studies both at theoretical as well as practical level, performed on real distribution networks, are needed. [ABSTRACT FROM AUTHOR]

Answering the right questions on pipeline condition assessment in light of system‐specific priorities helps utilities make cost‐effective decisions about infrastructure renewal. [ABSTRACT FROM AUTHOR]

The growing utilization of renewable energy sources (RES) that rely on inverters has adverse impacts on distribution systems, resulting in challenges such as power quality (PQ) issues and increased losses in the distribution network. One potential remedy for these issues involves the strategic allocation of open unified power quality conditioner (UPQC-O). This paper introduces a comprehensive two-layer integrated optimization model designed for the allocation of UPQC-O and RES in an active distribution network. The primary objective is to enhance energy efficiency, PQ, and economic benefits simultaneously. Furthermore, an innovative solver known as the multi-improved whale optimization method (multi-IWOA) has been implemented to address the optimization problem at hand. The efficacy of this approach was validated through testing on both the standard IEEE 69-bus radial distribution system and a real-world Indian 52-bus system. The evaluation results underscore the significance of optimally distributing UPQC-O resources while taking into consideration the integration of RES within the distribution system. It's worth noting that the proposed methodology achieved a substantial 56.72% cost reduction for the IEEE 69-bus radial distribution system and a significant 76.092% cost reduction for the Practical System Indian 52-bus system. This highlights the substantial advantages of the proposed methodology for the efficient management of UPQC-O and RES integration in distribution systems. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Deploying distributed generators (DGs) supplied by renewable energy resources poses a significant challenge for efficient power grid operation. The proper sizing and placement of DGs, specifically photovoltaics (PVs) and wind turbines (WTs), remain crucial due to the uncertain characteristics of renewable energy. To overcome these challenges, this study explores an enhanced version of a meta-heuristic technique called the prairie dog optimizer (PDO). The modified prairie dogs optimizer (mPDO) incorporates a novel exploration phase inspired by the slime mold algorithm (SMA) food approach. The mPDO algorithm is proposed to analyze the substantial effects of different dynamic load characteristics on the performance of the distribution networks and the designing of the PV-based and WT-based DGs. The optimization problem incorporates various operational constraints to mitigate energy loss in the distribution networks. Further, the study addresses uncertainties related to the random characteristics of PV and WT power outputs by employing appropriate probability distributions. The mPDO algorithm is evaluated using cec2020 benchmark suit test functions and rigorous statistical analysis to mathematically measure its success rate and efficacy while considering different type of optimization problems. The developed mPDO algorithm is applied to incorporate both PV and WT units, individually and simultaneously, into the IEEE 69-bus distribution network. This is achieved considering residential, commercial, industrial, and mixed time-varying voltage-dependent load demands. The efficacy of the modified algorithm is demonstrated using the standard benchmark functions, and a comparative analysis is conducted with the original PDO and other well-known algorithms, utilizing various statistical metrics. The numerical findings emphasize the significant influence of load type and time-varying generation in DG planning. Moreover, the mPDO algorithm beats the alternatives and improves distributed generators' technical advantages across all examined scenarios. [ABSTRACT FROM AUTHOR]

To optimize radial distribution systems, this study suggests the utilization of the Grey Wolf Optimizer (GWO), a hybrid metaheuristic optimization method, combined with the Sine Cosine method (SCA). The primary objective of this work is to enhance the distribution system by determining the most efficient network reconfiguration, sizing, and placement of various distributed energy sources in distribution system. The energy sources considered include capacitors, solar cells, wind turbines, biomass-based distributed generation units, and battery storage units. To achieve this goal, the proposed strategy incorporates the power loss sensitivity technique, which assists in identifying suitable candidate buses and accelerates the resolution process. Moreover, the model considers fluctuations in solar irradiance and wind speed using Weibull and Beta probability distribution functions, compensating for the intermittent nature of renewable energy sources and the variability in demand. To address power fluctuations, voltage surges, significant losses, and inadequate voltage stability challenges, battery energy storage, diesel generators, and dispatchable biomass DGs are employed to mitigate variability and enhance supply continuity. The proposed approach is evaluated and validated by comparing it to existing optimization strategies using IEEE 69-bus and 84-bus RDSs. The results demonstrate that the suggested technique achieves faster convergence to near-optimal solutions. The proposed methodology yields a significant reduction of up to 80% in power losses in the 69-bus system and a 35% reduction in the 84-bus system, signifying higher performance than existing methods. [ABSTRACT FROM AUTHOR]

This paper introduces a new algorithm designed to address the challenge of distribution network reconfiguration, employing the tabu search metaheuristic in conjunction with the voltage drop concept. Distinguishing itself from existing methods, our proposed approach not only utilizes voltage drop for obtaining the initial solution but also introduces a novel technique for generating a candidate solution neighborhood. This method leverages both randomness and voltage drop, ensuring a smooth and steady descent during algorithm execution. The primary goal of our algorithm is to minimize active power losses within distribution networks. To validate its effectiveness, the proposed method underwent testing on three commonly referenced distribution systems: the 33-Bus, 69-Bus, and 94-Bus systems, widely acknowledged in the literature. A pivotal aspect of our work involves the synergy of the tabu search algorithm with a combination of both random and deterministic methods for generating neighbors. This strategic amalgamation plays a crucial role, enabling rapid execution while consistently yielding high-quality solutions. Additionally, the adoption of the electric distance method for generating the initial solution adds significant value, offering a commendable solution with minimal computational effort. Comparative assessments against other algorithms documented in the literature underscore the superior efficiency of our proposed algorithm. [ABSTRACT FROM AUTHOR]

The growing deployment of distributed resources significantly affects the distribution grid performance in most countries. The optimal sizing and placement of these resources have become increasingly crucial to mitigating grid issues and reducing costs. Particle Swarm Optimization (PSO) is widely used to address such problems but faces computational inefficiency due to its numerical convergence behavior. This limits its effectiveness, especially for power system problems, because the numerical distance between two nodes in power systems might be different from the actual electrical distance. In this paper, a scalable bilevel optimization problem with two novel algorithms enhances PSO's computational efficiency. While the resistivity-driven algorithm strategically targets low-resistivity regions and guides PSO toward areas with lower losses, the connectivity-driven algorithm aligns solution spaces with the grid's physical topology. It prioritizes actual physical neighbors during the search to prevent local optima traps. The tests of the algorithms on the IEEE 33-bus and the 69-bus and Norwegian networks show significant reductions in power losses (up to 74% for PV, wind, and storage) and improved voltage stability (a 21% reduction in mean voltage deviation index) with respect to the results of classical PSO. The proposed network-oriented PSO outperforms classical PSO by achieving a 2.84% reduction in the average fitness value for the IEEE 69-bus case with PV, wind, and storage deployment. The Norwegian case study affirms the effectiveness of the proposed approach in real-world applications through significant improvements in loss reduction and voltage stability. [ABSTRACT FROM AUTHOR]

In the forthcoming decades, significant advancements will shape the construction and operations of distribution systems. Particularly, the increasing prominence of photovoltaic (PV) systems in the power industry will impact the security of these systems. This study employs Monte Carlo Simulation (MCS) in conjunction with genetic algorithm (GA) and differential evolution (DE) to address uncertainties. The probability density functions (pdf) for total voltage harmonic distortion (UTHD), individual voltage harmonic distortion (UIHDh), and RMS voltage (URMS) have been determined for utilization in chance constrained framework. In addition, the uncertainty effects of PV systems on grid losses for various solar radiation conditions are also investigated. Specifically, the paper aims to evaluate these impacts within the context of stochastic limits. The PV system sizing problem has been addressed inside the distribution system using a chance-constrained framework. A key contribution is the integration of GA, DE, and MCS into a cohesive approach, and the study evaluates the benefits of this approach through an analysis of outcomes derived from the stochastic method. The simulation results illustrate the advantages of the proposed stochastic GA methodology. [ABSTRACT FROM AUTHOR]

A major challenge in distribution systems is the issue of voltage drop along the distribution line resulting from an increased load capacity connected to the utility. A significant voltage drop can affect the performance of a distribution system and cause quality issues for end users, impacting the system's long-term sustainability and reliability. Therefore, regulations have been set stating that the voltage level should not be more that 5% higher or lower than the rated voltage. Thus, in this study, we aimed to evaluate the voltage level characteristics of a 22 kV distribution system that replicates the actual distribution system in the Provincial Electricity Authority. A voltage improvement technique based on distributed generation placement was proposed. In addition, the distribution system characteristics with and without distributed generation placement were evaluated under fault conditions. The results indicate that distributed generation placement in the distribution system can improve the voltage level along the distribution line. However, the level of increase in voltage depends on the size of the load, the capacity of the distributed generation, and the location of the distributed generation system on the distribution line. Furthermore, placing a distributed generation system with a minimum capacity at the proposed location can improve the voltage within the utility's standard level. Thus, the installation of a distributed generation system in the distribution system is beneficial in terms of voltage improvement in the distribution system and provides the power system with a sustainable method to address the issue of voltage drop. [ABSTRACT FROM AUTHOR]

Economic development relies on access to electrical energy, which is crucial for society's growth. However, power shortages are challenging due to non-renewable energy depletion, unregulated use, and a lack of new energy sources. Ethiopia's Debre Markos distribution network experiences over 800 h of power outages annually, causing financial losses and resource waste on diesel generators (DGs) for backup use. To tackle these concerns, the present study suggests a hybrid power generation system, which combines solar and biogas resources, and integrates Superconducting Magnetic Energy Storage (SMES) and Pumped Hydro Energy Storage (PHES) technologies into the system. The study also thoroughly analyzes the current and anticipated demand connected to the distribution network using a backward/forward sweep load flow analysis method. The results indicate that the total power loss has reached its absolute maximum, and the voltage profiles of the networks have dropped below the minimal numerical values recommended by the Institute of Electrical and Electronics Engineers (IEEE) standards (i.e., 0.95–1.025 p.u.). After reviewing the current distribution network's operation, additional steps were taken to improve its effectiveness, using metaheuristic optimization techniques to account for various objective functions and constraints. In the results section, it is demonstrated that the whale optimization algorithm (WOA) outperforms other metaheuristic optimization techniques across three important objective functions: financial, reliability, and greenhouse gas (GHG) emissions. This comparison is based on the capability of the natural selection whale optimization algorithm (NSWOA) to achieve the best possible values for four significant metrics: Cost of Energy (COE), Net Present Cost (NPC), Loss of Power Supply Probability (LPSP), and GHG Emissions. The NSWOA achieved optimal values for these metrics, namely 0.0812 €/kWh, 3.0017 × 106 €, 0.00875, and 7.3679 × 106 kg reduced, respectively. This is attributable to their thorough economic, reliability, and environmental evaluation. Finally, the forward/backward sweep load flow analysis employed during the proposed system's integration significantly reduced the impact of new energy resources on the distribution network. This was evident in the reduction of total power losses from 470.78 to 18.54 kW and voltage deviation from 6.95 to 0.35 p.u., as well as the voltage profile of the distribution system being swung between 1 and 1.0234 p.u., which now comply with the standards set by the IEEE. Besides, a comparison of the cost and GHG emission efficiency of the proposed hybrid system with existing (grid + DGs) and alternative (only DGs) scenarios was done. The findings showed that, among the scenarios examined, the proposed system is the most economical and produces the least amount of GHG emissions. [ABSTRACT FROM AUTHOR]

US Environmental Protection Agency (EPA) requires public water systems (PWSs) that disinfect to maintain a detectable disinfectant residual throughout the distribution system (DS) to protect consumers against risks from microbial pathogens. The EPA is tasked with proposing revisions to the Microbial, Disinfectant, and Disinfection Byproduct rules by July 2025. Potential revisions could include a numeric minimum disinfectant residual level in place of the "detectable" requirement. Twenty‐three states have defined a required DS numeric minimum disinfectant residual level. Only three such states, Louisiana, Colorado, and Pennsylvania, have made recent changes and have sufficient data available to assess implications. These states were used as case studies to assess the impact this regulatory change had on disinfectant residual levels and microbial and disinfection byproduct (DBP) occurrence in PWS DSs. Results included increases in disinfectant residual levels, decreases in total coliform positive occurrences and violations, and temporary increases in DBP occurrence and violations. [ABSTRACT FROM AUTHOR]

This paper examines the impact of different voltage unbalance definitions on voltage control in unbalanced distribution systems. Traditional voltage regulation methods for unbalanced systems rely on a single voltage unbalance definition with inconsistent adoptions. In such a situation, voltage control performance can vary between definitions, and improper choice leads to control failures. In this study, we perform numerical analysis and simulations to investigate the impact of different definitions. In the numerical analysis, two definitions quantifying negative and zero sequence components are used as reference definitions, and the theoretical error range of the remaining definitions is identified. The results are further verified by the simulations on the IEEE 123 node test system with phase‐by‐phase controlled three‐phase step voltage regulators and smart photovoltaic inverters. The control of the devices is optimized to minimize or limit a specific definition. The results show that the values of each definition at the same voltage condition can differ significantly, and inappropriate definitions may lead to significant uncertainty in the voltage control. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

According to recent power quality studies, voltage sags and voltage unbalance in medium-voltage and low-voltage distribution grids are the most common type of power quality problems. Power quality is an essential concern in the modern power system that can affect consumers and utility. In modern age, with an increase in sensitive and expensive electronic equipment, power quality is essential for the power system's reliable and safe operation. This paper proposes a hybrid technique-based unified power quality conditioner (UPQC) of microgrid in an EV charging station (EVCS), and it is the combination of recalling-enhanced recurrent neural network (RERNN) and sand cat swarm optimization (SCSO), together called as RERNN-SCSO. The SCSO creates the converter control signal, and the RERNN method forecasts the control signal from SCSO method. The proposed technique consists of series-active power filter (Se-APF), shunt-active power filter (Sh-APF), and DC-side capacitor as the parts of proposed technique. The major purpose of RERNN-SCSO technique is used to increase the power quality. Many loads, like highly inductive NLL, nonlinear loads (NLL) as well as unbalanced NLL (UNLL), are all linked at the point of common coupling (PCC). When the major structure of UPQC is built into the EVCS, an additional alternating current (AC)/DC converter will help with initialization. At this, the charging position provides charging service to the EVs as well as discharges the EVs into the power grid. The novelty of the proposed work consists in connecting EVs in parallel to the UPQC DC-side using double-layer microgrid (MC) and DC–DC converter. The proposed method ensures EV charging while efficiently handling quality of power issues like harmonic current, voltage disturbance, voltage sag and swell and voltage unbalance. RERNN-SCSO method performance is performed on the MATLAB/Simulink software to analyze the working performances and related with present techniques. RERNN-SCSO method-based THD under voltage sag condition as 2.03% and swell as 2.01%, which is lower than the existing methods. [ABSTRACT FROM AUTHOR]

Water utilities must help protect public health by making sure water distribution systems are effectively treated to guard against dangerous levels of Legionella pneumophila. [ABSTRACT FROM AUTHOR]

To improve the resilience of a distribution system against extreme weather, a fuel-based distributed generator (DG) allocation model is proposed in this study. In this model, the DGs are placed at the planning stage. When an extreme event occurs, the controllable generators form temporary microgrids (MGs) to restore the load maximally. Simultaneously, a demand response program (DRP) mitigates the imbalance between the power supply and demand during extreme events. To cope with the fault uncertainty, a robust optimization (RO) method is applied to reduce the long-term investment and short-term operation costs. The optimization is formulated as a tri-level defender-attacker-defender (DAD) framework. At the first level, decision-makers work out the DG allocation scheme; at the second level, the attacker finds the optimal attack strategy with maximum damage; and at the third level, restoration measures, namely distribution network reconfiguration (DNR) and demand response are performed. The problem is solved by the nested column and constraint generation (NC&CG) method and the model is validated using an IEEE 33-node system. Case studies validate the effectiveness and superiority of the proposed model according to the enhanced resilience and reduced cost. [ABSTRACT FROM AUTHOR]

The continuous growth of renewable energy and the load level has posed increasingly severe operational risks to distribution systems. In view of this, this paper combines state estimation with risk assessment, and uses the results of distribution system state estimation based on Unscented Kalman Filter as the input of risk assessment. With the combination, the sampling based on probability distributions in traditional risk assessment methods is no longer needed, thus avoiding the difficulty of updating probability distributions timely according to the latest information in real-time operation. By applying the proposed risk assessment method, the real-time assessment of operational risks in perspectives of bus voltage, branch power, and renewable energy utilization is achieved. Meanwhile, the weight of each risk index is properly determined according to both subjective and objective knowledge by using Analytic Hierarchy Process method and entropy weight method. Case studies show that the proposed method achieves effective assessment of comprehensive risks in the operation of distribution system.

This paper proposes a new solution for the classification of short-circuits in medium-voltage distribution grids. The solution requires the measurements of the phase voltages at the time of the short-circuit and the voltage amplitudes of the short-circuit wave resulting from the fault. The values of the voltage before the short-circuit are used to calculate the expected values of the short-circuit wave amplitudes for the different types of short circuits. These calculations are based on an analysis of the charge flow between the capacitances of the power line due to the disturbance. The short-time matrix pencil method was used to detect the incoming short-circuit waves. Tests of the algorithm were carried out on the IEEE 34-bus Feeder model, taking into account the influence of voltage sensors on the measured signal values. The classification algorithm uses only non-zero components due to their relatively low attenuation. Despite that, it achieves high performance in the classification of single- and two-phase short-circuits.

The widespread deployment of electric buses (EBs) offers substantial flexibility resources to support the service restoration and enhance the resilience of distribution system (DS). Therefore, this paper presents a comprehensive and collaborative service restoration strategy for resilient DS, considering the support of EBs. First, the restoration flexibility model for EBs is derived, which quantifies the restoration capability of EBs by dividing effective EB clusters. This model is integrated into the DS restoration process. Then, a two-step collaborative restoration method of DS is proposed with the aim of minimizing the load curtailment cost. The first step addresses the restoration planning problem to determine the dispatch decisions for EB clusters. Based on above planning solutions, the second step focuses on the real-time restoration problem of DS, obtaining optimal restoration results through coordinating EB clusters and other local resources. In this stage, the impact of natural disasters on the EB cluster dispatch in transportation system is considered, and a rolling optimization method is employed to deal with uncertainties from renewables, load, and EB cluster dispatch time. Finally, numerical simulations are conducted using the modified IEEE 33-bus distribution test system and a 29-node transportation system to verify the effectiveness of the proposed restoration method.

Accurately estimating load is essential for effective electric distribution planning, assets management, precise power flow predictions, accurate power losses calculations, and efficient integration of distributed energy resources. This work describes a dataset that was generated using Matlab and OpenDSS to produce several simulations in which load estimation is performed using a direct search method called pattern search. These simulations were conducted on three typical distribution feeders (IEEE 13-bus, 37-bus, and 123-bus) that support studies in distribution planning, assets management, power flow predictions, power losses calculations, and distributed resource integration. The dataset includes individual demand profiles of residential, commercial, and industrial consumers specified for the three distribution feeders, comprising 96 distinct scenarios. An optimization method was developed to obtain the dataset, which employs the pattern search technique to estimate loads through the optimization of objective functions and specified constraints. The load estimation quality was assessed for all three feeders, utilizing estimation quality indices proposed by the authors. These indices evaluated both the initial and proposed load estimation methods across the developed scenarios. Furthermore, the data provided in this article can be utilized for comparison with future load estimation studies, particularly regarding the quality of the method's results.

Many events in power networks which caused failure and outage of subsystems have root in design and initial selection of equipment and their specification. This concern will be important while the any kind of expanding and acclimation plan need to be executed to strengthen the existing infrastructures in order to provide better condition with regard to satisfied criteria. In Iran one of the most adverse environmental occurrences known Fine Dust, may force several distribution utilities to be changed due to new technical requirements. Medium voltage level regrading in way to decrease or unification of distribution system have been proposed in this paper to reach reduced faults and fast replacement of equipment in damaged situations. Voltage level changing and selection have been investigated in order to increase the network strengthen and reselection of equipment and redesigning critical sections of system against natural events that called fine dust with aim to attain an upgraded distribution utility. Operating of existing 33 kV and 11 kV network in 20 kV level and investigation of changing process have been done for Ahvaz medium voltage distribution system as a case study in these paper's sections. Calculations and results will be contained maximum current capacities in different line conductors, insulation and air clearances assessment, poles acceptable mechanical values, short circuit current and protection coordination for changing voltage level in distribution system.

Detecting High Impedance Faults (HIFs) in Distribution Systems (DSs) still requires effective solutions. HIFs can cause damage, such as shock hazards and bushfires. However, conventional protection cannot identify HIFs due to their low fault current, and most existing solutions neglect the multiple operating scenarios of the DSs. Thus, this paper proposes a new self-adaptive approach for HIF detection, considering the conductor fall period in a system with distributed generation, a condition usually overlooked in existing studies. The algorithm is based only on the fundamental component and third harmonic extracted from the neutral current using the Stockwell Transform. The approach was thoroughly evaluated, considering several scenarios, such as changes in the system loading, background noise, events that can cause false detection, and variations in the distributed generator power output. The tests considered HIFs signals from real field measurements and generated by a simulation model. The results showed that the algorithm is promising, with high detection rates for all the evaluated scenarios, which included over 400,000 HIF and 6,000 non-HIF cases. Additionally, a comparison proved the superiority of the proposed approach over existing methodologies.