Short-circuit calculation is one of the key functions within distribution management system, providing essential results for other applications such as adaptive relay coordination and settings, Fault Location, Isolation, and Supply Restoration (FLISR), and sizing the electrical equipment within substations. Climate changes have increased the risks of simultaneous occurrence of short circuits across the network, which can be caused by extreme weather events such as windstorms (tornados, hurricanes) or floods, or even earthquakes. This paper proposes a solution for multiple short-circuit calculations in multi-phase distribution network (MDN) leveraging the phase-domain modelling. Since multiple short-circuit calculations introduce additional modelling challenges, the sequence-domain approach cannot achieve the same level of accuracy as phase-domain modelling. The multiple-short circuit calculation software has been developed in conjunction with the power flow model as part of software for sequential analysis of meteorological resilience events. Therefore, the models of all network elements are non-matrix-based, which provides uniformity of the software modules used for power-flow and short-circuit analyses, while also increasing computational speed. This improvement can be critical when studying large sequences of resilience events (and restoration stages). The proposed solution was tested on small-scale networks, demonstrating several advantages over existing procedures reported in the literature. Furthermore, for the considered benchmark networks and hardware configurations, the proposed approach was observed to be two to six times faster compared to the literature-based solutions.

This paper presents a comprehensive review and analysis of the application of machine learning (ML) methodologies to state estimation (SE) in power systems (PSs) and multi-energy systems (MESs). Traditional PS-SE, heavily reliant on weighted least squares and Kalman filter techniques, has experienced challenges, such as difficulties to track the dynamics of the inverter-based resources (IBR) in transmission networks (TNs), limited observability and topology variations in distribution networks (DNs), or susceptibility to various types of data anomaly in both TNs and DNs. These challenges are being amplified in MES-SE; for instance, energy systems exhibit different dynamics and sampling rates, some of them may be unobservable, and the impact of bad data can propagate across energy systems. The emergence of ML brings notable potential for overcoming these challenges; for example, “good” predictions are derived from large sets of historic data, data classification and anomaly detection are automatized and made more reliable, complex relationships within models can be established, and algorithms can learn from generated results. This paper presents the fundamentals and challenges of PS-SE and MES-SE, provides an overview of relevant ML methods, and highlights the advantages of applying these methods in both PS-SE and MES-SE. The study concludes with a real-life example and outlines promising research directions for the application of ML in enhancing PS-SE and MES-SE results.

Real-time monitoring and control of distribution networks relies on a robust distribution system state estimation (DSSE). The use of pseudo measurements, typical for DSSE, may negatively affect estimation accuracy as their uncertainties are high. Increased integration of intermittent renewable generation makes active distribution networks more prone to sudden state changes. To overcome these challenges, this paper proposes a Variational Bayesian Unscented Kalman Filter (VBUKF). By efficiently adapting the prediction error covariance matrix and measurement noise covariance matrix, VBUKF copes with unpredictable sudden state changes and bad data, as well as unknown measurement noise. The proposed VBUKF makes use of a vector autoregressive process to capture temporal and spatial correlations in system states and improve prediction accuracy. Extensive simulations are conducted on three IEEE test systems with PV generations to demonstrate the performance of the proposed VBUKF in terms of estimation accuracy, convergence speed, numerical stability and scalability. Results obtained are compared with state-of-the-art state estimation algorithms to highlight the advantages of the proposed approach.

This paper proposes a probabilistic model for the flexibility assessment of shared charging stations for electric vehicles. Flexibility is modelled and evaluated in terms of the potential to reduce demand during the specified flexibility service window. Model is developed within the probabilistic framework to ensure that the randomness in modelled quantities is addressed. Main factors, which affect demand and available flexibility of charging stations, are identified and modelled in terms of the usage patterns of shared chargers, EV charging characteristics and customers’ charging preferences. Based on the proposed links between input, internal and output quantities, probability distributions of SoC value while charging, temporary charging duration at specified time, as well as the maximum aggregate charging power are calculated and presented. Finally, limits of available flexibility [kW] are quantified from the developed model for distinctive combinations of charging power rating, chargers’ location and flexibility service window. Flexible capacity is modelled and evaluated in line with the standardised active power services in the markets. Developed model is expected to be of particular interest in the distribution network planning.

Detekcija anomalija u elektroenergetskim sistemima važna je za pouzdanost i stabilnost mreže, a posebno u kontekstu integracije obnovljivih izvora energije i sve veće složenosti strukture elektroenergetskog sistema. U ovom radu se istražuje primena Extended Isolation Forest (EIF) algoritma za detekciju anomalija u raznim merenjima koje operatori prenosnih sistema dobijaju u svakodnevnom radu. EIF predstavlja poboljšanu verziju standardnog Isolation Forest algoritma, omogućavajući bolju obradu podataka visoke dimenzionalnosti i veće preciznosti u otkrivanju odstupanja. Rezultati istraživanja ukazuju na sposobnost da EIF precizno otkrije anomalije u realnim i simuliranim podacima. Prikazani pristup može doprineti razvoju naprednih sistema za online nadzor i prevenciju poremećaja u elektroenergetskim sistemima.

This paper presents a probabilistic model for the flexibility assessment of electric vehicles in residential areas. The flexibility is modelled and evaluated in terms of the aggregate charging demand which can be rescheduled to prevent exceeding network capacity. Developed model and corresponding calculations are based on the algebra of random variables. Main factors which affect the flexibility of electric vehicles are identified and modelled first. Modelled quantities and their mutual links are then incorporated into the probabilistic model. Cumulative distribution of charging duration, discrete probability of charging on particular day and discrete probabilities of possible start charging time are presented in the paper as the probabilistic model outputs. Finally, limits of available flexible capacity [kW] are calculated and presented as the main flexibility indicator for specified test cases. Presented results illustrate a potential for rescheduling charging sessions in different cases/scenarios. Developed model is expected to be of particular interest for distribution network planning during the 2020s and early 2030s.

This paper summarizes the technical activities of a three-year-long IEEE Task Force (TF) on State Estimation (SE) for Integrated Energy Systems (IES). It presents the formal definition and characteristics of IES, along with the comprehensive discussion on Electric Power Systems (EPS) model, and static and dynamic models associated with heating and natural gas systems. The paper also identifies the barriers of SE for IES, such as estimation modeling, observability analysis, and measurement requirements, together with addressing multi-scale dynamics. An extensive comparative analysis between Integrated Energy Systems-State Estimation (IES-SE) and more established Electric Power System-State Estimation (EPS-SE) is presented. The paper also provides future research needs and directions related to IES-SE.

This paper aims to investigate the impact of non-Gaussian measurement noise on state estimation (SE) results in distribution systems. To this end, the measurement noise is assumed to be distributed according to Gaussian or one of the following non-Gaussian probability distribution functions: Uniform, Laplace, Weibull and Gaussian mixture of two Gaussian components. The influence is investigated on three different state-of-the-art SE methods: weighted least squares (WLS) based static SE method, and two Kalman filter based forecasting-aided SE methods, namely extended Kalman filter (EKF) and unscented Kalman filter (UKF). Analyses are conducted on modified IEEE 37-bus system under different operating conditions, including quasi-steady state, sudden state changes and bad data. Performance of the methods in the presence of non-Gaussian measurement noise is compared against their performance when measurement noise is Gaussian distributed. The main conclusions were drawn, summarizing the impacts non-Gaussian measurement noise has on SE and proposing the solutions for overcoming some of the negative impacts.

Cilj ovog rada je da istraži uticaj postojanja negausovog šuma u merenjima na performanse estimatora stanja u distributivnim mrežama. Uticaj je ispitivan na primeru dva različita estimatora stanja: WLS (Weighted Least-Squares), koji je zasnovan na primeni metoda minimuma sume otežanih kvadrata odstupanja, i EKF (Extended Kalman Filter), koji je zasnovan na primeni Kalmanovog filtra. Performanse svakog od estimatora su poređene za slučaj kada šum merenja podleže Gausovoj ili nekoj od negausovih raspodela verovatnoće, kao što su: Uniformna, Laplasova, Vejbulova i model Gausove smeše sa dve Gausove komponente. Analize su sprovedene na modifikovanom IEEE test sistemu sa 37 čvorova pri različitim pogonskim uslovima, uključujući kvazistacionarni režim, iznenadne promene stanja i loša merenja. Izvedeni su glavni zaključci i predložena su rešenja za prevazilaženje potencijalnih problema u radu estimatora.

This paper proposes a new methodology for the tracking state estimation (SE) of multi-energy systems containing electricity, gas and heat networks. The three networks are modelled via quasi steady-state models, whereby different gas components form the gas mixture. The SE Kalman filter framework is extended to allow the application of the primal-dual decomposed constrained optimization. The primal problem is further decomposed into three sub-problems, corresponding to electricity, gas and heat networks. It is also proposed to solve the dual problem with a Newton – type second-order method. Efficient detection of bad data, without further aggravation of the measurement redundancy, is achieved by incorporating the Variational Bayesian approximation into the decomposed Kalman filter SE and developing the computation algorithms. The proposed methodology is tested on the developed regional and national multi-energy systems and its advantages are highlighted.

In the last price control review, the UK regulator has specified a common set of “network output measures” that includes both system-wide and individual asset metrics. This paper introduces a two-stage framework for the replacement planning and develops the second – simulation stage. It consists of two Monte Carlo procedures, where the latter is based on the proposed probabilistic health index (HI) methodology and application of several types of asset interventions, such as minimal, minor and major repairs and replacement. The probabilistic HI model makes use of proportional hazard models and Kijima II virtual – age model. The outputs are system-wide and nodal reliability indices, as well as asset interventions and asset profiles. IEEE test network is used to test the probabilistic HI methodology, which is then compared to the deterministic HI method applied in the UK.

This paper proposes a new forecasting-aided state estimation (FASE) method for distribution systems that mitigates issues with uncertain distributed generation (DG) and lost measurements. We utilize an Improved Particle Swarm Optimization (IPSO)-optimized Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for examination of historical DG output data. Based on identified DG output modes, it facilitates precise state prediction and data reconstruction,. The proposed method employs a Bidirectional Gated Recurrent Unit (BiGRU) neural network for state prediction and a particle filter (PF) for final state filtering. The method verification is provided through Python simulations of the Distribution Transformer Unit (DTU)7k distribution network system, demonstrating improved accuracy and robustness against sudden load change and bad data in measurements.

The rapid development of renewable sources has significantly increased interdependencies between electricity networks of all voltage levels, leading to bidirectional flows between transmission and distribution networks, and requiring analysis of Integrated Transmission-Distribution (ITD) network. The interconnectivity is further amplified by additional coupling with electricity, gas, heat and hydrogen networks, on both national and regional levels. Moreover, installation of solar, wind and storage on customers' premises has indicated that residential networks need to be included in the overall integrated model, called Integrated Transmission-Distribution-Residential (ITDR) network. The main goal of the paper is to develop a general model of the ITD/ITDR networks and to solve the power-flow problem on large-scale networks in an efficient way. The general model includes three-phase transmission and multi-phase distribution models, as well as accurate control strategies for traditional and electronically coupled electricity resources. The proposed model is solved via novel single-threaded power flow procedure, which incorporates new network elements' models and the developed algorithm for integrated power flow calculations in different domains. This is further improved by developing a multi-threaded approach. Analyses on a small-scale ITD network have shown that single- and multi-threaded approaches are, respectively, (1.5-4) and (2-5) times faster compared to the state-of-the-art procedures. As network size increases, the efficiency of the proposed multi-threaded procedure becomes more pronounced compared to the single-threaded one (up to 2.39 times).

The Anomaly Detection, Classification and Identification Tool (ADCIT) is an open source Matlab and Python code used for detection, classification and identification of anomalies in power system state estimation. Outputs of weighted least squares (WLS) and extended Kalman filter (EKF) state estimators, developed in Matlab, are used as inputs for machine learning algorithms developed in Python. The ADCIT can address hard anomaly cases; for example, it can detect and classify the case when load is abruptly changed at multiple nodes simultaneously, or when false data injection attack targets multiple states at the same time. Additionally, the ADCIT does not require retraining of the machine learning algorithm in the presence of network topology changes. Application of the ADCIT within power grid energy management system can help system operator to design proper countermeasures in case of an anomaly occurrence.

The increasing integration of Power Electronics (PE)-based renewable energy sources into the electric power system has significantly affected the traditional levels and characteristics of fault currents compared to the ones observed in power systems dominated by synchronous generating units. The secure operation of a renewable rich power system requires the proper estimation of fault currents with wide range of scenarios of the high share of renewables. Although the utilization of detailed and complex time-domain dynamic simulations allows for calculating the fault currents, the resulting modeling complexity and computational burden might not be adequate from the operational perspective. Thus, it is necessary to develop alternative quicker data-driven fault current estimation approaches to support the system operator. For this purpose, this paper utilizes an Artificial Neural Network (ANN)-based tool to estimate the characteristics of short circuit currents in power systems with high penetration of power electronics-based renewables. The short circuits against different penetration of renewables are produced offline using the DIgSILENT PowerFactory considering the control requirements for renewables (e.g., fault ride through requirement). The resulting dataset is utilized to train the ANN to provide the mapping between the penetration level and the characteristics of the short circuit currents. The application of the approach using the modified IEEE 9-bus test system demonstrates its effectiveness to estimate the components of short circuit currents (sub-transient current, transient current, and peak current) with high accuracy based only on the penetration of power electronics-based renewables.

In this paper, a real-time state estimation platform for distribution grids monitored by Phasor Measurement Units (PMUs) is developed, tested, and validated using Real Time Digital Simulator (RTDS). The developed platform serves as a proof-of-concept for potential implementation in an existing 50 kV ring network of the Dutch distribution utility Stedin medium voltage distribution grid located in the southwest (Zeeland area) of the Netherlands. To catch up with the fast sampling rates of PMUs, the platform incorporates computationally efficient techniques for state estimation and detection, discrimination and identification of anomalies like bad data and sudden load changes. Forecasting Aided State Estimation has been utilized to enable measurement innovations needed for fast anomaly detection, discrimination, and identification, whilst the Extended Kalman Filter (EKF) algorithm is selected to provide fast state forecasting and filtering. The platform has been tested under various normal and abnormal operating conditions considering different statistical properties of measurement noise as well as different bad data and sudden load change scenarios. To demonstrate advantages and disadvantages for embedding EKF into the platform, EKF is compared with Unscented Kalman Filter (UKF) in terms of estimation accuracy, computational efficiency, and compatibility with the module for anomaly detection, discrimination, and identification. The results of extensive simulations provide good hints about the feasibility of PMU-based real-time state estimation for the Stedin distribution grid.

Power system state estimation is being faced with different types of anomalies. These might include bad data caused by gross measurement errors or communication system failures. Sudden changes in load or generation can be considered as anomaly depending on the implemented state estimation method. Additionally, considering power grid as a cyber physical system, state estimation becomes vulnerable to false data injection attacks. The existing methods for anomaly classification cannot accurately classify (discriminate between) the above mentioned three types of anomalies, especially when it comes to discrimination between sudden load changes and false data injection attacks. This paper presents a new algorithm for detecting anomaly presence, classifying the anomaly type and identifying the origin of the anomaly, i.e., measurements that contain gross errors in case of bad data, or buses associated with loads experiencing a sudden change, or state variables targeted by false data injection attack. The algorithm combines analytical and machine learning (ML) approaches. The first stage exploits an analytical approach to detect anomaly presence by combining χ2-test and anomaly detection index. The second stage utilizes ML for classification of anomaly type and identification of its origin, with particular reference to discrimination between sudden load changes and false data injection attacks. The proposed ML based method is trained to be independent of the network configuration which eliminates retraining of the algorithm after network topology changes. The results obtained by implementing the proposed algorithm on IEEE 14 bus test system demonstrate the accuracy and effectiveness of the proposed algorithm.

This paper proposes a forecasting-aided state estimator (FASE) based on improved Bayesian deep long-short term memory (BDLSTM) neural network, which has significantly higher robustness against non-Gaussian distributed measurement noise and multiple types of uncertainties than existing methods. The proposed method optimizes neural network parameters using the variational autoencoder (VAE) algorithm and constructs the Variational Autoencoder-Bayesian Deep LSTM Neural Network (VAE-BDLSTM) prediction model. This model proficiently mitigates non-Gaussian noise problems and addresses the model uncertainty, stochastic uncertainty, and parameter uncertainty to an extent, thus improving the accuracy and robustness of state estimation. The unscented Kalman filter (UKF) is adopted as the nonlinear filter that combines predictions and measurements to produce the state estimate. Using the IEEE 39-bus system to simulate, and the results prove the effectiveness and practical value of the proposed method in improving prediction accuracy and estimator robustness.

The large uncertainties in wind power generation will bring great challenges to the analysis of optimal reactive power dispatch (ORPD). This paper considers a multi-objective ORPD strategy solved by a heuristic search algorithm that combines the elitist non-dominated sorting genetic algorithm with inheritance (i-NSGA-II) and a roulette wheel selection to optimize the operation of wind power integrated systems. The proposed ORPD strategy employs day-ahead predicted wind energy and load demand data to optimally set of the following control variables: i) optimal tap positions of on-load tap changers (OLTCs), ii) reactive demand set point of reactive power compensators and iii) active and reactive power outputs of wind farms (WFs) with the objectives to minimize: a) voltage deviations, b) active power loss, c) wind turbine harmonic distortions and d) number of switching operations of OLTCs. Because of the uncertainties of wind energy and load demand, hourly modifications of the day-ahead optimal results are also formulated to determine the real-time optimal reactive power dispatch. The proposed new ORPD strategy has been rigorously tested using IEEE 33-bus test system, PG&E 69-bus test system and modified real GB network. Results obtained confirmed the efficacy and applicability of the proposed strategy in both distribution and transmission networks.