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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]

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]

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]

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]

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]

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]

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]

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]

This article presents an original methodology to determine the optimal level of reactive energy transmission to low-voltage consumers supplied from MV/LV substations that guarantees the lowest total costs of reactive energy transmission through the DSO network and its generation in receiving installations within the reactive power compensation process. The average value of the optimal factor tgφ to be maintained by customers depends on the efficiency of the network, the characteristics of the load, and the market costs of energy losses due to the transmission of reactive energy through the network that are covered by the DSO and the costs of reactive energy generation in receiving installations. The results presented for real MV/LV substations operating in the Polish distribution network demonstrate the application of annual measurements of active and reactive energy consumed and generated registered by AMI systems to calculate the optimal reactive power compensation level. They can be applied to verify the permissible levels of reactive energy compensation applied by the DSOs until now within the yearly tariffs for customers. [ABSTRACT FROM AUTHOR]

The operation envelope of distribution networks can obtain the independent p-q controllable range of each active node, providing an effective means to address the issues of different ownership and control objectives between distribution networks and distributed energy resources (DERs). Existing research mainly focuses on deterministic operation envelopes, neglecting the operational status of the system. To ensure the maximization of the envelope operation domain and the feasibility of decomposition, this paper proposes a modified hyperellipsoidal dynamic operation envelopes (MHDOEs) method for distribution networks based on adjustable "Degree of Squareness" hyperellipsoids. Firstly, an improved convex inner approximation method is applied to the non-convex and nonlinear model of traditional distribution networks to obtain a convex solution space strictly contained within the original feasible region of the system, ensuring the feasibility of flexible operation domain decomposition. Secondly, the embedding of the adjustable "Degree of Squareness" maximum hyperellipsoid is used to obtain the total p-q operation domain of the distribution network, facilitating the overall planning of the distribution network. Furthermore, the calculation of the maximum inscribed hyperrectangle of the hyperellipsoid is performed to achieve p-q decoupled operation among the active nodes of the distribution network. Subsequently, a correction coefficient is introduced to penalize "unknown states" during the operation domain calculation process, effectively enhancing the adaptability of the proposed method to complex stochastic scenarios. Finally, Monte Carlo methods are employed to construct various stochastic scenarios for the IEEE 33-node and IEEE 69-node systems, verifying the accuracy and decomposition feasibility of the obtained p-q operation domains. [ABSTRACT FROM AUTHOR]

The high penetration of distributed photovoltaics (PV) in distribution networks (DNs) results in voltage violations, imbalances, and flickers, leading to significant disruptions in DN stability. To address this issue, this paper proposes a multi-timescale voltage regulation approach that involves the coordinated control of a step voltage regulator (SVR), switched capacitor (SC), battery energy storage system (BESS), and electric vehicle (EV) across different timescales. During the day-ahead stage, the proposed method utilizes artificial hummingbird algorithm optimization-based least squares support vector machine (AHA-LSSVM) forecasting to predict the PV output, enabling the formulation of a day-ahead schedule for SVR and SC adjustments to maintain the voltage and voltage unbalance factor (VUF) within the limits. In the intra-day stage, a novel floating voltage threshold band (FVTB) control strategy is introduced to refine the day-ahead schedule, enhancing the voltage quality while reducing the erratic operation of SVR and SC under dead band control. For real-time operation, the African vulture optimization algorithm (AVOA) is employed to optimize the BESS output for precise voltage regulation. Additionally, a novel smoothing fluctuation threshold band (SFTB) control strategy and an initiate charging and discharging strategy (ICD) for the BESS are proposed to effectively smooth voltage fluctuations and expand the BESS capacity. To enhance user-side participation and optimize the BESS capacity curtailment, some BESSs are replaced by EVs for voltage regulation. Finally, a simulation conducted on a modified IEEE 33 system validates the efficacy of the proposed voltage regulation strategy. [ABSTRACT FROM AUTHOR]

The construction of an energy distribution network can improve the system's ability to absorb new energy, and its stable and efficient operation has become more and more important. The security and stability analysis of a distribution network needs accurate distribution network model parameters as support. At present, the installation of PMU equipment in China's distribution network synchronous phase angle measurement unit is limited, which brings challenges to the parameter correction of the distribution network. In this paper, an automatic correction algorithm of distribution network parameters based on the CIM model and measurement data is proposed for the distribution network system without PMU. Firstly, the distribution network topology construction technology based on XML files and key fields of the distribution network is proposed, and the non/small impedance devices (such as switches) are merged and reduced. The breadth-first traversal algorithm is used to verify the connectivity of the constructed topology. Then, based on the topology construction and the least squares method, an iterative parameter correction technique is constructed. Finally, the accuracy and effectiveness of the proposed algorithm are verified in a standard IEEE 33-bus system distribution network and an example of the China Southern Power Grid. The topology connections constructed based on the CIM model have significantly enhanced the efficiency of parameter correction. [ABSTRACT FROM AUTHOR]

This study aims to minimize the overall cost of wind power, photovoltaic power, energy storage, and demand response in the distribution network. It aims to solve the source-grid-load-storage coordination planning problem by considering demand response. Additionally, the study includes a deep analysis of the relationship between demand response, energy storage configuration, and system cost. A two-level planning model is established for wind power and photovoltaic power grid connection, including demand response, wind power, photovoltaic power, and energy storage. The model minimizes the sum of the differences between the total load and the total new energy generation after demand response in each time period as the bottom-level objective and minimizes the overall cost of the distribution network as the top-level objective, achieving the coordinated configuration of wind power, photovoltaic power, and energy storage. The simplex method is used to solve the model, and the improved IEEE33 node system is used as an example for verification. The simulation results fully prove the model's correctness and the algorithm's effectiveness, supporting the coordinated planning of distribution networks. [ABSTRACT FROM AUTHOR]

The surge in disordered EV charging demand, driven by the rapid growth in the ownership of electric vehicles (EVs), has highlighted the potential for significant disruptions in photovoltaic (PV)-connected distribution networks (DNs). This escalating demand not only presents challenges in meeting charging requirements to satisfy EV owners and grid fast-charging stations (GFCSs) but also jeopardizes the stable operation of the distribution network. To address these challenges, this study introduces a novel model called SOR&KANO for charging decisions, which focuses on addressing the dual-sided demand of GFCSs and EVs. The proposed model utilizes the salp swarm algorithm-convolutional neural network (SSA-CNN) to predict the PV output and employs Monte Carlo simulation to estimate the charging load of EVs, ensuring accurate PV output prediction and efficient EV distribution. To optimize charging decisions for reserved EVs (REVs) and non-reserved EVs (NREVs), this study applies the multi-verse optimizer (MVO) in conjunction with time-of-use (TOU) tariff guidance. By integrating the SOR&KANO model with the MVO algorithm, this approach enhances satisfaction levels for GFCSs by balancing the charging demand, increasing utilization rates, and improving voltage quality within the DN. Simultaneously, for EVs, the optimized scheduling strategy reduces charging time and costs while addressing concerns related to range anxiety and driver fatigue. The efficacy of the proposed approach is validated through a simulation on a modified IEEE-33 system, confirming the effectiveness of the optimal scheduling methods proposed in this study. [ABSTRACT FROM AUTHOR]

As global climate change intensifies, extreme weather events are becoming more frequent, with ice disasters posing an increasingly significant threat to the stable operation of power distribution networks. Particularly during power outages for de-icing, multiple power islands may form within a distribution area, increasing the complexity of grid operations. Existing research has not fully considered the comprehensive coordination of stable operation of these power islands and de-icing maintenance schedules. Therefore, for the potential multi-island operation of distribution networks caused by freezing disasters, this paper first establishes a dynamic island partitioning model based on distribution network reconfiguration technology. Secondly, based on the characteristics of the de-icing phase, a de-icing maintenance schedule model is established. Finally, dispatch optimization of the distribution network is coordinated with the line de-icing maintenance schedule. By adjusting the de-icing strategies and network structure, the aim is to minimize the risk of load loss. The relevant case analysis indicates that the collaborative optimization model established in this paper helps power distribution networks to reduce their economic losses when facing adverse weather conditions. [ABSTRACT FROM AUTHOR]

With the ongoing development of new power systems, the integration of new energy sources is facing increasingly daunting challenges. The collaborative operation of shared energy storage systems with distribution networks and microgrids can effectively leverage the complementary nature of various energy sources and loads, enhancing energy absorption capacity. To address this, a shared energy storage capacity allocation method based on a Stackelberg game is proposed, considering the integration of wind and solar energy into distribution networks and microgrids. In this approach, a third-party shared energy storage investor acts as the leader, while distribution networks and microgrids serve as followers. The shared energy storage operator aims to maximize annual revenue, plan shared energy storage capacity, and set unit capacity leasing fees. Upon receiving pricing, distribution networks and microgrids aim to minimize annual operating costs, determine leased energy storage capacity, and develop operational plans based on typical daily scenarios. Distribution networks and microgrids report leasing capacity, and shared energy storage adjusts leasing prices, accordingly, forming a Stackelberg game. In the case study results, the annual cost of MGs decreased by 29.63%, the annual operating cost of the ADN decreased by 11.25%, the cost of abandoned light decreased by 60.77%, and the cost of abandoned wind decreased by 27.79% to achieve the collaborative optimization of operations. It is proven that this strategy can improve the economic benefits of all parties and has a positive impact on the integration of new energy. [ABSTRACT FROM AUTHOR]

The article presents a study on the influence of weather factors (ambient temperature) on the operational reliability of overhead low-voltage power lines with bare conductors. A method for determining the average failure intensity, average failure duration, average renewal intensity, and failure rate of overhead low-voltage power lines with bare conductors as a function of ambient temperature is presented. Based on many years of observations of power lines operated in electric power distribution networks in Poland, the empirical values of the above-mentioned reliability indicators were determined. An analysis of empirical distribution compliance with the assumed theoretical model was also carried out. The reliability studies conducted showed that the highest failure intensity of the considered power lines occurred at temperatures commonly found in Poland. [ABSTRACT FROM AUTHOR]

Distributed photovoltaic (PV) in the distribution network accounted for an increasing proportion of the distribution network, and the power quality of the distribution network of the power quality problem is more and more significant. In this paper, the voltage regulation methods for low-voltage distribution networks containing high-penetration PV are investigated. First, the working principles of the four voltage control methods are introduced: energy storage system configuration, regulating the reactive power output of PV inverters, restricting the active power output of PV, adjusting the switching positions of on-load regulator trap changer and distribution network reconfiguration, and then, in combination with the recent related research, the optimization of each method is compared horizontally with its respective concerns and characteristics. The optimization of each method is compared horizontally with the recent studies to find out the focus and characteristics of each method, and the shortcomings of each method are explored. Coordinated voltage control through multiple flexibility resources has become the mainstream voltage regulation scheme, and distribution network voltage regulation is considered from the perspective of flexibility resources. The three types of flexibility resources, namely, source, network, and storage, have been widely used in distribution network voltage regulation. Although load-side resources have become one of the main regulation resources of the new type of power system, the current study introduces less about the participation of load-side flexibility resources in voltage regulation of LV distribution networks and advancing the application of load-side resources in voltage regulation of LV distribution networks is the focus of future research. Then, the important role of load-side flexibility resources in voltage regulation is described in three parts, namely, the important role of load-side resources, the development trend, and the suggestions for promoting the coordination of source-network-load-storage flexibility resources, aiming to promote the application of load-side resources in voltage regulation in LV distribution networks, and the suggestions and programs are proposed for the technological challenges faced by voltage regulation. In the context of today's new power system emphasizing the interaction of source, network, load, and storage, new technologies and methods for solving voltage problems in LV distribution networks are prospected, with a view to providing certain reference value for the actual operation and optimization of distribution network systems. [ABSTRACT FROM AUTHOR]

Flexible multi-state switches (FMSSs), also called soft open point (SOP), are regarded as the core devices in flexible interconnected distribution network. However, due to their more complex control structure and nonlinear characteristics, there is a tight and complex coupling relationship between the transmission power limit and stability of two (or more) converter sides, which brings difficulties to the planning and power flow dispatching control of flexible interconnected distribution networks. In this paper, taking the back-to-back converter based FMSS (BTBC-FMSS) as the research object, the dynamic equivalent circuit connected to the distribution network is constructed, and the transmission active and reactive power expression of the converter on both sides considering PLL dynamics is deduced. The variation rules of transmission power limits on both sides of the device with various structures and control parameters are summarized. Furthermore, the stable operation domain of the BTBC-FMSS is constructed, and the effective measures to improve the transmission power stability of the device are given according to the analysis of the stable operation domain. Finally, a simulation model is built in Matlab/Simulink to verify the effectiveness of the analysis results. [ABSTRACT FROM AUTHOR]

Distributed photovoltaics (DPVs) are widely distributed and the output is random, which brings challenges to the safe operation of the distribution network, so the construction of photovoltaic aggregations can effectively participate in the flexible regulation of the power system. At present, the extraction of DPV clustering features is not sufficient, only considering the output characteristics of PVs. Certain PVs under some nodes may have a more pronounced regulation effect, but they may be ignored in the clustering process. To address the above problems, this paper proposes a DPV aggregation approach considering the distribution network topology. It combines the voltage sensitivity and the power curve and regards them as clustering features to form the DPV aggregation with the highest average voltage sensitivity participating in voltage regulation. The simulation on the IEEE 33-node system verifies that the proposed aggregation approach can select DPV aggregation more suitable for voltage regulation, and make full use of the aggregation to realize the optimal voltage regulation effect. [ABSTRACT FROM AUTHOR]

With the increasing number of electric vehicles (EVs), their uncoordinated charging poses a great challenge to the safe operation of the power grid. In addition, traditional individual-EV scheduling models may be difficult to solve due to the increasing number of constraints. Therefore, this paper proposes a cluster-based EV scheduling model. Firstly, electric vehicle clusters (EVCs) are formed based on the charging and discharging preferences of EV users and the expected time for EVs to leave. Secondly, the EVC energy and power boundary aggregation method based on the Minkowski addition algorithm is proposed. Finally, for the sake of reducing user charging cost and distribution network energy loss, and smoothing the daily load curve, an EVC scheduling model for EV participation in grid auxiliary services is proposed. The optimization model includes the reactive-power compensation of EV charging piles. The simulation results show that the proposed EVC scheduling model can greatly reduce the solution time compared to traditional individual-EV scheduling model. The model has high potential to be applied to large-scale EV scheduling. The reactive-power compensation provided by EV charging piles improves the voltage quality of the grid and enables more EVs to be connected to the grid. [ABSTRACT FROM AUTHOR]

The modern distribution network system is gradually becoming more complex and diverse, and traditional fault location methods have difficulty in quickly and accurately locating the fault location after a single-phase ground fault occurs. Therefore, this study proposes a new solution based on the Markov transfer field and deep learning to predict the fault location, which can accurately predict the location of a single-phase ground fault in the distribution network. First, a new phase-mode transformation matrix is used to take the fault current of the distribution network as the modulus 1 component, avoiding complex calculations in the complex field; then, the extracted modulus 1 component of the current is transformed into a Markov transfer field and converted into an image using pseudo-color coding, thereby fully exploiting the fault signal characteristics; finally, the Darknet-19 network is used to automatically extract fault features and predict the distance of the fault occurrence. Through simulations on existing models and training and testing with a large amount of data, the experimental results show that this method has good stability, high accuracy, and strong anti-interference ability. This solution can effectively predict the distance of ground faults in distribution networks. [ABSTRACT FROM AUTHOR]

Due to the complex topology, multi-line branches, and dense spatial distribution characteristics of a distribution network, potential disturbances and failures cannot be eliminated in real scenes, which means that higher levels of both reliability and stability are required in its corresponding protection system. For this reason, the timely monitoring and pinpoint identification of an underlying abnormal operation status in those protection systems must be ensured. To this end, a data-driven-based real-time anomaly detection ensemble is proposed in this paper. First, the kernel principal components investigation (KPCI) process is deployed to compress the dimensionality of input data, which can reduce the computational complexity within such high-dimensional data environments. Next, the isolated forest (IF) model is applied to excavate potential outliers according to the numeric range of the normal operating states of different features. Thus, a better detection performance in biased or sparse distributions can be achieved by reacting swiftly to those outliers. Finally, the operation data of the power distribution network protection system in a certain area is used as a simulation case. It is evident that compared with the single model IF detection method, combining the IF with the data dimension reduction model can effectively reduce data complexity. Due to the addition of kernel functions, KPCI can adapt to high-dimensional data environments better than standard PCI, and it also has certain advantages in calculation efficiency. This validates the theory that the proposed model has a high level of anomaly detection in practical applications, can assist in the automatic identification of and response to power distribution network security risks, effectively dig out potential system operational disturbances and state abnormalities, and achieve real-time anomaly monitoring and early warning. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-objective optimization framework for safe, reliable, and economic integration of electric vehicles (EVs) and renewable distributed generators (DGs) in distribution micro-grids. EV and DG coordination optimization with the use of vehicle-to-grid (V2G) technology along with system reconfiguration optimization is developed to provide collective revenues and address integrational complications that may occur by additional system loading due to EV charging and EV-DG energy exchanges. A Genetic Algorithm (GA) optimizes the EV charging/discharging in synergies with renewable DGs to maximize benefits that can be captured by their collaborative participation in electricity market and through renewable energy arbitrage. The developed EV charging/discharging optimization is implemented in a real 134-bus distribution network and is evaluated for its potential operational implications, namely, increased system losses. A system reconfiguration is then proposed to reduce the system losses by optimizing the flow of power through switching on/off the connections within the micro-grid and/or with other distribution systems. Simulation results demonstrate the efficiency of the proposed method in not only providing collective revenues, but also in enhancing the system operation by reducing the losses of the distribution grid. The collective benefits proposed by the developed optimization and validated by the simulation results facilitate transitioning to clean and eco-friendly sources of energy for generation and transportation, which in turn leads to more sustainable development of societies and communities. [ABSTRACT FROM AUTHOR]

To ensure safe and stable operation, accurate fault localization within active distribution networks is required, and this has attracted much attention. Influenced by many factors such as the control strategy, control performance, initial state of the distributed generators, and distribution network topology, it is still difficult to reliably locate complex and variable single-phase short-circuit faults relying only on a single feature quantity, while localization methods incorporating intelligent algorithms are affected by the choice of a priori samples and the fact that the solution process is a black-box model. To address this challenge, in this work, an expression for the single-phase short-circuit current vector of a distribution network containing distributed generators is derived, and the differences in magnitude and phase angle of the short-circuit current vectors upstream and downstream of the fault point are analyzed. Based on measurement theory, a fault confidence distribution function that reacts to the relative size of the current magnitude difference and phase angle difference is established, and the fusion fault confidence of the short-circuit current vector is constructed with the help of evidence theory. Finally, a method of locating single-phase short-circuit faults in distribution networks that contain distributed generators is proposed. The simulation results show that the ratio of the fusion fault confidence of the short-circuit current vector between faulted and non-faulted sections under the influence of different distributed generator capacities, fault locations, and transition resistances differ significantly. The proposed single-phase short-circuit fault localization method is both adaptive and physically interpretable and has clear boundaries, sound sensitivity, and engineering practicability. [ABSTRACT FROM AUTHOR]

Featured Application: The paper investigates the efficacy of grounding systems in power distribution networks during the transition from overhead to underground network configurations. Through scenario analysis and comparative evaluations, it aims to provide insights for optimal design methodologies, emphasizing safety and techno-economic criteria. Factors such as soil structure, fault current magnitude, and fault clearing times are examined to standardize the design of grounding systems in distribution networks. Designing and installing efficient grounding systems in power distribution networks is considered a complex and crucial task to ensure the reliable operation of power-protective schemes while mitigating hazardous potentials arising from faults, thereby safeguarding both personnel and equipment. This paper aims to offer guidance on designing effective grounding systems in distribution networks by assessing the influence of parameters such as soil structure, fault current magnitude, and fault clearing time. This involves proposing a more precise methodology for calculating hazardous potentials, leveraging software tools like PowerFactory, to accurately determine short-circuit (SC) currents and fault clearing times at specific locations where grounding grids are to be installed. Consequently, Distribution System Operators (DSOs) can design tailored grounding systems that optimize techno-economic considerations without unnecessary over-dimensioning, accounting for the unique characteristics of the Medium-Voltage (MV) Line and soil structure. [ABSTRACT FROM AUTHOR]

Developed to achieve the balancing of three-phase loads and improve their power factor, the BCC (Balancing Capacitive Compensator) is presented in this paper as having a broader capability, namely that of balancing the two-way power flow. BCCs are becoming useful in today's distribution networks with a high content of DERs (Distributed Energy Resources), where unbalanced power transfers to the higher voltage network occur more and more frequently as a result of the excess power generated. The article contains a case study in which, by means of Matlab-Simulink 2021 modelling, such a network is studied by considering two regimes corresponding to the two-way power flow. The numerical analysis of phase components and sequence components confirms the validity of the mathematical model concerning the BCC and also for the case of changing the way of power flow in the section controlled by the compensator. This demonstrates the possibility of extending the load balancing function of the BCC to that of balancing the two-way power flow and is an additional argument in support of replacing, in the more or less near future, conventional shunt capacitive compensators with capacitive balancing compensators. [ABSTRACT FROM AUTHOR]

This study proposes a charging demand forecasting model for electric vehicles (EVs) that takes into consideration the characteristics of EVs with transportation and mobile load. The model utilizes traffic information to evaluate the influence of traffic systems on driving and charging behavior, specifically focusing on the characteristics of EVs with transportation and mobile load. Additionally, it evaluates the effect of widespread charging on the distribution network. An urban traffic network model is constructed based on the multi-intersection features, and a traffic network–distribution network interaction model is determined according to the size of the urban road network. Type classification simplifies the charging and discharging characteristics of EVs, enabling efficient aggregation of EVs. The authors have built a singular EV transportation model and an EV charging queue model is established. The EV charging demand is forecasted and then used as an input in the support vector machine (SVM) model. The final projection value for EV charging load is determined by taking into account many influencing elements. Compared to the real load, the proposed method's feasibility and effectiveness are confirmed. [ABSTRACT FROM AUTHOR]

In this study, an optimized dual-layer configuration model is proposed to address voltages that exceed their limits following substantial integration of photovoltaic systems into distribution networks. Initially, the model involved segmenting the distribution network's voltage zones based on distributed photovoltaic governance resources, thereby elucidating the characteristics and governance requisites for voltages across distinct regions. Subsequently, a governance model for voltage limit exceedances, grounded in optimizing energy storage configurations, was formulated to mitigate photovoltaic power fluctuations by deploying energy storage systems. This model coordinates the reactive power output of photovoltaic installations with the active power consumption of energy storage systems, thereby augmenting voltage autonomy in the power grid. This study leveraged Karush–Kuhn–Tucker (KKT) conditions and the Big-M method to transform the dual-layer model into a single-layer linear model, thereby enhancing solution efficiency and precision. Finally, a simulation was carried out to demonstrate that the strategy proposed from this research not only achieves commendable economic efficiency, but also significantly improves the regional voltage effect by 28.7% compared to the optical storage capacity optimization model. [ABSTRACT FROM AUTHOR]

With the continuous development of renewable energy technologies, both domestically and internationally, the focus of energy research has gradually shifted towards renewable energy directions such as distributed photovoltaics and wind power. The penetration rate of renewable energy generation is constantly increasing, at the same time, the elements in the grid are becoming increasingly complex, and large-scale energy storage, as well as a variety of electricity loads such as electric vehicle charging piles and data centers are gradually appearing. Therefore, traditional distribution methods of the power grid are difficult to ensure the stable operation of the power system and cannot achieve efficient integration of renewable energy. Consequently, some scholars have proposed the concept of an energy internet. Compared to traditional power grids, the energy internet employs more comprehensive power electronics and communication technologies, enabling the interconnection of various new and traditional energy sources, and effectively integrating renewable energy. As the core device in the energy internet, the energy router plays a role in energy transformation and distribution, facilitating multi-information interconnection and multi-energy exchange within the energy internet. At the level of distribution network, the energy router can realize the efficient access of various forms of energy and the flexible control and management, which is of great significance for the optimal operation of distribution network. Against this backdrop, this paper reviews the development and current research status of energy routers, systematically analyzes the typical topologies and related control technologies of multi-port energy routers and summarizes and forecasts key issues and future development trends, aiming to provide thoughts and reference for subsequent related research. [ABSTRACT FROM AUTHOR]

Due to a rising share of renewable energy sources on the production side and electrification of transport and heating on the consumption side, the efficient management of flexibility in distribution networks is crucial for ensuring optimal operation and utilization of resources. Nowadays, the sensitivity-based approach is mainly used in medium-voltage (MV) networks for regulating voltage profiles with reactive power of distributed energy resources (DER). The main disadvantage of the simplified sensitivity-based method is its inaccuracy in case of a high deviation of the network voltage from the nominal values. Furthermore, it was also noted that despite the fact that the method is well described in the literature, there is a lack of systematic approach to its implementation in real-life applications. Thus, the main objective of this paper is to address this disadvantage and to propose an algorithm designed to calculate required consumer flexibility in near real-time to ensure distribution grid operation within operational criteria. In the first part of the paper, network state, including line loading and node voltages, is assessed to determine distribution network node capacity. By analyzing the sensitivity of network busbars to changes in consumption and production, our algorithm effectively identifies the most efficient nodes and facilitates strategic decision-making for resource allocation. We demonstrate the effectiveness of our approach through simulations of real-world distribution network data, highlighting its ability to enhance network flexibility and improve resource utilization. Leveraging sensitivity coefficients, the algorithm enables flexible consumption and production management across various scenarios, supporting the transition to a more dynamic and efficient power system. [ABSTRACT FROM AUTHOR]

In order to achieve the flexible and efficient utilization of distributed energy resources, microgrids (MGs) can enhance the self-healing capability of distribution systems. Conventional primary droop control in microgrids exhibits deviations in voltage and frequency and lacks research on voltage–frequency control during network reconfiguration. Therefore, this paper investigates the control strategy of secondary control for voltage and frequency during the process of reconstructing distribution networks to operate in the form of microgrids after faults. Firstly, the mathematical model of three-phase voltage-source inverters with droop control is analyzed. Then, inspired by the generator's secondary frequency control strategy, an improved secondary control method based on droop control is proposed, and the effectiveness of the improved droop control strategy is verified. Finally, simulation examples of distribution networks with various dynamic boundary topology changes are designed to simulate a relatively stable control result, validating the proposed strategy. [ABSTRACT FROM AUTHOR]

Timely and accurate fault location in active distribution networks is of vital importance to ensure the reliability of power grid operation. However, existing intelligent algorithms applied in fault location of active distribution networks possess slow convergence speed and low accuracy, hindering the construction of new power systems. In this paper, a new regional fault localization method based on an improved northern goshawk search algorithm is proposed. The population quality of the samples was improved by using the chaotic initialization strategy. Meanwhile, the positive cosine strategy and adaptive Gaussian–Cauchy hybrid variational perturbation strategy were introduced to the northern goshawk search algorithm, which adopted the perturbation operation to interfere with the individuals to increase the diversity of the population, contributing to jumping out of the local optimum to strengthen the ability of local escape. Finally, simulation verification was carried out in a multi-branch distribution network containing distributed power sources. Compared with the traditional regional localization models, the new method proposed possesses faster convergence speed and higher location accuracy under different fault locations and different distortion points. [ABSTRACT FROM AUTHOR]

The global capacity for renewable electricity generation has surged, with distributed photovoltaic generation being the primary driver. The increasing penetration of non-programmable renewable Distributed Energy Resources (DERs) presents challenges for properly managing distribution networks, requiring advanced voltage regulation techniques. This paper proposes an innovative decentralised voltage strategy that considers DERs, particularly inverter-based ones, as autonomous regulators in compliance with the state-of-the-art European technical standards and grid codes. The proposed method uses an optimal reactive power flow that minimises voltage deviations along all the medium voltage nodes; to check the algorithm's performance, it has been applied to a small-scale test network and on a real Italian medium-voltage distribution network, and compared with a fully centralised ORPF. The results show that the proposed decentralised autonomous strategy effectively improves voltage profiles in both case studies, reducing voltage deviation by a few percentage points; these results are further confirmed through an analysis conducted over several days to observe how seasons affect the results. [ABSTRACT FROM AUTHOR]

P2P power trading is necessary for efficiently using consumer electricity not subject to FIT. However, the execution rules for P2P power trading do not include restrictions on voltage, and there is a trade-off between the activation of the P2P power trading market through the mass introduction of PV and the optimization of the voltage of the power distribution system. In addition, there is a tendency for output curtailment to be biased toward consumers connected to the end of the grid. Since consumers cannot choose the interconnection location, there are concerns about unfairness. In this study, we investigate a new P2P model that includes voltage constraints for the execution rules of P2P power trading to avoid voltage deviation while ensuring benefits and fairness for the participants. In the proposed model, to increase the incentive to participate in the P2P power trading market, we consider compensating consumers who receive output curtailment signals due to voltage constraints. In addition, the profit is secured by differentiating the compensation cost unit price depending on the contract's availability. A case study was conducted on this model using the IEEE 33 bus system. The results show that the proposed model is superior. [ABSTRACT FROM AUTHOR]

Accurate line parameters are the basis for the optimal control and safety analysis of distribution networks. The lack of real-time monitoring equipment in grids has meant that data-driven identification methods have become the main tool to estimate line parameters. However, frequent network reconfigurations increase the uncertainty of distribution network topologies, creating challenges in the data-driven identification of line parameters. In this paper, a line parameter identification method compatible with an uncertain topology is proposed, which simplifies the model complexity of the joint identification of topology and line parameters by removing the unconnected branches through noise reduction. In order to improve the solving accuracy and efficiency of the identification model, a two-stage identification method is proposed. First, the initial values of the topology and line parameters are quickly obtained using a linear power flow model. Then, the identification results are modified iteratively based on the classical power flow model to achieve a more accurate estimation of the grid topology and line parameters. Finally, a simulation analysis based on IEEE 33- and 118-bus distribution systems demonstrated that the proposed method can effectively realize the estimation of topology and line parameters, and is robust with regard to both measurement errors and grid structures. [ABSTRACT FROM AUTHOR]

In distribution networks, time asynchrony exists between the phasor measuring unit (PMU) at both ends of a line, and the effective measurement time of the devices is short, leading to insufficient accuracy in phasor measurements. This paper proposes a fault location method for distribution networks that employ an additional inductance strategy to address the limited location accuracy caused by time asynchrony and the inadequate accuracy of phasor measurement devices. The method enhances the stability and accuracy of phase measurement by connecting an additional inductance after the online circuit breaker, thus extending the effective measurement time. It uses the symmetrical component method to obtain the positive-sequence and negative-sequence networks following a fault. Time asynchrony is treated as an equivalent asynchronous phase angle, which is then applied to the positive and negative-sequence voltage components. The impact of time asynchrony is mitigated by compensating for the phase angle difference using the ratio of the positive-sequence voltage component to the negative-sequence voltage component. This approach provides the fault location function and has improved the accuracy of fault location, which is advantageous for rapid fault repair. [ABSTRACT FROM AUTHOR]

As the penetration rate of new energy generation in distributed distribution networks continues to increase, the integration of numerous new energy power plants and associated power electronic devices presents challenges to the power quality of traditional power systems. Therefore, conducting power quality-related research in distribution networks is of significant importance for maintaining power system stability, safeguarding electrical equipment, and enhancing electrical safety. A framework for evaluating the overall power quality of new energy-penetrated distribution network systems based on the analytic hierarchy process (AHP) is proposed. This framework aggregates and calculates the global power quality index (GPQI) through averaging, thereby completing the assessment of power quality situations. By enhancing the computation speed of evaluation metrics through an improved S-transform and considering various disturbances such as diminished illumination, wind power disconnection, and high-current grounding, the GPQI values are used to assess power quality under diverse scenarios. Simulation and experimental results confirm the framework's close alignment with real scenarios and its effectiveness in evaluating power quality within distribution networks. This method is crucial for maintaining power system stability, protecting electrical equipment, and enhancing overall electrical safety within distribution networks. [ABSTRACT FROM AUTHOR]

The objective of the presented paper is to verify economically justified levels of reactive energy compensation in the distribution network in the new market conditions, including the extensive use of smart metering systems, new types of load, or distributed generation. The proposed methodology is based on the minimization of annual costs of losses caused by the flow of reactive energy to the supplied loads through the equivalent resistance of the distribution system determined on the basis of statistical energy losses in this network. The costs of losses are compared to the costs of using compensating devices expressed by the levelized costs of reactive energy generation. The results are the relations describing the optimal annual average value of the tgφ factor to be maintained by customers to optimize the cost of loss of the distribution network caused by reactive energy flows. The dependence of the optimal tgφ value on the analyzed load and network parameters is also discussed. The resulting optimal tgφ levels should be considered in the tariffication process of services offered by distribution system operators to improve capacity and limit the costs of power network operation due to reactive energy transmission. [ABSTRACT FROM AUTHOR]

In the context of national efforts to promote country-wide distributed photovoltaics (DPVs), the installation of distributed energy storage systems (DESSs) can solve the current problems of DPV consumption, peak shaving, and valley filling, as well as operation optimization faced by medium-voltage distribution networks (DN). In this paper, firstly, a price elasticity matrix based on the peak and valley tariff mechanism is introduced to establish a master–slave game framework for DN-DESSs under the DPV multi-point access environment. Secondly, the main model optimizes the pricing strategy of peak and valley tariffs with the objective of the lowest annual operating cost of the DN, and the slave model establishes a two-layer optimization model of DESSs with the objective of the maximum investment return of the DESSs and the lowest daily operating costs and call the CPLEX solver and particle swarm optimization algorithm for solving. Finally, the IEEE33 node system is used as a prototype for simulation verification. The results show that the proposed model can not only effectively reduce the operating cost of the distribution network but also play a role in improving the energy storage revenue and DPV consumption capacity, which has a certain degree of rationality and practicality. [ABSTRACT FROM AUTHOR]

In Italy, the availability of service areas (SAs) equipped with charging stations (CSs) for electric vehicles (EVs) on highways is limited in comparison to the total number of service areas. The scope of this work is to create a prototype and show a different approach to assessing the number of inlets required on highways. The proposed method estimates the energy requirements for the future electric fleet on highways. It is based on an energy conversion that starts with the fuel sold in the highway network and ends with the number of charging inlets. A proposed benchmark method estimates energy requirements for the electric fleet using consolidated values and statistics about refueling attitudes, with factors for range correction and winter conditions. The results depend on assumptions about future car distribution, with varying numbers of required inlets. The analysis revealed that vehicle traffic is a critical factor in determining the number of required charging inlets, with significant variance between different SAs. This study highlights the necessity of incorporating factors like weather, car charging power, and the future EV range into these estimations. The findings are useful for planning EV charging infrastructure, especially along major traffic routes and in urban areas with high-range vehicles relying on High-Power DC (HPDC) charging. The model's applicability to urban scenarios can be improved by considering the proportion of energy recharged at the destination. A key limitation is the lack of detailed origin–destination (OD) highway data, leading to some uncertainty in the calculated range ratio coefficient and underscoring the need for future research to refine this model. [ABSTRACT FROM AUTHOR]

This paper demonstrates the effectiveness of Demand Side Response (DSR) with renewable integration by solving the stochastic optimal operation problem (OOP) in the IEEE 118-bus distribution system over 24 h. An Improved Walrus Optimization Algorithm (I-WaOA) is proposed to minimize costs, reduce voltage deviations, and enhance stability under uncertain loads, generation, and pricing. The proposed I-WaOA utilizes three strategies: the fitness-distance balance method, quasi-opposite-based learning, and Cauchy mutation. The I-WaOA optimally locates and sizes photovoltaic (PV) ratings and wind turbine (WT) capacities and determines the optimal power factor of WT with DSR. Using Monte Carlo simulations (MCS) and probability density functions (PDF), the uncertainties in renewable energy generation, load demand, and energy costs are represented. The results show that the proposed I-WaOA approach can significantly reduce costs, improve voltage stability, and mitigate voltage deviations. The total annual costs are reduced by 91%, from 3.8377 × 107 USD to 3.4737 × 106 USD. Voltage deviations are decreased by 63%, from 98.6633 per unit (p.u.) to 36.0990 p.u., and the system stability index is increased by 11%, from 2.444 × 103 p.u. to 2.7245 × 103 p.u., when contrasted with traditional methods. [ABSTRACT FROM AUTHOR]

This paper presents a time-dependent reliability analysis created for a critical energy infrastructure use case, which consists of an interconnected urban power grid and a communication network. By utilizing expert knowledge from the energy and communication sectors and integrating the renewal theory of multi-component systems, a representative reliability model of this interconnected energy infrastructure, based on real network located in the Czech Republic, is established. This model assumes reparable and non-reparable components and captures the topology of the interconnected infrastructure and reliability characteristics of both the power grid and the communication network. Moreover, a time-dependent reliability assessment of the interconnected system is provided. One of the significant outputs of this research is the identification of the critical components of the interconnected network and their interdependencies by the directed acyclic graph. Numerical results indicate that the original design has an unacceptable large unavailability. Thus, to improve the reliability of the interconnected system, a slightly modified design, in which only a limited number of components in the system are modified to keep the additional costs of the improved design limited, is proposed. Consequently, numerical results indicate reducing the unavailability of the improved interconnected system in comparison with the initial reliability design. The proposed unavailability exploration strategy is general and can bring a valuable reliability improvement in the power and communication sectors. [ABSTRACT FROM AUTHOR]

The sampling rate of the wide-area synchronous phasor measuring device (D-PMU) in the distribution network is insufficient, and the localization sensitivity of the traditional localization method based on the time-domain Bergeron equation in the distribution network is insufficient. In this paper, an accurate location method of the distribution line grounding fault based on the time-domain' synchronous information calculation is proposed to solve the problem of limited location accuracy caused by a low sampling rate and the insufficient sensitivity of traditional methods. The method preprocesses the measurement data through low-frequency time-domain signal reconstruction and cubic spline interpolation. The fault current's different location criterion is constructed by using the voltage and current constraints at the fault point. By calculating the fault current difference at a limited number of calculated points, the accurate fault location under a low sampling rate is realized, which is beneficial to the rapid maintenance of faults and the shortening of the power outage time. [ABSTRACT FROM AUTHOR]