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The paper proposes a new stochastic multiobjective technoeconomic model for integrating photovoltaic (PV) and wind energy resources in electricity price (EP)‐driven distribution systems. The primary goal of this paper is to determine the optimal location and capacity for renewable energy‐based distributed generation, specifically PV and wind resources, while considering weather and system uncertainties. These uncertainties include stochastic variations in PV illumination intensity, wind speed, EP, and load fluctuations. To address these uncertainties, the paper employs scenario modeling techniques named as Latin hypercube sampling with Cholesky decomposition. This technique generates multiple correlated scenarios that represent uncertain variables. Subsequently, a scenario reduction technique is applied to identify the scenario with the highest probability. Later, a mathematical model is developed to minimize an objective function that encompasses various factors like system losses, node voltage deviations, the cost of purchasing power from the grid; and simultaneously maximize the total annual energy savings. The objective is to find optimal solutions that strike a balance between different objectives. To obtain an efficient optimum solution, this paper employs an effective meta‐heuristic technique named as JAYA algorithm. The results obtained by the JAYA algorithm are juxtaposed with those obtained using particle swarm optimization and genetic algorithm techniques. The proposed method is evaluated using Institute of Electrical and Electronics Engineers (IEEE) 33‐node and IEEE 69‐node test feeders to validate its feasibility and effectiveness. However, the effectiveness of the proposed method is not limited to any size of test systems. [ABSTRACT FROM AUTHOR]

This article explores the impact of electric vehicle (EV) charging on power systems and emphasizes the importance of careful analysis and planning. It discusses various topics related to EV charging, including EV hosting capacity, optimal placement of EVs and charging stations, technical impacts of Vehicle-to-Grid (V2G) technology, shared vehicle rental, decentralised collaboration frameworks, intelligent energy management schemes, EV demand response, and optimal sizing and siting of public EV charging stations. The article provides valuable insights and solutions for utilities, academia, and policymakers to address the challenges and opportunities associated with EV charging. Additionally, the article introduces a new approach for determining the limits of transformers and overhead lines in relation to EV charging, using Monte Carlo simulations and proposing new parameters for evaluating maximum demands. The authors stress the need for further analysis and attention to the impact of EVs on power systems. [Extracted from the article]

This paper proposes a new comprehensive load restoration (LR) method for electrical distribution networks. Since two main technologies of switching equipment are there in the modern distribution networks, namely manual switches (MSs) and remote‐controlled switches (RCSs), this article has benefited from this concept effectively. A two‐stage algorithm that provides the system operators with the ability to recover part of the loads in the shortest possible time by RCSs is proposed. After this step, the remaining loads will be restored by a combination of MSs and RCSs. The other strength of this algorithm is to provide accurate and practical solutions so that the sequence of switching actions is clearly defined. Also, using an innovative index called expected weighted energy not supplied as the objective function of the main problem will ensure the operators recover the maximum amount of load in the shortest time possible. This novel method was applied on a sample standard IEEE distribution test network. The simulation results proved the effectiveness of this proposed method. [ABSTRACT FROM AUTHOR]

Transmission lines, the core part of the transmission and distribution system in the smart grid, require effective, efficient, and reliable protective measures against faults to avoid severe damage to physical infrastructure and financial losses. Due to their growing popularity, machine learning models are used in fault detection and classification, whose performances can be severely affected by cyber‐attacks due to their data dependency, posing a critical concern. Hence, this paper introduces false data injection attacks to address the vulnerability of machine learning‐based fault detectors and classifiers. A comparative study of 9 detection models and 6 classification models under normal conditions and during a combination of two models of false data injection attacks is presented to evaluate the severity of cyber‐attacks. Experimental results show that highly accurate models in normal conditions are more susceptible to cyber‐attacks, with up to 69% and 28% degradations in accuracy for fault detectors and classifiers, respectively. Furthermore, the detection models are found to be more vulnerable to cyber‐attacks than the classification models. With no robust detectors and classifiers being found, this work addresses the importance of developing attack‐resilient fault detection and classification schemes considering their academic and industrial significance. [ABSTRACT FROM AUTHOR]

This paper presents a non‐intrusive method for identifying the load state of a distribution network. The method focuses on continuously varying loads. By considering the load on‐off state switching points and the continuous features at on state, a deep convolutional method considering non‐local spatiotemporal features is proposed. The addition of an attention component to the convolutional network enhances the non‐local feature extraction capability of the convolutional network. Ultimately, the effectiveness of the method is demonstrated in an experimental setting. In addition, this paper demonstrates that the proposed method can effectively integrate switching point features as well as persistent features through neural network visualization techniques. [ABSTRACT FROM AUTHOR]

As the most promising alternative to internal combustion engines (ICEs), electric vehicles (EVs) have an excellent development outlook. The charging route scheduling of EVs can simultaneously affect traffic congestion in the transportation network (TN) and power flow distribution in the power distribution network (PDN). The research on TN and PDN coupling networks based on the static traffic flow model is relatively mature; however, it ignores that the traffic flow will spread across periods in a short scheduling period. In this paper, a semi‐dynamic traffic flow model is proposed to represent the dynamic propagation characteristics of EVs and ICEs flow. Furthermore, the cost of carbon emission and system operation are combined as the overall goal of system optimisation. Since the model has become a more complex non‐linear model, this paper proposes to combine the heuristic sequential boundary tightening and binary expansion method to linearise the model. The study compared four cases and found that a 20% penetration rate of EVs can reduce carbon emissions by 4.2% while reducing the system's total cost by 10%. Moreover, the impact of network congestion on the spatiotemporal distribution of traffic flow and power flow in the coupled network is alleviated. [ABSTRACT FROM AUTHOR]

Due to the strong dependence between electricity generation and heating supply and the need for optimal use of renewable energies to supply power and heat loads, it is essential to provide an integrated energy management structure for electricity and heating systems. This paper studies the techno‐economic and environmental aspects of a combined electricity distribution and district heating system by considering multi‐type flexible energy resources and the environmental and practical constraints of both networks. The integrated energy system (IES) is equipped with wind power plants, combined heat and power plants, gas boilers, heat pumps, multi‐energy storage systems, electric vehicle parking lots, and multi‐energy demand response, as well as the IES operator can take part in the energy market to supply thermal and electrical demands. Besides, a robust strategy is adopted to control the power price uncertainty in the day‐ahead scheduling of IES, where a robustness parameter is considered to adjust the risk level of scheduling. The numerical results show that technical and environmental constraints can significantly increase the operation cost of IES by 11.8% and 3.6%, respectively. In addition, multi‐type flexible resources can improve the operational flexibility of IES by 16.1%. [ABSTRACT FROM AUTHOR]

The UHV DC tension insulator strings are often large‐tonnage suspension porcelain or glass insulators with high mechanical strength. The single insulator is heavy, so two workers working at the same time are required to make a replacement on insulator strings in live working conditions. One of the key problems that must be solved in live working conditions is to select an appropriate way to reach the working position and realize the potential transfer between two workers. In this paper, the electric field calculation model of live working of ±800 kV tension strings was established. The process that two workers enter and leave the working position along the tension string and different conditions that two workers replace insulators were simulated and analyzed, and distribution characteristics of the electric field of insulator strings and live workers were obtained. According to the calculation results, when the two workers replace the tension insulators in different tension strings, it is easy to realize the potential transfer, so as to reach the same potential for work. The study results of this paper provide a basis for the selection of replacement method of large‐tonnage single insulator by two workers, and a theoretical basis for UHV DC live working. [ABSTRACT FROM AUTHOR]

This paper deals with power generation through solar photovoltaic (SPV) system and its implementation as grid‐tied and stand‐alone systems. The major setbacks of SPV system of low conversion efficacy, that can be enhanced through the maximum power point tracking (MPPT) algorithm. A modified incremental conductance (MIC), termed as error based incremental conductance MPPT, has been proposed, and its behaviour is comprehensively compared with classical perturb and observe (P&O) and incremental conductance (IC) techniques. Dealing with the on‐grid application of SPV system, maximum power obtained through MIC is fed to a three‐phase grid operating at unity power factor (UPF), and the quality of grid current is monitored. Further, the power obtained through MIC finds its application in designing an SPV‐diesel generator (DG) based hybrid renewable energy system (HRES) for areas either not connected to the grid or have insufficient fossils. The stochastic nature of source or uncertain load demand leads to deviation in system frequency. This paper proposes an intelligent, comprehensive supervisory optimal‐fuzzy‐proportional‐integral‐derivative (O‐F‐PID) controller for load frequency control (LFC). The performance of proposed O‐F‐PID controller has been vividly compared with the designed optimal‐PID (O‐PID) and conventional PID (C‐PID) controllers under varying source and load demand conditions. [ABSTRACT FROM AUTHOR]

The ever‐evolving electricity distribution network calls for new effective, transparent and fair market procedures to be implemented. This paper offers a systematic framework for real‐time trading in prosumer communities. A data‐driven robust optimization model that deals with uncertainty is proposed using the flexibility requirement envelope concept. Using machine learning techniques, high quality point and quantile forecasts are prepared to form the envelope. Next, this data is fed into the optimization problem to match the peers and the distribution grid, trading power, and flexibility in a short‐term look‐ahead horizon. To increase the quality of forecasts, a clustering approach accompanied by a pattern recognition machine is proposed to determine the cluster to which the recently observed net‐load data belongs. To provide forecasts, a forecasting machine is developed using a hybrid of tree‐based ensemble learning models, which provides intuitive, robust, and accurate results. Finally, the peer matching is done in a decentralized fashion using the alternating direction method of multipliers (ADMM), while each agent calculates its battery storage aging cost. Case studies demonstrate the hybrid model's capability in providing high quality power and flexibility forecasts compared to single models and ARIMA models and its positive impact on reducing the overall cost of the system. [ABSTRACT FROM AUTHOR]

Combined heat and power (CHP) systems are employed to supply electrical and thermal loads of distribution networks (DNs). However, the heat loss can significantly hinder the heat transfer through pipelines from CHP systems to consumers Therefore, the optimal placement and sizing of CHP systems to reduce losses, including electrical and thermal losses, are very crucial. To minimize electrical loss, many methods have been proposed for optimal resource allocation. In this paper, a new approach for the optimal allocation of CHP systems to minimize electrical loss and heat transfer loss (HTL) is presented. Accurate modeling of HTL through the pipeline is performed. The proposed method is applied to 33‐bus, 69‐bus, and 84‐bus test systems. According to the simulation results, it is concluded that considering HTL in the resources optimization problem leads to major changes in the selection of CHP systems' optimal size and location. To solve the optimization problem, a new enhanced imperialist competitive algorithm (E‐ICA) is presented. The proposed algorithm enhances the performance and efficiency of the ICA algorithm by improving exploitation and exploration to prevent getting trapped in local optimums. Comparison of CHP systems allocation results confirms the superiority of the E‐ICA algorithm over other methods. [ABSTRACT FROM AUTHOR]

The increasing integration of distributed photovoltaics may further aggravate the over‐voltage and three‐phase unbalance issues of low‐voltage distribution networks with three‐phase four‐wire structures. The voltage control method based on the AC and DC side power flow control and the three‐phase power control capability of voltage source converter in hybrid AC/DC low‐voltage distribution networks is a solution for the improvement of the above power quality issues. In this paper, an accurately improved sensitivity matrix calculation method considering shunt admittance based on the ABCD parameters is proposed in hybrid AC/DC low‐voltage distribution networks with a three‐phase four‐wire structure. The presented ABCD parameters of the feeders consider the influence of the coupling effect among phases and the neutral line on sensitivity calculation, which makes the sensitivity calculation simple. Then, a power‐voltage control method for voltage source converters based on three‐phase four‐wire sensitivity matrices of the AC side is proposed considering the constraints from the voltage source converter and DC side power flow in hybrid AC/DC low‐voltage distribution networks, which can effectively address the over‐voltage and unbalanced issues. Simulations are performed to verify the proposed sensitivity calculation method and voltage control method. [ABSTRACT FROM AUTHOR]

The virtual power plant (VPP) can aggregate small‐scale distributed resources to provide flexibility for the transmission system. The flexibility provision capability of the VPP is restricted by technical operating limits of the distribution system to which distributed resources are connected. To facilitate the flexibility provision of the VPP while securing the operation of the local system, this paper leverages the dynamic line rating (DLR) to enlarge the VPP flexibility region. The flexibility region of the VPP is defined as the allowable range of active and reactive power output that the VPP can execute subject to operating constraints of the internal system. With the DLR, the current carrying capacity of the distribution network is dynamically adjusted based on actual environmental conditions, which helps to remove barriers from transmission limits that hinder the integration of distributed flexibility. A convex hull‐based method with an explicit accuracy guarantee is proposed to approximate the flexibility region with the DLR. Case studies based on IEEE‐33 and IEEE‐123 test feeders with real‐world operation data validate the proposed framework. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to the problem of defining an investment policy in battery energy storage systems in active distribution networks, taking into account a diversity of uncertainties. The proposed methodology allows the selection of type, capacity, and location of battery energy storage systems in distribution networks with distributed generation and electric vehicle charging stations. A mixed‐integer stochastic programming problem is cunningly approached with a metaheuristic, where fitness calculation with stochastic scenarios is performed by introducing an approximation to the operation costs in the form of a polynomial neural network, generated according to the Group Method of Data Handling—GMDH method, with strong computing speeding‐up. The quality of this approximation for heavy Monte Carlo simulations is assessed in a first case study using a 33‐bus distribution test system. The optimization planning model is then validated in the same test system using real data collected from solar and wind sources, demand, prices, and charging stations. Four types of batteries are compared considering degradation impact. The results demonstrate the practicality and advantages of this process. [ABSTRACT FROM AUTHOR]

The electric power systems are experiencing a transformation to be characterized by power electronic devices, for example, voltage source converters (VSCs). However, the large‐capacity VSC will interact with the synchronous generators (SGs) and could lead to low‐frequency oscillation (LFO). It becomes a hurdle for the secure and stable operation of modern electric power systems. Therefore, this paper analyses the oscillatory instability mechanism of the SG with the penetration of grid‐forming (GFM) and grid‐following (GFL) VSC. The distinctive transfer function model of the SG‐VSC system is first developed. Then, through parameter sensitivity and transfer function analysis, the impact of three types of VSC is investigated, encompassing GFM‐VSC and GFL‐VSCs with two conventional reactive control schemes. It proves that the voltage control of the VSC can be detrimental to the damping torque of the SG. According to the theoretical analysis, a supplementary compensator is proposed to suppress the LFO induced by the VSC. Finally, case studies in a two‐area test system validate the analytical results and robustness of the proposed enhancement method. [ABSTRACT FROM AUTHOR]

For a few decades, electric power growth and demand have increased globally. To fulfil this demand, distribution generators (DGs) have been installed in the power system. To get numerous benefits such as comprising reduction of power losses, improvement of (voltage, current, rpm) transient stability, leads to economic benefits. DGs integration in the distribution power system (DPS) was reported as an optimal generator placement (OGP) problem to maximize these benefits. This paper performs wind turbine (WT) integration while considering the internal generator's (rpm) transient stability under fault conditions. A power loss index (PLI) method is implemented to select potential candidate buses for generator placement. A self‐sorting analytical approach (SSA) is also used to determine the optimal bus and WT size. The IEEE 14‐bus DPS is used as the OGP test system to demonstrate the proposed method's effectiveness in terms of power losses reduction, system transient stability, and economic benefits. The IEEE 14 Bus system was considered using MATLAB with a 3.2 GHz processor to perform the said approach. WT integration suggested in case‐2 results in a 65.80% power losses reduction, which leads to a 60.88% enhancement of economic benefits of the DPS. [ABSTRACT FROM AUTHOR]

With the increasing penetration of large‐scale distributed renewable energies, the electricity distribution network is growing more complex with challenges brought by the intermittency and uncertainty of renewable energies. Microgrid (MG), one of feasible solutions to integrating renewable energies, has shortages because of limited capacity. Therefore, MG cluster (MGC) was proposed as an improved solution. To efficiently and friendly integrate renewable energies, a novel honeycomb‐like MG cluster (H‐MGC) is proposed in this paper. Considering the uncertainty of renewable energies, a robust optimisation method of the siting and sizing of energy storage system (ESS) constrained by emergency reserve is proposed. Combining the column‐and‐constraint generation algorithm and big‐M method, the proposed optimisation model is decomposed into mixed integer second‐order cone programming master problem and sub‐problems, which are alternately solved. Based on the optimal results of study case, the siting and sizing results of ESS are analysed, as well as the operating characteristics of H‐MGC. Compared to existing MGC system, the proposed H‐MGC has a more flexible operation mode. The related costs of ESS are reduced, and the operational life is relatively extended, under the condition that the emergency reserve of system is satisfied. [ABSTRACT FROM AUTHOR]

Power flow calculations are an essential stage in many planning and control applications for distribution systems.To use these in control applications, however, the calculation time needs to be improved, and this can be done by the use of a trained ANN. This paper presents the considerations for constructing ANNs for DSs, and describes a method for training the system in order to support switching events representing a change in topology. The solutions for three DSs, balanced as well as unbalanced, are presented and the various considerations affecting the most appropriate ANN construction are discussed. The results are compared to the solution from the classical complex Newton‐Raphson and the fixed‐point iterative methods. The solutions have very high precision and good results are found for switched laterals. The computational performance is also compared and an improvement of two orders of magnitude is observed. [ABSTRACT FROM AUTHOR]

With the advent of small‐scale heat and electricity producers in distribution energy systems, the interdependencies between energy carriers have been increased. Moreover, the rapid deployment of micro CHP, electric heat pumps, electricity‐to‐heat appliances etc., calls for new local market frameworks to be employed in distribution energy systems. In response, this paper presents a new energy market framework based on the concept of peer‐to‐peer negotiations to facilitate energy transactions between agents at the distribution level while addressing the interdependencies between different energy carriers. Moreover, linear optimization problems are proposed to investigate the optimal strategies of market participants with various configurations. These optimization problems not only obtain the optimum strategy of agents to participate in the day‐ahead peer‐to‐peer electricity market, the day‐ahead heat market, and gas distribution systems but also obtain their optimal operation scheduling for the entire next day. The numerical results successfully demonstrate the applicability and merits of the proposed market framework. [ABSTRACT FROM AUTHOR]

In a transmission line, sag is intentionally provided to relax the tension on the wire when placed between two terminals. However, thermal stress and extreme weather conditions can cause increases in transmission line sag, which can eventually damage transmission line infrastructure. In this regard, this paper examines a number of sag measurement techniques based on physical or optical properties and integrated with signal processing and communication technologies. The performance of different sag measurement methods is compared in terms of accuracy, precision and key feature. Several challenges and limitations of the existing sag measurement mechanisms are reported. A number of recommendations to address the identified challenges are made. Moreover, an efficient communication framework for sag related information exchange is proposed. This comprehensive review of the sag measurement techniques will facilitate the evolution of new and efficient real‐time sag measurement and monitoring methods. [ABSTRACT FROM AUTHOR]

Geomagnetically induced currents (GICs) are an impact of space weather that can occur during periods of enhanced geomagnetic activity. GICs can enter into electrical power grids through earthed conductors, potentially causing network collapse through voltage instability or damaging transformers. It would be beneficial for power grid operators to have a forecast of GICs that could inform decision making on mitigating action. Long lead‐time GIC forecasting requires magnetospheric models as drivers of geoelectric field models. However, estimation of the geoelectric field is sensitive to high‐frequency geomagnetic field variations, which operational global magneto‐hydrodynamic models do not fully capture. Furthermore, an assessment of GIC forecast uncertainty would require a large ensemble of magnetospheric runs, which is computationally expensive. One solution that is widely used in climate science is "downscaling," wherein sub‐grid variations are added to model outputs on a statistical basis. We present proof‐of‐concept results for a method that temporally downscales low‐resolution magnetic field data on a 1‐hr timescale to 1‐min resolution, with the hope of improving subsequent geoelectric field magnitude estimates. An analog ensemble (AnEn) approach is used to select similar hourly averages in a historical data set, from which we separate the high‐resolution perturbations to add to the hourly average values. We find that AnEn outperforms the benchmark linear‐interpolation approach in its ability to accurately drive an impacts model, suggesting GIC forecasting would be improved. We evaluated the ability of AnEn to predict extreme events using the FSS, HSS, cost/loss analysis and BSS, finding that AnEn outperforms the "do‐nothing" approach. Plain Language Summary: Forecasting space weather impacts on ground‐based systems, such as power grids, requires the use of computer simulations of the disturbance of the Earth's magnetic field by the solar wind. However, these computer simulations are often too smooth, underestimating small and fast variations in the Earth's magnetic field, which are important for modeling induction hazards that may affect power grids. In this paper we present a proof‐of‐concept scheme that attempts to introduce realistic high‐frequency variations using the idea of looking at how the field has previously behaved in historical events. We test the model and find that it allows for better impact forecasting than if our scheme is not used. Key Points: Operational global MHD models do not fully capture the ground‐level magnetic field variability important for modeling induction hazardsWe provide a proof of concept model to statistically introduce realistic, high‐resolution perturbations with which to drive an impacts modelOur downscaling scheme outperforms a reference linear‐interpolation approach under a range of metrics [ABSTRACT FROM AUTHOR]

This paper assesses the viability of providing down and up spinning reserves by renewable energy resources (RES) in island power systems. The process consists of evaluating the impact of providing spinning reserve on the system operation costs of different islands by simulating the unit commitment problem. The assessment is carried out for La Palma (small size) and Tenerife (medium size) island power systems, and by considering different wind source availability scenarios for sample weeks of different seasons in current and future years. This paper differentiates between up and down reserves and studies their impacts separately. Results show that enabling RES to provide just down spinning reserve has economic benefits for all scenarios, by reducing over 40% the amount of thermal generation and over 30% the systems costs for high wind scenarios. It also confirms that employing variable deloading of wind energy as a source of up reserve is advisable, mainly in scenarios with high share of wind sources. In some scenarios, using RES as reserve provider, reduces the amount of thermal generation more than 50%, compared to when RES does not participate as a source of reserve, and can even lead to a full RES coverage of demand. [ABSTRACT FROM AUTHOR]

An introduction to articles in the issue is presented on topics including planning and operation of microgrids as part of cellular energy systems, modelling and simulation of microgrids within cellular energy systems, and ancillary services in cellular energy systems incorporating microgrids.

This study underscores the contexts and crisis of privatisation in Nigeria's power sector to apprehend the level of prepaid meters distribution to consumers, the conflicting billing methods adopted and its impact on both the electricity consumers and revenue generation stream of the Enugu Electricity Distribution Company (EEDC). The descriptive survey method and correlation design were used, with sample drawn from Enugu metropolis, Nigeria. The results reveal that the introduction of prepaid metering and billing system did not improve electricity power distribution to consumers. There are still huge metering gaps in prepaid meter distribution to consumers resulting in distorted/partial implementation of both prepaid and estimated billing systems at the same time. This leads to imposition of non‐cost reflective tariffs on electricity consumers. It undermines efficiency, encourage corruption, meter scam by EEDC workers and low revenue generation for EEDC. The Nigeria Electricity Regulatory Commission (NERC) should use taskforce to strictly monitor the distribution of prepared meter in Enugu metropolis by the Discos and promptly sanction the EEDC for infractions, to guarantee adequate electricity consumers protection in Nigeria. [ABSTRACT FROM AUTHOR]

In recent years, the importance of deregulated power systems has grown significantly, resulting in positive effects on stability, reliability, innovation, and investment in new energy grid technology. The competitive landscape among energy providers and distributors has empowered consumers to not only save money on their energy bills but also incorporate sustainable energy sources into the grid. To efficiently manage electricity distribution, deregulated power systems must include a smart grid and microgrid (MG). Herein, the potential for sustainable expansion of these systems, as well as their economic and environmental implications, are examined. A comprehensive grid system that integrates smart grids and MGs can offer a complete solution, catering to the evolving energy needs of communities and businesses. The advantages of establishing such a system, including improved grid stability, reliability, and increased utilization of renewable energy sources (RES), are highlighted. Furthermore, the integration of MGs and smart grids enhances the management of distributed generation, allowing power companies to optimize system operations for profitability and efficiency. By following these suggestions, businesses and stakeholders in the power sector can enhance the efficiency and responsibility of their systems, resulting in benefits for both the economy and the environment. [ABSTRACT FROM AUTHOR]

This paper shows the characteristics of an algorithm that calculates the corona losses of transmission lines. The line can have up to four circuits, with any configuration, with single or bundled conductors, and taking into account the line unbalance. This is achieved by modelling the lines in phase quantities, for active conductor, and calculating the corona losses for each one. The results are given per active conductor and average per bundle. In lines with several conductors per phase the results are given per circuit and a total one. These characteristics allow choosing the most convenient configuration and spacing for the conductor bundles, which will reduce and balance corona losses. The results found with this method show that the value of the losses calculated with the formulas with transmission lines symmetrically transposed is conservative. The configuration and the number of conductors per phase have a noted influence in the value and the asymmetry of the corona losses. In double circuit lines, corona losses can be reduced by changing the arrangement of the phases of circuit two in reference to circuit one, with the advantage that, at the design stage, those changes are costless. [ABSTRACT FROM AUTHOR]

In order to extract the fault characteristics of the distribution power system automatically, a new method for fault identification based on parameter optimized variational mode decomposition (VMD) and convolutional neural network (CNN) is proposed. First, the VMD is used to decompose electric signals of the main transformer, including the current of the low‐voltage inlet line, bus voltage, and so on. The obtained intrinsic mode functions should be arranged in sequence from low to high according to the central frequency. The VMD can obtain the time‐frequency matrices. In order to decide the decomposition layers of the VMD adaptively, the partial mean of multi‐scale permutation entropy is used to optimize it. Then, the time‐frequency matrixes can be converted into pixel matrices by pseudo‐colour coding. The pixel matrices which are appropriately compressed can be used as input of CNN. The CNN can autonomously extract the eigenvectors of fault. Finally, the classification algorithms of support vector machine (SVM), naive Bayesian classifier (NBC), extreme learning machine (ELM), and random forest (RF) are used to train and test the feature vectors extracted by CNN to verify the effectiveness of the method. The experiments show that the proposed method has excellent stability, fast convergence speed, and high precision. The technique can effectively complete the fault identification of the distribution network. [ABSTRACT FROM AUTHOR]

Widespread adoption of electric vehicles (EVs) would significantly increase the electrical load demand in power distribution networks. Most previous studies investigated EV charging demand based on drivers' trip habits, but the impact of psychological bearing ability (PBA) about the range anxiety on EV drivers' charging decision are ignored. Here a novel forecast method considering drivers' PBA for predicting nodal charging demand of EVs is proposed. The charging decision model considering PBA is established based on an improved Richards model, and the spatial‐temporal dynamics model is established based on the non‐homogeneous Markov chain (NMC) and random trip chain. Meanwhile, the Monte Carlo simulation (MCS) is adopted to avoid the disaster of dimensionality in large scale EVs charging problem. The proposed method is illustrated by an actual system integrated traffic network and power grid. The simulation results demonstrate that drivers' PBA will significantly affect the charging decision, then changes the spatial‐temporal distribution of charging power demand. The conclusion is that the drivers with lower PBA have a higher charging demand, and the impact of drivers' PBA on charging power has a close relation with the initial battery level (IBL) of EVs. [ABSTRACT FROM AUTHOR]

A continuous supply of electricity is necessary to maintain an acceptable standard of life, and the power distribution system's overhead line components play a crucial role in this matter. In Pakistan, identifying defective parts often necessitates human involvement. An unmanned aerial vehicle was used to gather a collection of 10,343 photos to automate this procedure. Using supervised and unsupervised machine learning methods, a number of automated anomaly detection systems were created. Support vector machine, random forest, VGG16, and ResNet50 were used as supervised machine learning models, and a convolutional auto‐encoder was used as the unsupervised machine learning model. VGG16 achieved the best accuracy of 99.00% while random forest achieved the worst accuracy of 72.49%. The convolutional auto‐encoder was successful in distinguishing between normal and abnormal components. The aforementioned machine learning models can be put on unmanned aerial vehicles to immediately identify defective parts. [ABSTRACT FROM AUTHOR]

An inverter‐based microgrid is a small‐scale power network governed by a distributed control system. In this system, the nodes are the digital controllers of the power inverters, normally located at separate points within the microgrid. A relevant issue is that these controllers operate at different frequencies due to inherent clock deviations in the local hardware oscillators. This paper evaluates the effects of these clock deviations on the performance of microgrids equipped with inverters that emulate the operation of synchronous machines. A systematic procedure is presented to derive steady‐state expressions of the inverter active power and microgrid frequency as a function of clock drift rates. This procedure is applied to swing and governor equations of the virtual synchronous generators, revealing the mechanism that allows clock drifts to be absorbed, making their presence negligible. In addition, it allows recognising the controllers that should never be implemented in a distributed control system, since they cause an unsatisfactory behaviour that can even lead to a blackout in the microgrid. Therefore, the relevance of this study is the identification of the control schemes that are most sensitive to clock drifts, which makes it easier to choose the most suitable control implementation for a particular application. Furthermore, technical guidelines are reported to help researchers on developing control solutions more robust to clock drifts. In this study, the theoretical results are validated by experimental tests in a laboratory microgrid. [ABSTRACT FROM AUTHOR]

The electric power system is one of the most important critical infrastructures of a country. Recently, the number of natural and man‐made disasters is increased, which can impose extensive damages and costs to the power system. A resilient power system can withstand against, adapt to and recover from these disasters. Power system resilience is quantified by mathematical tools which are called "resilience metrics". Currently, a lot of resilience metrics are proposed in the power system literature. In this paper, based on the extensive research in the critical infrastructure resilience literature which specifically concentrates on the "area‐based" resilience metrics, a new area‐based resilience metric is proposed which can measure the power system resilience considering the government policymaker criteria, which are rarely noticed before. The proposed and conventional area‐based resilience metrics are evaluated based on the real data from the 2012 Superstorm Sandy in the USA, which led to significant damage to the power distribution system. The simulation results show that the proposed area‐based resilience metric is very simple, can successfully address actual power system performance curves and is more meaningful and tangible than the conventional area‐based metrics for the government policymaker. The proposed area‐based resilience metric has also a general form and can be used for other critical infrastructures. [ABSTRACT FROM AUTHOR]

Lightning strikes to a substation happen frequently, which has a serious impact on the safety of the power system. Therefore, it is important to evaluate the effect of the direct lightning to the substation for the sake of protection. However, since the propagation and attachment of the lightning leader is a typical random process, it is hard to sufficiently represent the effect of the direct lightning by some deterministic models. In order to take into consideration the randomness of the lightning propagation process, in this paper, a three dimensional lightning fractal model that reproduces the tortuosity and branching of natural lightning is established. The proposed model is then applied to the effect analysis of the direct lightning to a typical 220 kV substation. Moreover, the protection effect of different distribution and height schemes of lightning rods is investigated using the proposed model. This work helps to understand the lightning strike risk that the substations face and also provides a promising way to improve and optimize the protection design. [ABSTRACT FROM AUTHOR]

This study proposes a TOPSIS‐based method for assessing the ability of distribution networks to accept electric vehicles. This method establishes an assessment index system in terms of the rationality, safety, and economy of the distribution network operation, and assesses the capacity of the distribution network in all aspects. Firstly, a fuzzy theory‐based model of users' charging psychology under the influence of time‐of‐use electricity price was constructed, and the spatio‐temporal distribution of EV charging loads in the target area was predicted using travel chain theory and Monte Carlo methods. Secondly, considering the rationality, safety and economy of the distribution network operation, a comprehensive evaluation index system for acceptability has been constructed. Then, a comprehensive weighting method for evaluation indexes based on AHP and entropy weight method is proposed, and the improved TOPSIS is used to evaluate the acceptance capacity of the distribution network when EV charging loads are connected in different ways. Finally, a typical IEEE33 distribution network is used to simulate the time and space distribution of the charging load, and taking the charging load access schemes proposed in this paper to verify the effectiveness of the evaluation method. [ABSTRACT FROM AUTHOR]

Upgrading protection schemes in distribution networks can help electric utilities to improve the reliability of their supply. Fuses and reclosers are still the primary protective devices used in distribution grids, and their optimal allocation allows isolating faults with the least number of interrupted customers. However, most models used to select and locate protective devices optimally do not consider their coordination or rescue schemes for the fuses, resulting in unfeasible or impractical solutions. This paper presents a mixed‐integer linear program model for the simultaneous selection, localization, and coordination of protective devices (reclosers and fuses), considering the fuses' rescue schemes based on electrical criteria. Local reliability indices and economic penalties for violating minimum standards are integrated into the model, thereby providing new decision‐making elements. The proposed approach is tested in different distribution networks to showcase its key features. Computational experiments show that the proposed model can satisfactorily handle the trade‐off between costs and reliability. [ABSTRACT FROM AUTHOR]

Microgrids suitable operation depends on the system's proper design, based on accurate component models. A complete model considering entire system dynamics is a vital necessity for a successful design. Using dynamic load in microgrid small‐signal model results in a model that shows transient and steady‐state dynamics, since designing a low‐inertia system like microgrid need extra accuracy. In this paper, an inverter‐based microgrid's small‐signal model in islanded operation mode containing a dynamic load model has been achieved. The Exponential Recovery Load (ERL) model is presented here to study the dynamic behavior of load. Microgrid stability analysis has been carried out for both static and dynamic load models. To attain this goal, the entire component equations are obtained and linearized around an operating point, then the state‐space model is formed and eigenvalues are derived. System stability condition has been investigated by plotting eigenvalues and root locus method. In the final step, a simulation on Matlab/Simulink is performed to show the microgrids operation in the presence of a static and dynamic load. [ABSTRACT FROM AUTHOR]

This paper studies the power production optimization problem of multiple offshore wind farms (OWFs) considering different maintenance demands and requirements under the wind power uncertainty. A decentralized robust operation optimization model is developed, in which OWFs and a maintenance service provider (MSP) are treated as individual stakeholders with different interests but coupled by maintenance resources. Based on the analytical target cascading (ATC) algorithm, the model is decoupled into independent models for MSP and each OWF. The operation and objectives of individual entities can be autonomously optimized only considering their own conditions. For each OWF, the power production planning coupled with maintenance scheduling is formulated as a two‐stage robust optimization model and solved by a column‐and‐constraint generation (C&CG) algorithm to tackle the wind power uncertainty. Numerical experiments demonstrate the effectiveness of the proposed model and the applicability of the integrated solution method to the studied problem. Results illustrate that the developed model shows a better guarantee for individual interests of OWFs, lower communication burden, and better information privacy than the centralized optimization model, especially in large‐scale systems. The integrated solution framework can guarantee a quick convergence and optimality. Moreover, it reduces impacts of uncertain factors on power productions and maintenance schemes. [ABSTRACT FROM AUTHOR]

Dispatchable renewable generation is essential for 100% renewables power system. It is in the interest of both power system operators and customers since it reduces the need for flexibility and reserve. To this end, battery energy storage systems (BESS) are proposed for integration in the renewable power plant. This paper presents the optimal dispatch unit for a dispatchable hybrid solar‐wind power plant with BESS framework. It achieves optimal dispatchable renewable generation (from dispatchable hybrid renewable (solar‐wind) power plant with BESS, DHRB, operator perspective), subject to operational limits, by exploiting the synergy of wind and solar energy and combining it with storage capability of BESS using two different operation strategies, maximisation of revenues and maximisation of renewables harvesting. A continuous BESS degradation model is incorporated in the proposed rolling‐algorithm‐based‐optimal dispatch unit to improve the accuracy of results. The applicability of the proposed methodology and the performance of the operation strategies are demonstrated using a case study and the operation strategies are compared. Further, the effect of BESS size on its degradation and dispatchable power in a hybrid solar‐wind power plant with BESS are investigated through BESS lifetime. An indicative economic analysis is carried out to provide ground for the extra investment on and sizing of BESS for a dispatchable hybrid renewable power plant. [ABSTRACT FROM AUTHOR]

The effects of sampling frequency on the efficacy of the POD technique for reconstructing the flow field of a wind turbine wake are studied in a neutral atmospheric boundary layer (ABL) using large eddy simulations (LES) coupled with an actuator disk model. The objective of this paper is to study how the sampling frequency over a fixed period of time affects the POD modes. The results showed that sampling frequency has little influence on the convergence of POD modes and the extraction of large‐scale coherent structures. Meanwhile, sampling frequency has negligible influence on the kinetic energy contained in each mode of the first 25 modes. When the mode number is greater than 25, it affects the distributed energy in each mode and the accumulative energy in a given number of modes, and the accuracy of the reconstructed flow field. In present study, the flow field reconstructed using the smallest sampling frequency can be as accurate as that with the largest sampling frequency, while the former can save about 86% of the computational cost. For a given number of POD modes, a smaller sampling frequency may lead to more accurate flow field reconstruction. The sampling frequency mainly affects the small‐scale structures. [ABSTRACT FROM AUTHOR]

This paper presents a new protection scheme with low communication capacity requirement for protecting lines connected to a doubly‐fed induction generator (DFIG)‐based wind farm (WF). The proposed relaying scheme addresses the line protection challenges which stem from the non‐synchronous frequency component of the current fed from a DFIG‐based WF during a short‐circuit fault. The fault current frequency of a DFIG‐based WF deviates from the synchronous frequency during a fault, which affects the operation of distance relays. In such a scenario, the distance relay located at the WF terminal may lose its coordination with downstream relays, resulting in unnecessary tripping. The proposed scheme relies on the impedance trajectory captured by the local relay, local fault current characteristics, and the frequency tracking of the fault current measured at the distance relays located at the two ends of the transmission line connected to a DFIG‐based WF. The reliable performance of the proposed scheme is verified on a 4‐bus test system under balanced and unbalanced faults during super‐ and sub‐synchronous operating modes of the DFIG as well as its robustness against power system disturbances. [ABSTRACT FROM AUTHOR]

Accessibility estimation is significant to the offshore wind farm operation and maintenance (O&M) due to the extremely limited weather window and its sensitive effects on O&M tasks. Wave forecasting can be one solution to help maintenance decision‐making. However, the uncertain and dynamic properties of wave forecasts are seldom considered in the accessibility estimation process. This paper presents an uncertain accessibility estimation method based on a multi‐step probabilistic wave height forecasting (MPWHF) model and Monte Carlo simulation. First, an MPWHF model is proposed using the wavelet decomposition and the sequence to sequence (Seq2Seq) network with quantile outputs. Second, the O&M missions are randomly given a start time and simulated in the O&M flow chart by the Monte Carlo method. Finally, several access indexes, including accessibility probability, delay time, and delay probability, are evaluated based on the simulation results. Verification of the proposed MPWHF model and uncertain accessibility estimation is based on 7‐year observation data of a buoy station. The results show that the MPWHF model outperforms other counterparts and the probability of offshore accessibility is nonlinearly dependent on the weather limits and the O&M required time. [ABSTRACT FROM AUTHOR]

This paper proposes a frequency‐control scheme for Doubly Fed Induction Generator (DFIG)‐based (Type‐3) wind turbines to improve the primary‐frequency‐control when the grid power balance is disturbed. The increasing penetration level of renewable energy sources, like wind power plants, reduces the total available inertia of modern grids, which deteriorate the frequency response in case of sudden power‐mismatches. The proposed closed‐loop participation of wind power plant interacts with the thermal units to reduce the frequency nadir and frequency settling‐time, during the inertial and primary stages. The designed disturbance observer decreases the uncertainties in the estimation of grid parameters, which results in robust PI performance in adjusting the ancillary power provided by wind turbines. Certain measures considered within the control loop to limit the rate‐of‐change‐of‐frequency within the permissive range to avoid the protective relays tripping. Comparative simulations studies on modified IEEE 9‐bus and 68‐bus test systems verify the effectiveness and advantages of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive real‐time energy scheduling method (RT‐EMS) for a microgrid, using a Lyapunov optimization‐based real‐time approach. Inaccuracy in day‐ahead predictions can result in non‐optimal solutions to the energy scheduling problem. Although the real‐time optimization method eliminates the need to deal with the prediction uncertainties, it ignores the valuable statistical information used in day‐ahead stochastic approaches and provides suboptimal solutions to the problem. The proposed adaptive approach combines the advantages of both the stochastic day‐ahead and the RT‐EMS and reduces the real‐time operational cost of the microgrid. The proposed method moves the RT‐EMS solution towards the optimal solution, by adding a penalty term to the objective function. Numerical results are provided to demonstrate the improved performance of the proposed adaptive method. [ABSTRACT FROM AUTHOR]

Dynamic voltage restorer (DVR) plays an essential role in achieving high‐quality voltage restoration in the distribution system. This paper presents an elliptical‐trajectory‐based approach to guarantee continuous power delivery by considering the active and reactive power limits during voltage sags. Besides, two parameters are proposed in the accurate mathematical analysis of current references. Thus, the control objective can be fulfilled by the flexible current injection strategy that combines a precise balance between the positive and negative sequences. The proposed control method can determine the required power injections by selecting the two proper values to support that the converter output current is smaller than the maximum rated value. Under the presented control, the maximum power limit and flexible power dispatch are combined together to develop four operation schemes. The main advantage is the smooth transition process during changes in the operation schemes and the disappearance of active and reactive power oscillations. Also, these four control states are applied to reveal the flexibility of the proposed designs. Finally, the hardware tests in a laboratory prototype are implemented to validate the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

Unexpected imbalances between supply and demand during real‐time operation in the wake of high penetration of renewable energy resources necessitate system operators to improve flexibility of the system. Moreover, increasing growth in flexible distributed resources installation in distribution networks with multiple MicroGrids (MGs) such as fuel‐based distributed generators, storage units, and controllable loads makes distribution system operators be able to manage energy imbalances locally through exploiting potentials of aforementioned resources in flexibility enhancement. This paper proposes a receding horizon data‐driven chance‐constrained energy flexibility trading framework for active distribution networks with multiple MGs. Specifically, a distance‐based confidence set is constructed in a data‐driven manner to apply net load uncertainty into this modelling. According to this proposed data‐driven approach, the energy flexibility is modelled using flexibility envelope notion to demonstrate its dynamics in different scenarios in an imminent horizon. Furthermore, MGs' flexibility trading problem is modelled as an exact potential game that possesses a pure strategy Nash Equilibrium (NE). An iterative decentralized algorithm based on alternating direction method of multipliers is developed to achieve the NE strategy of MGs. Numerical results are also represented to show the performance efficiency of the proposed energy flexibility trading framework. [ABSTRACT FROM AUTHOR]

The implementation of synchronous phasor measurement units (PMUs) in distribution networks provide accurate and GPS‐based synchronous data which support the grid flexible reconfiguration, such as island partition and island reconnection. Utilizing the high resolution PMU data, the phase angle droop control can be implemented on islanded microgrids (MGs) without causing the deviation on frequency brought from traditional frequency droop control, consequently support the synchronous and seamless transition. In this paper, a phase angle droop control method considering the incremental cost of distributed generation (DG) is proposed utilizing distributed finite‐time protocol to realize cost minimization and consensus active power sharing of DG units. In addition, an observer‐based voltage controller with finite‐time convergence velocity is implemented to meet the target of DG units' voltage recovery and the accuracy of reactive power sharing. The proposed method is implemented through a sparse communication topology and exchange information with their own neighbours only. The effectiveness, robustness and scalability of the proposed distributed control method are verified through case studies. [ABSTRACT FROM AUTHOR]

Here, a novel tri‐level energy market model aimed at addressing the challenges posed by demand side management (DSM) in the electricity distribution company (EDC) is introduced. DSM has emerged as a new strategy employed by EDCs to manage and control electricity demand by encouraging end‐users to modify their electricity consumption patterns. This is achieved through the participation of demand response (DR) aggregators, which play a crucial role in assisting end‐users with strategies and technologies to reduce their electricity consumption during peak hours. The proposed tri‐level energy market model consists of four distinct players: EDC, microgrids, aggregators, customers. The interactions between these four actors are modelled within a tri‐level game framework, where the EDC and aggregators act as leaders, and the micro‐grids and customers are followers. This multi‐level and multi‐player game structure allows for a more realistic representation of the complexities involved in DSM programs within the energy market. To demonstrate the effectiveness of the proposed model, a real case study is utilized, showing that the new model better resembles real‐life market conditions. The results illustrate how the tri‐level energy market model can significantly reduce demand fluctuations during peak hours, leading to improved efficiency and effectiveness within DSM programs. [ABSTRACT FROM AUTHOR]

Electricity demand forecasting plays a vital role in power system planning, operation, transmission design, and financial risk management. In this study, we employ a Gaussian Process model to estimate the probability distributions of short‐term electricity demand. To account for input uncertainty in multi‐period ahead forecast, we adopt an error propagation procedure that improves upon the naive recursive forecasting. This method is shown to perform well in terms of multi‐step ahead point and probabilistic forecasts. We illustrate the usefulness of the suggested estimator with an application to short term demand forecasting of Texas electricity market. [ABSTRACT FROM AUTHOR]

This work is motivated by the need in overcoming the electricity crisis in Gaza, which is initiated due to political reasons and the spread of COVID‐19. Building quarantine centers is one of the most important means used in combating the COVID‐19, but connecting these centers to the electricity distribution network at the appropriate time is not always possible and increases the burden on the local utility company. This article proposed a hybrid off‐grid energy system (HES) to effectively energize the quarantine COVID‐19 center in Gaza economically and environmentally. To achieve this aim, the estimated load profile of the quarantine center is fed to the HOMER‐Pro program. In addition, the various systems components are introduced to the program, then modeled, and optimized. The developed approach was tested using a real case study considering realistic input data. HOMER‐Pro program is used to simulate and optimize the system design. The results revealed the potential of the HES to provide environment‐friendly, cost‐effective, and affordable electricity for the studied quarantine center, as compared to just the diesel generators system. For the considered case study, it is found that the PV‐wind‐diesel generators HES can cover the connected load with the lowest cost ($ 0.348/kWh) in comparison to other possible HES structures. Taking into consideration the price of harmful emissions, the wining system shows a reduction of 54.89% of the cost of energy (CoE) compared to other systems. For the considered case study, it is found that a combination of 150 kW PV, 200 kW wind, and two diesel generators with capacities of 500 and 250 kW can hold 100% of the electrical load required to keep the quarantine COVID‐19 center in operation. The initial capital cost of this HES is $510,576 where the share of wind energy, solar PV, inverter, and diesel‐electric generators are $320,000, $83,076, $25,000, and $82,500, respectively. The replacemen cost ($55,918) is due to diesel generators. The total operation and maintainance cost (O&M) is $268,737, that is, 25.6% for wind turbines, 1.2% for inverters, and 70.7% for diesel electric generators. The PV/wind/diesel generators HES generate 1,659,038 kWh of electricity. The total energy requirement of 1,442,553 kWh, which means a surplus of 212,553 kWh of energy/year. The total energy (kWh) is an integration of energy sources which are 427,276 (25.8%), 274,500 (16.5%), and 857,263 (57.7%), due to wind, solar and diesel generators respectively. The cost of yearly consumed fuel is $437,828.769. The payback period for the winning system is 1.8 years. Finally, it is proved that the developed approach gives a reasonable solution to the decision‐makers to find a fast, economic and reliable solution to energize the quarantine centers. [ABSTRACT FROM AUTHOR]

We propose an endogenous merger algorithm to evaluate the impact of government‐provided incentives on consolidation patterns for services such as electricity distributors, school boards, hospitals and municipalities. The algorithm replicates the observed industry reconfiguration, with calibrated parameters used to simulate consolidation patterns that would have resulted from policy incentives. We apply the method to the case of Ontario, where transfer tax reductions have been proposed to incentivize consolidation of electricity distributors. We find that the proposed incentive would have no impact on efficiency and consolidation, and even subsidies would still leave many more electricity distributors than desired by policy makers. [ABSTRACT FROM AUTHOR]

Short‐circuit accidents hinder the supply of electric power in distribution lines. If a short‐circuit occurs, the power supply is interrupted and can be resumed only after the short‐circuit point is repaired. Identifying the short‐circuit point is time consuming because it is necessary to confirm whether the overcurrent passage indicator is operating and visually confirm the short‐circuit point. Therefore, we propose a method of installing an abnormal detection line under the high‐voltage line and identifying the short‐circuit point based on the current ratio before and after the short‐circuit flowing to the current‐measuring devices installed at both ends of the abnormal detection line. Because the current flowing in a high‐voltage distribution line at the time of the short‐circuit depends on the short‐circuit resistance value, the range of application of the short‐circuit resistance value is also discussed. We show that the method using abnormal‐detection lines can be applied to estimate the high‐resistance ground faults and disconnection points, and the ability to estimate short‐circuit points leads to the efficient use of the method using abnormal detection lines. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]