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With the growth of the load in the electricity networks, sufficient investment in the generation and lines expansion should be made in order to provide the energy needed by consumers with the lowest possible investment and operation costs. This issue is especially important in distribution networks, which are faced with the uncertainties of renewable energy generation and the development of microgrids and related issues. In this article, the planning of generation and lines expansion has been modeled with the aim of minimizing the total costs of microgrids, based on the cooperative approach. For this purpose, a bi‐level model has been developed; on the upper level, microgrids make investment decisions with a cooperative approach, and a constrained stochastic formulation has been developed with considering operational uncertainties on the lower level. Also, in this article, in order to ensure the supply of critical loads in island conditions, the self‐sufficiency index is defined. Three case studies have been considered to ensure the effectiveness of the developed model. In case 1, each microgrid will be able to supply its load only by generating of its units and purchasing from the retail market. In case 2, the possibility of trading with other microgrids in a non‐cooperative approach will also be available to the microgrids operators, and in case 3, microgrids can exchange energy with other microgrids in a cooperative manner. The simulation results showed that due to the possibility of using nearby microgrid resources, the cost of microgrid load supply in case 2 was reduced by 4.84% compared to case 1. Also, this cost in case 3 was reduced by 5.23% and 0.38%, respectively, compared to cases 1 and 2, due to the use of a cooperative manner in microgrid load providing. [ABSTRACT FROM AUTHOR]

The flexibility of soft open point (SOP) in spatial power regulation enhances the distribution network's (DN) integration of large‐scale renewable energy sources. However, the high cost of SOP and its limited capability for temporal power regulation impede its widespread adoption. Given the rapid expansion of 5G base stations (BSs), utilizing their energy storage to participate in DN planning and operation optimization provides a promising solution. Therefore, this paper proposes an optimal planning method of SOP in DN, considering collaborations with 5G BSs. The objective is to enhance DN's power regulation in both temporal and spatial dimensions, while minimizing the investment cost of SOP and fully utilizing the unused capacity in base station energy storage (BSES). Firstly, the flexible regulation models of SOP and 5G BS are established, with the real‐time dispatchability of BSES formulated. Then, a bi‐level optimization model is proposed, where the planning layer aims to minimize the total cost, while the operational layer aims to decrease the average voltage deviation. Additionally, an improved Shapley value method based on interactive power is developed for benefit allocation, which enhances the engagement of 5G BSs to participate in DN regulation. The effectiveness of proposed method is validated by simulation results. [ABSTRACT FROM AUTHOR]

As distributed generators (DG) and power electronic devices become more integrated, distribution networks have evolved from being passive to active, and power flow has shifted from unidirectional to bidirectional. This transformation has negatively impacted power quality, leading to weakened fault transient attributes and increased harmonic complexities. Consequently, the efficacy of traditional relay protection has diminished, elevating the risk of misoperation or misjudgment and compromising the safety of distribution networks. In response to these challenges, a fault section location method for active distribution network based on characteristic wave coupling is proposed to expand the fault difference. This method explores the principles of characteristic wave coupling, discusses characteristic wave parameter selection theory, examines the start‐up control strategy for characteristic wave coupling, and establishes a protection action criterion by comparing the energy difference of characteristic waves based on the fault identification principle. Subsequently, by utilizing multiple inverter interfaced distributed generators to actively couple characteristic waves into the distribution network during faults, achieves rapid identification and location of fault sections. Finally, the effectiveness of the method is substantiated through simulation results in MATLAB/Simulink and experimental outcomes obtained from a low‐voltage active distribution network experimental platform based on dSPACE1103. [ABSTRACT FROM AUTHOR]

The integration of large‐scale distributed power sources increases the voltage fluctuation in AC/DC hybrid distribution network (AD‐HDN). Power electronics devices such as photovoltaic (PV) inverters, soft open point (SOP), and voltage source converters (VSCs) can be utilized for voltage/var control (VVC) to alleviate the risk of voltage fluctuation and violation. This paper proposes a distributed optimal VVC method in power electronics dominated AD‐HDN. Firstly, the reactive power and voltage characteristics of PV inverters, SOP, and VSCs are analysed, and an optimal VVC optimization model for AD‐HDN to minimize node voltage deviation, PV curtailment, and network loss is proposed. Then, the second‐order cone (SOC) relaxation technique is used to re‐formulate the model into a convex optimization model. A distributed optimal VVC framework based on the alternating direction method of multipliers (ADMM) is constructed. Based on the residual balance principle and relaxation technique, an accelerated ADMM method is further proposed to solve the proposed model. Finally, case studies are conducted on the IEEE 33‐node and 85‐node systems to verify the superiority and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

A large scale of distributed photovoltaics is accessed through a low voltage distribution network in a single‐phase or two‐phase, which causes three‐phase unbalance and over‐voltage problems. These problems can be solved by using a voltage source converter to realize AC/DC interconnection and flexible power transfer between lines. How to utilize the characteristics of voltage source converters to optimize the power qualities, and fully promote distributed PV systems consumption in low voltage distribution network is an important research point. This paper proposes a power regulation model of VSC. The goal of this model is to adjust and optimize over‐voltage and three‐phase unbalance problems. By constructing a voltage‐power sensitivity calculation model for a three‐phase four‐wire system and integrating different control modes and adjustment capabilities of voltage source converters, a control strategy based on voltage source converters is proposed to address three‐phase unbalance and over‐voltage issues. Finally, the simulation results demonstrate the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

Owing to the rapid increase in electric vehicle integration and the uncoordinated charging behaviour of electric vehicles, the overloading risk of distribution transformers has deteriorated. This impact caused by large‐scale electric vehicle integration can be effectively reduced through the orderly guidance of electric vehicle charging behaviours. Here, a dispatching strategy for charging loads is proposed to address the problems of the uncoordinated charging demand in electric vehicles and overloading risk of distribution transformers in residential areas. First, an edge‐side dynamic index of the electric vehicle hosting capacity is proposed to guide the optimal dispatching of charging loads. Subsequently, an optimal dispatching model of the charging loads is established based on edge computing. The edge‐side dispatching strategy for the charging loads is then further updated considering the participation willingness of electric vehicle users. Finally, the effectiveness of the proposed control strategy is validated using a modified residential distribution network in Tianjin. The results show that the proposed strategy can effectively decrease the overloading risk of distribution transformers while realizing the efficient operation of electric vehicles on the edge side. [ABSTRACT FROM AUTHOR]

A dc voltage restorer (dc‐VR) with optimal droop control is developed to reduce the power loss and solve the dynamic current imbalance simultaneously of the zonal dc distribution system integrating multi‐parallel energy storage. Firstly, the power loss model composed of line loss and converter loss is built and it is proved to be a strictly convex function with respect to droop coefficients, which indicates the power loss can be mitigated by optimizing the current distribution. Thus, an image‐based droop optimization method is proposed to search for the optimal droop coefficients. Then, the economy of voltage compensation on power loss reduction is investigated and an ESS‐combined dc‐VR is designed, including an interleaved parallel architecture and a capacitor‐integrated electric spring (C‐ES). Unified virtual inertia is proposed for interleaved parallel bridge arms to make their dynamic performance consistent. And the newly introduced C‐ES can keep the bus voltage at the rated level to reduce the system cost. Therefore, the problem of dynamic currents imbalance is addressed from the points of converter structure (dc‐VR) and control system (unified inertia), and the power loss can be reduced by voltage recovery (C‐ES) and optimizing the current reallocation which is achieved by updating sharing coefficients from the image‐based droop optimization method. Finally, the simulation cases validate the effectiveness of the proposed optimal‐droop dc‐VR on dynamic current balance and power loss reduction, and hardware in the loop experiment results prove the consistent dynamic characteristics of the dc‐VR's arms. [ABSTRACT FROM AUTHOR]

The challenges facing active distribution networks have highlighted the position of the distribution system state estimation (DSSE) process in the distribution management systems as its most important function. Here, regarding the extensive scale of distribution networks and the weaknesses of centralized methods, the decentralized implementation of the DSSE process has received considerable attention. However, predefined network partitioning is supposed in previous works and zone size effects on the performance of the DSSE process have not been assessed. In response, a method for finding the optimal number of network zones and their size is proposed here. For this purpose, initially, an algorithm is used to partition the network into all possible configurations with different sizes. Subsequently, performance metrics affected by zone sizes, such as execution time, accuracy of the DSSE results, and reliability in achieving the results at the control centre, are modelled. Finally, by applying the decentralized DSSE method across all partitioning scenarios and calculating performance metrics, the most efficient and cost‐effective partitioning scenario can be identified. The performance of the proposed method is evaluated using the modified 77‐bus UK distribution network as an active test case, and the findings are subsequently presented and analysed. [ABSTRACT FROM AUTHOR]

With the development of the Internet of Things (IoT) in power distribution and the advancement of energy information integration technologies, the explosive growth in network data volume caused by massive terminal devices connecting to the power distribution network has become a significant challenge. Multi‐terminal collaborative computing is a key approach to addressing issues such as high latency and high energy consumption. In this article, fog computing is introduced into the computing network of the power distribution system, and a cloud‐fog‐edge collaborative computing architecture for intelligent power distribution networks is proposed. Within this framework, an improved weighted K‐means method based on information entropy theory is presented for node partitioning. Subsequently, an improved multi‐objective particle swarm optimization algorithm (MWM‐MOPSO) is employed to solve the task resource allocation problem. Finally, the effectiveness of the proposed architecture and allocation strategy is validated through simulations on the OPNET and PureEdgeSim platforms. The results demonstrate that, compared to traditional cloud‐edge service architectures, the proposed architecture and task offloading scheme achieve better performance in terms of processing latency and energy consumption. [ABSTRACT FROM AUTHOR]

The introduction of cloud‐native technology has significantly changed the architecture of applications and the mechanism for the collaborative operation of components in power distribution edge computing terminals (PDECT). To develop an effective quantitative analysis tool for PDECT performance, the composition and characteristics of cloud‐native PDECT are studied, and the modelling and simulation of cloud‐native PDECT are proposed. Subsequently, modelling is implemented through the simulation software CloudSim, achieving the simulation of microservices, containers, declarative configuration, and container orchestration with the consideration of power distribution scenarios. Then, by the proposed simulation scenario module, various elements of the power distribution scenarios can be self‐defined. Finally, by demonstrating the principles and implementation mechanisms of the proposed modelling method and simulation tool, and comparing simulation results for different service time ranges, access devices, resource configurations of PDECT, request occurrence rates, and resource scheduling strategies, the validity and effectiveness of the proposed modelling method and simulation tool are verified. [ABSTRACT FROM AUTHOR]

This paper presents an approximate power flow model‐based forecasting‐aided state estimation estimator for power distribution networks subject to naive forecasting methods and nonlinear filtering processes. To this end, this estimator designs a voltage perturbation vector around the priori‐determined nominal value as the dynamic state variable, which enables more detailed depictions of voltage changes. Then, a state transition model incorporating nodal power variation is derived from the approximate power injection model. The constant state transition matrix working on power variations only consists of nodal impedance, which reduces the extensive parameter tuning effort when facing different estimation tasks. Furthermore, an approximate branch power flow observation equation is proposed to improve the filtering efficiency. The observation matrix with branch admittance information presents the linear filtering relationship between power flow measurements and forecasted states, omitting the complex iterative updates of the Jacobian matrix for nonlinear measurements. Finally, the overall estimated voltage state at each time sample is entirely obtained by combining the filtered voltage perturbation vector with the priori‐determined nominal value. Numerical simulation comparisons on a symmetric balanced 56‐node distribution system verify the performance of the proposed estimator in terms of accuracy and robustness under normal and abnormal conditions. [ABSTRACT FROM AUTHOR]

The role of distributed generations (DGs) in a modern scenario is very useful for improving the system performances indexes like minimization of the total real and reactive power losses (ILP, and ILQ) of the system, voltage profile improvement (IVD), better voltage regulation (IVR), increasing the short circuit current capacity (ILC) and apparent power intake in the distribution networks. In this paper the novelty of the DGs are placed and sized with genetic algorithm (GA) in distribution network for improving system performance indexes. The system performance indexes such as ILP, ILQ, IVD, ILC, and IVR are considered for the planning of DGs. In this proposed work, 16-bus, 37-bus, 69-bus test systems is considered as a test systems, and constant impedance (Z), current (I), and power (P) load models is considered as a load. The proper placing of DGs in the distribution networks meets the challenge of more demand for electricity which can be achieved with enhanced load ability of the system with voltage stability and frequency stability also. [ABSTRACT FROM AUTHOR]

Active distribution networks (ADNs) are consistently being developed as a result of increasing penetration of distributed energy resources (DERs) and energy transition from fossil‐fuel‐based to zero carbon era. This penetration poses technical challenges for the operation of both transmission and distribution networks. The determination of the active/reactive power capability of ADNs will provide useful information at the transmission and distribution systems interface. For instance, the transmission system operator (TSO) can benefit from reactive power and reserve services which are readily available by the DERs embedded within the downstream ADNs, which are managed by the distribution system operator (DSO). This article investigates the important factors affecting the active/reactive power flexibility area of ADNs such as the joint active and reactive power dispatch of DERs, dependency of the ADN's load to voltage, parallel distribution networks, and upstream network parameters. A two‐step optimization model is developed which can capture the P/Q flexibility area, by considering the above factors and grid technical constraints such as its detailed power flow model. The numerical results from the IEEE 69‐bus standard distribution feeder underscore the critical importance of considering various factors to characterize the ADN's P/Q flexibility area. Ignoring these factors can significantly impact the shape and size of Active Distribution Networks (ADN) P/Q flexibility maps. Specifically, the Constant Power load model exhibits the smallest flexibility area; connecting to a weak upstream network diminishes P/Q flexibility, and reactive power redispatch improves active power flexibility margins. Furthermore, the collaborative support of reactive power from a neighboring distribution feeder, connected in parallel with the studied ADN, expands the achievable P/Q flexibility. These observations highlight the significance of accurately characterizing transmission and distribution network parameters. Such precision is fundamental for ensuring a smooth energy transition and successful integration of hybrid renewable energy technologies into ADNs. [ABSTRACT FROM AUTHOR]

With the high penetration and flexible access of inverter‐interfaced distributed generators (IIDGs), it is gradually becoming difficult for traditional protection schemes to meet the requirements for the safe operation of distribution networks (DNs). Active protection schemes based on power electronic equipment provide a new approach. On the basis of the controllability of voltage source converters, a method for active high‐frequency signal injection and a selection principle for the corresponding control parameters are proposed. Considering the impact of T‐connected load branches on the protected line, the high‐frequency current characteristics at the three terminals during internal and external faults are analysed. On this basis, an active protection scheme based on high‐frequency current is proposed for DNs with IIDGs. The performance of the proposed scheme is verified via PSCAD/EMTDC simulation software. The test results show that the proposed scheme can reliably trip during internal faults and identify faulty phases, which has better endurance to fault resistance. [ABSTRACT FROM AUTHOR]

Commercial off‐the‐shelf (OTS) photovoltaic systems coupled with battery energy storage units (PV‐BES) are typically designed to increase household self‐consumption, neglecting their potential for voltage regulation in low voltage distribution networks (LVDNs). This work proposes an enhanced sensitivity‐based combined (ESC) control method for voltage regulation, using BES control as level 1 and reactive power compensation as level 2. A centralized controller manages charging/discharging intervals, while local inverters handle real‐time power rates and reactive power, ensuring effective LVDN voltage regulation. The BES set points are obtained concerning the measured local bus voltage and according to enhanced sensitivity coefficients. The enhancement algorithm ensures that the full capacity of BES is utilized and that there is adequate capacity during charging and discharging time intervals. The proposed method, tested on 8‐bus and 116‐bus LV test feeders, outperforms OTS and an adaptive decentralized (AD) control method by completely preventing overvoltage issues, minimizing various changes in the direction of BES power, and reducing voltage deviation without significantly affecting consumers' grid dependency. [ABSTRACT FROM AUTHOR]

Aiming to improve the voltage distribution and realize the proportional sharing of active and reactive power in the distribution network (DN), this article proposes a distributed optimal control strategy based on the grouping cooperation mechanism of the distributed generation (DG). The proposed strategy integrates the local information of the DG and the global information of the DN. Considering the high resistance/reactance ratio of DN, distributed optimization control strategies for node voltage control and active power management are developed with the consensus variable of active utilization rate. And distributed strategy for reactive power management is proposed with a consensus variable of reactive utilization rate. The convergence of the distributed control system for each group is proved. The validity and robustness of the proposed strategy are verified by several simulations in the IEEE 33‐bus system. [ABSTRACT FROM AUTHOR]

Electric distribution networks increasingly host distributed energy resources based on power electronic converter (PEC) toward active distribution networks (ADN). Despite advances in computational capabilities, electromagnetic transient models are limited in scalability because of their reliance on exact data about the distribution system and each of its components. Similarly, the use of the DER_A model, which is intended to examine the combined dynamic behavior of many DERs, is limited by the difficulty in parameterization. There is a need for improved dynamic models of DERs for use in large power system simulations for stability analysis. This paper proposes an aggregate model‐free, data‐driven approach for deriving a dynamic partitioned model (DPM) of ADNs. Detailed residential distribution feeders were first developed, including PEC‐based DERs and composite load models (CMLDs), from which the aggregated DPM was derived. The performance was evaluated through various case studies and validated against the detailed ADN model and state‐of‐the‐art DER_A model with CMLD. The data‐driven DPM achieved a fitpercent${\it fitpercent}$ of over 90%, accurately representing the aggregated dynamic behavior of ADNs. Furthermore, the DPM significantly accelerated the simulation process with a computational speedup of 68 times compared to the detailed ADN and a 3.5 times speedup compared to the DER_A CMLD model. [ABSTRACT FROM AUTHOR]

The high adoption of electric vehicles (EVs) and the rising need for charging power in recent years calls for advancing charging service infrastructures and assessing the readiness of the power system to cope with such infrastructures. This paper proposes a novel model for the integrated operation of dynamic wireless charging (DWC) and power distribution systems offering charging service to in‐motion EVs. The proposed model benefits from a hierarchical design, where DWC controllers capture the traffic flows of in‐motion EVs on different routes and translate them into estimations of charging power requests on power distribution system nodes. The charging power requests are then communicated with a central controller that monitors the distribution system operation by enforcing an optimal power flow model. This controller coordinates the operation of distributed energy resources to leverage charging power delivery to in‐motion EVs and mitigate stress on the distribution system operation. The proposed model is tested on a test distribution system connected to multiple DWC systems in Salt Lake City, and the findings demonstrate its efficiency in quantifying the traffic flow of in‐motion EVs and its translation to charging power requests while highlighting the role of distributed energy resources in alleviating stress on the distribution system operation. [ABSTRACT FROM AUTHOR]

To devise more scientifically rational interruptible load contracts, this paper introduces a novel model for interruptible load contracts within modern electric power systems, grounded in data mining techniques. Initially, user characteristics are clustered using data mining technology to determine the optimal number of clusters. Building on this, the potential for different users to participate in interruptible load programs is analysed based on daily load ratios, yielding various user‐type parameters. Furthermore, the paper develops an interruptible load contract model that incorporates load response capabilities, enhancing the traditional interruptible load contract model based on principal‐agent theory through considerations of user type parameters and maximum interruptible load limits. The objective function, aimed at maximizing the profits of the electric company, is solved, and lastly, through the use of real data, a case study analysis focusing on commercial users with the strongest load response capabilities is conducted. The results affirm the efficacy of the proposed model. [ABSTRACT FROM AUTHOR]

The continuous increase in energy demand strains distribution networks, resulting in heightened power losses and a decline in overall performance. This negatively impacts distribution companies' profits and increases consumer electricity costs. Optimal distributed generation (DG) allocation in distribution networks can mitigate these issues by enhancing power supply capabilities and improving network performance. However, achieving optimal DG allocation is a complex optimization problem that requires advanced mathematical techniques. Nature-inspired (NI) swarm intelligence (SI)-based optimization techniques offer potential solutions by emulating the natural collective behaviors of animals. This paper reviews the application of NI-SI algorithms for optimal DG allocation, specifically focusing on reducing power losses as a key objective function. The review analyzes a significant body of literature demonstrating the effectiveness of NI-SI techniques in addressing power loss challenges in distribution networks. Additionally, future research directions are provided to guide further exploration in this field. [ABSTRACT FROM AUTHOR]

Second‐life batteries (SLBs), which are batteries retired from electric vehicles (EVs), can be used as energy storage systems to enhance the performance of distribution networks. Two issues should be addressed particularly for the optimal sizing of SLBs. Compared with fresh batteries, the failure rate of SLBs is relatively high, and timely and preventive replacement is needed. In addition, the flexibility introduced by EVs and installed SLBs should be coordinated to achieve optimal economic benefits. This paper focuses on the efficient utilization of SLBs by highlighting reliability‐flexibility concerns in optimal sizing. The model is formulated as a bi‐level model. On the upper‐level, considering the operational reliability constraints of SLBs, decisions regarding the investment and replacement of SLBs are optimized. Distribution network operations are improved on the lowerlevel, with an effective spatiotemporal flexible dispatch strategy for EVs. Finally, a linearized process for the optimal sizing of SLBs is presented and efficiently implemented. The Sioux Falls network and IEEE 69‐node distribution network are coupled as the test system. According to the simulation results, when the state of health of the SLBs decreased to 70%, the conditions were unreliable. The differences in the optimal SLB size and costs considering reliability and flexibility are highlighted. [ABSTRACT FROM AUTHOR]

The integration of alternative energy sources, storage systems, and modern loads into the distribution grid is complicating its operation and maintenance. Variability in individual generation and consumption elements dynamically affects voltage profiles, which in turn undermines efficiency and power quality. This study proposes to address this dynamical variability using an online reconfiguration approach that involves opening and closing switches to modify the grid's topology and adjust voltage levels in response to load/generation variations. Other grid optimization techniques, based on reconfiguration, typically focus on static, fully instrumented grids with predictable parameters and homogeneous changes, aiming to minimize power losses but overlooking the dynamics of variable grid elements. This study proposes a testing approach that is dependent on the estimated transient status of the grid only using a limited number of measurement units and considering the individual‐stochastic variations of loads and generators. The proposed approach was tested on the IEEE 33‐bus test feeder with up to five varying distributed generators. The results confirm that the algorithm consistently finds a reconfiguration alternative that could enhance system efficiency and voltage profiles, even in the face of dynamic load/generator behavior, demonstrating its effectiveness and online adaptability for grid operation and management tasks. [ABSTRACT FROM AUTHOR]

When a severe fault occurs in the distribution network, all or parts of it may be disconnected from the upstream network. Partitioning of these islanded areas is a solution to supplying the affected loads. Due to the variable nature of loads and renewable distributed generation (DG), the static model of partitioning with a fixed nature during island operation cannot be suitable. Therefore, in this article, considering the variable nature of loads and renewable distributed generation, a dynamic model is presented for the island partitioning to restore more valuable loads, which is suitable for quick decision‐making in emergencies. Also, a method for deciding on the mode of charging and discharging storage systems in emergencies is proposed. This model considers time limitation, uncontrollable DGs, controllable DGs and their control, controllability, and priority of loads, tie‐switches, storage systems, simultaneous faults, different situations of unintentional islanding of the distribution network, position of switches, and variable nature of loads and distributed generations. So, it is more comprehensive than the previous methods. Applying the proposed model to the modified IEEE 69‐bus system with controllable and uncontrollable generation and storage systems assuming different scenarios shows the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Natural disasters would destroy power grids and lead to blackouts. To enhance resilience of distribution systems, the sequential load restoration strategy can be adopted to restore outage portions using a sequence of control actions, such as switch on/off, load pickup, distributed energy resource dispatch etc. However, the traditional strategy may be unable to restore the distribution system in extreme weather events due to random sequential contingencies during the restoration process. To address this issue, this paper proposes a distributionally robust sequential load restoration strategy to determine restoration actions. Firstly, a novel multi‐time period and multi‐zone contingency occurrence uncertainty set is constructed to model spatial and temporal nature of sequential line contingencies caused by natural disasters. Then, a distributionally robust load restoration model considering uncertain line contingency probability distribution is formulated to maximize the expected restored load amount with respect to the worst‐case line contingency probability distribution. Case studies were carried out on the modified IEEE 123‐node system. Simulation results show that the proposed distributionally robust sequential load restoration strategy can produce a more resilient load restoration strategy against random sequential contingencies. Moreover, as compared with the conventional robust restoration strategy, the proposed strategy yields a less conservative restoration solution. [ABSTRACT FROM AUTHOR]

Introduction: Accurate prediction of line losses in distribution networks is crucial for optimizing power system planning and network restructuring, as these losses significantly impact grid operation quality. This paper proposes a novel approach that combines advanced feature selection techniques with Stacking ensemble learning to enhance the effectiveness of distribution network loss analysis and assessment. Methods: Utilizing data from 44 substations over an 18-month period, we integrated a Stacking ensemble learning model with multiple feature selection methods, including correlation coefficient, maximum information coefficient, and tree-based techniques. These methods were employed to identify the key predictors of power loss in the distribution network. Results: The proposed model achieved a Mean Absolute Percentage Error (MAPE) of 3.78% and a Root Mean Square Error (RMSE) of 1.53, demonstrating a substantial improvement over traditional linear regression-based prediction methods. The analysis revealed that historical line loss and line active power were the most influential predictive variables, while the inclusion of time-related features further refined the model's performance. Discussion: This study highlights the efficacy of combining multiple feature selection methods with Stacking ensemble learning for predicting power loss in 10 kV distribution networks. The enhanced accuracy and reliability of the proposed model offer valuable insights for electrical engineering applications, potentially contributing to more efficient and sustainable energy distribution systems. Future research could explore the applicability of this approach to other distribution network voltage levels and investigate the incorporation of additional environmental and network-specific factors to further improve power loss prediction. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are transforming the transportation sector, driven by the rapid expansion of charging infrastructure, including fast-charging stations (FCSs), significantly reducing charging time compared to standard charging stations. Despite the advantages of faster charging, the substantial power demand of EVs poses significant technical challenges for distribution networks. In particular, the existing literature has a research gap regarding how FCSs may impact or interact with step-voltage regulators' (SVRs) tap operations. In this study, we characterize and evaluate the effects of fast recharging at varying penetration levels (PLs) on SVRs' tap operations using probabilistic simulations and sensitivity analysis. To address these challenges, we propose a local and innovative application of the Volt/Var control on EV fast charging. The proposed application aims to inject reactive power into the network, depending on the FCS's nominal active power, when the bus voltage connected to the FCS exceeds a minimum value. Our research on an actual feeder in northern Brazil reveals that reducing the active power supplied to the vehicle or oversizing the charging station power converters is unnecessary. Furthermore, our strategy reduces the probability of undervoltage violations and minimizes SVR tap changes, mitigating EVs' impact on voltage quality. [ABSTRACT FROM AUTHOR]

This study investigates the IEEE 69-bus distribution network with three wind turbines (WTs) connected at the same buses of three battery energy storage systems (BESSs), with three 20- or 30-outlet electric vehicle charging stations (EVCSs) for charging electric vehicles (EVs). The honey badger algorithm (HBA) is adopted to minimize daily energy loss. The HBA determines the best size and position for three WT-BESS buses and three EVCS buses. The HBA calculates BESS size and operation mode to minimize daily energy loss. The demand of EVCSs varies throughout the day depending on the random choice of the number and state of charge of EVs entering the station. This results in the active and reactive energy losses and utility input energy decreasing by 63.5%, 60.6% and 59.6%, respectively, and the minimum voltage increasing from 0.9256 to 0.9839 pu. The network voltage profile and stability are also improved. [ABSTRACT FROM AUTHOR]

Summary: The existing single‐phase grounding (SPG) fault section location methods typically suffer from difficulty in feature selection, limited feeder terminal units (FTUs) configuration, and excessive dependence on communication, which weaken their generalization and robustness. To overcome these challenges, an SPG fault section location approach based on feature subset optimization is proposed. First, the relation between the position of FTU and its three‐phase current variation is analyzed, and its fault features are extracted to construct the candidate feature sets as feature subset optimization objects. Then, genetic algorithm and support vector machine (SVM) are combined to select the optimal feature subset with small dimensions and recognition error, which avoids the empirical errors of artificial feature selection. To reduce the cumulative errors, the SVM hyperparameters are simultaneously optimized. Finally, the SVM model is trained based on the optimal feature subset and hyperparameters. In the absence of zero‐sequence current measurement, three‐phase currents measured by FTU are locally processed to locate the fault section by the trained SVM. The experimental results verified the effectiveness and feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

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Abstract The flexibility of soft open point (SOP) in spatial power regulation enhances the distribution network's (DN) integration of large‐scale renewable energy sources. However, the high cost of SOP and its limited capability for temporal power regulation impede its widespread adoption. Given the rapid expansion of 5G base stations (BSs), utilizing their energy storage to participate in DN planning and operation optimization provides a promising solution. Therefore, this paper proposes an optimal planning method of SOP in DN, considering collaborations with 5G BSs. The objective is to enhance DN’s power regulation in both temporal and spatial dimensions, while minimizing the investment cost of SOP and fully utilizing the unused capacity in base station energy storage (BSES). Firstly, the flexible regulation models of SOP and 5G BS are established, with the real‐time dispatchability of BSES formulated. Then, a bi‐level optimization model is proposed, where the planning layer aims to minimize the total cost, while the operational layer aims to decrease the average voltage deviation. Additionally, an improved Shapley value method based on interactive power is developed for benefit allocation, which enhances the engagement of 5G BSs to participate in DN regulation. The effectiveness of proposed method is validated by simulation results.

Abstract With the growth of the load in the electricity networks, sufficient investment in the generation and lines expansion should be made in order to provide the energy needed by consumers with the lowest possible investment and operation costs. This issue is especially important in distribution networks, which are faced with the uncertainties of renewable energy generation and the development of microgrids and related issues. In this article, the planning of generation and lines expansion has been modeled with the aim of minimizing the total costs of microgrids, based on the cooperative approach. For this purpose, a bi‐level model has been developed; on the upper level, microgrids make investment decisions with a cooperative approach, and a constrained stochastic formulation has been developed with considering operational uncertainties on the lower level. Also, in this article, in order to ensure the supply of critical loads in island conditions, the self‐sufficiency index is defined. Three case studies have been considered to ensure the effectiveness of the developed model. In case 1, each microgrid will be able to supply its load only by generating of its units and purchasing from the retail market. In case 2, the possibility of trading with other microgrids in a non‐cooperative approach will also be available to the microgrids operators, and in case 3, microgrids can exchange energy with other microgrids in a cooperative manner. The simulation results showed that due to the possibility of using nearby microgrid resources, the cost of microgrid load supply in case 2 was reduced by 4.84% compared to case 1. Also, this cost in case 3 was reduced by 5.23% and 0.38%, respectively, compared to cases 1 and 2, due to the use of a cooperative manner in microgrid load providing.

Abstract A large scale of distributed photovoltaics is accessed through a low voltage distribution network in a single‐phase or two‐phase, which causes three‐phase unbalance and over‐voltage problems. These problems can be solved by using a voltage source converter to realize AC/DC interconnection and flexible power transfer between lines. How to utilize the characteristics of voltage source converters to optimize the power qualities, and fully promote distributed PV systems consumption in low voltage distribution network is an important research point. This paper proposes a power regulation model of VSC. The goal of this model is to adjust and optimize over‐voltage and three‐phase unbalance problems. By constructing a voltage‐power sensitivity calculation model for a three‐phase four‐wire system and integrating different control modes and adjustment capabilities of voltage source converters, a control strategy based on voltage source converters is proposed to address three‐phase unbalance and over‐voltage issues. Finally, the simulation results demonstrate the effectiveness of the proposed strategy.

Abstract Owing to the rapid increase in electric vehicle integration and the uncoordinated charging behaviour of electric vehicles, the overloading risk of distribution transformers has deteriorated. This impact caused by large‐scale electric vehicle integration can be effectively reduced through the orderly guidance of electric vehicle charging behaviours. Here, a dispatching strategy for charging loads is proposed to address the problems of the uncoordinated charging demand in electric vehicles and overloading risk of distribution transformers in residential areas. First, an edge‐side dynamic index of the electric vehicle hosting capacity is proposed to guide the optimal dispatching of charging loads. Subsequently, an optimal dispatching model of the charging loads is established based on edge computing. The edge‐side dispatching strategy for the charging loads is then further updated considering the participation willingness of electric vehicle users. Finally, the effectiveness of the proposed control strategy is validated using a modified residential distribution network in Tianjin. The results show that the proposed strategy can effectively decrease the overloading risk of distribution transformers while realizing the efficient operation of electric vehicles on the edge side.

Abstract The integration of large‐scale distributed power sources increases the voltage fluctuation in AC/DC hybrid distribution network (AD‐HDN). Power electronics devices such as photovoltaic (PV) inverters, soft open point (SOP), and voltage source converters (VSCs) can be utilized for voltage/var control (VVC) to alleviate the risk of voltage fluctuation and violation. This paper proposes a distributed optimal VVC method in power electronics dominated AD‐HDN. Firstly, the reactive power and voltage characteristics of PV inverters, SOP, and VSCs are analysed, and an optimal VVC optimization model for AD‐HDN to minimize node voltage deviation, PV curtailment, and network loss is proposed. Then, the second‐order cone (SOC) relaxation technique is used to re‐formulate the model into a convex optimization model. A distributed optimal VVC framework based on the alternating direction method of multipliers (ADMM) is constructed. Based on the residual balance principle and relaxation technique, an accelerated ADMM method is further proposed to solve the proposed model. Finally, case studies are conducted on the IEEE 33‐node and 85‐node systems to verify the superiority and effectiveness of the proposed method.

Abstract As distributed generators (DG) and power electronic devices become more integrated, distribution networks have evolved from being passive to active, and power flow has shifted from unidirectional to bidirectional. This transformation has negatively impacted power quality, leading to weakened fault transient attributes and increased harmonic complexities. Consequently, the efficacy of traditional relay protection has diminished, elevating the risk of misoperation or misjudgment and compromising the safety of distribution networks. In response to these challenges, a fault section location method for active distribution network based on characteristic wave coupling is proposed to expand the fault difference. This method explores the principles of characteristic wave coupling, discusses characteristic wave parameter selection theory, examines the start‐up control strategy for characteristic wave coupling, and establishes a protection action criterion by comparing the energy difference of characteristic waves based on the fault identification principle. Subsequently, by utilizing multiple inverter interfaced distributed generators to actively couple characteristic waves into the distribution network during faults, achieves rapid identification and location of fault sections. Finally, the effectiveness of the method is substantiated through simulation results in MATLAB/Simulink and experimental outcomes obtained from a low‐voltage active distribution network experimental platform based on dSPACE1103.

Abstract The introduction of cloud‐native technology has significantly changed the architecture of applications and the mechanism for the collaborative operation of components in power distribution edge computing terminals (PDECT). To develop an effective quantitative analysis tool for PDECT performance, the composition and characteristics of cloud‐native PDECT are studied, and the modelling and simulation of cloud‐native PDECT are proposed. Subsequently, modelling is implemented through the simulation software CloudSim, achieving the simulation of microservices, containers, declarative configuration, and container orchestration with the consideration of power distribution scenarios. Then, by the proposed simulation scenario module, various elements of the power distribution scenarios can be self‐defined. Finally, by demonstrating the principles and implementation mechanisms of the proposed modelling method and simulation tool, and comparing simulation results for different service time ranges, access devices, resource configurations of PDECT, request occurrence rates, and resource scheduling strategies, the validity and effectiveness of the proposed modelling method and simulation tool are verified.

Abstract This paper presents an approximate power flow model‐based forecasting‐aided state estimation estimator for power distribution networks subject to naive forecasting methods and nonlinear filtering processes. To this end, this estimator designs a voltage perturbation vector around the priori‐determined nominal value as the dynamic state variable, which enables more detailed depictions of voltage changes. Then, a state transition model incorporating nodal power variation is derived from the approximate power injection model. The constant state transition matrix working on power variations only consists of nodal impedance, which reduces the extensive parameter tuning effort when facing different estimation tasks. Furthermore, an approximate branch power flow observation equation is proposed to improve the filtering efficiency. The observation matrix with branch admittance information presents the linear filtering relationship between power flow measurements and forecasted states, omitting the complex iterative updates of the Jacobian matrix for nonlinear measurements. Finally, the overall estimated voltage state at each time sample is entirely obtained by combining the filtered voltage perturbation vector with the priori‐determined nominal value. Numerical simulation comparisons on a symmetric balanced 56‐node distribution system verify the performance of the proposed estimator in terms of accuracy and robustness under normal and abnormal conditions.

Abstract The challenges facing active distribution networks have highlighted the position of the distribution system state estimation (DSSE) process in the distribution management systems as its most important function. Here, regarding the extensive scale of distribution networks and the weaknesses of centralized methods, the decentralized implementation of the DSSE process has received considerable attention. However, predefined network partitioning is supposed in previous works and zone size effects on the performance of the DSSE process have not been assessed. In response, a method for finding the optimal number of network zones and their size is proposed here. For this purpose, initially, an algorithm is used to partition the network into all possible configurations with different sizes. Subsequently, performance metrics affected by zone sizes, such as execution time, accuracy of the DSSE results, and reliability in achieving the results at the control centre, are modelled. Finally, by applying the decentralized DSSE method across all partitioning scenarios and calculating performance metrics, the most efficient and cost‐effective partitioning scenario can be identified. The performance of the proposed method is evaluated using the modified 77‐bus UK distribution network as an active test case, and the findings are subsequently presented and analysed.

Abstract With the development of the Internet of Things (IoT) in power distribution and the advancement of energy information integration technologies, the explosive growth in network data volume caused by massive terminal devices connecting to the power distribution network has become a significant challenge. Multi‐terminal collaborative computing is a key approach to addressing issues such as high latency and high energy consumption. In this article, fog computing is introduced into the computing network of the power distribution system, and a cloud‐fog‐edge collaborative computing architecture for intelligent power distribution networks is proposed. Within this framework, an improved weighted K‐means method based on information entropy theory is presented for node partitioning. Subsequently, an improved multi‐objective particle swarm optimization algorithm (MWM‐MOPSO) is employed to solve the task resource allocation problem. Finally, the effectiveness of the proposed architecture and allocation strategy is validated through simulations on the OPNET and PureEdgeSim platforms. The results demonstrate that, compared to traditional cloud‐edge service architectures, the proposed architecture and task offloading scheme achieve better performance in terms of processing latency and energy consumption.

Abstract A dc voltage restorer (dc‐VR) with optimal droop control is developed to reduce the power loss and solve the dynamic current imbalance simultaneously of the zonal dc distribution system integrating multi‐parallel energy storage. Firstly, the power loss model composed of line loss and converter loss is built and it is proved to be a strictly convex function with respect to droop coefficients, which indicates the power loss can be mitigated by optimizing the current distribution. Thus, an image‐based droop optimization method is proposed to search for the optimal droop coefficients. Then, the economy of voltage compensation on power loss reduction is investigated and an ESS‐combined dc‐VR is designed, including an interleaved parallel architecture and a capacitor‐integrated electric spring (C‐ES). Unified virtual inertia is proposed for interleaved parallel bridge arms to make their dynamic performance consistent. And the newly introduced C‐ES can keep the bus voltage at the rated level to reduce the system cost. Therefore, the problem of dynamic currents imbalance is addressed from the points of converter structure (dc‐VR) and control system (unified inertia), and the power loss can be reduced by voltage recovery (C‐ES) and optimizing the current reallocation which is achieved by updating sharing coefficients from the image‐based droop optimization method. Finally, the simulation cases validate the effectiveness of the proposed optimal‐droop dc‐VR on dynamic current balance and power loss reduction, and hardware in the loop experiment results prove the consistent dynamic characteristics of the dc‐VR's arms.

Abstract Active distribution networks (ADNs) are consistently being developed as a result of increasing penetration of distributed energy resources (DERs) and energy transition from fossil‐fuel‐based to zero carbon era. This penetration poses technical challenges for the operation of both transmission and distribution networks. The determination of the active/reactive power capability of ADNs will provide useful information at the transmission and distribution systems interface. For instance, the transmission system operator (TSO) can benefit from reactive power and reserve services which are readily available by the DERs embedded within the downstream ADNs, which are managed by the distribution system operator (DSO). This article investigates the important factors affecting the active/reactive power flexibility area of ADNs such as the joint active and reactive power dispatch of DERs, dependency of the ADN's load to voltage, parallel distribution networks, and upstream network parameters. A two‐step optimization model is developed which can capture the P/Q flexibility area, by considering the above factors and grid technical constraints such as its detailed power flow model. The numerical results from the IEEE 69‐bus standard distribution feeder underscore the critical importance of considering various factors to characterize the ADN's P/Q flexibility area. Ignoring these factors can significantly impact the shape and size of Active Distribution Networks (ADN) P/Q flexibility maps. Specifically, the Constant Power load model exhibits the smallest flexibility area; connecting to a weak upstream network diminishes P/Q flexibility, and reactive power redispatch improves active power flexibility margins. Furthermore, the collaborative support of reactive power from a neighboring distribution feeder, connected in parallel with the studied ADN, expands the achievable P/Q flexibility. These observations highlight the significance of accurately characterizing transmission and distribution network parameters. Such precision is fundamental for ensuring a smooth energy transition and successful integration of hybrid renewable energy technologies into ADNs.

Abstract Electric distribution networks increasingly host distributed energy resources based on power electronic converter (PEC) toward active distribution networks (ADN). Despite advances in computational capabilities, electromagnetic transient models are limited in scalability because of their reliance on exact data about the distribution system and each of its components. Similarly, the use of the DER_A model, which is intended to examine the combined dynamic behavior of many DERs, is limited by the difficulty in parameterization. There is a need for improved dynamic models of DERs for use in large power system simulations for stability analysis. This paper proposes an aggregate model‐free, data‐driven approach for deriving a dynamic partitioned model (DPM) of ADNs. Detailed residential distribution feeders were first developed, including PEC‐based DERs and composite load models (CMLDs), from which the aggregated DPM was derived. The performance was evaluated through various case studies and validated against the detailed ADN model and state‐of‐the‐art DER_A model with CMLD. The data‐driven DPM achieved a fitpercent of over 90%, accurately representing the aggregated dynamic behavior of ADNs. Furthermore, the DPM significantly accelerated the simulation process with a computational speedup of 68 times compared to the detailed ADN and a 3.5 times speedup compared to the DER_A CMLD model.

Abstract Aiming to improve the voltage distribution and realize the proportional sharing of active and reactive power in the distribution network (DN), this article proposes a distributed optimal control strategy based on the grouping cooperation mechanism of the distributed generation (DG). The proposed strategy integrates the local information of the DG and the global information of the DN. Considering the high resistance/reactance ratio of DN, distributed optimization control strategies for node voltage control and active power management are developed with the consensus variable of active utilization rate. And distributed strategy for reactive power management is proposed with a consensus variable of reactive utilization rate. The convergence of the distributed control system for each group is proved. The validity and robustness of the proposed strategy are verified by several simulations in the IEEE 33‐bus system.

Abstract With the high penetration and flexible access of inverter‐interfaced distributed generators (IIDGs), it is gradually becoming difficult for traditional protection schemes to meet the requirements for the safe operation of distribution networks (DNs). Active protection schemes based on power electronic equipment provide a new approach. On the basis of the controllability of voltage source converters, a method for active high‐frequency signal injection and a selection principle for the corresponding control parameters are proposed. Considering the impact of T‐connected load branches on the protected line, the high‐frequency current characteristics at the three terminals during internal and external faults are analysed. On this basis, an active protection scheme based on high‐frequency current is proposed for DNs with IIDGs. The performance of the proposed scheme is verified via PSCAD/EMTDC simulation software. The test results show that the proposed scheme can reliably trip during internal faults and identify faulty phases, which has better endurance to fault resistance.

Abstract Commercial off‐the‐shelf (OTS) photovoltaic systems coupled with battery energy storage units (PV‐BES) are typically designed to increase household self‐consumption, neglecting their potential for voltage regulation in low voltage distribution networks (LVDNs). This work proposes an enhanced sensitivity‐based combined (ESC) control method for voltage regulation, using BES control as level 1 and reactive power compensation as level 2. A centralized controller manages charging/discharging intervals, while local inverters handle real‐time power rates and reactive power, ensuring effective LVDN voltage regulation. The BES set points are obtained concerning the measured local bus voltage and according to enhanced sensitivity coefficients. The enhancement algorithm ensures that the full capacity of BES is utilized and that there is adequate capacity during charging and discharging time intervals. The proposed method, tested on 8‐bus and 116‐bus LV test feeders, outperforms OTS and an adaptive decentralized (AD) control method by completely preventing overvoltage issues, minimizing various changes in the direction of BES power, and reducing voltage deviation without significantly affecting consumers' grid dependency.

Abstract Rapidly increasing uptake of Electric Vehicles (EVs) is expected to have a significant impact on electrical power distribution networks. Considerable work has been carried out to understand this impact and quantify the distribution networks hosting capacity, with and without network management solutions. However, the current body of knowledge does not have a comprehensive review of the research done to‐date on this topic which is vital to understand the scope of the existing studies, the data used in analysing the impact, and, most importantly, the findings. A comprehensive yet focused review of impact of EV charging on distribution networks is presented by delving into the main factors restricting EV hosting capacity and the strategies used to maximise EV hosting capacity by managing the aforementioned impacts. The authors comprehensively summarise the approaches used to quantify the impact, network and data types, and the proposed solutions to increase network hosting capacity. Moreover, the shortcomings in the existing work are identified and recommendations for future research are provided to help stakeholders understand the current state‐of‐the‐art, make informed decisions, and to be considered by future researchers.

Abstract The high adoption of electric vehicles (EVs) and the rising need for charging power in recent years calls for advancing charging service infrastructures and assessing the readiness of the power system to cope with such infrastructures. This paper proposes a novel model for the integrated operation of dynamic wireless charging (DWC) and power distribution systems offering charging service to in‐motion EVs. The proposed model benefits from a hierarchical design, where DWC controllers capture the traffic flows of in‐motion EVs on different routes and translate them into estimations of charging power requests on power distribution system nodes. The charging power requests are then communicated with a central controller that monitors the distribution system operation by enforcing an optimal power flow model. This controller coordinates the operation of distributed energy resources to leverage charging power delivery to in‐motion EVs and mitigate stress on the distribution system operation. The proposed model is tested on a test distribution system connected to multiple DWC systems in Salt Lake City, and the findings demonstrate its efficiency in quantifying the traffic flow of in‐motion EVs and its translation to charging power requests while highlighting the role of distributed energy resources in alleviating stress on the distribution system operation.

Abstract To devise more scientifically rational interruptible load contracts, this paper introduces a novel model for interruptible load contracts within modern electric power systems, grounded in data mining techniques. Initially, user characteristics are clustered using data mining technology to determine the optimal number of clusters. Building on this, the potential for different users to participate in interruptible load programs is analysed based on daily load ratios, yielding various user‐type parameters. Furthermore, the paper develops an interruptible load contract model that incorporates load response capabilities, enhancing the traditional interruptible load contract model based on principal‐agent theory through considerations of user type parameters and maximum interruptible load limits. The objective function, aimed at maximizing the profits of the electric company, is solved, and lastly, through the use of real data, a case study analysis focusing on commercial users with the strongest load response capabilities is conducted. The results affirm the efficacy of the proposed model.

Abstract The integration of alternative energy sources, storage systems, and modern loads into the distribution grid is complicating its operation and maintenance. Variability in individual generation and consumption elements dynamically affects voltage profiles, which in turn undermines efficiency and power quality. This study proposes to address this dynamical variability using an online reconfiguration approach that involves opening and closing switches to modify the grid's topology and adjust voltage levels in response to load/generation variations. Other grid optimization techniques, based on reconfiguration, typically focus on static, fully instrumented grids with predictable parameters and homogeneous changes, aiming to minimize power losses but overlooking the dynamics of variable grid elements. This study proposes a testing approach that is dependent on the estimated transient status of the grid only using a limited number of measurement units and considering the individual‐stochastic variations of loads and generators. The proposed approach was tested on the IEEE 33‐bus test feeder with up to five varying distributed generators. The results confirm that the algorithm consistently finds a reconfiguration alternative that could enhance system efficiency and voltage profiles, even in the face of dynamic load/generator behavior, demonstrating its effectiveness and online adaptability for grid operation and management tasks.