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Abstract With the increasing integration of distributed energy resources (DERs) into distribution systems, the optimization of system operation has become complex, facing challenges such as inadequate consideration of market participants’ benefits, poor computational efficiency, and data privacy concerns. This paper introduces the concept of a virtual power plant (VPP) as a solution for energy integration and management. To strike a balance between operational safety and the interests of market participants, a dual‐layer model is proposed. This model considers the benefits of both Distribution System Operators (DSO) and VPP, while also enhancing the consideration of distribution network constraints. The DSO considers AC optimal power flow and utilizes penalty functions to ensure network security in case of violations. To enhance computational efficiency and privacy, the paper presents the parameter‐sharing twin delayed deep deterministic policy gradient approach. This approach allows multiple intelligent agents to share a neural network model, effectively reducing the computational load. During the training process, only essential data is exchanged among the agents, ensuring the privacy of sensitive information. The effectiveness of the proposed model and the algorithm is validated through a case study on an IEEE 33‐node system.

The excessive integration of renewable distributed generation (RDG) and electric vehicles (EVs) could be considered the two most problematic elements representing the greatest threat to the distribution network (DN) technical operation. In order to avoid going beyond technical limitations, the term hosting capacity (HC) was proposed to define the highest permitted amount of distributed generation (DG) or EVs that can be integrated safely into the DN. The connection of RDGs was first brought to the attention of researchers and DN operators since it accounts for the most notable portion of these technical issues. Hence, the phrase ‘DG-HC’ was initially proposed and evolved significantly over the last few years. Currently, EV integration in most DNs worldwide is still low, but given the worldwide support for clean transportation options, expectations are raised for a significant increase. As a result, it is anticipated that over the next years, the effect of EV integration on the DN will be highly noticeable, requiring greater attention from researchers and DN operators to define the accepted limits of EV penetration levels, ‘EV-HC,’ which is expected to pass along the same line of DG-HC. This article provides an in-depth review of both DG-HC and EV-HC. It first analyses how the DG-HC research has grown over the years and then studies the published EV-HC papers, illustrating to what extent there is a similarity between them and, finally, employs these analyses to expect future development in the EV-HC research area. This article includes the different uses of the term HC, the most common performance indices of DG-HC, the various methods for assessing DG-HC, the different techniques for DG-HC enhancement, the effects of integrating EVs on the DG-HC, and finally, calculating and enhancing methods for EV-HC.

Abstract In the context of rare but high‐impact extreme scenarios, such as natural disasters, it is crucial to utilize all available resources, including microgrids and distributed power sources, to restore critical loads in the distribution network as much as possible. This paper proposes a two‐stage resilience enhancement strategy for distribution networks considering post‐disaster topology reconstruction and demand response, aiming to facilitate the recovery of critical loads after disasters. In the first stage, a heuristic algorithm is introduced to determine the post‐disaster topology of the distribution network. In the second stage, by employing a step‐wise elastic load curve, user demand response is incorporated to maximize the socio‐economic value of the restoration, and resilience metrics are computed. Finally, the effectiveness of the proposed resilience restoration strategy is validated through simulations on Case33 and Case69 systems.

Abstract This paper presents a multi‐objective restoration scheme for improving the resilience of integrated electricity and natural gas distribution systems against extreme weather events. The coupling constraints of electricity and gas networks are tackled properly using a linearized optimal power flow (OPF). Distributed generators, power‐to‐gas facility, rescheduling of generation/storage units, and microgrid formation are employed as operational resources/measures for restoring the integrated energy system after the event landfall. An adaptable directed multi‐commodity flow‐based microgrid formation is utilized, that is, the network configuration is dynamically changed in accordance with time‐variant load priority weights. The proposed method was successfully examined on an integrated electricity and natural gas distribution system comprised of the modified IEEE 33‐bus distribution network and a 14‐node natural gas distribution network. Numerical results showed that using microgrid formation increased the supplied critical load of integrated electricity and natural gas distribution system by about 16%. Moreover, due to making benefit of the power‐to‐gas unit, the supplied critical load increased by about 12.3%. respectively. While utilizing energy storage systems along with the power‐to‐gas unit facilitated the exchange of energy between the power distribution network and natural gas distribution network regarding time‐variant load priority weights, the supplied critical load increased by about 13%.

Abstract Cascaded H‐bridge power electronic transformers (CHB‐PET) can efficiently connect AC/DC distribution networks, realizing power transmission and electric energy conversion. Once a pole‐to‐pole DC fault occurs on the low‐voltage direct current (LVDC) side of CHB‐PET, the fault current has a fast rise and high amplitude, which easily leads to the rapid blocking of power electronic devices and makes it difficult for DC line protection to obtain fault information. A stepwise blocking scheme of PET is proposed for LVDC network protection. When the fault on the LVDC side of PET is detected, cascaded H‐bridge and low‐voltage H‐bridge of dual‐active‐bridge are blocked first, and then high‐voltage H‐bridge of dual‐active‐bridge is blocked after a 5 ms delay. The stepwise blocking can provide fault information for DC line protection. On this basis, wavelet packet transform is employed to extract different frequency components of fault current. The energy of the specific frequency band corresponding to the switching frequency of DAB and the total energy of all frequency bands contained in the fault current is calculated; then their ratio can be utilized to construct the protection scheme. Finally, the PSCAD/EMTDC simulation results verify the feasibility and effectiveness of proposed protection scheme based on stepwise blocking of CHB‐PET.

Abstract In the case of a single‐phase grounding fault in the distribution network, the transient zero‐sequence current (TZSC) tends to be non‐linear and non‐stationary. The faulty line selection is relatively difficult. The distributed power access further brings many difficulties to faulty line selection. This work proposes a novel method of faulty line selection using spatial image generation and deep learning. At first, the optimal smooth denoising model can be used to smooth the zero‐sequence current for each line, reducing the external environment electromagnetic interference. Then, the treated zero‐sequence current is mapped into the colorful floral hoop image by using symmetrized Hilbert transform pattern (SHTP). The SHTP transforms the one‐dimensional time domain signal into the two‐dimensional space domain image, enhancing invisible information and obtaining more abundant feature information. Finally, the deep features of the SHTP floral hoop image are extracted by means of deep learning method. In order to improve the faulty line selection universality, a mixed sample library containing three different topologies is established, including the 10 kV radial distribution network, IEEE‐13 node model, IEEE‐34 node model and StarSim platform. The comparisons show that the proposed method has a more noticeable visualization effect on fault features, higher classification precision rate, and better anti‐noise performance.

Abstract Earth faults is a challenging fault type to locate in resonant grounded networks due to their naturally low fault current, and the problem increases with an increased fault impedance. This paper describes the detailed implementation and laboratory testing of a method for detection, location and clearing of earth faults with very small fault currents. The method consists of two indicators used in the fault detection stage, where their simultaneous operation ensures selective fault detection and faulty feeder selection. One of these indicators also enables continuous fault indication throughout a sectionalizing process. The laboratory tests demonstrate that both indicators function as intended, and it is the current sensors which ultimately limit the attainable sensitivity. Faults up to 15 kΩ were detected successfully in the laboratory network based on phase current measurements, while the sectionalizing indicator showed much higher sensitivity and functioned as intended in a 50 kΩ fault. Measurements from one field test in a 22 kV network corroborate the laboratory results and demonstrate the expected earth fault indicator response.

The electric utility industry is moving towards a deregulated and competitive market to meet customer expectations. So, system performance and reliability assessments are getting targeted more than before. Several performance measures of reliability are developed in the literature. In this research, the NEPLAN Simulator reliability analysis module is used to determine all the reliability indices in different cases of study considering the effect of the Advanced Distributed Automation System (ADAS). The analysis also benefits from the presence of Penetration of Distributed Generation (DG) units and Soft Open Points (SOP) to enhance system reliability alongside power quality. Therefore, this paper provides a methodology based on a cost/benefit study for distribution networks to define the best location of DG units and SOP devices that leads to better reliability indices. The objectives of the study are demonstrated and investigated through Bus 4 of the standard reliability Roy Bollington Test System (RBTS). NEPLAN uses a tool to apply the Reliability Centered Maintenance (RCM) strategy, which leads to a substantial reduction of maintenance expenses. Simulation results indicate a significant reduction in system reliability cost by 65.8% with significant enhancement in the average of all reliability indices and at each load point too such as Customer Average Interruption Duration Index (CAIDI) which is improved from 40.899 to 29.883, Energy not supplied also is declined by 52.34%. It is also worth saying that, even using DG units or SOP devices separately leads to positive results but the best outcomes are obtained with an appropriate combination of both.

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.

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The primary methods of assessing the reliability of distribution networks comprise analytic and simulation methods. However, both approaches require the identification and computation of network topology, which precludes their expression in explicit, continuous functions, consequently impeding the incorporation of reliability constraints into planning and operational optimization models. To tackle this restriction, the present work puts forth a novel linear‐programming‐based reliability assessment method that is mathematically formulated, considering distribution automation (DA) and distributed generations (DGs), consisting of both conventional and renewable energy sources. In this paper, the clustering method and the scenario‐based method are used to model DGs. Next, a mixed integer linear programming (MILP) model, considering the DA and DGs with the System Average Interruption Duration Index (SAIDI) as the optimization objective, is proposed. Finally, the feasibility and effectiveness of the proposed method are verified in a 37‐node distribution network system. [ABSTRACT FROM AUTHOR]

With large‐scale renewable energy connected to the distribution network, the traditional method to solve the distribution network voltage/var power optimization model can hardly meet the needs of online optimization of the distribution network. Therefore, a Bayesian optimization eXtreme Gradient Boosting (BO‐XGBoost) based optimization strategy for voltage/var of the distribution network considering the cost of the photovoltaic (PV) power is proposed to improve the solving speed of the optimization model. First, the voltage/var optimization model of the distribution network considering the LCOE of the PV system is established. The optimal reactive power output command data set of the PV generation in the distribution network is obtained by solving the established optimization model. The XGBoost model is used to mine the nonlinear mapping relationship between the state information of the distribution network and the optimal reactive power output of the PV system; then the Bayesian algorithm is used to complete the adaptive optimization of the hyperparameters of the XGBoost model. Finally, the PG&E 69 node power distribution system is used to verify the advantages of the proposed strategy in improving the performance of the XGBoost algorithm, increasing the solving speed of the voltage/var optimization model, and reducing the LCOE of PV generation. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are rapidly replacing conventional fuel vehicles, offering powerful, emission-free performance. This paper introduces an innovative three-phase bidirectional charger for grid-to-vehicle (G2V) and vehicle-to-grid (V2G) applications, strengthening the connection between EVs and the power grid. The charger employs a two-stage power conversion approach with advanced converters and a simplified dq-based charging control strategy. An efficient AC-DC converter facilitates smooth transitions between modes, responding to grid directives for active and reactive power. A soft-switching dual active bridge (SS-DAB) DC-DC converter optimally interfaces with the EV battery pack, while dual active LCL filters suppress harmonics, enhancing system performance. Simulated results confirm the charger's effectiveness in a 3.5-kW prototype using MATLAB/Simulink. The proposed SS-DAB converter-based bidirectional on-board charger introduces a groundbreaking unified Voltage Source Converter (VSC) control approach, enabling efficient power transfer in both vehicle-to-grid (V2G) and grid-to-vehicle (G2V) modes. This innovation ensures rapid dynamic response, exceptional steady-state performance, and robustness against grid demand changes, optimizing EV integration. [ABSTRACT FROM AUTHOR]

Considering the importance of the renewable energy sector in the distribution systems, energy operation, and management which are connected to the distribution network (DN) in the form of multiple microgrids (MMGs) is crucial in reducing cost and pollution. Hence, this paper aims to propose optimal energy management for MMGs in the DN. Different objective functions have been taken into account in this optimization, including network cost, pollution reduction, and distribution network power losses. To design the multi‐objective optimization problem, a fuzzy method has been adopted for simultaneous multi‐objective calculations. Furthermore, the effect of the placement of distributed generations (DGs) and microgrids (MGs) is considered to reduce the distribution network power losses. Information gap decision theory (IGDT) has formulated uncertainties about renewable sources and consumers. To solve this optimization problem, a new method of the modified Harris Hawk optimization (MHHO) algorithm has been implemented, compared with the original HHO and genetic algorithm (GA). Finally, the proposed method has been analysed under the IEEE 33‐bus distribution network for a 24‐hour time horizon, including three MGs considering different renewable energy sources (RESs). The simulation results have demonstrated the high performance of the allocated network with the MHHO algorithm compared to the other scenarios. [ABSTRACT FROM AUTHOR]

Increasing the penetration level of distributed generation (DG) units requires overcoming the technical challenges associated with their integration into the distribution systems, especially protection problems. Change in the current profile of the distribution system due to the presence of DG units disrupts the operation of the conventional fuse saving coordination (FSC) scheme. The first objective of this paper is to provide an overview of the state‐of‐the‐art of FSC schemes in distribution systems with distributed generators that has not been systematically presented yet. In addition to comparing the features of reliability, cost, speed, implementation, calculation burden, and requirements, the impact of presence of distributed generations on the performance of the conventional FSC scheme is investigated in details. The second objective of this paper is to propose an FSC restoration scheme for minimizing the challenges of previous works. Using a quasi‐voltage current term, the proposed scheme modifies the adjustable time coefficient of the recloser in two ways of pro and plus. The former scheme provides an approximate FSC with a simple setting while the latter scheme provides complete coordination at the expense of a more complex setting. No need for voltage measurement makes its implementation practical in available distribution systems. The effective performance of the proposed FSC scheme is verified through extensive simulation studies in the ETAP environment. [ABSTRACT FROM AUTHOR]

This paper proposes three‐phase steady‐state models for distributed generators (DGs) with different negative control strategies. An augmented rectangular load flow method is used for three‐phase distribution systems. For two types of DGs with power electronic devices: photovoltaic power generators and doubly fed induction generators, the nodal voltage equations and output power equations are derived. Moreover, under unbalanced conditions, the strategies for mitigating the effects caused by negative sequence components are formulated as functions of the nodal voltage and the injected current. Furthermore, the models are incorporated into an augmented rectangular load flow method with nodal voltage and injected current as state variables in three‐phase systems. Case studies are conducted to verify the correctness and accuracy of the proposed models by comparing the results obtained from MATLAB and PSCAD. [ABSTRACT FROM AUTHOR]

As the penetration of renewable distributed generation (RDG) continues to grow, the stochastic and intermittent nature of its output imposes significant challenges on distribution networks (DNs), such as source-load mismatch and voltage fluctuations, which seriously affects the safety and reliability of the system. Thus, this paper presents a stochastic optimal allocation method for a battery energy storage system (BESS) in the DN, with the consideration of annual load growth, BESS degradation, and DN operation, aiming to minimize the overall cost of DNs and harvest more renewable energy. Based on the rainflow-counting concept, BESS degradation is efficiently modeled and linearized to improve solvability. Additionally, to address the uncertainties of RDG outputs and loads, a stochastic optimization (SO) method is adopted. Furthermore, considering that a large number of integer variables of the BESS allocation model may cause a heavy computational burden, a feasibility pump-based solution algorithm is introduced to accelerate the solving speed. Finally, the effectiveness of the proposed BESS allocation method and the solution algorithm is verified on a 33-bus DN system through comparative analyses, showing high efficiency and performance. [ABSTRACT FROM AUTHOR]

This article presents the methodology and results developed as part of the Integration of Energy Resources through Local Electricity Markets (IREMEL) project, whose aim is to assess the capability of flexibility markets to manage eventual distribution network (DN) congestion produced by a high penetration of distributed energy resources (DERs), including photovoltaic (PV) panels, battery energy storage systems (BESSs), and electric vehicles (EVs). The distribution system simulator OpenDSS has been used to simulate three Spanish DNs under multiple DER penetration scenarios considering an urban and rural low-voltage network and an industrial medium-voltage DN. Likewise, the congestion events detected in the annual simulations have been used to measure the potential of flexibility markets under different DER penetrations and energy pricing. The results suggest that oversized distribution networks could prevent a profitable flexibility market implementation since the simulations developed in this article shows that networks with high congestion levels are prime candidates to solve this issue through a market mechanism. Likewise, the results suggest that a proper price for the energy managed through a local flexibility market (LFM) could have a bigger effect on market viability than DER penetration. [ABSTRACT FROM AUTHOR]

In recent times, a significant amount of power loss and system instability due to high voltage deviation experienced by modern power systems, in addition to the pressing issues challenging the power industry such as pollution—especially the emission of greenhouse gases—and aging infrastructures, have posed a serious threat to system operations. Distributed generation has been identified as one main solution capable of reducing pollution when solar and wind power are used and, hence, rejuvenating dilapidated infrastructures and redeeming climatic changes. This paper presents a novel two-stage approach for the identification of suitable locations for DG placement and the sizing of DG for loss reduction and voltage stability enhancement. The first stage explored the use of a network structure to develop a coupling factor (CF) approach that was non-iterative in nature to determine suitable DG locations. In the second stage, the size of the DG was determined using the particle swarm optimization (PSO) algorithm. The main objective was to obtain an optimal voltage profile of the system under consideration while lowering the power loss in the system and ensuring network stability amidst DG incorporation. The model design, optimization and simulation were carried out using the MATLAB 2016a environment and the IEEE 33-bus test system, in which DG was integrated. The influence of increasing the level of DG placement in the system was then investigated. The forward/backward sweep method was applied to monitor the optimization process. The voltage profiles for both the base case when no DG was integrated and the case of incremental DG integration were considered. The results obtained for both single and multiple DG integration are compared with those obtained using the existing methods. The results show the efficiency and applicability of the new non-iterative scheme in the quick identification of DG locations for voltage profile enhancement and network real power loss reduction in radial distribution networks. [ABSTRACT FROM AUTHOR]

The steady-state and dynamic phases of operation can both be improved, the transmission corridors have been controlled using FACTS and flexible AC systems. To provide variable AC power flow regulation in the distribution corridors of an MV distribution system, Using the FACTS idea, this project suggests extending power distribution networks. The management of renewable energy sources (RERs) and batteries as a type of energy storage will be the main focus. The distribution network operator (DNO) can use the suggested FACPFCS outputs to help with peak shaving, supporting voltage and amperage, reducing long line sag, grid mitigation of reverse power flow, relieving congestion, delaying distribution upgrade, and a storage bus with regulation that can act as a load or a generator. [ABSTRACT FROM AUTHOR]

A power line carrier (PLC) communication characteristics‐based method is proposed for single‐phase‐to‐ground fault location in neutral isolated medium voltage meshed distribution networks in this paper. The carrier signals with a time‐varying frequency and constant amplitude are processed by a set of PLC transmitters and receivers, whose placement is optimized by regarding the power network as an undirected graph. Two signal encoding and decoding algorithms for the PLC terminals are proposed to avoid using expensive timing systems between the terminals. The fault location technique is implemented by comparing the cosine similarity of amplitude attenuation and phase offset between the fault and a feature library. The node corresponding to the maximum cosine similarity of the characteristics between the present fault and the library is selected as the location of the current fault. Only one set of low‐cost PLC communication terminals and the widely available power lines are needed in the fault location system, making this approach highly practical. Numerical simulations using MATLAB/Simulink have been performed to verify the technique's feasibility. The results show that the method can accurately locate faults in neutral isolated medium voltage meshed distribution networks. Besides, the presented approach achieves a high level of accuracy in estimating transition resistance values. [ABSTRACT FROM AUTHOR]

The efficient, reliable, and resilient supply of electricity has become essential for social and economic well‐being of the modern society. However, more frequent occurrence of extreme weather events has exposed inequity in the planning and operation practices of power distribution systems, evidenced in higher vulnerability and longer power interruptions for some parts of the grid as compared to others. This paper proposes an equity‐aware power distribution system restoration model in an effort to ensure a more equitable yet resilient power distribution operation after outages. To this end, the proposed equity‐aware distribution system restoration model balances the efficiency of the restoration operation and the equitable allocation of distributed energy resources among affected customers after an outage, while prioritizing the critical infrastructure (e.g. hospitals). The results demonstrate the effectiveness of the proposed framework to ensure a more equitable restoration process as measured by the proposed fairness and restoration performance indices. [ABSTRACT FROM AUTHOR]

Accurately forecasting regional distributed photovoltaic (DPV) power is crucial in mitigating the negative impact of high DPV penetration on the reliability and resilience of the distribution network. However, most of the current photovoltaic power forecasting methods suffer from two key problems: (1) ignoring the asymmetric influence relationship among DPV sites; (2) lack of consideration of dynamic spatiotemporal correlation among DPV sites. As a result, these methods are unable to fully adapt to the characteristics of DPV, making it challenging to directly apply the existing forecasting methods to improve the accuracy of DPV power forecasting. To conquer this limitation, a dynamic directed Graph Convolution Neural Network (DDGCN) is applied to regional DPV ultra‐short‐term power forecasting. Unlike the conventional Graph Convolution Neural Network (GCN) based forecasting methods, the proposed method improves GCN to process the directed graph. On this basis, to capture the dynamic and directed adjacency relationship among graph nodes, a temporal attention mechanism is proposed and combined with the directed GCN model. In this way, the dynamic and asymmetric/directed relationships among DPV sites can be taken into account. It is worth noting that the DPVs' adjacency relationship can be constructed without any prior knowledge by end‐to‐end model training. The simulation experiment proves that the prediction accuracy can be further improved by taking into account the dynamic directed relationship among the sites via a real DPV power dataset. [ABSTRACT FROM AUTHOR]

Distributed energy resources (DERs) provide flexible load restoration strategies, which can effectively enhance the resilience of active distribution systems (ADSs). Whereas, the widespread DERs in ADSs complicates the supply‐demand relationship and make the system resilience difficult to access. Therefore, this paper proposes a simulation‐based resilience assessment algorithm of ADSs considering the microgrid formation based on grid‐edge DERs. Microgrid formation is used to depict the resilience gain of grid‐edge DERs on ADSs. Specifically, a resilience assessment framework for ADSs is firstly proposed, where the uncertainty of component state and supply‐demand is modelled based on probability statistics. Then the mixed integer linear programming is used to search for optimal load restoration strategies with microgrid formation. On this basis, a set of resilience indices are defined to quantitatively analyse the resilience of ADSs, and a resilience assessment algorithm with uncertainty scenario generation is proposed to obtain these indices. Furthermore, extensive numerical results based on a modified IEEE 123‐bus feeder validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

With the increasing integration of distributed energy resources (DERs) into distribution systems, the optimization of system operation has become complex, facing challenges such as inadequate consideration of market participants' benefits, poor computational efficiency, and data privacy concerns. This paper introduces the concept of a virtual power plant (VPP) as a solution for energy integration and management. To strike a balance between operational safety and the interests of market participants, a dual‐layer model is proposed. This model considers the benefits of both Distribution System Operators (DSO) and VPP, while also enhancing the consideration of distribution network constraints. The DSO considers AC optimal power flow and utilizes penalty functions to ensure network security in case of violations. To enhance computational efficiency and privacy, the paper presents the parameter‐sharing twin delayed deep deterministic policy gradient approach. This approach allows multiple intelligent agents to share a neural network model, effectively reducing the computational load. During the training process, only essential data is exchanged among the agents, ensuring the privacy of sensitive information. The effectiveness of the proposed model and the algorithm is validated through a case study on an IEEE 33‐node system. [ABSTRACT FROM AUTHOR]

This paper introduces a comprehensive performance evaluation algorithm explicitly designed for secondary equipment in substations, specifically targeting the relay protection system. In contrast to the current evaluation systems, this novel method navigates the complex internal interconnections and mechanisms inherent within secondary system equipment. Such complications have previously impeded the accuracy and breadth of evaluations, thereby limiting the degree of precision and innovation attainable within substations. The proposed approach effectively integrates the improved Analytic Hierarchy Process entropy weight (IAHP‐EW) method with the Learning Vector Quantization (LVQ) neural network. Initially, the IAHP‐EW method identified the comprehensive evaluation indicators and their corresponding weights for relay protection devices. Following weight allocation, these evaluation indicators are scrutinized and computed utilizing the multivariate regression analysis algorithm, resulting in performance evaluation outcomes for the relay protection system. These outcomes are subsequently classified and utilized in training the LVQ neural network, promoting the network's capacity to autonomously evaluate the performance status of the relay protection system. To corroborate the viability and effectiveness of this proposed performance evaluation and prediction algorithm, empirical operating data from a local substation is used. The results suggest a significant improvement in the evaluation accuracy of secondary equipment performance, indicating potential for practical application and a valuable contribution to the field through the introduction of a novel approach to performance assessment of substation relay protection systems. [ABSTRACT FROM AUTHOR]

The dynamic nature of distribution networks raises fresh issues with how such electrical systems function. These networks have some characteristics that indicate the need for better monitoring and control capabilities, including dispersed generation employing renewable resources, changing load profiles, and rising reliability requirements. Phasor measurement units (PMUs) offer simultaneous voltage and current phasor measurements at various places and offer a variety of options for gauging the condition and health of the power distribution network. In this regard, a cost‐optimized PMU with some unique features for distribution systems is presented in this work. These features include a fuzzy inference system to determine the root cause of potential electrical disturbances and methods to estimate electrical parameters through measured field data, which are necessities. This study takes into account the modelling of PMUs, utilizing a process for fault detection and classification with a fuzzy inference network. The 9‐bus distribution network's dependability model is built once the components and their functions are first outlined. The proposed model is then used to calculate the availability of the presented model, which has been examined to provide an analogous reliability model for PMUs. Depending on the specific manufacturer, the PMU's design and specs will change. To extract phase and size measurement features for the proposed model adaptive neural‐fuzzy inference system network's training, two PMU structures and associated reliability models are described here. When merging input data for fuzzy neural network prediction using MATLAB software, fuzzy sets are taken into account for error classification analysis. [ABSTRACT FROM AUTHOR]

In the context of rare but high‐impact extreme scenarios, such as natural disasters, it is crucial to utilize all available resources, including microgrids and distributed power sources, to restore critical loads in the distribution network as much as possible. This paper proposes a two‐stage resilience enhancement strategy for distribution networks considering post‐disaster topology reconstruction and demand response, aiming to facilitate the recovery of critical loads after disasters. In the first stage, a heuristic algorithm is introduced to determine the post‐disaster topology of the distribution network. In the second stage, by employing a step‐wise elastic load curve, user demand response is incorporated to maximize the socio‐economic value of the restoration, and resilience metrics are computed. Finally, the effectiveness of the proposed resilience restoration strategy is validated through simulations on Case33 and Case69 systems. [ABSTRACT FROM AUTHOR]

Integrating the self‐healing capability realizes the automated protection of smart distribution systems. This article presents a fault location scheme for low voltage (LV) distribution systems based on the information collected from the smart meters. First, a fault condition is detected and classified by processing the current signal at the secondary side of the distribution transformer by an intelligent electronic device. Then, the faulty feeder and section are determined by calculating the fault‐imposed component of nodal voltages. The reliable performance of the proposed scheme is verified through several case studies using a real semi‐rural distribution system. [ABSTRACT FROM AUTHOR]

Earth faults is a challenging fault type to locate in resonant grounded networks due to their naturally low fault current, and the problem increases with an increased fault impedance. This paper describes the detailed implementation and laboratory testing of a method for detection, location and clearing of earth faults with very small fault currents. The method consists of two indicators used in the fault detection stage, where their simultaneous operation ensures selective fault detection and faulty feeder selection. One of these indicators also enables continuous fault indication throughout a sectionalizing process. The laboratory tests demonstrate that both indicators function as intended, and it is the current sensors which ultimately limit the attainable sensitivity. Faults up to 15 kΩ were detected successfully in the laboratory network based on phase current measurements, while the sectionalizing indicator showed much higher sensitivity and functioned as intended in a 50 kΩ fault. Measurements from one field test in a 22 kV network corroborate the laboratory results and demonstrate the expected earth fault indicator response. [ABSTRACT FROM AUTHOR]

In the case of a single‐phase grounding fault in the distribution network, the transient zero‐sequence current (TZSC) tends to be non‐linear and non‐stationary. The faulty line selection is relatively difficult. The distributed power access further brings many difficulties to faulty line selection. This work proposes a novel method of faulty line selection using spatial image generation and deep learning. At first, the optimal smooth denoising model can be used to smooth the zero‐sequence current for each line, reducing the external environment electromagnetic interference. Then, the treated zero‐sequence current is mapped into the colorful floral hoop image by using symmetrized Hilbert transform pattern (SHTP). The SHTP transforms the one‐dimensional time domain signal into the two‐dimensional space domain image, enhancing invisible information and obtaining more abundant feature information. Finally, the deep features of the SHTP floral hoop image are extracted by means of deep learning method. In order to improve the faulty line selection universality, a mixed sample library containing three different topologies is established, including the 10 kV radial distribution network, IEEE‐13 node model, IEEE‐34 node model and StarSim platform. The comparisons show that the proposed method has a more noticeable visualization effect on fault features, higher classification precision rate, and better anti‐noise performance. [ABSTRACT FROM AUTHOR]

Cascaded H‐bridge power electronic transformers (CHB‐PET) can efficiently connect AC/DC distribution networks, realizing power transmission and electric energy conversion. Once a pole‐to‐pole DC fault occurs on the low‐voltage direct current (LVDC) side of CHB‐PET, the fault current has a fast rise and high amplitude, which easily leads to the rapid blocking of power electronic devices and makes it difficult for DC line protection to obtain fault information. A stepwise blocking scheme of PET is proposed for LVDC network protection. When the fault on the LVDC side of PET is detected, cascaded H‐bridge and low‐voltage H‐bridge of dual‐active‐bridge are blocked first, and then high‐voltage H‐bridge of dual‐active‐bridge is blocked after a 5 ms delay. The stepwise blocking can provide fault information for DC line protection. On this basis, wavelet packet transform is employed to extract different frequency components of fault current. The energy of the specific frequency band corresponding to the switching frequency of DAB and the total energy of all frequency bands contained in the fault current is calculated; then their ratio can be utilized to construct the protection scheme. Finally, the PSCAD/EMTDC simulation results verify the feasibility and effectiveness of proposed protection scheme based on stepwise blocking of CHB‐PET. [ABSTRACT FROM AUTHOR]

This paper presents a multi‐objective restoration scheme for improving the resilience of integrated electricity and natural gas distribution systems against extreme weather events. The coupling constraints of electricity and gas networks are tackled properly using a linearized optimal power flow (OPF). Distributed generators, power‐to‐gas facility, rescheduling of generation/storage units, and microgrid formation are employed as operational resources/measures for restoring the integrated energy system after the event landfall. An adaptable directed multi‐commodity flow‐based microgrid formation is utilized, that is, the network configuration is dynamically changed in accordance with time‐variant load priority weights. The proposed method was successfully examined on an integrated electricity and natural gas distribution system comprised of the modified IEEE 33‐bus distribution network and a 14‐node natural gas distribution network. Numerical results showed that using microgrid formation increased the supplied critical load of integrated electricity and natural gas distribution system by about 16%. Moreover, due to making benefit of the power‐to‐gas unit, the supplied critical load increased by about 12.3%. respectively. While utilizing energy storage systems along with the power‐to‐gas unit facilitated the exchange of energy between the power distribution network and natural gas distribution network regarding time‐variant load priority weights, the supplied critical load increased by about 13%. [ABSTRACT FROM AUTHOR]

The N-1 criterion is a critical factor for ensuring the reliable and resilient operation of electric power distribution networks. However, the increasing complexity of distribution networks and the associated growth in data size have created a significant challenge for distribution network planners. To address this issue, we propose a fast N-1 verification procedure for urban distribution networks that combines CIM file data analysis with MILP-based mathematical modeling. Our proposed method leverages the principles of CIM file analysis for distribution network N-1 analysis. We develop a mathematical model of distribution networks based on CIM data and transfer it into MILP. We also take into account the characteristics of medium voltage distribution networks after a line failure and select the feeder section at the exit of each substation with a high load rate to improve the efficiency of N-1 analysis. We validate our approach through a series of case studies and demonstrate its scalability and superiority over traditional N-1 analysis and heuristic optimization algorithms. By enabling online N-1 analysis, our approach significantly improves the work efficiency of distribution network planners. In summary, our proposed method provides a valuable tool for distribution network planners to enhance the accuracy and efficiency of their N-1 analyses. By leveraging the advantages of CIM file data analysis and MILP-based mathematical modeling, our approach contributes to the development of more resilient and reliable electric power distribution networks. [ABSTRACT FROM AUTHOR]

The distribution system is easily affected by extreme weather, leading to an increase in the probability of critical equipment failures and economic losses. Actively scheduling various resources to provide emergency power support can effectively reduce power outage losses caused by extreme weather. This paper proposes a mobile energy storage system (MESS) scheduling strategy for improving the resilience of distribution networks under ice disasters. First, the influence of wind and ice loads on power transmission lines is analyzed, and a detailed fault statistical model of transmission lines under an ice disaster is established. Then, the MESS scheduling problem considering the coupling of transportation-distribution networks is transformed into a two-stage optimization problem. The first stage determines the optimal configuration scheme for MESS, and in the second stage, the optimal path selection for MESS can be obtained. Finally, the effectiveness and feasibility of the algorithm proposed in this paper are verified through an improved IEEE-33 node testing system. [ABSTRACT FROM AUTHOR]

The precise fault localization holds significant importance in reducing power outage duration and frequency in power systems. The widespread application of synchrophasor measurement technology (PMU) has laid the foundation for achieving accurate fault localization in distribution networks. However, fault localization methods based on PMU often suffer from a significant decrease in accuracy due to topological reconstruction and inaccurate parameters. To address these challenges, this paper proposes a fault location method for distribution networks based on Multi-head Graph Attention Networks (GATs). The proposed method begins by modeling the distribution network as a graph, where nodes represent network components and edges represent the connections between these components. GATs have been employed to learn the underlying relationships between topological structure and electrical characteristics of the distribution network. The results demonstrate that our approach outperforms traditional fault location methods in terms of accuracy and speed. The proposed method achieves high precision which reducing the time required for fault location and enabling faster response times for network maintenance personnel.

Abstract With the increase in population and the growth of technology, the load demand has increased and major changes in spinning reserve are unavoidable. Short‐term forecasting to hourly predict the required load and spinning reserve is of great importance. All of the power system studies in planning and operation fields are depend on short‐term hourly load forecasting. In this work, the problem of load forecasting and spinning reserve based on deep learning (DL) algorithms and traditional methods is investigated with the help of the proposed information combination system. The proposed method tries to reduce the weaknesses of the stated methods and increase the accuracy of the predicted signal. First, short‐term predicting of load and spinning reserve is performed using a combination of adaptive network‐based fuzzy inference system (ANFIS) and meta‐heuristic algorithms including differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO). The ANFIS‐PSO is selected as the best ANFIS combination in load and spinning reserve prediction with a lower error criterion than other methods. Also, the long short‐term memory (LSTM) network can provide good accuracy for load and spinning reserve forecasting. Therefore, the combination of ANFIS‐PSO and LSTM is used to reduce the average error and error variance.

Abstract Precise energy management in distribution power system requires high‐precision time synchronization among large‐scale deployed devices. Multiple clock sources‐based time synchronization possesses advantages of reliability, high precision, and robustness, but still faces several challenges such as coupling between time synchronization error and delay, as well as different timescales between clock source and clock weight optimization. In this paper, a multi‐clock source time synchronization model is constructed and a problem is formulated to minimize the synchronization error and delay through jointly optimizing large‐timescale clock source selection and small‐timescale weight selection. A reinforcement learning‐based multi‐timescale multi‐clock source time synchronization algorithm named RL‐M2 is proposed to solve the formulated problem from a learning perspective. Besides, a lossless switching method is proposed to address the switching problem for multiple clock sources. Simulation results demonstrate the superior performance of RL‐M2 and the lossless switching method in time synchronization delay and error.

Abstract Microgrids are a new concept resulting from decentralized power sources, which have changed energy utility grid topography. As this promising field is penetrated, control must be streamlined immediately. This paper analyses key control mechanisms in energy utility networks, including centralized, decentralized, and distributed setups. The focus is on the auxiliary category as the various benefits and drawbacks of each approach is investigated. Information interchange architectures that enable multiple control techniques in the auxiliary control domain are examined using distributed energy networks. The research emphasizes the necessity of auxiliary control in ensuring reliable and effective utility infrastructure operation but notes the lack of a comprehensive scholarly reference. Additionally, the study explores key concerns in utility grids' auxiliary control mechanisms, emphasizing information‐sharing system limits. The goal is to demonstrate the importance of auxiliary control and evaluate its challenges, such as latency, which require new solutions for a seamless transition to a sustainable energy future through rigorous academic assessment. At the auxiliary level, complex, decentralized regulatory mechanisms are recommended to handle transmission channel bandwidth and operational overhead in energy utility networks. These innovative measures have the potential to transform the fundamental structure of our energy landscape, heralding in unprecedented efficiency and sustainability.

In the context of vigorous development of new energy sources, optimizing the arrangement of phase measurement units (PMUs) is crucial to realize real-time data monitoring for distribution network (DN) condition assessment. Firstly, we propose a multi-objective model for optimal PMU placements. Secondly, we classified the buses based on the Jacobi matrix and the grid structure, and calculated the unobservable risk probability of the system by estimating the state of the PMUs installed on the buses, so as to obtain the observability of the system. Finally, two simulations of PMU placements in the DN implementing the IEEE39 bus are designed and the placement schemes are determined through solving the non-dominated Pareto solution sets by NSGA-II. The verifications show that the model reduces the placement number of PMUs, resulting in a cost reduction of 35.4% and an unobservable risk indicator δ reduction of 13.8%, and achieves an optimal placement of PMUs.

Abstract This paper presents a reliability evaluation method for distribution network with distributed generations considering feeder fault recovery and network reconfiguration, and mainly addresses issues: 1) insufficient consideration of the characteristics for the components and distributed generations (DGs) of distribution network such as time sequences, randomness and intermittency; 2) changes in the feeder area of the distribution network after the access of DGs; and 3) multi‐dimensional reliability index for distribution network with DGs has not been formed perfectly. Firstly, Markov reliability model for distribution network components/DGs is established in our study. Then, considering the fault recovery and network reconfiguration of the feeder area, island partitioning method of distribution network with DGs is also proposed. Moreover, a reliability evaluation model is presented for distribution network with DGs based on sequential Monte Carlo method. Case study is performed on the improved IEEE RBTS BUS6 F4 feeder system, and the research results indicate the validity and effectiveness of the proposed method. In addition, the system reliability indicators, such as system average interruption frequency index (SAIFI), system average interruption duration index (SAIDI), customer average interruption duration index (CAIDI), and average system availability index (ASAI), are studied and discussed in detail from three dimensions: the access of DGs, the types of DGs, and the capacity of DGs.

Abstract Nowadays, due to network expansions, system operators are dealing with natural events including system failures. Evaluating the system readiness to maintain the network interconnections during severe faults are known as an important challenge of today's distribution networks. To do this, this paper presents an online non‐model‐based scheme of evaluating the system resiliency and supplying critical loads within severe natural conditions. In this case, by developing system resiliency as multi‐metric decision making problem and proposing the Choquet integral and graph theoretic methodology, the system resiliency is evaluated. The proposed resiliency scheme is an online non‐model‐based approach can be used for all distribution system configurations with the potential of natural event failures. In this case, by proposing online resiliency criteria based on the system critical loads and branches and developing the proposed criteria on Choquet integral scheme in the form of optimization procedure, the system resiliency is evaluated. The validity of the proposed approach is carried out on two different systems including the IEEE 123‐bus test system and the Consortium for Electric Reliability Technology Solutions (CERTS) microgrid. Simulation results present effectiveness of the proposed scheme to provide proper protective switching strategy and design the system resiliency criteria for future distribution systems.

Abstract Controlling and operating modern distribution networks are challenging tasks. The implementation of monitoring systems and the development of tools are helping to address this challenge. State estimators are one of the most promising tools for this purpose. These are used to reconstruct the current state of a system based on measurement outputs, which can then be used to design controllers that can adjust the inputs to achieve the desired output. This article deals with the problem of estimating the state of a real‐world distribution network. A combination of conventional supervisory control and data acquisition is used to estimate the state, together with a synchrophasor monitoring system. The proposed method uses the Fortescue transformation to separate the three‐phase coupled equations into three independent modal measurement equations. Single‐phase estimators based on the extended Kalman filter are used to solve three sub‐problems associated with the sequence reference frame, which correctly capture the transient and steady‐state behaviour of the distribution network. The proposed estimation method is easy to implement and robust to measure model noise and uncertainties. Furthermore, an already‐established distribution network tests the estimation scheme under various conditions.

Abstract This paper proposes a mixed‐integer non‐linear programming (MINLP) model for the multi‐period active low voltage (LV) distribution network expansion planning problem, which can be solved by off‐the‐shelf optimization solvers. In addition, the wide‐spread penetration of solar resources, optimal loading of transformers, and modeling of the multiple solar generation patterns are considered in the model. The solar resources are of roof‐top household‐scale photovoltaic panel type, developed using a host‐owned business model and are able to exchange energy with the distribution company (DISCO). This model enables the DISCO, as the owner of the network, to minimize the investment and operation costs while being able to determine the optimal location and capacity of the distribution transformers, the service zone of distribution transformers, the optimal route of the distribution lines, and the total and annual planning costs. In order to check the efficiency of the proposed model, a 0.4 kV distribution test network is considered to plan the LV network of a residential district over a planning horizon of 5 years. Results show the effectiveness of the proposed model in dynamic expansion planning of the active LV distribution network. Also, results illustrates that the penetration of solar sources can be a great tool to improve the technical, economic, and environmental criteria for planning studies. Specifically while reducing the billing costs of customers, it favors the government to reduce the pollution costs, and it enables the DISCO to reduce the loss costs and maintain the voltage in the standard boundaries.

Abstract The structure complexity and the extended geographical area are among the factors that create challenges in accurate fault location and detection in distribution networks. The single‐phase to ground (SPG) faults are considered as most frequent reasons for distribution network interruption, which can threaten the network's reliability. Signal processing methods are usually used as common approaches to distinguish and locate faults in distribution networks. This paper proposes a novel scenario to select optimal wavelet packet transform (WPT) coefficients for SPG fault location and a method based on superimposed voltage energy to distinguish the faulty phase according to these coefficients. Furthermore, employing the energies of the superimposed voltages helps to minimize the effects of high resistance faults (HRFs) and load encroachment on the efficiency of the faulty phase detection part. Comparing the obtained results with other scenarios demonstrates the considerable efficiency of the proposed scenario. Finally, with the help of general regression neural networks (GRNN) as machine‐learning tools, a new algorithm is derived for detecting and locating the SPG fault and capacitor bank switching overvoltage (COV). Simulation results are implemented on the IEEE 34‐bus standard distribution network, demonstrating the efficiency and superiority of the suggested method.

Abstract A novel optimal allocation framework for photovoltaic generations in an integration system of buildings‐to‐distribution‐network using improved backtracking search optimization algorithm is proposed here. In the proposed framework, photovoltaic generations are optimally allocated to optimize the overall performance of a buildings‐to‐distribution‐network regarding the efficient active power usage of photovoltaic generations, the energy savings, and voltage profile improvement of distribution network. The effects of building active demand response on the photovoltaic generations' optimal allocation are considered in the proposed framework. An improved backtracking search algorithm using two new operators is developed to optimize the active power reduction factors and locations of photovoltaic generations. The test results of IEEE 33‐bus and 69‐bus systems demonstrate that the developed framework can take full advantage of photovoltaic generation power and the active demand response of buildings to coordinate the efficient active power usage of photovoltaic generations, the voltage profile improvement and energy savings of a buildings‐to‐distribution‐network. In addition, the improved backtracking search optimization algorithm converges faster than genetic algorithm, classical backtracking search optimization algorithm, and bird swarm algorithm.