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Adenosine triphosphate (ATP) assays have a faster turnaround time and higher sensitivity than traditional cultivation methods for microbial monitoring. Challenges implementing ATP testing include incompatibility with chlorine quenching agents and hold time sensitivity, which are not well-studied. Chlorinated distribution system samples were collected from two Canadian utilities, Metro Vancouver (n = 40 samples) and Halifax Water (n = 283). No significant correlations were observed between heterotrophic cell count (HPC) and cellular ATP, suggesting these do not correlate well in waters with low biological activity (median HPC < 2 CFU/mL). However, interpretation of HPC and cATP results (based on the HPC guideline of 100 CFU/mL and cATP of 10 pg/mL) yielded the same conclusion for 95% of samples, suggesting a potential decision-making framework to replace HPC with cATP. Moreover, cATP correlates better with free chlorine (p < 0.04) compared with HPC for one of the studied systems. Importantly, adding chlorine quench (10% sodium thiosulfate) did not produce significantly different cATP results, nor did analyzing at various hold times of 4-, 6-, and 24-h. This study supports the integration of ATP testing into existing sampling procedures for water utilities, as a sensitive, fast, and reliable monitoring method. [ABSTRACT FROM AUTHOR]

With the rapid development of the economy and society, the demand for power quality is constantly increasing. As a crucial part of grid situational awareness, distribution network state estimation plays a vital role in providing critical data support for other advanced applications, which is significant for ensuring the safe and reliable operation of the distribution network. Therefore, this paper proposes a dynamic state awareness method for distribution networks based on the robust adaptive cubature Kalman filter (RACKF). The proposed solution, grounded on the core computational concept of the cubature Kalman filter (CKF), constructs a robust noise statistical estimator (NSE) composed of a biased NSE and an unbiased NSE to adapt to the unknown and time-varying process noise parameters in the dynamic state estimation process. The proposed solution can ensure that the calculated process noise parameters always meet the constraints and guarantee the robustness of the algorithm. In addition, an estimation strategy for the fusion of multi-time scale measurement data is developed according to the RACKF-based dynamic state estimation features in order to realize system state corrections and updates. The results of simulation experiments and comparisons with traditional CKF methods demonstrate the accuracy and superiority of the proposed solution. [ABSTRACT FROM AUTHOR]

With the proposed "double carbon" target for the power system, large-scale distributed energy access poses a major challenge to the way the distribution grid operates. The rural distribution network (DN) will transform into a new local power system primarily driven by distributed renewable energy sources and energy storage, while also being interconnected with the larger power grid. The development of the rural DN will heavily rely on the construction and efficient planning of the microgrid (MG) within the agricultural park. Based on this, this paper proposes a two-stage optimal scheduling model and solution strategy for the microgrid distribution network with multi-source agricultural load aggregation. First, in the first stage, considering the flexible agricultural load and the market time-of-use electricity price, the economic optimization is realized by optimizing the operation of the schedulable resources of the park. The linear model in this stage is solved by the Lingo algorithm with fast solution speed and high accuracy. In the second stage, the power interaction between the MG and the DN in the agricultural park is considered. By optimising the output of the reactive power compensation device, the operating state of the DN is optimised. At this stage, the non-linear and convex optimization problems are solved by the particle swarm optimization algorithm. Finally, the example analysis shows that the proposed method can effectively improve the feasible region of safe operation of the distribution network in rural areas and improve the operating income of a multi-source agricultural load aggregation agricultural park. [ABSTRACT FROM AUTHOR]

Accurate identification of network topology and line parameters is essential for effective management of distribution systems. An innovative joint estimation method for distribution network topology and line parameters is presented, utilizing a power flow graph convolutional network (PFGCN). This approach addresses the limitations of traditional methods that rely on costly voltage phase angle measurements. The node correlation principle is applied to construct a node correlation matrix, and a minimum distance iteration algorithm is proposed to generate candidate topologies, which serve as graph inputs for the parameter estimation model. Based on the topological dependencies and convolutional properties of AC power flow equations, a PFGCN model is designed for line parameter estimation. Parameter refinement is achieved through an alternating iterative process of pseudo-trend calculation and neural network training. Training convergence and loss function values are used as feedback to filter and validate candidate topologies, enabling precise joint estimation of both topologies and parameters. The proposed method's accuracy, transferability, and robustness are demonstrated through experiments on the IEEE-33 and modified IEEE-69 distribution systems. Multiple metrics, including MAPE, IAE, MAE, and R2, highlight the proposed method's advantages over Adaptive Ridge Regression (ARR). In the C33 scenario, the proposed method achieves MAPEs of 4.6% for g and 5.7% for b, outperforming the ARR method with MAPEs of 7.1% and 7.9%, respectively. Similarly, in the IC69 scenario, the proposed method records MAPEs of 3.0% for g and 5.9% for b, surpassing the ARR method's 5.1% and 8.3%. [ABSTRACT FROM AUTHOR]

Copyright of Scientia Iranica. Transaction D, Computer Science & Engineering & Electrical Engineering is the property of Scientia Iranica 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.)

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press 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.)

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press 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.)

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press 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 aim of this research is to evaluate the performance of the distribution network by connecting wind distributed generation (DG) and determining the optimal location and size using the particle swarm optimization (PSO) technique, once the wind DG is connected at the optimal location, the output of wind turbines is not constant but varies with changes in wind speed. Wind turbines are designed to generate the energy from the wind. As the output of the wind turbines changes, it influences the power flow and voltage levels in the distribution network. The injection of power from the wind turbines can cause variations in voltages within the distribution network. Additionally, the changing power flow may contribute to power losses in the distribution system. In this paper, the voltages and active power losses are evaluated with the change in wind speed for the IEEE 15 Bus system by conducting load flow analysis in MATLAB. The results reveal optimized solutions that contribute to reduced power losses, increased renewable energy generation, and improved voltage profiles. This research underscores the potential of PSO-based optimization in conforming more efficient and sustainable distribution networks. [ABSTRACT FROM AUTHOR]

With the rapid development of 5G base station construction, significant energy storage is installed to ensure stable communication. However, these storage resources often remain idle, leading to inefficiency. To enhance the utilization of base station energy storage (BSES), this paper proposes a co-regulation method for distribution network (DN) voltage control, enabling BSES participation in grid interactions. In this paper, firstly, an energy consumption prediction model based on long and short-term memory neural network (LSTM) is established to accurately predict the daily load changes of base stations. Secondly, a BSES aggregation model is constructed by using the power feasible domain maximal inner approximation method and Minkowski summation to evaluate the charging and discharging potential and adjustable capacity of BSES clusters. Subsequently, a BSES demand assessment and optimal scheduling model for low voltage regulation in DN is developed. This model optimizes the charging and discharging strategies of BSES to alleviate low voltage problems in DN. Finally, the simulation results effectively verify the feasibility of the proposed optimal scheduling method of BSES for voltage regulation in DN. [ABSTRACT FROM AUTHOR]

In distribution networks with distributed generators (DGs), power generation and load demand exhibit increased randomness and volatility, and the line parameters also suffer more frequent fluctuations, which may result in significant state shifts. Existing model-driven methods face challenges in efficiently solving uncertain power flow, especially as the size of the system increases, making it difficult to meet the demand for rapid power flow analysis. To address these issues, this paper proposes an SVR-based interval power flow (IPF) prediction method for distribution networks with DGs integration. The method utilizes intervals to describe system uncertainty and employs Support Vector Regression (SVR) for model training. The input feature vector consists of the intervals of active power generation, load demand, and line parameters, while the output feature vector represents the intervals of voltage or line transmission power. Ultimately, the SVR-based IPF prediction model is established, capturing the linear mapping relationship between input data and output IPF variables. Simulation results demonstrate that the proposed method exhibits high prediction accuracy, strong adaptability, and optimal computation efficiency, meeting the requirements for rapid and real-time power flow analysis while considering the uncertainty in distribution networks with DGs integration. [ABSTRACT FROM AUTHOR]

With the increasing penetration of electric vehicles (EVs) in road traffic, the spatial and temporal stochasticity of the travel pattern and charging demand of EVs as a mode of transportation and an electrical load have generated different degrees of congestion impacts on both the power grid and the transportation network. Based on this, this paper proposes a power–transportation-coupled distribution network optimization strategy based on stochastic user traffic equilibrium theory. First, a stochastic user equilibrium-mixed traffic flow allocation model that expresses user non-completely rational path selection behavior is established, and the traffic flow equilibrium solution is obtained using an improved method of successive algorithm and mapped to charging loads. Second, a distribution network power flow optimization model under the coupled power–transportation architecture is established to optimize the operation state of the distribution network by combining distributed resources such as energy storage, demand response loads, wind power, photovoltaic, and gas turbines. [ABSTRACT FROM AUTHOR]

In the background of the global commitment to low-carbon renewable energy transformation, new energy sources such as distributed wind–solar storage and charging and single-phase loads, such as electric vehicles, are connected to the distribution network. Due to the asymmetric access of load and the randomness of load power consumption, the problem of three-phase imbalance is becoming more and more prominent, and the uncertainty is increasing. In this paper, by analyzing the three-phase load imbalance problem of the distribution network, starting from the cause of three-phase load imbalance, the three-phase imbalance calculation method of the distribution network is deduced. The access mode of a distributed power flow controller in the distribution network and the three-phase imbalance compensation method are studied. The control strategy of the distributed power flow controller for the three-phase imbalance control of the distribution network is proposed. The simulation results show that the proposed method can effectively control the three-phase asymmetry problem of the distribution network, the configuration is more flexible, and the capacity utilization rate is higher. [ABSTRACT FROM AUTHOR]

Copyright of Zhejiang Electric Power is the property of Zhejiang 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.)

With the increasing number of electric vehicles (EVs), a large amount of charging load will affect the safety and economic operation of the distribution network (DN). In this paper, an optimization dispatching strategy for integrated electricity and natural gas systems (IEGS) with fast charging stations (FCSs) is proposed to address the impact of fast charging loads. Firstly, the FCS load model is established based on the simulation of vehicle traveling and vehicle charging. Secondly, a natural gas supply model is established, which includes constraints such as the adjustment range, direction, and total number of supply flow adjustments. Then, an optimization dispatching strategy for IEGS based on energy storage and gas storage is proposed. Finally, the effectiveness of the proposed strategy is verified. The simulation results show that when the proposed strategy is applied, the voltage of FCS node will be stabilized within a good range. And the energy purchase cost will be reduced by more than 50%. The proposed method improves the carrying capacity of DN for FCSs. [ABSTRACT FROM AUTHOR]

The access of a high proportion of photovoltaic (PV) will change the energy structure of the distribution network (DN), resulting in a series of safety operation risks. This paper proposes a two-stage PV hosting capacity (PVHC) calculation model to assess the maximum PVHC, considering the uncertainty and active management (AM). Firstly, we employ a robust optimization model to characterize the uncertainty of sources and loads in DN with PV and analyze the worst-case scenarios for PVHC. Subsequently, we construct a PVHC calculation model that takes into account AM, and convert the model into a mixed-integer second-order cone (MISOC) model using linearization techniques. Finally, we apply "heuristic optimization + CPLEX solver" to solve the model and introduce overvoltage and overcurrent indices to analyze the safety of the DN under PV limit access. Case studies are carried out on the IEEE 33-bus system and a practical case. Results show that (1) only the uncertainty that reduces the load or increases the output efficiency will affect PVHC; (2) for DN limited by overvoltage, AM can better improve PVHC; however, for DN limited by maximum transmission power, the effect of AM is low; (3) for most DN, SVC can improve PVHC, but the effect is modest. And network reconfiguration can significantly increase PVHC on the system with poor branch network, even reaching 150% of the original PVHC. [ABSTRACT FROM AUTHOR]

With the rapid development of 5G base station construction, significant energy storage is installed to ensure stable communication. However, these storage resources often remain idle, leading to inefficiency. To enhance the utilization of base station energy storage (BSES), this paper proposes a co-regulation method for distribution network (DN) voltage control, enabling BSES participation in grid interactions. In this paper, firstly, an energy consumption prediction model based on long and short-term memory neural network (LSTM) is established to accurately predict the daily load changes of base stations. Secondly, a BSES aggregation model is constructed by using the power feasible domain maximal inner approximation method and Minkowski summation to evaluate the charging and discharging potential and adjustable capacity of BSES clusters. Subsequently, a BSES demand assessment and optimal scheduling model for low voltage regulation in DN is developed. This model optimizes the charging and discharging strategies of BSES to alleviate low voltage problems in DN. Finally, the simulation results effectively verify the feasibility of the proposed optimal scheduling method of BSES for voltage regulation in DN. [ABSTRACT FROM AUTHOR]

With the development of China's national economy and the acceleration of urbanization, the production and life of society are increasingly dependent on power supply. Thus, how to ensure the safety and reliability of electric energy is becoming more and more important. It would cause substantial losses in case of a large‐scale blackout, which is what we need to avoid. The recovery speed of the power outage is one of the most vital core issue, fast recovery of power supply in the distribution network is a significant way of improving emergency disposal efficiency. In order to make full use of the emergency resources and improve the efficiency of distribution network emergency repair, this paper presents a coordinated optimal scheduling of emergency power supply vehicles and emergency repair teams in the case of multiple faults. First, the emergency repair disposal path optimization model is created, with the goal of minimizing emergency disposal time and outage losses. Second, an emergency power supply vehicle scheduling model is established, with the goal of maximizing the economic benefit. And the optimal dispatch model of the emergency power supply vehicle with the highest value of the recovery load is built by using the transfer strategy matrix. Finally, the binary particle swarm optimization (BPSO) algorithm is used to solve the optimization models in the IEEE33 node distribution network model. The results indicates that the optimized scheduling strategy considering the coordinated dispatch of emergency power supply vehicles and emergency repair teams can be obtained, and the validity of the proposed method is verified. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Aiming at the problems of high peak value fault current, fast rising speed, and being unable to ensure the reliability of the power supply in the non-fault zone in a multi-terminal DC system, a new cascade flexible current limiter and mechanical DC circuit breaker for medium- and high-voltage distribution networks are proposed. Firstly, the flexible current limiter is triggered by differential under-voltage protection to achieve the effect of interpole voltage clamping, suppressing the fault current and improving the dynamic recovery characteristics of the DC system after fault clearing. Secondly, according to the breaking speed of the DC circuit breaker, the action time of the current limiter can be set flexibly. The directional pilot protection signal of the circuit breaker is used to ensure the continuous action of the current limiter at the converter station side in the fault zone, until the circuit breaker acts to isolate the fault. The protection strategy can also avoid the blocking of the converter station and reduce the requirements for the breaking speed and breaking capacity of the circuit breaker. Finally, a four-terminal medium voltage distribution network model is built in MATLAB/SIMULINK, and the effect of the current limiter and the feasibility of the proposed protection strategy are verified by simulation. [ABSTRACT FROM AUTHOR]

Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco 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.)

Copyright of Integrated Intelligent Energy is the property of Editorial Department of Integrated Intelligent Energy 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.)

We investigate the influence of precipitation patterns on the differences between Missouri corn and soybean elevator prices and nearby Chicago Board of Trade futures prices, also referred to as basis. Basis reflects the crop transportation network costs, and we use basis data to estimate the sensitivity of this network to precipitation. The estimates show that corn and soybean basis differ in terms of the immediate effect of weekly local precipitation. The growing‐season precipitation exhibits a U‐shaped relationship for both crops, reflecting the role of growing‐season precipitation on yields and the sensitivity of transportation system stress to crop volumes. We also find that the basis is affected by river transportation disruptions proxied by regional precipitation, which indicates when water levels might be too high or too low for normal barge traffic. Given the climate projections, there are concerns regarding precipitation's influence on the infrastructure that links Missouri crops to export facilities, crop growing conditions, and collection point disruptions. Hence, this research speaks to both the state of this part of the food system now and the potential impacts of changing weather patterns on the US crop distribution network. [EconLit Citations: Q13, L90]. [ABSTRACT FROM AUTHOR]

In the context of constructing new power systems, distribution networks are increasingly incorporating distributed resources such as distributed photovoltaic (PV) systems, decentralized wind turbines (WTs), and new types of energy storage system (ESS), which may lead to prominent issues such as voltage overruns and reverse heavy overloads in the distribution network. While distributed resources are valuable for voltage regulation, their regulation characteristics vary with their operation means, and the randomness and volatility of renewable power generation will also influence the optimization and regulation of voltage in the distribution network. This paper proposes a multi-timescale reactive power optimization and regulation method for distribution networks in a multi-source interactive environment. Firstly, the voltage regulation characteristics of distributed PV systems, decentralized ESSs, and distributed WTs are analyzed. Based on this analysis, a multi-timescale voltage optimization scheme for distribution networks using the MPC method is proposed, which optimizes the voltage regulation strategies for each distributed resource in a rolling manner. Furthermore, an event-triggered real-time voltage zoning control strategy based on voltage sensitivity is proposed to address the real-time sudden voltage overlimit problems. The modified IEEE 33-node system is used to verify the performance of the proposed method. Simulation results indicate that the issue of voltage overruns at distribution network nodes has been improved, and the intraday rolling optimization yields results are more realistic compared with the day-ahead optimization method. [ABSTRACT FROM AUTHOR]

The distribution network is the main component of the power system, which undertakes the important function of power transmission and distribution. Fast and accurate distribution network fault location is one of the important means to ensure the safe and stable operation of the power system and is helpful in guiding troubleshooting, shortening the power outage time, reducing the workload of manual inspection, and reducing the social and economic losses caused by faults. Due to the development of the new distribution network, the comprehensive influence of many factors has put forward new challenges to the traditional distribution network fault location methods. In this paper, a distribution network fault location method based on the graph neural network is proposed. Firstly, the distribution network is treated as non-Euclidean graph data; secondly, variational graph auto-encoders (VGAE) are used to mine the underlying information of nodes and improve the overall anti-noise performance of the fault location method. Then the GraphSAGE model is used to aggregate the neighbor information of nodes, fully consider the influence of the surrounding lines on the target lines, and improve the output of the model to locate the distribution network line where the fault occurred. The experimental example analysis based on OpenDSS simulation software (version 9.8) proves that the proposed method has high accuracy and anti-interference, and the accuracy reached 97.81%. Moreover, the positioning result is still good in the new intelligent distribution network scenario with distributed power access, with an accuracy of 95.07% in the hybrid power generation scenario. [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.)

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.)

This study proposed a method for optimizing a radial distribution network by integrating wind turbine allocation, considering fluctuating load demands, through the use of a hybrid Grey Wolf Optimizer-Genetic Algorithm (HGWOGA). This approach aims to decrease the network's energy loss costs. By incorporating genetic algorithm techniques, the method enhances the Grey Wolf Optimizer's efficiency, speeding up convergence and avoiding local optima. The strategy determines the network's open lines and the placement and capacity of wind turbines, adhering to radiality and operational constraints. It categorizes load levels into residential, commercial, and industrial, providing a comprehensive analysis of energy losses and their cost implications under various scenarios, including constant and dynamic loads. The study suggests that managing time-varying demand offers a more accurate depiction of network challenges, enabling effective reconfiguration throughout different demand phases. Moreover, HGWOGA demonstrates its ability to find the global optimum efficiently, even with reduced population sizes—a feat not achievable with the Grey Wolf Optimizer alone. Comparative analyses reveal HGWOGA's effectiveness in curbing network energy loss costs better than previous methodologies. By simultaneously applying network reconfiguration and wind turbine allocation, as opposed to merely reconfiguring the network, this approach notably reduces power loss, diminishes the cost of losses, and enhances the voltage profile. This synergistic strategy leverages the dynamic allocation of wind turbines within the network, optimizing energy flow and distribution efficiency, thereby offering a substantial improvement over conventional network reconfiguration methods. [ABSTRACT FROM AUTHOR]

In this paper, the problem of simultaneous charging of Electrical Vehicles (EVs) in distribution networks (DNs) is examined in order to depict congestion issues, increased power losses, and voltage constraint violations. To this end, this paper proposes an optimal EV charging schedule in order to allocate the charging of EVs in non-overlapping time slots, aiming to avoid overloading conditions that could stress the DN operation. The problem is structured as a linear optimization problem in GAMS, and the linear Distflow is utilized for the power flow analysis required. The proposed approach is compared to the one where EV charging is not optimally scheduled and each EV is expected to start charging upon its arrival at the residential charging spot. Moreover, the analysis is extended to examine the optimal siting of small-sized residential Photovoltaic (PV) systems in order to provide further relief to the DN. A mixed-integer quadratic optimization model was formed to integrate the PV siting into the optimization problem as an additional optimization variable and is compared to a heuristic-based approach for determining the sites for PV installation. The proposed methodology has been applied in a typical low-voltage (LV) DN as a case study, including real power demand data for the residences and technical characteristics for the EVs. The results indicate that both the DN power losses and the voltage profile are further improved in regard to the heuristic-based approach, and the simultaneously scheduled penetration of EVs and PVs could yield up to a 66.3% power loss reduction. [ABSTRACT FROM AUTHOR]

This article proposes a voltage compensation control strategy for the distribution network based on the unified power quality conditioner (UPQC) with battery energy storage systems (BESS) to achieve comprehensive management of the power quality and improve the system dynamic responses. Specifically, aiming at that amplitude and phase jump of the terminal voltage, a capacity configuration compensation scheme is proposed to achieve voltage vector compensation. The series converter is designed to compensate for the amplitude and phase of the load voltage. The parallel converter is designed to realize power compensation and ensure that the grid voltage and current are sinusoidal and in phase. A voltage control approach based on the nonlinear disturbance observer (NDO) is developed for BESS to stabilize the DC bus voltage and improve the anti-interference performance of the current. An NDO is designed to accomplish current disturbance tracking and provide disturbance feedforward compensation. Simulations validate that the proposed approach is effective and feasible. [ABSTRACT FROM AUTHOR]

In order to mitigate the impact of DG (distributed generation) integration into distribution networks on grid safety and economic operation, it is necessary to optimize the planning of DG. Considering the uncertainties and correlations of wind and photovoltaic outputs, a bi-level planning model was established. The upper-level planning model was formulated with the objective function of minimizing the comprehensive costs, which included investment costs of DG, operation and maintenance expenses, active distribution network electricity purchase costs, micro gas turbine fuel costs, pollution control costs, and network loss costs. The lowerlevel operational model aimed to minimize the annual comprehensive operational costs for each scenario, subjected to constraints such as power balance, node voltage, branch capacity, and DG penetration rate. To address the issues of slow convergence speed and local optima in the whale optimization algorithm, three improvements were introduced: tent mapping, the incorporation of inertia weight, and a nonlinear convergence factor. The proposed improvements were validated through simulation case studies. The results demonstrate that the improved method enhances the performance of the whale optimization algorithm, thereby providing a more effective solution to the proposed model. [ABSTRACT FROM AUTHOR]

In response to global energy, environment, and climate concerns, distributed photovoltaic (PV) power generation has seen rapid growth. However, the intermittent and uncertain nature of PVs can cause voltage fluctuations in distribution systems, threatening their stability. To address this challenge, this paper proposes an active distribution network voltage optimization method, of which the main contribution is the development of a comprehensive voltage optimization strategy that integrates day-ahead prediction and real-time adjustment, significantly enhancing the stability and efficiency of distribution networks with high PV penetration. In the day-ahead prediction stage, the forecast scenarios of load and PV output guide network reconfiguration for improved voltage distribution. In the real-time operation stage, flexible regulation of PV and energy storage systems is used to adjust power outputs, further optimizing voltage quality. Simulations on the IEEE 33-bus system show that the method effectively improves voltage distribution, enhances renewable energy consumption, and ensures the safe, economic operation of the distribution system. [ABSTRACT FROM AUTHOR]

A single-phase grounding fault often occurs in 10 kV distribution networks, seriously affecting the safety of equipment and personnel. With the popularization of urban cables, the low-resistance grounding system gradually replaced arc suppression coils in some large cities. Compared to arc suppression coils, the low-resistance grounding system features simplicity and reliability. However, when a high-resistance grounding fault occurs, a lower amount of fault characteristics cannot trigger the zero-sequence protection action, so this type of fault will exist for a long time, which poses a threat to the power grid. To address this kind of problem, in this paper, a hybrid grounding system combining the low-resistance protection device and fully controlled power module is proposed. During a low-resistance grounding fault, the fault isolation is achieved through the zero-sequence current protection with the low-resistance grounding system itself, while, during a high-resistance grounding fault, the reliable arc extinction is achieved by regulating the neutral-point voltage with a fully controlled power module. Firstly, this paper introduces the principles, topology, and coordination control of the hybrid grounding system for active voltage arc extinction. Subsequently, a dual-loop-based control method is proposed to suppress the fault phase voltage. Furthermore, a faulty feeder selection method based on the Kepler optimization algorithm and convolutional neural network is proposed for the timely removal of permanent faults. Lastly, the simulation and HIL-based emulated results verify the rationality and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

With the rapid development of distribution networks and increasing demand for electricity, the pressure of power supply for medium- and low-voltage distribution networks (M&LVDNs) is increasingly significant, especially considering the large scale of customers at the low-voltage (LV) level. In this paper, an outage sequence optimization method for low-voltage distribution networks (LVDNs) that considers the importance of users is proposed. The method aims to develop an optimal outage sequence strategy for LV customers in case of medium-voltage (MV) failure events. First, a multi-dimensional importance indicator system for LV users is constructed, and the customers are ranked using a modified Analytic Hierarchy Process–Entropy Weight (AHP-EW) method to determine their priorities during outages. Then, an elastic net regression-based method is used to identify the topology of the LV network. Finally, an outage sequence optimization model based on the user importance is proposed to reduce the load-shedding level. Extensive case studies are conducted in the modified LV distribution network. The results show that the proposed method results in fewer outage losses throughout the restoration periods than traditional methods and effectively improves the reliability of the power supply to LV users. [ABSTRACT FROM AUTHOR]

The placement of electric vehicle charging stations (EVCS) and battery swapping stations (BSS) is crucial to meet demand while minimizing their impact on the power system network. This article addresses the placement and scheduling of EVCS and BSS within a radial distribution network (RDN) overlaid with a road network. Electric Vehicle Routing Problem (EVRP) and Traffic Assignment Problem (TAP) is introduced to reduce EV energy consumption during trips to EVCS and BSS. A Bi-layer optimization approach is used, with the outer layer minimizing energy loss by allocating EVCS and BSS optimally, and the inner layer optimizing scheduling considering associated costs. Uncertainties related to EV is considered on a real-time trip basis and modelled using 2m-Point estimation method. The problem is solved using Differential Evolution (DE) and Prairie Dog Optimization (PDO) techniques. PDO results in a 9.6% reduction in total energy losses and a 22.5% decrease in total associated costs by optimally placing EVCS at 69, 25,114 and BSS at 19, 84,113 of the RDN compared to DE. Inclusion of 2-m PEM uncertainty increases total energy loss by 8.76% but decreases total associated costs by 6.97% compared to real-time uncertainty cases, enhancing the realism of the results. [ABSTRACT FROM AUTHOR]

The penetration of renewable energy resources for distribution networks can significantly impact the security of power supply systems. To improve the penetration efficiency, a hierarchical optimization planning for a distributed generation (DG) network using the improved sand cat swarm optimization (ISCSO) is proposed to determine the optimal location and capacity of DG into the distribution network. Firstly, the sensor is used to collect data, and the ISCSO algorithm is used to construct a complex nonlinear DG planning problem, thus reducing the impact of DG uncertainty. Second, a DG hierarchical planning model is established to select the optimal location and capacity of DG access to the distribution network. Finally, in the classical test system, the effectiveness of the proposed model is verified by setting up multiple cases. The test results confirm that the total annual cost and power loss of the DG system can be reduced by 13.1 and 40.5%, respectively. [ABSTRACT FROM AUTHOR]

Introduction: In response to the issues of complexity and low efficiency in line loss calculations for actual distribution networks, this paper proposes a fast power flow calculation method for distribution networks based on Neo4j graph models and a hierarchical forward-backward sweep parallel algorithm. Methods: Firstly, Neo4j is used to describe the distribution network structure as a simple graph model composed of nodes and edges. Secondly, a hierarchical forward-backward sweep method is adopted to perform power flow calculations on the graph model network. Finally, during the computation of distribution network subgraphs, the method is combined with the Bulk Synchronous Parallel (BSP) computing model to quickly complete the line loss analysis. Results and Discussion: Results from the IEEE 33-node test system demonstrate that the proposed method can calculate network losses quickly and accurately, with a computation time of only 0.175s, which is lower than the MySQL and Neo4j graph methods that do not consider hierarchical parallel computing. [ABSTRACT FROM AUTHOR]

With the high penetration of renewable energy, the addition of a large number of energy storage units, and flexible loads, the source-load-storage structure of active distribution networks is becoming increasingly complex, making optimization and scheduling more challenging. In response to issues as difficult global information acquisition, less consideration of flexible load and energy storage unit access, individual deception, and insufficient security in the optimization scheduling process of active distribution networks, this paper constructed a distribution network optimization scheduling model that includes sources, loads, and storage. It proposed a distributed optimization scheduling strategy for source-load-storage distribution networks, combined with alliance chains. This strategy is based on the FISCO BCOS consortium chain platform, with blockchain multi-agent nodes forming a consortium chain network. The consistency variables are the incremental cost of distributed power generation and the incremental benefits of flexible loads. Distributed scheduling calculations were carried out using a consensus algorithm that includes leadership nodes. By combining the data storage mechanism and consensus algorithm advantages of the consortium chain, the centrality of leadership nodes is eliminated, achieving optimal power allocation in the distribution network at a minimum economic cost. The simulation results show that the distributed optimization scheduling strategy proposed in this paper can achieve optimal allocation of minimum cost in the distribution network and converge quickly in various scenarios such as non-flexible load fluctuations, leader node switching, node joining or leaving, and changes in power exchange instruction in the distribution network. It demonstrates good robustness and stability. [ABSTRACT FROM AUTHOR]

The great challenge of an electric power engineer is to satisfy the power/energy demand with the existing power generation which increases sharply due to growth in industrials, commercial, and urbanization. An increase in power generation leads to not only financial obligatory but also environmental issues. To operate the network under optimum conditions, appropriate control strategies based on systematic planning methods are the only option that can improve the performance of the Distribution Network (DN) by way of reducing the power/energy losses with enhancement in node voltages. In this study, Dandelion Optimizer (DO), a novel, swift, resilient optimization tool specially designed to tackle highly non-linear optimization problems has been utilized which is inspired by the natural movement of plant seeds. This innovative approach has been employed to pinpoint the ideal nodes and to determine the optimal capacitor capacities in a radial DN. The primary objective is to maximize the Financial Benefits (FB) by minimizing both real and reactive power losses, capacitor purchases, and its associated costs while ensuring that all equality and inequality constraints are met. No sensitivity factorbased index has been employed to identify the most suitable nodes for installing capacitors. The proposed optimization technique (DO) needs to search for both the optimal nodes for placing capacitors and the suitable capacities. To showcase the performance and the effectiveness of DO, three well-known DNs viz. a 10-node, IEEE 33-node, and a 62-node (extracted from the Indian 118 bus test system) have been taken. The simulation results obtained have been compared with those from other techniques documented in the literature to emphasize the FBs. It has been found that the proposed optimization technique excels in delivering high-quality solutions. [ABSTRACT FROM AUTHOR]

With the increase in the proportion of photovoltaic and wind power access, the scale and form of distribution networks are becoming more and more complex. The traditional single distribution network vulnerability assessment method is difficult to use to identify the vulnerable links in the distribution network. Therefore, this paper proposes a method for identifying and evaluating vulnerable links in distribution networks based on minimum discriminant information. First, considering the influence of distributed grid connection, an improved probabilistic power flow calculation method is proposed, which improves the calculation efficiency and accuracy. Second, considering the correlation degree, transmission capacity, and voltage stability of branches in the distribution network, the identification index of vulnerable lines is defined. Based on power quality and operating state, the identification index of vulnerable nodes in a distribution network is defined. Finally, based on the indicators of vulnerable nodes and vulnerable lines, the vulnerable links in the distribution network are comprehensively evaluated based on the principle of minimum discriminant information, and the vulnerable links of the entire distribution network are evaluated according to different degrees of vulnerability. The rationality and effectiveness of the proposed method are verified via an example analysis of actual power grid data. [ABSTRACT FROM AUTHOR]

Taking into account the multiple demand differences and the existence of distribution network faults, the study conducts a multiple demand-side resource regulation capability assessment for distribution network safety and security as a way to achieve distribution network resource regulation optimization. The study considers multiple demands and adopts the correlation depth wandering algorithm to assess node faults in the distribution network, and proposes a quantitative assessment strategy for multiple demand-side resource regulation capability, and constructs an index system for assessing the resource regulation capability of the distribution network, and carries out the assessment of the resource regulation capability of the multiple demand-side resources. The results show that the regulation efficiency of wind power, thermal power, solar power and hydro power is above 20%, i.e., the combined regulation efficiency of the four energy sources is able to reach above 80%. The results show that the multi-demand-side resource regulation capability for distribution network security can be significantly improved, and the findings of the study have an important practical value for the electric power industry in terms of resource regulation and security, as well as providing a strong support for the sustainable development of the industry. [ABSTRACT FROM AUTHOR]

Project‐based interorganizational networks are temporary and dynamic, and significantly different from stable networks for mass production. However, empirical studies on project‐based interorganizational networks are scant. Drawing upon the network literature and distinguishing between production and distribution networks, we investigate how the centrality of a project in industry networks and tie strength of the project's member firms affect project performance in terms of product quality and sales. Using data of 821 Chinese movie projects during 2015–2018, we find empirical evidence that a project's centrality in the production network enhances product quality and that in the distribution network elevates product sales. Interestingly, weak ties in the production team contribute to product quality while strong ties in the distribution team improve product sales. This study advances the literature on project‐based industries and interorganizational networks. It highlights the distinction between production and distribution networks in project‐based industries and sheds light on the differential roles of a project's centrality and tie strength in both networks. These findings also offer novel insights to managers in project‐based industries. Highlights: In project‐based industries such as the movie industry, the industry network comprises various companies and can be categorized into production and distribution networks.When a movie is produced by companies at the center of the production network, it tends to have better quality; and when it is distributed by companies central in the distribution network, it is more likely to achieve box office success.When production companies collaborate too frequently in the past, it could actually be detrimental to product quality; but when distribution companies have strong connections, it usually helps boost box office sales. [ABSTRACT FROM AUTHOR]

The arrangement of distributed generation networks through optimal capacitor placement and sizing is critical for modern power systems, particularly in the context of renewable energy sources with inherent uncertainties. As renewable sources such as wind and solar introduce variability in power generation, the stability and efficiency of the distribution network can be significantly affected. This study addresses the challenge of optimizing distribution network configurations by integrating capacitor placement and distributed generation sizing under these renewable energy uncertainties. Using a self-adaptive Kho-Kho optimizer, the research aims to minimize operational costs while improving technical parameters, such as voltage stability and loss reduction. The proposed method was applied to the IEEE 33-bus standard network, yielding significant improvements. Specifically, a capacitor was placed at bus 9 with a capacity of 2260 kVar, and a 110-kW distributed generation unit was installed at bus 14. Post-optimization results indicated a reduction in power loss costs from 4.11 x 10^5 to 1.05 x 10^5 units, representing a 25.54% decrease. Additionally, energy purchased from the upstream network dropped by 19.81%, and the total operational cost decreased by 9.35%, from 8.77 x 10^6 to 7.95 x 10^6 units. These results underscore the effectiveness of the proposed optimization strategy in enhancing network performance while minimizing costs, offering a valuable approach for future distribution network management. Further research will explore multi-objective algorithms and the incorporation of additional types of distributed generation to continue improving reliability and stability. [ABSTRACT FROM AUTHOR]

With the advancement of new power system construction, distribution networks are gradually transforming from being a simple energy receiver and distributor to being an integrated power network that integrates sources, networks, loads, and energy storage with interactive and flexible coupling with the upper-level power grid. However, traditional distribution networks lack active control and distribution capabilities, failing to meet the demands of network transformation and upgrading. To address this issue, this paper proposes a method for solving AC/DC power flow calculation considering an electric energy router (EER) based on an alternating iterative method. Initially, the model for the multi-port EER and three types of power flow models for the AC distribution network, DC distribution network, and EER are constructed. By leveraging the properties of the EER and the hybrid power flow calculation model, a method is proposed for calculating the power flow in the AC/DC hybrid distribution network considering the EER. Finally, by solving the power flow in a medium- and low-voltage AC/DC distribution system, the adaptability of the proposed method is compared. The results demonstrate that the AC/DC hybrid distribution network power flow calculation method established in this paper, which incorporates the EER, possesses high accuracy and adaptability, with an error margin of less than 0.05%. [ABSTRACT FROM AUTHOR]

Flexible resources, including district heat networks (DHN) and battery energy storage systems (BESS), can provide flexible regulation capability for distribution networks (DN), thereby increasing the absorption capacity for renewable energy. In order to improve the operation economy of DN and ensure the information privacy of different operators, a distributed cooperative dispatch method of DN with DHN and BESS considering flexible regulation capability is proposed. First, a distributed cooperative dispatch framework of DN-DHN-BESS is constructed. Then, an optimal dispatch model of DHN under constant flow-variable temperature control strategy is established in order to utilize the heat storage capacity to provide flexible regulation capability for DN. Next, the optimal dispatch models of BESS and DN are established with the objective of minimizing the operation cost, respectively. Finally, a solution method based on the alternating direction multiplier method of distributed cooperative dispatch for DN-DHN-BESS is proposed. Case studies are performed on a system consisting of a 33-node DN and a 44-node DHN, and simulation results demonstrate that the proposed method differs from the centralized dispatch method by only 0.52% in the total system cost, and the proposed method reduces the total system cost by 34.5% compared to that of the independent dispatch method. [ABSTRACT FROM AUTHOR]

This article examines the impact of meteorological conditions represented by ambient temperature, ambient humidity, wind speed, and daily precipitation sum on the reliability of low-voltage cable lines. Cable line reliability is crucial to the stability and safety of power systems. Failure of cable lines can lead to power outages. This can cause serious economic and social consequences, as well as threaten human safety, especially in the public sector and critical infrastructure. In addition, any interruption of cable lines generates costs related to repairs, operational losses, and possible contractual penalties. This is why it is so important to investigate the causes of power equipment failures. Many power system failures are caused by weather factors. The main purpose of this article is to quantify the actual impact of weather conditions on the performance and reliability of power equipment in distribution networks. Reliability indicators (failure rate, failure duration, restoration rate, and failure coefficient) for low-voltage cable lines were calculated as a function of weather conditions. Empirical values of the indicators were determined based on many years of observations of power lines operating in the Polish power system. An analysis of the conformity of their empirical distribution with the assumed theoretical model was also conducted. By quantifying the impact of specific weather factors on the operation of power equipment, it becomes possible to identify the ranges in which failures are most likely. [ABSTRACT FROM AUTHOR]

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Copyright of Journal of Shanghai Jiao Tong University (1006-2467) is the property of Journal of Shanghai Jiao Tong University 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.)

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.)

Water distribution is a critical system that involves engineered hydrologic and hydraulic components to provide water supply to a continuously growing population. Ensuring a sufficient and uniform water supply through a well-designed network is essential to meet the increasing water demand. The present study focuses on analyzing the water demand of the public water supply to facilitate effective planning, development, and operation of water supply and distribution networks. The main objective of the study is to analyze the existing water distribution network at West Moorings using the Watergems Software. To conduct this analysis, various data points are needed, such as the population of the area, water demand, distribution network layout, and water pump information. Additionally, details regarding the length, nodes, and diameter of the pipes are essential for the analysis. These data are input into the Watergems Software to perform analyses related to pressure, head loss, and elevation. The results of the analysis provide valuable information on pressure and flowrate at different nodes and head loss along various pipes in the network. By comparing the results obtained from the Watergems Software with actual data, the study aims to locate the leaks within the water distribution network at Westmooring, Trinidad and Tobago. In conclusion, Watergems identified leakage nodes within the West Moorings Network, and data analysis helped narrow down the leak's location in the field. This process involved model calibration using field pressure and flowrate data from WASA, and emitter coefficients were categorized by the Darwin Calibrator function. Accurate boundary inputs, such as the number of leakage nodes, Initial Emitter Coefficient Spacing, and time intervals, were essential for precise data assumptions. [ABSTRACT FROM AUTHOR]