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The damage of wind turbines suffered from lightning strikes has been a key issue for the safe and reliable operation of wind farms. Accurate determination of the annual lightning flash number to a wind turbine is essential for designing proper lightning protection measures. The interaction of downward and multiple upward leaders (MULs) is studied in this paper, which also considers the stochastic nature and branched behaviour of the lightning attachment phenomenon. Firstly, with an improved stochastic lightning model, the relationship among the striking distance, the height of wind turbines and the return stroke current is established. Moreover, a modified method for predicting the annual lightning flash number strikes to a wind turbine is proposed. The simulation results show that the striking distance and the collection area not only depend on the return stroke current, but also on the height and blade angle. Besides, the comparations between the calculation results and field statistics indicate that the conventional electric geometry model is not satisfied with the need of lightning protection for wind turbines. Note that there is only a difference of 4% between the modified method and the observation value. These quantitative researches provide the guidance of calculation results for the optimization of the blade lightning protection system (LPS) design. [ABSTRACT FROM AUTHOR]

Reducing carbon emissions is a crucial way to achieve the goal of green and sustainable development. To accomplish this goal, electric vehicles (EVs) are considered system‐schedulable energy storage devices, suppressing the negative impact of the randomness and fluctuation of renewable energy on the system's operation. In this paper, a coordination control strategy aimed at minimising the carbon emissions of a distribution network between EVs, energy storage devices, and static var compensators (SVCs) is proposed. A model‐free deep reinforcement learning (DRL)‐based approach is developed to learn the optimal control strategy with the constraint of avoiding system overload caused by random EV access. The twin‐delayed deep deterministic policy gradient (TD3) framework is applied to design the learning method. After the model learning is completed, the neural network can quickly generate a real‐time low‐carbon scheduling strategy according to the system operating situation. Finally, simulation on the IEEE 33‐bus system verifies the effectiveness and robustness of this method. On the premise of meeting the charging demand of electric vehicles, this method can optimise the system operation by controlling the charge‐discharge process of EVs, effectively absorbing the renewable energy in the system and reducing the carbon emissions of the system operation. [ABSTRACT FROM AUTHOR]

This paper presents an open‐access Matlab/Simulink‐based power system simulation toolbox (MatPSST) for research and education. In MatPSST, dynamic modeling is implemented by Simulink. Only the initialization process is coded in Matlab. This structure makes full use of Simulink, enabling users to fully use the rich solvers and built‐in toolboxes in Matlab/Simulink, such as small‐signal analysis, without additional programming. The interaction with real‐time platforms like RTLAB and dSPACE in MatPSST is convenient through the Simulink interface. In addition, MatPSST provides a uniform interface and flexible framework for user‐defined functions. Users do not need to consider the interaction between models and dynamic solvers when implementing state‐of‐the‐art user‐defined models. To avoid the heavy work (replicate units and change parameters) in modeling detailed wind farm or photovoltaic station with hundreds of renewable generation units, the idea of the vector‐variable‐based model is proposed with MatPSST. Case studies introduce some selected application examples of MatPSST in research and education and demonstrate its accuracy by the comparisons with Simscape and PSAT. The cases reveal the advantages of MatPSST in promoting power system research and education. [ABSTRACT FROM AUTHOR]

This paper presents a novel fully decentralized and intelligent energy management system (EMS) for a smart microgrid based on reinforcement learning (RL) strategy. The purpose of the proposed EMS is to maximize the benefit of all microgrid entities comprising customers and distributed energy resources (DERs). Due to unpredictable features of renewable energy sources and variability of consumers' demands, designing the microgrid EMS is a complicated task. To overcome this issue, the multi‐agent hour‐ahead energy management problem is modelled as a finite Markov decision process. The microgrid entities are considered as intelligent agents. The optimal policy of agents is obtained through a newly developed framework of the model‐free Q‐learning algorithm to maximize the benefit of all renewable and non‐renewable energy resources and battery energy storage system. The degradation model of the battery is considered to reduce the number of battery replacements. To ensure customers' comfort, customers' expenses are decreased without demand curtailment via introducing two types of load shifting techniques. The microgrid operation is analysed under four scenarios comprising no‐learning, generator‐learning, customer‐learning, and whole‐learning. the performance of the proposed algorithm is compared to the Monte Carlo method and simulation results on the real power‐grid dataset show the superiority of the algorithm. [ABSTRACT FROM AUTHOR]

Undervoltage load shedding (UVLS) is the last line of defense to ensure the safe and stable operation of the power system. The existing UVLS technique has difficulty adapting and generalizing to new topology variation scenarios of the power network, which greatly affects the reliability of the control strategy. This paper proposes a UVLS emergency control scheme based on a graph deep reinforcement learning method named GraphSAGE‐D3QN (graph sample and aggregate‐double dueling deep q network). During offline training, a GraphSAGE‐based feature extraction mechanism of the power grid with topology variation is designed that can better capture the changes in system state characteristics. A novel reinforcement learning framework based on D3QN is developed for UVLS modeling, which reduces overestimations of control action and leads to a better control effect. Then, online emergency decision‐making is achieved. The simulation results on the modified IEEE 39‐bus system and IEEE 300‐bus power system show that the proposed UVLS scheme can always provide more economical and reliable control strategies for power networks with topology variations and achieves better benefits in both adaptability and generalization performances for previously unseen topology variation scenarios. [ABSTRACT FROM AUTHOR]

Despite improving various technical indices such as operation and voltage profile, the integration of synchronous distributed generations (SDGs) may lead to the loss of protection coordination in the distribution network (DN). To tackle the problem, this paper presents a multi‐objective siting and sizing of SDGs on DN considering operation, economic and protection coordination indices simultaneously. The proposed scheme is structured in the framework of a four‐objective optimization problem, which aims to minimize the energy losses, the worst voltage security index (VSI), planning cost of SDGs and the protection index (PI) (the deviation of coordination time interval). Therefore, it is constrained to power flow equations, VSI, and protection coordination of overcurrent relays (OCRs). Then, the Pareto optimization based on the method of the weighted functions summation obtains an integrated single‐objective problem for the proposed scheme. Next, a hybrid evolutionary algorithm formed by merging particle swarm optimization (PSO) and crow search algorithm (CSA) is incorporated to achieve the optimal solution with unique response approximate conditions. Eventually, the suggested scheme is applied on distribution portion of IEEE 30‐bus ring and IEEE 69‐bus radial DN and numerical results confirm the efficient performance of SDG planning in terms of technical indicators and protective devices coordination. [ABSTRACT FROM AUTHOR]

Monitoring and alarm play an important role in preventing trip‐out caused by wildfires for high‐voltage transmission lines. This paper proposes a multi‐Doppler weather radar‐based method for monitoring and alerting of wildfires near transmission lines. Firstly, characteristic parameters are mathematically proposed to distinguish wildfire's radar echoes. Then, the four‐site neighbourhood algorithm and the multithreading method are used to extract all possible wildfire echo units detected by various Doppler weather radars, and a cumulative probability is calculated to identify the wildfire. Finally, a regional block search method is used to quickly locate wildfire‐affected transmission towers. The membership function of spread time in the fuzzy set is proposed to determine the alarm level, which takes into account the influences of environment, topography, and vegetation on wildfire spread rate. The application to a provincial power grid demonstrates that the proposed method has an accuracy of 82.4%, a missing alarm rate of 27.4%, and a delay of fewer than 15 min. In addition, the joint observation of wildfires by multi‐Doppler weather radars and satellites indicates a promising application prospect for transmission line wildfire fighting. [ABSTRACT FROM AUTHOR]

Accurately evaluating the response capability of electric vehicles (EVs) is very important for an EV aggregator (EVA) to formulate reasonable bidding strategies in ancillary service markets. Most of the methods proposed in the existing literature only evaluate EVs' temporal response capability while ignoring their spatial distribution. In addition, the evaluation results provided by the existing methods are typically deterministic, which fails to characterize the uncertainty of EV trip. The above two issues pose high risk of economic loss for the EVA. To this end, a probabilistic evaluation method of spatial‐temporal response capability for EVA is proposed in this paper. The gravity model is adopted to calculate a spatial transfer probability matrix describing EV owners' trip characteristics between different areas at each time in a region which is divided into several different areas according to the evaluation requirements. Then, the trip chains of EVs are modelled based on the spatial transfer probability matrix, and the states of charge (SOCs) of EVs are tracked in the process. The spatial‐temporal response capability of EVA is evaluated based on charging–discharging states and states of charge of EVs, and the probabilistic evaluation results of response capability are obtained by multiple Monte Carlo simulations. The effectiveness of the proposed method is verified on a real dataset from San Francisco, CA, USA. [ABSTRACT FROM AUTHOR]

With the high penetration of distributed generation and gradual increase in loop network structures, existing over‐current protection in distribution networks is difficult to apply. The use of current differential protection has become an ideal solution, and high performance and commercialization of 5G communication provides an effective way to solve the problem of differential protection channel transmission. Aiming at distribution network differential protection based on 5G, this paper adopted an adaptive frequency conversion transmission mechanism to significantly reduce the data flow of differential protection. In terms of protection strategy, the differential allowable signal and the differential protection startup logic are separated, and a novel action method and protection logic suitable for weak infeed distribution networks are proposed. The strategy significantly shortens the protection action time under wireless communication. Furthermore, a comprehensive synchronization scheme using zero‐crossing synchronization of forward sampling points and cycle numbers based on the second pulse signal was established. This scheme can effectively improve the reliability of data synchronization for differential protection. Finally, the method proposed in this study was verified using the dynamic model experiment of real‐time digital simulation (RTDS). [ABSTRACT FROM AUTHOR]

Presenting cloud energy storage system (CESS) in the landscape of storage devices exposes microgrids (MGs) to a substantial change. Employing a specific type of inverter namely synchronverter as the connecting interface for CESS gains high inertial response in MGs with high penetration level of inverter‐based distributed generations (IBDGs). However, high fault contribution of synchronverters can lead to challenging issues. Nevertheless, presence of fault resistance which results in malfunctioning of the recloser‐fuse pair intensifies the cruciality of the dilemma. In this paper, at the outset, the effects of contribution of synchronverter‐based CESS and also fault resistance on recloser‐fuse protection are explored and then, a new algorithm is proposed for tackling these bottlenecks. To overcome these hurdles, the conventional recloser‐fuse scheme is applied at first. Then, by using the obtained schemes for fast and slow operations of the recloser, a specific part of them is set in a manner that the fuse‐saving task is performed properly even when the synchronverter‐based CESS is deployed. On the other hand, the other part of the recloser scheme is altered in a way that the maloperation of the recloser‐fuse pair owing to the fault resistance is alleviated. Adequate numerical analyses are conducted to evaluate the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper proposes a novel energy and reserve scheduling model for power systems with high penetration of wind turbines (WTs). The objective of the proposed model is to minimize the total operation cost of the system while static and dynamic security is guaranteed by preserving the frequency nadir, RoCoF, and quasi‐steady‐state frequency in the predefined range. Likewise, a supervisory, control, and data acquisition (SCADA) system is developed which allows Vanadium Redox Flow Batteries (VRFBs) to continuously communicate and participate in the primary frequency response. To cope with the uncertainties, adaptive information gap decision theory is used that ensures a target operating cost for the risk‐averse operator of the power system. The proposed scheduling model is applied on a modified IEEE 39 bus test system to verify the impacts of the fast reserve provided by the VRFBs in the dynamic frequency security enhancement of the power system with high penetration of WTs. [ABSTRACT FROM AUTHOR]

This paper proposes an intelligent approach based on the empirical Fourier decomposition (EFD) to identify harmonic sources at the point of common coupling (PCC) when different inverter‐based distributed generations (DGs) like microturbine (MT), battery energy storage system (BESS), photovoltaic (PV), superconducting magnetic energy storage (SMES), wind turbine with a permanent magnet synchronous generator (PMSG), and doubly‐fed induction generator (DFIG) wind turbine are presented. In order to decrease memory storage and computational burden, strife feature selection is used. Applying just voltage signals consumes less processing time and decreases measurement devices. Moreover, the whale optimization algorithm (WOA) as the optimizer of the parameters of the support vector machine (SVM) classifier is used. Consequently, the results from the proposed method can be helpful for both engineers and researchers to plan and develop a better strategy to mitigate harmonic distortion. [ABSTRACT FROM AUTHOR]

This paper presents an optimal bidding strategy for a technical and commercial virtual power plant (VPP) in medium‐term time horizon. A VPP includes various distributed energy resources (DERs) that can participate in the Pool and Futures markets. Although medium/long‐term scheduling provides the opportunity to participate in the futures market, it also raises the possibility of unit failure. In this regard, the impact of distributed generation (DG) units' failure, as an important challenge in VPP, is incorporated in the proposed model. The model is formulated as a risk‐constrained two‐stage stochastic problem. The VPP signs futures market contracts in the first stage, and in the second stage, it participates in the day‐ahead (DA) market and manages its DERs. Long short‐term memory neural network and scenario generation and reduction methods are used to capture the uncertainty parameters of electrical load, DA market prices, wind speed, and solar radiation in the proposed model. The performance of proposed model is investigated in different cases. The obtained results show that the VPP can compensate the losses caused by the DG units' failure through taking advantage of the arbitrage opportunity. [ABSTRACT FROM AUTHOR]

For the ultra high‐voltage dc (UHVDC) transmission with hierarchical connection mode (HCM) at the inverter side, local commutation failure (LCF) at one layer after the ac fault may cause concurrent commutation failure (CCF) at the non‐fault layer. The inter‐layer couplings at the ac and dc sides add the difficulty to suppress the CCF. The fault instant may decide the dc current variation during the commutation, which affects the suppression effect. This paper shows the mechanism of the CCF including the inter‐layer ac and dc coupling, and proposes a control method for the inverter at the fault and non‐fault layers. First, an analytical expression among the dc current, ac voltage, and extinction angle of both layers is newly derived to find the dominant factors to the CCF. Second, to improve the calculation accuracy, a three‐point sampling function using Newton interpolation is newly proposed to predict the dc current. Finally, a coordinated control strategy based on the constant extinction area, the overlap‐arc area, and the dominant factor of the CCF is proposed to adjust the firing angle and suppress the CCF. The simulation results using the PSCAD/EMTDC software are given to verify the control effect of the proposed method against the CCF. [ABSTRACT FROM AUTHOR]

In order to ensure the reliable interruption of high voltage direct current circuit breaker (HVDC CB), the breaking test of HVDC CB plays an important role in its performance verification. In this paper, the interruption process of the HVDC CB is analyzed, and the equivalent requirements for the breaking test of HVDC CB are summarized. In the breaking test of high voltage and high current, the transient interruption voltage (TIV) duration is short, which leads to insufficient voltage stress. A new synthetic test circuit for testing the interruption performance of HVDC CBs is proposed, which can solve the problem of insufficient TIV duration caused by insufficient test capacity. Subsequently, the working principle of the test circuit is introduced in detail and the design method of the circuit parameters is given. The short‐circuit current breaking test simulation of HVDC CB is carried out. The simulation results fit most well with the results of actual test cases, which verifies the universality of this test method. Finally, a test platform is built to verify the principle of the test circuit. TIV rose to 2.5 kV compared to the predetermined system voltage of 1.67 kV during the test, which means that the key stress provided by the test circuit can meet the expected requirements. [ABSTRACT FROM AUTHOR]

This paper determines the optimal capacity of solar photovoltaic (PV) and battery energy storage (BES) for a grid‐connected house based on an energy‐sharing mechanism. The grid‐connected house, also mentioned as house 1 where it is relevant, shares electricity with house 2 under a mutually agreed fixed energy price. The objective is to minimize the cost of electricity (COE) for house 1 while decreasing the electricity cost of house 2. Practical factors such as real data for solar insolation, electricity consumption, grid constraint, ambient temperature, electricity rate, and battery degradation are considered based on actual data. The developed methodology is examined by taking the actual load data of two houses in South Australia. Different scenarios of contract years between the houses are investigated to make it more practical in real life. Sensitivity analyses are conducted for the sharing of energy between the houses and by changing parameters like export power limitation, load of houses, and costs of PV and BES. Likewise, operational analysis is done for two days of summer and winter. It is found that when energy sharing is applied, the optimal design of the PV‐BES system will achieve lower COE for both houses. [ABSTRACT FROM AUTHOR]

Cloud energy storage is considered a promising application in future power systems. It focuses on optimally leveraging the capacity of centralized large‐scale energy storage compared with the requirements of small‐scale localized users. In this paper, to satisfy the small‐ and medium‐scale timely energy storage requirement from localized users, the concept of the cloud‐based location sharing energy storage is proposed. The modular mobile energy storage system is flexibly configured and deployed at different sites to fulfil the long‐term seasonally dynamic transformer capacity increment and short‐term daily energy arbitrage based on economic values. To optimize the overall incomes of the energy storage investment, a two‐step user potential identification algorithm is proposed to discover the most valuable users at different time scales from the regional power usage profile. Then, the number/capacity optimal planning algorithm is proposed to optimally share the mobile energy storage system among users seasonally and total profits are further increased through daily energy arbitrage at less valuable seasons. Finally, the structure and dispatching strategy of the cloud‐based location sharing energy storage management system is illustrated. Case study results prove that the proposed identification algorithm can excavate the most valuable users at different time scales and the optimal planning and operation strategy is able to guarantee the overall income benefits. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive control framework for the flexible and effective management and control of clustered DC nano‐grids (NGs) in an islanded DC microgrid system. It is assumed that each NG contains a photovoltaic (PV) system, a battery energy storage system (BESS), local loads, and a gateway (GW) module. Each NG has a hierarchical control system consisting of a decision‐making module and low‐level controllers. The decision‐making module ensures various desirable features including plug‐and‐play operation of NGs, maximum utilization of PV power generations, and avoiding state of charge (SoC) violation of batteries. Moreover, an adaptive model predictive control (AMPC) strategy is proposed to regulate the voltage of the NG local DC buses in the presence of non‐linear loads. This approach improves the performance of the NG voltage control system and reduces the current ripples of BESSs, thereby enhancing the lifetime of the batteries. In addition, a smart switching consensus‐based control strategy is designed that provides flexible power sharing among the NGs to balance the SoC of BESSs in which the BESSs altogether imitate the behaviour of a single cloud energy storage system (ESS). Finally, the performance of the proposed control system is verified by simulating the DC microgrid in MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

Cloud energy storage system (CESS) can effectively improve the utilization rate of the energy storage system (ESS) and reduce the cost. However, there is a lack of a model designed for large‐scale renewable energy power plants (REPPs). Due to the volatility and intermittency of renewable energy power generation, as well as the demand of following scheduling plan and market arbitrage, it is also necessary to configure ESS for REPPs. However, if the REPP builds ESS by itself, the investment is relatively high. Therefore, the application of CESS on the renewable energy generation side can reduce the investment cost and increase the revenue by utilizing the difference between actual output and demand. Considering the uncertainties of renewable energy, this paper proposes a robust optimal configuration model of CESS based on the cooperative game. Firstly, the CESS model on the generation side is developed to describe the formation mechanism of ESS supply and demand. Then, the proposed model aims at maximizing the revenue of REPPs. The participants of the coalition are each REPP. By taking the renewable power uncertainty into consideration, the novel nested column‐and‐constraint generation (nested C&CG) method is utilized to solve the proposed model based on the min–max–min form. Furthermore, the Shapley‐value method is used to distribute the benefits to each member of the grand coalition. Finally, case studies verify the rationality and validity of the proposed model. [ABSTRACT FROM AUTHOR]

This paper explores residential microgrids (MG) which not only deploy smart algorithms and energy management strategies for appliances scheduling but also make benefit of insulated wall structures and modern windows ending to smart buildings with sustainable architecture. This amendment is shown to have a remarkable effect on avoiding thermal energy losses and hence lowering energy consumption of heat, ventilation, and air conditioning system (HVAC) in smart and sustainable homes. Moreover, frequency selective surfaces applied in modern windows are shown to greatly improve in‐service telecommunication signal transfer ratio, assuring a reliable communication for inhabitants. The proposed model adopts the most recent concept of cloud energy storage system (CESS) unit to provide a public access to charge/discharge capacity for smart home owners. Accordingly, a simple but applicable capacity sharing strategy of CESS is developed for the energy exchanges of smart homes in the MG. By establishing such an access to CESS, the proposed model allocates optimal shares of charge/discharge capacities for home owners, minimizes the daily operation cost of each home and grants an optimal operation of household appliances. The proposed model is formulated as a mixed integer linear programming (MILP) problem and is assessed in several operating strategies. [ABSTRACT FROM AUTHOR]

The main aim of this paper is to improve dynamic performance of a multi‐terminal DC (MTDC) grid in a wind‐integrated AC power system through DC voltage control. The proposed approach is based on design of robust H∞ controller and loop shaping along with mutual quality decomposition method (MQDM). For extracting transfer function of the plant, model of Master‐Slave control system is converted to standard feedback control system. Then, based on the obtained difference between singular‐value curves of the main open‐loop system and the proposed method, the weighting functions of robust H∞ controller are selected. In the next step, by using the MQDM technique, the transfer function ofthe system in state‐space form is evaluated to extract gain of the robust controller. For practicality of the conducted approach, singular‐value order reduction Hankel algorithm along with frequency response analysis have been used for designing the gain of the robust controller. The developed method has been implemented on the MTDC converter in the wind farm side to control the voltage of DC grid. The simulations are accomplished in MATLAB, where by considering different uncertainties in the DC grid, wind unit, and the AC grid, robustness of the proposed approach has been clearly demonstrated. [ABSTRACT FROM AUTHOR]

Disturbance‐resisting ability (DRA) of the restored network needs to be considered when making a restoration scheme of power systems, to ensure system security and reduce secondary blackout risk. This paper proposes an optimisation method of whole‐process restoration decision‐making with DRA of the restored network in consideration. Firstly, a load rate balance (LRB) index of the restored network is defined to represent the DRA index during restoration. Then, an optimisation model of whole‐process restoration decision‐making is established, considering the LRB index of the restored system. To solve the model by a linear programming method, the non‐linear terms in the model are transformed into linear forms. Finally, a time‐step decoupling method to reduce solving difficultness of the established model with the LRB index is derived, with purpose of accelerating the solving process. The numerical results of the IEEE 30‐bus system and a practical power system verify effectiveness of the proposed method to obtain a restoration scheme to balance the system restoration benefit and security. [ABSTRACT FROM AUTHOR]

The transmission line is a fundamental asset in the power grid. The sag condition of the transmission line between two support towers requires accurate real‐time monitoring in order to avoid any health and safety hazards or power failure. In this paper, state‐of‐the‐art methods on transmission line sag monitoring are thoroughly reviewed and compared. Both the direct methods that use the direct video or image of the transmission line and the indirect methods that use the relationships between sag and line parameters are investigated. Sag prediction methods and relevant industry standards are also examined. Based on these investigation and examination, future research challenges are outlined and useful recommendations on the choices of sag monitoring methods in different applications are made. [ABSTRACT FROM AUTHOR]

The Volt/Var optimization (VVO) problem is used for scheduling the voltage regulation and reactive power compensation equipment in distribution networks to minimize power loss and voltage violation. In order to solve the VVO problem for a forthcoming time horizon, it is necessary to predict some parameters such as load demand and renewable energy production. The prediction of these parameters is always accompanied by uncertainty that robust optimization can be used to solve this concern. This paper presents a scenario‐based robust Volt/Var optimization (RVO) method that significantly reduces the number of scenarios required for the worst‐case approach. Solving the VVO problem with the worst‐case scenarios reduces the computational burden and maintains the voltage security of the distribution network against the severe events. The proposed RVO is formulated based on a mixed‐integer second‐order cone programming (MISOCP) model in which Volt/Var control (VVC) equipment is scheduled over a two‐stage strategy. The proposed method is validated using modified 33‐bus and 69‐bus IEEE test systems. The results demonstrate that the proposed RVO method maintains the network's voltage profile within the acceptable range against uncertainties. [ABSTRACT FROM AUTHOR]

Energy storage devices become an indispensable part of modern power systems with high renewable energy penetration level. To reduce the operating costs, it is a promising way to allow the sharing and leasing of energy storage devices. In this paper, a bi‐lever optimized dispatch scheme is proposed to improve the usage efficiency of cloud energy storage in multi microgrids (MMG) system. Minimizing the operating costs of shared cloud energy storage is the main task of the upper lever while maximizing the profits of MMG is the goal of the lower lever. Moreover, the transaction cost and benefit between the two levers play an important role in system level optimization. This leads to a hybrid optimization problem with both discrete decision variables and continuous decision variables. To solve the problem, a relaxation‐based bi‐lever reformulation and decomposition algorithm is developed. The effectiveness of the proposed bi‐lever dispatch optimization model is verified by carrying out numerical experiments in three scenarios. It is shown that the proposed cloud energy storage service can effectively reduce the operating cost of MMG. [ABSTRACT FROM AUTHOR]

The fault of the tie line between the photovoltaic (PV) station and the grid is a serious fault for the PV station. It will cause the PV station to operate into an unintentional island. The uncontrolled Island voltage and frequency will inevitably lead to the disconnection of the inverter in the station. This situation will bring great losses to PV operators and impact to the power grid. In order to deal with the tie line fault, this paper analyzes the operation characteristics of PV stations in case of tie line fault firstly. Then a tie line fault ride‐through method based on cooperative strategy of small capacity energy storage (ES), relay protection and PV inverters is proposed. The islanding switching control strategies of PV and ES are designed respectively. The cooperative strategy of protection, PV controller and ES controller is formulated as well. The real‐time digital simulator (RTDS) closed‐loop test platform including line protection device, ES controller and PV controller is built to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Converter interfaced generation (CIG) may switch into different control modes under different fault scenarios, which poses a significant challenge to the fault calculation of modern power systems. This paper first establishes a reduced fault analysis model of CIG considering control mode switching. By neglecting the fast dynamic processes of the converter controller, CIG with each control mode can be expressed by equality and inequality constraints. Then, an alternative iteration algorithm is proposed to determine the control mode of each grid‐tied converter after fault, which mainly contains the estimation and discrimination steps. In the estimation step, the voltage of the point of common coupling (PCC) and output current of CIG are obtained by simultaneously solving the equality constraints of CIG and the reduced network equation. In the discrimination step, the inequality constraints of CIG are utilized to judge and revise the control mode of each converter. With alternative execution of the estimation and discrimination steps, the fault network can be solved. Simulation studies based on PSCAD/EMTP validate that the proposed method has high accuracy in fault calculation of the CIG embedded system under various fault locations and fault resistances. [ABSTRACT FROM AUTHOR]

Voltage sags are the most frequent and impactful disturbances in industrial power grids, leading to high financial losses for industrial clients. The identification of the cause and its relative location is crucial for the contractual relation between the energy provider and the industrial customers. This paper proposes a methodology to identify the origins of voltage sags based on instantaneous symmetrical components and dynamic time warping. Short‐Time Fourier and Fortescue transform are implemented in the pre‐processing step using the voltage and current waveforms. Then, a distance‐based classification strategy to identify the sources of voltage sags is used. It relies on a four‐dimension time‐series signature used as features. Moreover, a confidence index associated with the classification output is provided. The proposal offers an easy implementation in industrial applications with no previous recorded data. It has the benefit of using a reduced‐size reference database entirely composed of synthetic data. The main advantages of the proposed method are its generalization capabilities and the possibility of raising an alert based on the confidence index. The obtained classification accuracy on synthetic data with seven causes is 100%. The method reaches a classification F1‐score higher than 99% with field measurements representing five classes obtained from three different industrial sites. [ABSTRACT FROM AUTHOR]

To seek the optimal defense strategy against malicious attacks, this paper proposes a novel defender–attacker–operator model applicable to the cyber‐physical power system. The proposed model considers the interdependence in functionality and topology of the cyber network and the power network as well as the cyber network's constraints. The defender aims to minimize the load curtailment caused by the attacker, while the latter intends to maximize the power loss. The operator in the bottom level takes corrective action to minimize the attack consequence. This tri‐level model is decomposed into a master problem and a subproblem, based on which the column‐and‐constraint generation algorithm is implemented to obtain the optimal solution. Comparative case studies based on IEEE RTS‐79 system are carried out to demonstrate the advantage of the proposed method, and to investigate the impact of the energy coupling strength, the topologically independent link and the defensive resource. The effectiveness of this model is validated by the sensitivity analysis. [ABSTRACT FROM AUTHOR]

This paper deals with the real‐time optimal operation of active distribution networks (ADNs) hosting hybrid hydrogen‐electricity refuelling stations by benefiting from renewable energy sources (RESs), conversion facilities, and energy storage systems. The hybrid refuelling stations, which are controlled by ADN operator, supply electricity and hydrogen for electric vehicles (EVs) and hydrogen vehicles (HVs), respectively. In addition, the deployment of water equipment technologies in the ADNs, is considered by utilizing water well pumps in the hybrid stations to serve water demand. The principal aim is to minimize the expected operation cost, including the cost of purchasing power from the upstream grid and maintenance and operation costs of each hybrid refuelling station. Various technical and physical constraints are considered to ensure the reliable operation and realistic scheduling of ADNs in the presence of hybrid refuelling stations. This study employs a hybrid information gap decision theory (IGDT)‐stochastic approach to address the uncertain behaviour of wholesale market price, electricity demand of EVs in refuelling stations, RESs output power and nodal demand of ADN to reach a risk‐averse strategy. The developed approach is coded under GAMS software and the effectiveness of the approach is validated by testing on the modified IEEE 33‐bus test system. [ABSTRACT FROM AUTHOR]

With the emerging penetration of renewables and dynamic loads, the understanding of grid edge loading conditions becomes increasingly substantial. Load modelling researches commonly consist of explicitly expressed load models and non‐explicitly expressed techniques, of which artificial intelligence approaches turn out to be the major path. This paper reveals the artificial intelligence‐based load modelling technique to enhance the knowledge of current and future load information considering geographical and weather dependencies. This paper presents a recurrent neural network based sequence to sequence (Seq2Seq) model to forecast the short‐term power loads. Also, a feature attention mechanism, which is along channel and time directions, is developed to improve the efficiency of feature learning. The experiments over three publicly available datasets demonstrate the accuracy and effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

The connection of constant power loads (CPL) in a low power dc system leads to a reduction in the stability margin of the system. Most of the existing techniques increase the stability margin by modifying the controller for CPL or source converter at the cost of deterioration in the performance of converters. Few publications have suggested methods to determine the value of dc‐link capacitance required for stable operation of dc microgrid with CPL. These methods do not necessarily give the minimum possible capacitance as a solution. This paper suggests a method to determine the lower bound on the value of capacitor, for stable operation of dc system. A dc system with both linear and CPL loads is considered. Detailed mathematical analysis is used to determine the range of capacitance to ensure the stability of equilibrium point. Further, this paper discusses the effects of variation in linear loads on the selection of capacitor. The procedure of capacitor selection for a low power dc system is included. Using the real‐time digital simulator and a digital signal processor, controller hardware‐in‐loop based validation is carried out for the proposed approach. [ABSTRACT FROM AUTHOR]

This paper considers the problem of false data injection attacks (FDIAs) on load frequency control of interconnected smart grids (ISGs) with delayed electric vehicles (EVs) and renewable energies. By intruding incorrect information, unauthorised users can corrupt the system information leading to degradation in the performance and disruptions of ISGs. In this paper, a model of ISGs subject to FDIAs in aggregator of EVs and power plants is first presented. This mathematical representation comprises dynamic interactions of power plants, delayed EVs, renewable energies and FDIAs on both system states and outputs. Based on recent advanced techniques on functional observers and matrix inequalities for time‐delay systems, then a new distributed functional observers based scheme is developed to realise the tasks of detecting and isolating FDIAs. Also, an effective procedure presented in tractable linear matrix inequalitiesis build with an optimisation process for the synthesis of the detector. The proposed detector is distributed, of reduced order, avoids the risk of centralised malicious incidents, therefore easy for implementation and monitoring tasks. The stability of ISGs and contribution of EVs subject to FDIAs are also discussed. Comprehensive simulations are given to demonstrate the effectiveness of our proposed method by using three‐area ISGs. [ABSTRACT FROM AUTHOR]

Due to the greater penetration of renewable energy resources and the increasing complexity of the distribution system, moving towards a smart distribution system is essential and achievable via advanced information and communication technologies. These technologies come with side effects: not only do they change the structure and functionality of the system, their availability and efficiency alter the operation of the system as a whole. The aim of this paper is to examine the reliability of a cyber‐physical microgrid as a part of a smart distribution grid to evaluate the impact of the integration of information and communication technologies into the system and the impact of non‐dispatchable renewable energy resources, that is, photovoltaic and wind farms. This paper proposes a framework for developing reliability assessment tools for a grid‐connected microgrid with a hierarchical three‐level and communication‐based control system. Emphasis is laid on incorporating the interdependencies between the cyber system and the microgrid and on detailed models of renewable energy resources. [ABSTRACT FROM AUTHOR]

The reality of modern power grids requires the use of flexibilities from generation, load and storage. These flexibilities allow system operators to modify a transformer loading in a smart way. Therefore, power constraints of transformers can be overcome by using the appropriate flexibility. However, transformers have a physical limit of energy transfer which cannot be overpassed. This energy limit represents the unique transformer's loading profile, ensuring the highest energy transfer under a given ambient temperature profile. The paper explains how the energy limit can be calculated. Typical characteristics of an energy limit are estimated in cold continental climate of Russia and warm temperate climate in France. Maximal, minimal and mean loadings are identified for each month. Loading durations of energy limit are determined for each cooling system. It is found that winding temperatures of transformers, operating at energy limits, remain in the vicinity of design winding temperature. Therefore, transformer operation at energy limit avoids a high temperature stress and simultaneously maximizes the energy transfer. The application of energy limits for power system problems is briefly explained along the paper. Energy limit application can reduce an energy cost, maximize a renewable generation and increase a hosting capacity of distribution network. [ABSTRACT FROM AUTHOR]

During summer 2018, the Nordic system's kinetic energy dropped below a critical level. As a consequence, Svenska kraftnät, the Swedish transmission system operator, requested the largest production unit to reduce its power output to guarantee system's security. This action resulted in a deviation from the generation dispatch determined by the market and in high costs for the Nordic transmission system operators. In this regard, this paper presents a tool for comparing mitigation strategies from an economic point of view and evaluates potential economic benefits of utilizing the emergency power control functionality of high‐voltage direct current lines for the provision of fast reserves as a compliment to frequency containment reserves. Moreover, the analysis is extended to the years 2020 and 2025 using inertia estimations from the Nordic transmission system operators. The findings of the paper suggest that the frequency of redispatching actions will increase in the future and that the cost of security for Nordic transmission system operators could be reduced by 70% if high‐voltage direct current links are used for frequency support. [ABSTRACT FROM AUTHOR]

This paper presents a novel mixed integer linear programming‐Newton approach for solving constrained AC power flow problems, based on a recently proposed linear programing‐Newton method. The linear programming‐Newton method overcomes some deficiencies of the classical Newton's method, while retaining its quadratic convergence in the neighbourhood of a solution. The discrete nature of some control variables, such as transformer tap ratios and bus shunt susceptances, are often ignored in power flow algorithms due to difficulties that they introduce. The violation of operational limits in power flow algorithms is typically corrected in ex‐post procedures, such as PV‐PQ bus switching, which may lead to infeasibilities. Finally, under some circumstances, power flow problems may not have a solution; in these cases, a desirable property of a power flow algorithm would be the ability to obtain a nearly feasible solution. The approach proposed in this paper handles these difficulties in a unified framework and is able to deal directly with discrete variables and operational limits. When there are no power flow solutions, a nearly feasible solution that minimizes a merit function is obtained. Numerical experiments show that the proposed approach is robust and obtains accurate solutions in the presence of discreteness conditions and operational limits. [ABSTRACT FROM AUTHOR]

High impact–low probability (HILP) incidents, such as hurricanes, usually and gravely damage electric distribution networks. A resilient distribution network must have an ability to recover itself with a fast restoration methodology against the effects of HILP events. Due to improve the electric distribution network resiliency against HILP incidents, this paper suggests a probabilistic–proactive distribution network operation model based upon the chaos theory (P‐PDNOMC(. Here, a resilience index based upon operational cost and load shedding cost is employed to decrease the effects of a hurricane as an HILP incident. P‐PDNOMC is formulated as a framework that consists of a novel hurricane modelling and operation scheduling in normal and emergency conditions with uncertainties considering. It should be noted that a prediction approach based on the chaos theory and the least‐squares support vector machine (LS‐SVM) is also designed to consider the provisional and spatial behaviour of the hurricane. Furthermore, this paper proposes a novel optimization framework for damaged lines determination based upon the P‐PDNOM C (DLsP‐PDNOM C) by considering multi‐zone and multi‐period damaged equipment budget constraints. Here, the load shedding cost is applied to present the efficiency of the proposed model. The numerical results indicate the resiliency enhancement of the electric distribution network in the face of HILP incidents. [ABSTRACT FROM AUTHOR]

This paper presents several methods for performing two types of static load model conversion: exponential to ZIP & ZIP to exponential model conversion. In general, these conversions are inaccurate due to non‐equivalence of exponential and ZIP (second‐order polynomial) models. A numerical analysis is conducted using generated datasets of load models to analyse the error and to compare the accuracy of the presented methods. The results of the analysis indicate that the optimal selection of conversion method depends on a number of factors, including normalisation of load models, ZIP model type (accurate or constrained) and expected use of the converted model. In addition, a case study is conducted to analyse the impact of conversion error on load flow results. The results of the case study indicate that a significant difference in load flow results can occur when the load models are converted. Recommendations for conversion method selection are given in the discussion section of the paper. [ABSTRACT FROM AUTHOR]

The temperature gradient and mismatching between the thermal expansion of the core and flange readily lead to cracks and discharges on the core surface of the dry-type valve-side bushing, which severely impact the safety of power systems. It is vital to clarify the cracking risk of bushing cores under temperature gradients and establish corresponding control methods. The mechanical properties of epoxy resin impregnated paper (ERIP) material were measured in this study at different temperatures, and a thermal–mechanical coupling simulation model was established. The thermal and stress distributions of the core were obtained and the cracking risk was defined accordingly. The crack development mode was explored as it relates to the phase-field mode. Various elastic cushion materials affecting the stress distribution of the core were investigated. The results show that the mechanical properties of the ERIP material decrease rapidly as the temperature increases. When under severe working conditions, the maximum first principal stress of the core may be significantly higher than the tensile strength of the ERIP material resulting in significant axial cracks. Adding an elastic cushion layer made of polyurethane rubber can effectively relax the interface stress and reduce the cracking risk. [ABSTRACT FROM AUTHOR]

Due to deforestation, greenhouse gases and increasing air conditioner systems, the temperatures are changing drastically and the effects are prominent in summer and winter seasons. These ambient temperature variations are affecting the transmission system parameters and also the voltages at various buses. Changes in the transmission line parameters have non-negligible impact on system performance and present Energy Management applications are not considering these temperature variations in the power system state estimation. In this paper, impact of temperature variations on the power system Voltage Stability assessment is considered. Voltage stability assessment methods make use of the load flow results. Load flow results without considering the temperature variations are inaccurate in the real-time and leading to the erroneous results in the voltage stability assessment. In view of this, studies have been carried out on IEEE 30-bus, 118-bus and 300-bus systems, incorporating the seasonal temperature variations on various types of conductors, and also demonstrated the impact with conventional and ZIP type load models. In this paper, four voltage stability indices NLVSI, $L_{mn}$Lmn , FVSI and VCPI(power) are used for identifying the proximity of voltage collapse point, critical line, critical bus voltage, critical bus angle, total system active power loss, total system reactive power loss etc. [ABSTRACT FROM AUTHOR]

A wide range of remedial action schemes (RAS) have been developed and deployed in field to take automated control actions to maintain system reliability and stability during adverse system conditions. According to the regulatory standards from North American Electricity Reliability Corporation (NERC), RAS applications deployed in field and their functionalities are required to be tested periodically. To do testing for the RAS, regular process that requires taking critical components offline is usually labor‐intensive and time‐consuming, especially for complex applications utilizing multiple measurement units and controllers from different substations. To overcome these challenges, this paper presents a remote testing architecture for remedial action schemes. Our proposed remote testing architecture is capable of accessing test signal generators, phasor measurement units (PMUs), RAS control units from multiple substations, managing tests for both centralized and distributed implementations of RAS. To address the effectiveness of the remote testing architecture, this paper presents the minimal wind curtailment applications with detailed alternative formulations of optimization models for RAS and highlights performance of the proposed remote testing of RAS. Simulation results for an IEEE standard test system and representative industry test cases demonstrate effective performance of the developed RAS testing mechanism. [ABSTRACT FROM AUTHOR]

One of the most important tools for condition monitoring is the gas chromatography test of transformer oil, which is known as dissolved gas analysis (DGA). In this research, the DGA results of >3000 power transformers operating in Iran's power grid were carefully studied and from among them, the results related to transformers suspicious of being faulty were used to validate the fault detection accuracy of the presented fuzzy inference system (FIS). In most of the previously published papers, the detection and isolation of transformer faults has been based on one or two of the following parameters: absolute concentrations of free and dissolved gases in transformer oil, total dissolved combustible gases, total combustible gases, ratios of some gases to each other, and the rates of gas increase. However, in this research, most of these parameters have been used for fault detection and isolation, according to the IEC 60599 standards. Also, no attempt has been previously made to detect the decomposition of insulation papers of transformers; but the presented FIS is able to detect this fault as well. The overall performance accuracy of the presented system is F 1 = 91.2%, which seems to be a suitable value. [ABSTRACT FROM AUTHOR]

The frequency‐dependent characteristics of the impedance parameter and the admittance parameter in the transmission line model of the transformer windings (TWs) are mainly result of the skin effect of the conductor and the complex permittivity's variation with the frequency of oil‐paper, respectively. In this study, an accurate fractional transmission line model of TWs considering the frequency‐dependent parameters is proposed. The measured permittivity of oil‐paper samples is fitted based on the Cole–Cole model with single dielectric relaxation process, and then simplify the fitting model. The structure coefficient per unit length (PUL) corresponding to the complex capacitance of the TWs can be obtained based on the finite element software FEMM. Combining the simplified model with the structure coefficient, the fractional admittance PUL of TWs is derived. Then the fractional transmission line model of the TWs is determined. Finally, the calculated results of fractional transmission line model considering the frequency‐dependent parameters are compared with the experimental measurements, and the results demonstrate the accuracy of the proposed model. [ABSTRACT FROM AUTHOR]

Demand response programs (DRPs) have paved a meaningful role in the power supply–demand balance in a smart grid. Also, a residential community with the presence of renewable energy sources (RESs) and electric vehicles (EVs) provides a new way to tackle growing concerns about energy efficiency and environmental pollution. The inherent uncertainty of RESs generation and EVs behaviour leads to difficulty in the economic scheduling of the demand side. Different types of uncertainty modelling have been investigated, such as Monte Carlo (MC) simulation, fuzzy method, and robust optimization. They are faced with many scenarios and computational complexity. This paper uses the information gap decision theory (IGDT) method to study variations of uncertainty radius on residential community electricity costs. Therefore, to achieve an optimal strategy for scheduling the appliances considering the deep uncertainties of RESs and EVs, a novel IGDT‐based demand response scheduling for a residential community is proposed. Impacts of different levels of uncertainties are studied. The simulation results depict the privileges of the proposed method when confronting deep uncertainties. By increasing the radius of the uncertainty of RES and the initial charge of EVs, energy consumption costs grew 20% and 2%, respectively, which indicates the system operator can manage the costs effectively. [ABSTRACT FROM AUTHOR]

The extra‐high voltage (EHV) transformer winding disks are subjected to dynamic electromagnetic forces caused by short‐circuit currents. These periodically decaying impact forces may endanger the safety of the transformer. The winding constraint conditions around the circumference are different due to the influence of axial pressure structures and magnetic boundaries. Moreover, this difference becomes more evident as the voltage and capacity increase. To investigate the spatial differences and time effects of EHV transformer short‐circuit faults, this paper proposes a reliability evaluation method based on dynamic relative displacement (DRDEM) for the first time. Firstly, the suitable and efficient analysis method is summarized by comparing the deviation among different calculated impedances and the measured value. Then, the space‐time effect of windings is obtained by the static magnetic field and dynamic structural field based on equivalent compression supports. Furthermore, a 50 MVA/110 kV transformer is designed for the destructive short‐circuit test to simulate the short‐circuit ability feature of the EHV transformer. The disassembly phenomena after all the tests indicate that the DRDEM is more adaptable in characterizing the winding ability to withstand short circuit when investigating the spatial differences and time effects. [ABSTRACT FROM AUTHOR]

Power‐Flow (PF) solution in isolated microgrids has attracted notable attention recently, because these systems present various particularities compared with the traditional PF solution in large meshed transmission networks. In this sense, this paper develops a novel Newton‐like PF solver for isolated microgrids. The new proposal is based on the Modified Midpoint method and shows high convergence order with relatively low computational burden. These characteristics bring superior theoretical performance compared with the standard Newton–Raphson (NR) method and other high order techniques, which has been conventionally used. Extensive simulations are performed on various small‐, and large‐scale benchmark Microgrids under different loading conditions and R/X ratios. Results provided serve to confirm the theoretical features of the developed solver, outperforming the NR technique as well as other recently developed solvers in all the studied systems with acceptable reliability even under high stressed conditions. [ABSTRACT FROM AUTHOR]

This paper presents a research work which focuses on generating synthetic data to enrich the training‐set of a deep learning (DL) based classification system to classify power system transient events using PMU frequency measurements. The synthetically improved training‐set is shown to increase the classification performance compared to the case when only the actual‐data training‐set is used. The proposed classification system helps to reveal high‐frequency transient variation information out of PMU measurements collected at a relatively much lower rate, especially when a small set of training‐data exists. Synthetic PMU frequency data is generated based on the DFT analysis statistics on the limited‐size PMU frequency data. Generation of the synthetic data is achieved by re‐synthesis of the PMU frequency data using inverse DFT, which imitates the DFT frequency and phase behaviour for each event type separately. Then the DL structure is trained to classify the power system transients using the synthetically enriched train set. The proposed method of generating synthetically supported training‐set has lower computational complexity compared to the existing methods in the literature and helps to obtain improved classification results. It can be used to increase the classification performances of power quality devices performing transient event analysis, especially for those with access to a limited set of training‐set. [ABSTRACT FROM AUTHOR]

Even though shunt compensation of transmission lines (TLs) is a common practice to limit the overvoltages stemming from the Ferranti effect, it may cause transient and steady‐state resonant overvoltages when one phase is left open due to the single‐pole‐open condition following the temporary phase‐to‐ground (Ph‐g) fault. Furthermore, the existing shunt reactors potentially increase the duration of the secondary arc current at the fault point and hence prevent offering fast single‐pole reclosing. To exploit the mentioned issues, in recent studies, a neutral reactor is introduced, which is installed in the neutral point of the shunt reactors; nonetheless, obtaining the appropriate amount of it is remarkably challenging. In this paper, an optimal neutral reactor (ONR) is determined based on an optimization problem, which minimizes the neutral reactor cost subject to limiting both resonant overvoltages and the duration of the secondary arc current. In modelling, the Laplace domain is employed rather than the phasor domain to consider both transient and steady‐state conditions since this technique allows having better control over transient overvoltages as well as transient secondary arc. [ABSTRACT FROM AUTHOR]

Optimal power flow (OPF) plays an important role in the secure and economical operation of the distribution network. This paper proposes a novel generalized linear‐constrained optimal power flow (GLOPF) model for both radial and mesh distribution networks, which iteratively approaches the optimal solution to the original OPF problem. The first‐order Taylor series approximation method is applied to linearize the non‐linear and non‐convex power flow constraints for a given Taylor expansion point (TEP). In addition, the objective function of the GLOPF model is only added with an easy‐compute second‐order penalty on state variables, which is at least positive semi‐definite and guarantees the convexity of the quadratic optimization. Then a TEP‐based iterative (TEP‐I) method is proposed in the GLOPF model to construct a series of linear‐constrained convex quadratic optimizations, which avoids the complex calculation of quasi‐Hessian or Hessian matrices in traditional sequential programming methods. The converged solution to the GLOPF model is proved to satisfy the Karush–Kuhn–Tucker conditions of the original OPF problem. Furthermore, the GLOPF model at first and second iterations can be implemented as the 'cold‐start' and 'warm‐start' LOPF models, respectively. Case studies verify the super‐linear convergence and high accuracy of the proposed GLOPF model. [ABSTRACT FROM AUTHOR]