Rechargeable battery banks have been widely utilised in islanded microgrids as energy storage systems to complement the instant power imbalance in real-time. However, the cycle degradation becomes an unavoidable concern of the battery energy storage systems (BESSs) in achieving microgrid economic dispatch (ED). In this study, a novel degradation cost model based on an online auction algorithm is proposed for real-time management of BESS. To settle the intermittent distributed sources in real-time operation, a Wasserstein ambiguity set is adopted to characterise the uncertainties. Meanwhile, the authors newly reformulate the real-time microgrid ED as a two-stage distributionally robust optimisation (DRO) problem. To improve the tractability and scalability of the DRO problem, a model predictive control (MPC)-based data-driven approach is proposed, in which a novel affine policy namely extended event-wise affine adaption is properly employed. Through extensive case studies, the numerical results demonstrate the effectiveness of the proposed approach.

With a high penetration level of renewable energy resources (RESs) in the distribution network (DN) and microgrids (MGs), how to realise the coordination between the two entities while takes the uncertain RESs into consideration becomes an urgent problem. A data-driven distributionally robust economic dispatch (DRED) model for both DN and MGs is proposed in this study, wherein the 1-norm and ∞-norm are used to construct the confidence set for the probability distribution of the uncertainties based on historical data. The DN and each MG are considered as independent entities to minimise their own operation cost. The alternating direction method of multipliers is utilised to coordinate the power exchange between DN and MGs and realise the autonomy of each entity. The column and constraint generation algorithm is used to solve the proposed data-driven DRED model for each entity. Considering the special structure of the proposed DRED problem, a duality-free decomposition method is adopted. Thus the computational burden is reduced. Numerical results on a modified IEEE 33-bus DN with three MGs validate the effectiveness of the proposed method.

In this study, a novel data-driven based dynamic pricing framework is proposed for sharing rooftop photovoltaic (PV) energy in a single apartment building. In this framework, the input includes the load data, electricity price data, and PV power data and the output includes the pricing strategy for local PV generations. Specifically, the building energy management system operator is responsible for setting internal uniform prices of their own rooftop PV productions to facilitate the local PV energy sharing with apartment building users, aiming to maximise the economic profits. To protect the privacy of apartment building users and meanwhile improve the computational efficiency, a neural network is designed for simulating their demand response (DR) behaviours. Besides, the uncertain rooftop PV generations can be duly addressed by using a well-trained long short-term memory network, which can capture the future trends of rooftop PV generations in a rolling-horizon manner. With the information on PV predictions and DR results, a model-free reinforcement learning method is developed for finding the near-optimal dynamic pricing strategy. The simulation results verify the effectiveness of the proposed framework in making dynamic pricing strategies with partial or uncertain information.

Security-constrained unit commitment (SCUC) is one of the most important optimisation problems for operation planning of power systems. Indeed, with the fast expansion of power grids and the increasing growth of heterogeneous market participants in electricity markets, the day-ahead SCUC continuously encounters significant performance challenges. To improve the computational performance of SCUC and achieve solutions of good quality, an integrated framework which combines a data-driven approach and a variable-aggregation method is presented in this study. The variable-aggregation method effectively approximates the original network security constraints with a reduced number of variables. Moreover, as aggregated constraints could reduce the feasible region of the original SCUC and potentially degrade solution quality for certain SCUC instances, it is preferable to only apply such an aggregation approach towards difficult SCUC instances. Therefore, a data-driven classification method, by adopting relevant SCUC input data as features, is integrated to first predict whether a SCUC instance is ‘easy’ or ‘hard’. To this end, the integrated framework could improve the overall performance of SCUC instances, by and large, in terms of computational efficiency and solution quality. Numerical results illustrate the effectiveness of the proposed integrated framework.

High-density synchrophasors provide valuable information for power grid situational awareness, operation and control. Unfortunately, due to factors including communication instability and hardware failure, their data quality can be greatly deteriorated by anomalies. Since the anomalies can impact the performance of the synchrophasor applications, it is of paramount significance to propose a model to detect anomalies in synchrophasor. In this study, a convolutional neural network model is established to detect and classify the anomalies in the synchrophasor measurements. Four types of anomalies observed in actual synchrophasors including erroneous patterns, random spikes, missing points and high-frequency interferences are considered in this study. The proposed model is extensively evaluated via field-collected measurements from the synchrophasor network in Jiangsu grid, China. The superior performance of the proposed model indicates the great potential of using deep learning for the detection of abnormal synchrophasor measurements.

The synchronised phasor measurement units (PMUs), serving as ‘GridEye’, provide data that improve the observability and controllability of the power grid due to its high accuracy and high-upload rate. However, missing or abnormal data may seriously affect its applications. This study proposes a method to recover the missing or abnormal amplitude data in PMU measurements (i.e. the active power, reactive power, positive sequence current, and voltage amplitude), based on the historical PMU data obtained from both ends of the line, which is independent of the transmission line parameters and the phase angle that may be influenced by synchronisation. First, the issues in the quality of measured PMU data are analysed, and the motivation of the proposed method is stated. Then, a method to screen out the bad data from the historical data is proposed based on the density-based spatial clustering of applications with noise. Furthermore, the model of data recovery is established, and the recovery method, which recovers the voltage amplitude, active power, reactive power, and current amplitude in sequence, using historical amplitude data to calculate related recovery coefficients is proposed. Finally, the effectiveness and practicability of the proposed method are verified by examples of simulation and measured data.

This paper investigates the false data injection attacks (FDIA) in an AC smart island and the detection solution of the attack on distributed energy resources in a smart island. In this study, a new scheme of FDIA detection is proposed based on wavelet singular values as input index of deep learning algorithm. In the proposed method, switching surface based on sliding mode control breaks down for adjusting accurate factors of wavelet transform and then features of wavelet coefficients are extracted by singular value decomposition. Indexes are determined according to the wavelet singular values in switching surface of voltage and current which defines the input indexes of deep machine learning and detecting FDIA. This cyber-protection plan has been put forward for cyber diagnostic and examined in different types of attacks happening in voltage and current signals derivation of measuring sensors as well as sending and receiving data from communication and control systems. The main priority of the suggested detection plan is the high capability to detect FDIA with a high accuracy. To show the effectiveness of the proposed method, simulation studies are performed on AC smart island in MATLAB/Simulink environment.

Electromagnetic interference (EMI) diagnostics aid in identifying insulation and mechanical faults arising in high voltage (HV) electrical power assets. EMI frequency scans are analysed to detect the frequencies associated with these faults. Time-resolved signals at these key frequencies provide important information for fault type identification and trending. An end-to-end fault classification approach based on real-world EMI time-resolved signals was developed which consists of two classification stages each based on 1D-convolutional neural networks (1D-CNNs) trained using transfer learning techniques. The first stage filters the in-distribution signals relevant to faults from out-of-distribution signals that may be collected during the EMI measurement. The fault signals are then passed to the second stage for fault type classification. The proposed analysis exploits the raw measured time-resolved signals directly into the 1D-CNN which eliminates the need for engineered feature extraction and reduces computation time. These results are compared to previously proposed CNN-based classification of EMI data. The results demonstrate high classification performance for a computationally efficient inference model. Furthermore, the inference model is implemented in an industrial instrument for HV condition monitoring and its performance is successfully demonstrated in tested in both a HV laboratory and an operational power generating site.

Precise power transformer fault diagnosis involves incorporating multi-source monitoring information. Uncertain information, missing data, usually occurs in transformer fault cases and diagnosis tasks. To address these challenges, the authors proposed an integrated method of comprehensive transformer fault diagnosis. Diagnostic transformer rules extracted from fault cases form a decision-making table, whereby the main transformer monitoring information and fault types serve as conditional and decision attributes, respectively. Different fault-warning symptoms of the conditional attributes and corresponding decision attributes constitute diagnostic rules. Each obtained symptom in a diagnostic task is evidence supporting different fault types. A modified basic probability assignment (BPA) calculation method is proposed to determine the fault type probability by the obtained symptom. To address contradictory evidence, the symptom significance is introduced to design an improved combination rule incorporating all calculated BPA values to accomplish fault diagnosis. The obtained diagnostic results indicate that more symptoms and a higher symptom significance enable reliable transformer fault diagnosis. The recognition rate of the authors’ method reaches 91.2% with 12–14 symptoms and 94.3% for a 0.9 symptom significance coefficient. It is demonstrated that compared with other combination rules, their method attains a suitable performance (contradiction coefficient K = 0.9 at an 81.3% recognition rate) in realising contradictory information fusion.

Data-driven and artificial intelligence based transformer protection has attracted increasing attention but not been widely applied in the power system owing to the poor generalisation ability. In this study, a feature transferring method is proposed for a knowledge-based artificial neural network (ANN) to develop a transformer protection with an improved generalisation ability. Normally, power experts can reliably identify the running states based on the professional knowledge of only focusing on the unsaturated parts of equivalent magnetisation curve (voltage of magnetising branch-differential current). In order to imitate the power experts, the images of equivalent magnetisation curves whose saturated parts are removed are defined as source domain and the original samples are target domain. An ANN named as S:ANN is firstly trained through the source domain where the extracted features are equivalent to the professional knowledge. Then another ANN with the same structure as S:ANN is trained through the target domain and named as T:ANN. It is specially designed for T:ANN that adaptive layers are employed between S:ANN and T:ANN to reduce the feature differences. Finally, simulations and experiments reveal that the knowledge-based ANN namely the determined T:ANN shows a better generalisation ability through paying more attention to the unsaturated parts.

In the energy distribution networks, the most important and valuable equipment is oil-immersed distribution transformers. Besides, due to the key role of these transformers and their multiplicity, their lifetime monitoring is inevitable. The life of a transformer depends on the weakest solid insulation material (i.e. paper insulation). On the other hand, monitoring the transformer insulation status requires accurate information to be available about the oil temperature at every moment. Therefore, it is important to control and predict the oil temperature rise in the transformer. In this study, a new model based on fundamental heat transfer theory is proposed for thermal behaviour prediction of top oil of indoor distribution transformers using the concept of thermal resistance, namely electro-thermal resistance model (E-TRM). In E-TRM, the thermal resistance network is formed by following three-dimensional heat transfer path and assigning thermal resistance to each path. To evaluate the proposed E-TRM, the results of this model are verified with experimental results. Moreover, the E-TRM is used to predict the thermal behaviour of the indoor transformer in the overloading condition. At the end, the transformer loss of life is estimated based on the oil temperature and a normal cyclic overloading strategy is presented for overloading management.

Monitoring a large and geographically dispersed solar generation fleet can be a challenging undertaking. At Dominion Energy, there is currently over 1.7 GW of transmission and distribution-connected solar generation. Performing the post-mortem analysis and ensuring the safe and reliable operation on more than 100 solar facilities can take substantial engineering efforts. In this study, a renewable generation data collection platform is presented to address the increasing need for solar generation monitoring. The data platform consists of data collection, event storage, real-time alerts, and data analytics applications. The data analysis system can support various technical aspects in solar integration, including grounding monitoring, inverter fault response and model validation, power quality monitoring, and equipment overvoltage protection. The proposed data platform greatly improves the visibility of solar generation and ensures its successful integration.

Recently, power system reliability has been challenged due to the increment of electrical demand. When an outage occurs, locating the outage may take a long time because of the distribution system's radial structure and the presence of various elements. To decrease the outage detection time, this study proposes to classify the equipment-related outage causes to diagnose the faulty equipment at the time of outage occurrence. To this end, available historical outage, load and weather data sets are integrated, and various features are defined. Then, binary classifiers are developed to classify each equipment's failures against others'. To enhance classifiers' performance, this study also proposes to use cost function and ensemble models. The results of applying proposed classifiers show the accuracy of the proposed method and improvements in outcomes.

Determining an economical and reliable spare transformer strategy is an essential and challenging task for power utility companies. This study proposes a probabilistic cost-benefit analysis-based spare transformer strategy incorporating condition monitoring information. Not only the actual wearing condition of each transformer, but also the impacts of a transformer ageing process on spare transformer strategy are considered by the proposed method. First, the actual wearing condition and functional age of each transformer are estimated based on condition monitoring information. Secondly, the ageing process and ageing failure rate of each transformer are modelled by combining a Weibull distribution and its functional age. Thirdly, based on probabilistic cost-benefit analysis, the outage costs, as well as economic benefits under different inventory sizes, are estimated. The transformer's ageing process is modelled and considered in the determination of the optimal number and the acquisition timing of spare transformers. Finally, a transformer substation group and its condition monitoring information, and the real spare transformer strategy collected from a regional power grid in China were used to demonstrate an application of the proposed method. The results indicate that the spare transformer strategy determined by the proposed method not only enables the power utilities to avoid economic losses but also guarantees a high cost-benefit ratio of spare transformer strategy.

Accurate power system state estimation (SE) is essential for power system control, optimisation, and security analyses. In this work, a model-free and fully data-driven approach was proposed for power system static SE based on a conditional generative adversarial network (GAN). Comparing with the conventional SE approach, i.e. weighted least square (WLS) based methods, any appropriate knowledge of the system model is not required in the proposed method. Without knowing the specific model, GAN can learn the inherent physics of underlying state variables purely relying on historic samples. Once the model has been trained, it can estimate the corresponding system state accurately given the system raw measurements, which are sometimes characterised by incompletions and corruptions in addition to noises. Case studies on the IEEE 118-bus system and a 2746-bus Polish system validate the effectiveness of the proposed approach, and the mean absolute error is <1.2 × 10⁻³ and 5.3 × 10⁻³ rad for voltage magnitude and phase angle, respectively, which indicates a high potential for practical applications.

In this study, on the basis of a new agent-based situational awareness platform, the amount of severity in closeness to voltage instability occurrence is determined. In the proposed platform (with the aid of the agents of the disturbance detection unit that are responsible for perception of environmental events and understanding the current situation and the agents of the early enough instability prediction unit that predicts instability occurrence), the novelty is in creating the agents of clustering units that are responsible for prediction of the future status, i.e. severity in closeness to voltage instability occurrence through determining the severity class. This clustering unit maps the reactive power-based features obtained from unconstrained power flow analyses, to the instability time; without any need of post-disturbance data, i.e. the method belongs to just-after-disturbance algorithms. Then, it estimates the severity of the closeness to instability for any test scenario according to its membership degree related to the pre-defined severity clusters. The simulation results show acceptable overall accuracy (up to 99%) in performing early warning task for operators of power systems.

Typhoons have substantial impacts on power systems and may result in major power outages for distribution network users. Developing prediction models for the number of users going through typhoon power outages is a high priority to support restoration planning. This study proposes a data-driven model to predict the number of distribution network users that may experience power outages when a typhoon passes by. To improve the accuracy of the prediction model, twenty six explanatory variables from meteorological factors, geographical factors and power grid factors are considered. In addition, the authors compared the application effect of five different machine learning regression algorithms, including linear regression, support vector regression, classification and regression tree, gradient boosting decision tree and random forest (RF). It turns out that the RF algorithm shows the best performance. The simulation indicates that the accuracy of the optimal model error within ±30% can reach up to 86%. The proposed method can improve the prediction accuracy through continuous learning on the existing basis. The prediction results can provide efficient guidance for emergency preparedness during typhoon disaster, and can be used as a basis to notify the distribution network users who are likely to lose power.

Topology identification (TI) is an essential problem in the distribution network due to exponentially growing power grid size in recent years. In this study, it is reformulated as a regularised alternating convex optimisation problem. Then an application based on the current injection model is proposed. Compared to the traditional algorithm optimising -norm, which may lead to overfitting, a new -norm minimisation problem with -norm regularisation is proposed to solve the trade-off problem with non-convex constraints. The proposed method reduces the size of the training data set compared with the traditional TI method. Simulation results show that the recovery performance of the proposed algorithm is superior to the traditional one in additive white Gaussian noise scenario.

In actual power system operation, there is a significant imbalance between the number of stable and unstable samples, and the misclassification costs of the two classes of samples are different. In addition to the class imbalance, there is another imbalance of regional samples in the feature space. In order to reduce the impact of class imbalance and regional imbalance on the performance of the model, a method of dual cost-sensitivity factors for transient stability assessment is proposed. The method sets the balance factor and modulating factor in the loss function of light gradient boosting machine. The former corrects the classification bias caused by class imbalance, and the latter focuses on improving the classification accuracy of overlapping area samples. Combining the two factors, the model not only improves the accuracy of unstable samples, but also reduces the misjudgement of stable samples. In online application, a fast update scheme with memory function is proposed. In this scheme, incremental learning is used to update the model with fewer samples and less computational time, so as to achieve better evaluation performance. Case studies on three power systems demonstrate the generalisation performance of the proposed model and the effectiveness of the update scheme.

Large shares of renewable energy production in the electricity grid make grid expansion and new technologies necessary. The unavailability of grid models to address upcoming research questions led to the development of open source grid models. Our work contributes to establish the open_eGo model for grid simulations by validating its assumptions and results for a rural region with high share of wind energy. In particular, assumptions on electrical parameters and the graph structure of the model are compared to the grid owner's model along with a validation of AC load flow results at the model boundaries. It was found that the graph structure deviates in the degree of nodes and connection characteristic. These deviations are less exterior nodes and a lower maximum degree of nodes as well as a higher number of parallel lines in the open_eGo model. The AC load flow results differ slightly in active power and significantly in reactive power, but are more reliable than an aggregation of loads and generation to the extra high voltage (EHV) nodes. Concluding, the open_eGo model has a limited usability for simulating, understanding and optimising DSO grid operation but can enhance EHV-only analysis in large area contexts.

Load modelling is significant to ensure the accuracy of power system simulation. In previous research on load modelling, various optimisation algorithms have been widely applied. However, the achievement of the global optimal solution depends on the quality of the initial feasible solutions (IFSs). In this study, an enhanced measurement-based load modelling approach with ensemble learning-based initialisation is proposed to solve this problem. In the proposed method, an ensemble intelligent machine (EIM) is trained offline to provide high-quality IFSs based on which the load model parameters can be identified through optimisation. The input features of the EIM are extracted through numerical subspace state-space system identification from the measurement data, while the output of the EIM is the estimated load model parameters. Then, based on the offline generated samples, a group of individual intelligent units (IIUs) is trained and selected first, after which they are integrated to form an EIM. The enhanced load modelling approach is tested in a simulation case for the Guangdong power grid. The results show that the EIM has better performance than all the IIUs, and the identification accuracy of the load model parameters can be improved with the EIM estimated parameters as the IFSs.

An affordable, stable, reliable and sustainable power supply is the need of the hour, keeping in view the journey to a smart green grid. The present study and derivation of adaptive critical angle threshold (ACAT) contributes to this effect. The ACAT serves as a key index and an indicator to grid operator in proactive mitigation of angular instability in the grid, which is seen to be one of the major initiator of blackouts worldwide. The synchrophasor technology enables accurate calculation of ACAT on real time basis by time synchronised sampling of local voltage and current phasors. The real time calculation makes the index adaptive to variation in system topologies as well as system vulnerabilities. Furthermore, the local system model formed for deriving ACAT has less computational burden while capturing the dynamics of external system, without requirement of intricate power system details. The angular separation of the station bus under supervision can be monitored with respect to the critical angle and serve as a key indicator for initiation of preventive actions during contingencies. The devised methodology is tested using standard IEEE test systems as well as a practical 400/220 kV state grid system using ETAP/MATLAB.

With a large number of inverter-interfaced renewable power generation, fault-induced delayed voltage recovery (FIDVR) events have become a serious threat to power system stability assessment. This study proposes a novel data-driven method based on probabilistic prediction, ensemble learning, and multi-objective optimisation programming (MOP) to rapidly predict the FIDVR severity index for real-time FIDVR assessment. Distinguished from the existing single machine learning (ML) algorithm data-driven method, the proposed method combines different randomised learning algorithms to acquire a more diversified ML outcome. The probabilistic prediction models the uncertainties existing in the prediction process, which quantifies the prediction confidence over a progressive observation window. Besides, the FIDVR can be evaluated through the time-adaptive framework to achieve the best FIDVR speed and accuracy with the MOP framework. The simulation results on the New England 10-machine 39-bus system display its preponderance over the single ML, and also demonstrate its better speed and accuracy performance in FIDVR assessment.

Probabilistic forecasting of solar photovoltaic (PV) generation is critical for stochastic or robust optimisation-based power system dispatch. This study proposes a randomised learning-based hybrid ensemble (RLHE) model to construct the prediction intervals of probabilistic PV forecasting. Three different randomised learning algorithms, namely extreme learning machine, randomised vector functional link, and stochastic configuration network, are ensembled as a hybrid forecasting model. Besides, bootstrap is used as the ensemble learning framework to increase the diversity of training samples. For each algorithm, a decision-making rule is designed to evaluate the credibility of the individual outputs and the incredible ones are discarded at the output aggregation step. The weight coefficients of the aggregated outputs of the three algorithms are then optimised to compute the final point forecast results. Based on the point forecast results, the prediction intervals are constructed considering both model misspecification uncertainty and data noise uncertainty. The variance in model misspecification uncertainty is directly calculated with the individual outputs and the variance in data noise uncertainty is separately trained with an RLHE model. The proposed method is tested with an open dataset and compared with several benchmarking approaches.

Transport electrification is becoming the mainstream as a means to improve efficiency, performance, and sustainability of transportation systems. Electrical vehicles (EVs) can help to de-carbonise the environment, but a downside is the technical issues presented to the low-voltage distribution network. To quantify the stochastic nature of transport-affected electrification, probabilistic load flow is employed. Monte Carlo-based simulation is applied to accommodate the probabilistic uncertainties associated with variable EV charging patterns. This study considers high-power charging (up to 11 kW) at the domestic level while monitoring power quality variations (voltage drop, voltage unbalance factor, voltage sag) standards. This work focuses on the Irish and UK, distribution system operator's–transmission system operator's perspectives, as it will help to identify the likely impacts due to high-EV charger proliferation at household locations. The results indicate that if a 3.68 kW charger is used at the domestic level, it is possible for 40% of total household consumers to connect EVs directly to the distribution network without any power quality breaches. Furthermore, the proliferation of EV can be increased up to 100% if constrained to the start, and middle portions of the network (relative to the feeder substation transformer). For higher charger capacities (up to 11 kW), a bottleneck is presented regarding a resultant voltage unbalance factor.

Load forecasting is a complex non-linear problem with high volatility and uncertainty. This study presents a novel load forecasting method known as deep neural network and historical data augmentation (DNN–HDA). The method utilises HDA to enhance regression by DNN for monthly load forecasting, considering that the historical data to have a high correlation with the corresponding predicted data. To make the best use of the historical data, one year's historical data is combined with the basic features to construct the input vector for a predicted load. In this way, if there is C years' historical data, one predicted load can have C input vectors to create the same number of samples. DNN–HDA increases the number of training samples and enhances the generalisation of the model to reduce the forecasting error. The proposed method is tested on daily peak loads from 2006 to 2015 of Austria, Czech and Italy. Comparisons are made between the proposed method and several state-of-the-art models. DNN–HDA outperforms DNN by 44%, 38% and 63% on the three data sets, respectively.

The increasing penetration of renewable energy sources causes complex uncertainties of the power system. To capture such uncertainties in power system planning, an important step is to generate representative scenarios. In this work, a long short term memory (LSTM) auto-encoder based approach is proposed to generate representative scenarios in an integrated hydro-photovoltaic (PV) power generation system, which consists of feature extraction by LSTM Encoder, scenario clustering in feature domain by combining gap statistics method and K-means++, and representative scenario reconstruction by using LSTM Decoder. Compared with traditional scenario selection and generation methods, the proposed method can better capture the patterns of multivariate time-series data in both temporal and spatial dimensions. A case study in southwest China is used to demonstrate the effectiveness of the proposed method, which outperforms other existing methods by achieving the lowest SSE and DBI indices of 0.89 and 0.12, respectively, and obtaining the best SIL and CHI scores of 0.93 and 2.30, respectively, In addition, the case study shows the proposed model setup works more stable for scenario generation.

Power networks, especially distribution networks, have been undergoing substantial changes since the application of new technologies. Technology development in the early part of the 21st century has opened up new horizons for automated, efficient and reliable power grids. New technologies, while enhancing the capability of the electrical networks and providing opportunities and innovative solutions to the challenges of future networks, can also cause drawbacks that should be investigated and taken into account. Distributed generation (DG) is one of the new technologies that improves the operation of power grids. Despite tangible benefits that integration of DG units brings to electrical grids, their notable impacts on protection systems of power networks raise many challenges and concerns on how a fault should be detected and isolated in active distribution networks. Many attempts have been made to investigate the downside of the interconnection of DG units and methods to mitigate their impacts have been proposed. This study reviews the impact of DG integration on protection systems addressed in other research works and recapitulates suggested methods provided by scholars.

This study proposes a model for multi-phase, multi-winding, lossy transformers. A methodology is developed to decompose such transformers into a sub-network of multi-conductor -sections, shunts and idealised (lossless) two-winding transformers. The approach, therefore, can be used to include three-phase transformer models in any unbalanced power flow or optimal power flow tool that is able to represent these three basic components. The study derives the mathematical formulation and an implementation is provided in Julia/JuMP/PowerModelsDistribution.jl. The obtained voltage profile for several distribution test cases deviates from OpenDSS by at most . A case study applies this model to optimise the tap settings in the context of conservative voltage reduction. This illustrates that the optimised tap settings can vary by as much as 30% depending on the vector group of the transformer.

The rapid rise in the grid integration of low-carbon technologies, e.g. renewable energy sources (RESs) and plug-in electric vehicles (PEVs), has led to several challenges in distribution networks (DNs). This is due to the intermittent generation of RESs and uncertain loads of PEVs, both of which necessitate enhancing the flexibility requirements at the distribution level so as to accommodate the high penetration of these clean technologies in the future. To address such issues, this study proposes a mixed-integer linear programming-based expansion planning model for DNs considering the impact of high RES and PEV penetration, the associated uncertainties, and providing flexibility requirements at the distribution level. In this respect, the spherical simplex unscented transformation, an analytical uncertainty modelling method, is implemented in the planning model to take into account the forecasting errors of the uncertain green technologies. Also, in order to estimate the electric vehicle parking lot demand at each load node of the network, a new approach for PEV-charging model is suggested. To investigate the effectiveness and efficiency of the proposed probabilistic planning model, it is implemented on two test DNs, and the obtained results are thoroughly discussed.

The transition from the conventional power systems to smart grids has led to the modernisation of the measuring infrastructure used for their monitoring and control. Among the devices used to accomplish these tasks, phasor measurement units (PMUs) play a key role since they can provide extremely accurate synchronised voltage and current phasor measurements. The optimal PMU placement (OPP) problem, which focuses on minimising the number of PMUs for full system observability, has long been a popular research topic. The vast majority of the available OPP techniques have treated each transformer tap setting (voltage turns ratio and phase-shifting angle) as a fixed network parameter. Such an assumption can lead to misdirecting residuals in adjacent valid measurements when the modelled tap setting is incorrect. The aim of this study is to propose a substation-oriented OPP method based on a binary semidefinite programming algorithm, considering the observability of the transformer tap settings and the limited PMU channel capacity. The method is illustrated using an 8-bus test system. Numerical results using different size IEEE systems are presented and discussed. The proposed approach is further applied to the Polish 3120-bus system to show its efficacy in solving the OPP problem for large-scale power systems.

Economic losses caused by power disturbances such as voltage sags and short interruptions have become an important concern for high-tech enterprises in recent years. A potential solution to address this challenge is to provide value-added services such as voltage sag insurance to help enterprises to manage voltage sag risks. This study presents a severity index-based voltage sag insurance scheme including the liability, period, premium and compensation. Voltage sag severity is used as the index of insurance to define the trigger and limit of liability. The production value of enterprises suffered with voltage sags is obtained by introducing economics thus can accurately quantify the interaction between technical indexes of voltage sag and the economic indexes required for premium. Finally, case studies of three high-tech enterprises located in China have proved the correctness and rationality of the proposed insurance scheme.

A reasonable division of the unbalanced contribution is the premise of responsibility division and mitigation. As a large amount of renewable energy integrates into the power system, the disturbance of the system increases and the system impedance is no longer much smaller than the load impedance. The traditional unbalanced responsibility division method is difficult to calculate accurately. This study proposes a new method for calculating unbalanced contribution, which can effectively avoid the limitations of traditional methods. The robust independent component analysis is used to estimate the equivalent source signal. The sparse component analysis method is used to construct the screening criterion to screen the equivalent source signals to obtain estimated signals that are more in line with the real source signals. Then the equivalent negative sequence impedance of upstream and downstream is calculated, and the unbalanced responsibility is divided. This method is still effective when the system impedance is not much different from the load impedance. At the same time, the calculation accuracy is high and the anti-interference ability is strong. Simulation and field test verify the correctness and effectiveness of the proposed method.

Considering a large number of stochastic scenarios when optimising the flexible resource capacity for isolated microgrids not only improves the accuracy and credibility of the results but also enables the use of probabilistic statistical methods to obtain a scheme that balances robustness and costs. However, the consideration of massive scenarios can drastically lower the computational efficiency, and there is a lack of research on configuration methods that can result in a compromise. In this study, a characteristic-matching method is proposed to enhance the computational efficiency of optimisation, and a statistics-based prism filtering method is designed to obtain the compromise scheme for use by decision-makers. Specifically, the massive scenario set is divided into four subsets. The characteristic-matching method is proposed to obtain a near-optimal solution, which is used as the initial iteration value to accelerate the computational speed. Then, a characteristic-matching-based bi-level optimisation method is proposed to solve massive scenarios with high computational efficiency. Moreover, a prism filtering method is designed to select a compromise scheme with high economic benefits using scenario coverage, load curtailment, power curtailment and economy indicators. Simulation results verify the effectiveness of the proposed models and methods.

This study presents a new index for fast and reliable protection of the synchronous generator (SG) against loss of field (LOF) incidence, focusing on the operation time, reliability in clustering LOF, power system disturbance (PSD), and practical application. To this end, all allowable electrical parameters at SG output are acquired and thoroughly analysed. Analytical analysis of the parameters indicates that reactive power (Q _SG) and load angle (δ _SG) parameters have better conditions to improve the mentioned factors. Hence, based on the δ _SG increment and Q _SG decrement during LOF events, a new index based on the ratio of deviations of the δ _SG over Q _SG (Δδ _SG /ΔQ _SG) is proposed as a suitable criterion owing to remarkable fast sensitivity to LOF and PSD events. In the proposed algorithm, analysis of the events by the proposed Δδ _SG /ΔQ _SG index is dependent on ΔV _SG  < 0 condition. Extensive simulation studies demonstrated that the proposed LOFPS is a straightforward method to immediately and securely distinguish between LOF and PSD events, and it also has minimum operation time than other methods. Moreover, numerical data affirmed the greater changes of the Δδ _SG /ΔQ _SG index during LOF events than those in PSD events, giving rise to the easy clustering of both LOF/PSD events.

With the increase penetration of new energy and electric vehicles (EVs), the nodal voltage violations (NVVs) problem is widely concerned. Voltage predictive regulation methods can prevent NVVs and improve the performance of traditional voltage control. A fast and reliable forecasting method for NVVs is a key requirement to support this application. However, it is time-consuming to forecast NVVs using a Monte Carlo simulation-based probabilistic load flow method and the traditional deterministic method based on load forecasting has low reliability due to it not considering the uncertainty of load forecasting errors. A novel fast forecasting method for NVVs with shorter computational time and higher reliability is proposed. In the proposed method, the uncertainty of the load forecasting is taken into account and the impact of active/reactive power change on NVVs can be quantified like the traditional deterministic method. Thus, this method can assist in correcting voltage regulation plans so that voltage constraints can be satisfied as much as possible. Finally, the IEEE 69-bus test system and real historical load series are used to validate the effectiveness of the proposed method.

The Publisher is retracting this article, ‘Energy storage system and demand response program effects on stochastic energy procurement of large consumers considering renewable generation’, IET Gener., Transm. Distrib., 2016, 10, 1, pp. 107–114, doi: 10.1049/iet-gtd.2015.0473. The paper has been retracted due to an overlap with a previously published paper by the authors, entitled ‘Stochastic energy procurement of large electricity consumer considering photovoltaic, wind-turbine, micro-turbines, energy storage system in the presence of demand response program’, Energy Conver. Manage., 2015, 103, (1), pp. 1008–1018