Search

The paper proposes methodology for development of dynamic equivalent model of hybrid renewable energy source (HRES) plant suitable for reliable assessment of the overall transient stability of the realistic, large power systems. Transient stability index is used for system stability assessment, and consequently for the evaluation of the model performance. The historical plant production data and transmission network short-circuit fault statistics, together with probabilistically modelled uncertainties associated with system operation, are used to generate a set of realistic dynamic responses of the plant over the period of one year. The use of clustering algorithms enabled unsupervised grouping/clustering of responses and development of the low order equivalent models in the form of a transfer function with voltage as an input and real and reactive power as outputs. The methodology is illustrated on a number of case studies using two HRES plant configurations. It is demonstrated that a very few low order dynamic models can be used to model the HRES plant during the year for sufficiently accurate transient stability assessment of the power system. The possibility of deriving these equivalent models based on forecasted HRES plant composition makes them attractive for practical application in power system dynamic security studies. [ABSTRACT FROM AUTHOR]

This paper investigates the effect of modelling of different aspects of wind farms (WF) as a first step towards development of the equivalent harmonic model initially of WF, and later of other types of converter connected generation, for studies of harmonic propagation and mitigation in power electronics (PE) rich transmission networks. The aim of the study was to establish a set of scaling factors that can be used when modelling the entire WF as a single probabilistic harmonic generator in harmonic propagation studies, considering different WF configurations, weather conditions, harmonic cancellation effects, individual wind generator availability, cable disconnection, and the influence of the non-ideal external grid. The importance of modelling resonance and individual characteristic harmonics on the equivalent modelling of WF has also been established. The results facilitate the assessment of global contribution of PE connected RES to harmonic levels in transmission networks and more generally harmonic propagation studies in large PE rich transmission networks. [ABSTRACT FROM AUTHOR]

This paper proposes a method for the optimal deployment of Corrective Control Measures (CCMs) for the improvement of transient stability in uncertain power systems. First, the critical oscillation patterns of the disturbed system are identified using Hierarchical Clustering (HC) with statistically defined algorithm parameters. This is followed by the identification of critical generators where the application of CCMs will have maximum impact. The results show that a significant reduction in multi-machine unstable events can be achieved by the strategic deployment of CCMs. The proposed method is demonstrated on a modified IEEE 68 bus NETS-NYPS test network with Renewable Energy Sources (RES) and realistically modelled system uncertainties. [ABSTRACT FROM AUTHOR]

This paper proposes a methodology for advanced demand side management (DSM) in the distribution network (DN), catering at the same time for the requirements of the network operator, transmission or distribution, the available flexibility of the demand side, and the preservation of network performance. The basic premise of the study is that the distribution network operator is providing flexibility to the transmission system operator through load shaping. Different, one or more, indicators can be chosen to assess preservation of network performance. In this paper, for illustrative purposes of the methodology, the steady state voltage stability index, namely the load margin, is chosen as the network performance indicator. This is evaluated before and after a DSM action, in order to analyse the possible effect of DSM on network loadability. Load at each DN bus is represented using a realistic composite load model comprising controllable and uncontrollable loads. The DSM is initially performed based on optimal power flow calculation to ensure that the distribution system load profile at the grid supply point follows the required (pre-specified) load profile during a 24 hour period. Following this, a particle swarm optimisation is used to modify DSM program in those time steps of the planning horizon where load margin is reduced. The methodology is illustrated on a number of case studies using modified IEEE 33 bus distribution network model. The results illustrate the extent to which the available flexibility of demand can be used in support of transmission network operation when the preservation of network performance and appropriate load modeling are considered. [ABSTRACT FROM AUTHOR]

A high level of delivered power quality is becoming one of the key performance indicators for both contemporary and future power networks. The increased proliferation of converter connected generation and load in power networks will lead to increased levels of harmonics in distribution networks. A general knowledge of the distribution network performance in terms of harmonics is essential for distribution network operators for both operating and planning networks while considering power quality performance. The main limitation is the reduced monitoring infrastructure compared to transmission networks. This paper presents an efficient method for assessment of harmonic levels along medium voltage radial distribution feeders based on a single measurement at a substation. The methodology is illustrated in a case study involving a real life medium voltage 35 bus radial distribution feeder. [ABSTRACT FROM AUTHOR]

This paper proposes a voltage sag estimation approach based on a deep convolutional neural network. The proposed approach estimates the sag magnitude at unmonitored buses regardless of the system operating conditions and fault location and characteristics. The concept of system area mapping is also introduced via the use of bus matrix, which maps different patches in input matrix to various areas in the power system network. In this way, relevant features are extracted at various local areas in the power system and used in the analysis for higher level feature extraction, before feeding into a fully-connected multiple layer neural network for sag classification. The approach has been tested on the IEEE 68-bus test network and it has been demonstrated that the various sag categories can be identified accurately regardless of the operating condition under which the sags occur. [ABSTRACT FROM AUTHOR]

This paper presents parameter values and ranges of static load models used in a power system analysis for the representation of both individual electrical devices and aggregate system loads. The paper discusses and correlates load models and their parameters following statistical processing of the responses of around 100 transmission system operators and utilities around the world, who participated in a survey initiated by CIGRE Working Group C4.605, with the corresponding information on load models and their parameter values from the existing literature. According to the survey, the most frequently used static load models are voltage dependent, and voltage and frequency dependent exponential load models. The identified typical parameter values and ranges of these static load models, both for low voltage devices and for aggregate loads at higher voltage levels, are results of the analysis of a large number of data. Based on these results, the paper provides recommendations for their further use in power system studies and also introduces a novel method for obtaining the mean values and ranges of parameters of the aggregate system load models. [ABSTRACT FROM AUTHOR]

Demand decomposition (disaggregation) presents the process of assessing time-varying participation of different load categories within the total active or reactive load. Information on load composition is highly beneficial for different demand side management applications. In order to decompose total forecasted load of aggregated households where only some are monitored by smart meters (SMs) with submetering capabilities, a two-level methodology is proposed. At the first level, load disaggregation of the load monitored by SMs is done based on measurements of power consumed by each home appliance, and at the second, the disaggregation of the total forecasted load is performed using artificial neural network. This paper investigates the required percentage of users in an aggregation that should be equipped with a SM with submetering capabilities in order to forecast (within certain confidence level) the load composition of the overall aggregated demand. The methodology was first tested on a UK statistics-based load model, and then validated on a real pilot site's consumption dataset. The results show that even with 5% SM coverage, one can forecast, with high confidence, the composition of the load at the substation (aggregation point). In other words, there is no techno-economic justification for submetering technologies to be installed at every user's premise; a limited installation of such devices would suffice. [ABSTRACT FROM AUTHOR]

With an ever growing complexity, the power grids are designed and operated with an increasingly reduced stability margin. Under such circumstances, the adequate modeling of existing and new power system loads, with all its challenges, is receiving renewed attention. To simplify and to large extent automate the task of load modeling, this paper presents a methodology and associated software tool—Automated Load Modeling Tool to automatically (without human intervention) develop load models and derive corresponding model parameters from recorded power system responses. The approach facilitates automatic identification of load models and derivation of corresponding parameters for three different types of load models, i.e., polynomial, dynamic exponential, and composite load model. Several case studies using real life measurements at 11 kV distribution buses are used to test and validate developed methodology and software tool. [ABSTRACT FROM PUBLISHER]

Presents the best paper awards for 2019-2021 and includes the list of reviewers who contributed to this publication in 2021. [ABSTRACT FROM AUTHOR]

This paper introduces a probabilistic framework for transient stability assessment (TSA) of power systems with high penetration of renewable generation. The critical generators and areas of the system are identified using a method based on hierarchical clustering. Furthermore, statistical analysis of several transient stability indices is performed to assess their suitability for TSA of reduced inertia systems. The proposed framework facilitates robust assessment of transient stability of uncertain power systems with reduced inertia. [ABSTRACT FROM AUTHOR]

Prediction and shaping of dynamic response of the demand will enable advanced control algorithms for active demand management as well as improved stability assessment of the power system. Based on previously developed demand disaggregation approach, this paper develops a methodology for day-ahead prediction and shaping of dynamic response of the demand at bulk supply points without having to perform field measurements. The methodology is broadly based on application of artificial neural network and Monte Carlo simulations and incorporates multiple approaches namely, load forecasting, load disaggregation and the component-based load modelling approach. The input data include standard rms measurements at bulk supply points and actual and day-ahead forecasted weather data and does not rely on having access to detailed customer surveys or high-resolution load signatures. Measured dynamic responses of the demand from the substations of the local utility are used for validation. Load shifting and shaping of dynamic response of the demand are also illustrated. [ABSTRACT FROM PUBLISHER]

This paper proposes a two-stage methodology for online identification of power system dynamic signature using phasor measurement unit (PMU) measurements and data mining. Only transient stability status is usually predicted in the literature to assist with corrective control, without the dynamic behavior of generators in the event of instability. This paper uses traditional binary classification to identify transient stability in the first stage, and then develops a novel methodology to predict the nature of unstable dynamic behavior in the second stage. The method firstly applies hierarchical clustering to define patterns of unstable dynamic behavior of generators, and then applies different multiclass classification techniques, including decision tree, ensemble decision tree and multiclass support vector machine to identify characterized unstable responses. The proposed methodology is demonstrated on a multi-area transmission test system. High prediction accuracy at both stages of identification is demonstrated. [ABSTRACT FROM AUTHOR]

This paper critically evaluates a number of uncertainty importance measures for use in power system stability studies. Sensitivity analysis of uncertain system parameters is vital as new technologies proliferate and the total level of system uncertainty grows. Accurate assessment of the importance of different uncertainties can guide power system operators towards parameters which will require the greatest levels of mitigation or increased monitoring in order to reduce the uncertainty and its subsequent impact. Local and global sensitivity analysis techniques are described and evaluated within this paper, including nonparametric methods, variance-based approaches, and distribution-based techniques. The techniques are illustrated using a large 295-bus realistic network model of a generic distribution system. Numerical experiments on dynamic models are used in order to assess the impact of uncertainties on the mitigation of system frequency excursions using single-site and distributed energy storage devices. [ABSTRACT FROM AUTHOR]

The paper proposes a framework for the selection of optimal configuration of line compensation devices, thyristor controlled series capacitors (TCSC) and conventional fixed series capacitors, for reducing the risk of subsynchronous resonance (SSR) in the network while maintaining required power transfer. The methodology developed in the paper is based on the robust risk evaluation of SSR that takes into consideration the severity of subsynchronous resonance and probability of its occurrence. The subsynchronous resonance risk index developed previously is used to assess the severity of subsynchronous resonance. The line outage model is employed to determine the probability of contingencies potentially leading to subsynchronous resonance. It is demonstrated that with relatively small participation of TCSC, even with the most basic control, in series compensation of lines, the risk of SSR can be successfully managed. [ABSTRACT FROM PUBLISHER]

This paper proposes a methodology to efficiently estimate the probability of small-disturbance rotor angle instability of uncertain power systems. Traditional Monte Carlo (MC) approaches are computationally intensive and inefficient, particularly when used to study low probability conditions which result in small disturbance instabilities and develop into serious outage events high impact. The proposed methodology uses importance sampling to focus on conditions which contain the high information content required to make relevant decisions about low probability events. Latin hypercube sampling (LHS) is used to efficiently bound the search space and identify operating conditions leading to a marginally stable or unstable system response. The proposed approach is demonstrated on a model of a multi-area transmission network with a significant capacity of intermittent generation connected through a multi-terminal voltage source converter-based high voltage direct current (VSC-HVDC) grid. It is demonstrated that the methodology yields accurate results with just a small fraction of the sample points required using a conventional numerical MC approach. [ABSTRACT FROM PUBLISHER]

This paper investigates the economic value of voltage sag mitigation devices to end users. The focus is on sensitive industrial plants. Different types of power system faults are simulated on a typical British distribution network model to obtain voltage sag profiles at various locations in the network. The cost-effectiveness of various mitigation devices is investigated for industrial-size end users with various process characteristics and sensitivities, to obtain the optimal investment profile for each end-user plant. The sensitivity of the assessment to various levels of initial financial loss resulting from process interruption and network fault rates is also investigated. The findings would serve as valuable references for industrial end users who are considering investment in mitigation devices to reduce the financial losses caused by voltage sags. [ABSTRACT FROM PUBLISHER]

This paper proposes a new method for the probabilistic risk assessment of rotor angle instability in power systems using fuzzy inference systems (FISs). The novel two-step approach first models the stochastic uncertainties present within the power system to produced probability density functions (pdfs) for stability indicators. These stability indicators are established for both small and large disturbance rotor angle stability analysis. The pdfs produced are subsequently decomposed into regions based on user-specified threshold values. The outputs from this decomposition are analyzed using fuzzy techniques to complete the risk assessment of instability. The methodology is applied to a multi-area test network into which a VSC-MTDC grid has been embedded to support power transfer from a number of large wind farms. This new combination of probabilistic and fuzzy techniques is shown to provide an effective methodology for quantifying the influence of system uncertainties on the risks of rotor angle stability. [ABSTRACT FROM PUBLISHER]

This paper introduces a stochastic methodology to assess annual performance of distribution network and individual customers with respect to voltage sags, long duration interruptions, equipment trips, and process trips. These values are then used to evaluate financial losses in the entire distribution network. For each of the large number of simulated faults throughout the network, a probability of sag and long duration interruption occurrences is evaluated at each network bus. The methodology then includes for the first time the concept of process immunity time in order to assess as accurately as possible the resulting financial loss due to process interruption. To increase the accuracy of the assessment of financial loss, the methodology also considers the possibility of equipment and process restart, reliability, and type of fault. The methodology is illustrated on a real distribution network and it shows that inclusion of PIT in financial loss assessment results in a reduction of about 25% in estimated financial loss. [ABSTRACT FROM AUTHOR]

The paper presents a methodology for estimation of dynamic response of aggregate load at distribution network buses by taking into account daily variation in demand composition and generic dynamic responses of different types of load. Dynamic load models for different load categories obtained from field or laboratory measurements or through appropriate mathematical modeling are used in combination with hourly load composition at the given bus. The load composition is derived based on past demand surveys, and daily loading curves for different classes of customers. Uncertainties in both dynamic load models/responses of individual loads and load compositions at different times of the day are modeled probabilistically. With established dynamic signatures of different load categories and load compositions at different times of the day, Monte Carlo simulations are used to estimate probabilistic real and reactive power responses, including ranges of variation of these responses, for every hour of the day for a given or anticipated mix of demand. [ABSTRACT FROM PUBLISHER]

This paper presents a risk-based probabilistic small-disturbance security analysis (PSSA) methodology for use with power systems with uncertainties. This novel addition to existing dynamic security assessment (DSA) techniques can be used to quantify the small-disturbance stability risks associated with forecasted operating conditions. This approach first establishes the probability density functions (PDFs) for the damping of the critical oscillatory electromechanical modes by modeling the stochastic variation of system uncertainties, such as loading levels, intermittent generation sources, and power flows through voltage-source converter–high-voltage direct current (VSC–HVDC) lines. The produced PDFs are then combined with severity measures (either simple risk matrices or continuous functions) in order to quantify the risk of stability issues for the system associated with the forecasted operating scenario. In addition, the PSSA is used to establish risk-based operational limits and the concept of a probabilistic security margin is introduced to more accurately represent the probabilistic operation of uncertain power systems. The proposed techniques are demonstrated using a multiarea meshed power system incorporating two VSC-HVDC systems, one of which is connected to a large wind farm. [ABSTRACT FROM AUTHOR]

Real-time load composition knowledge will dramatically benefit demand-side management (DSM). Previous works disaggregate the load via either intrusive or nonintrusive load monitoring. However, due to the difficulty in accessing all houses via smart meters at all times and the unavailability of frequently measured high-resolution load signatures at bulk supply points, neither is suitable for frequent or widespread application. This paper employs the artificial intelligence (AI) tool to develop a load disaggregation approach for bulk supply points based on the substation rms measurement without relying on smart meter data, customer surveys, or high-resolution load signatures. Monte Carlo simulation is used to generate the training and validation data. Load compositions obtained by the AI tool are compared with the validation data and used for load characteristics estimation and validation. Probabilistic distributions and confidence levels of different confidence intervals for errors of load compositions and load characteristics are also derived. [ABSTRACT FROM PUBLISHER]

The paper investigates the effect of age related failure of power transformers on the identification of most critical transformer sites for system reliability. The end-of-life failure model of power transformers is modified first to integrate loading conditions effect. The adopted Arrhenius-Weibull probability distribution, which represents the effect of thermal stress on the transformer's end-of-life failure, was compared with the commonly used Gaussian probability distribution model. The sensitivity of results to the uncertainty in model parameters is thoroughly assessed, and acceptable level of uncertainty is determined. The results demonstrated the importance of integration of loading conditions into the failure model. The sensitivity analysis revealed that the identification of critical transformer sites is not significantly affected by the uncertainty in the failure model parameters and that approximate ranges of parameters can be used instead of accurate values without significant, if any, loss in accuracy. The case studies were performed on a realistic transmission test system with 154 power transformers. [ABSTRACT FROM PUBLISHER]

The paper investigates the effect of a transformer unplanned outage on the failure probability of the remaining transformers in the network. The probability of consequential dependent failures of transformers might ultimately lead to a multiple or cascading failure. New probabilistic indicators based on the transformer's unavailability under outage conditions have been formulated for individual power transformers and transformer sites to help with this assessment. The effect of thermal stress of transformer on its unavailability due to the new loading condition is taken into account as well as the age and the original loading of the transformer. The Arrhenius–Weibull life-stress model was adopted for assessment of the transformer unavailability. The study identifies both transformers whose outage could initiate cascading failure and those that are the most vulnerable to consequential failure. The effectiveness of the proposed probabilistic indicators in identifying the most critical transformers for multiple failure events is demonstrated on a realistic transmission test system with 154 power transformers. [ABSTRACT FROM PUBLISHER]

This paper presents a probabilistic framework for making decisions about the replacement of ageing power equipment. The framework involves three steps: first, to identify the most important and critical components of the system for overall system reliability; secondly, to perform Pareto analysis to relate the replacement of the components to the effect on system reliability indices; finally, to determine the optimum scenario for replacement based on a comparison between the cost of unreliability due to deferring the replacement and the saving on reinvestment cost. The proposed approach is illustrated on a meshed test system modeled using U.K. transmission system parameters, a representative transformer age profile and regulatory energy not supplied values. The results demonstrate the feasibility of the framework for application in the area of power system reliability, and show its feasibility for informing replacement decisions. [ABSTRACT FROM PUBLISHER]

This paper proposes a methodology for evaluation of risk of subsynchronous resonance (SSR) in meshed compensated AC networks. The developed methodology takes into account the severity of SSR problem in different network configurations caused by fixed series compensation and probabilistic behavior of the power systems due to random outages of the lines. SSR risk evaluation considers severity of SSR problem, probabilities of different network configurations for different contingencies and different operating conditions of turbine generator. Probability of each contingency is determined through line outage model while the probabilities of different operating conditions are calculated from multilevel load duration curve. Using developed risk matrix, it is demonstrated that the degree of risk of SSR that the generators in the network are exposed to can be evaluated for different network configurations, different contingencies and different line compensation levels. [ABSTRACT FROM AUTHOR]

The paper presents a generic probabilistic framework for assessing the accuracy of online prediction of power system transient stability based on phasor measurement unit (PMU) measurements and data mining techniques. It allows fair comparison of different data mining models in terms of the accuracy of the prediction. To illustrate the concept, a decision tree (DT) method is used as an example of a data mining technique. It is implemented in a 16-machine, 68-bus test power system. Generator rotor angles and speeds provided by PMUs during post-fault condition are chosen as predictors. The performance of the DT based prediction method is tested using a wide variety of disturbances with probabilistically modeled locations, durations, types of fault and the system loading levels. The accuracy of prediction is approximately 98.5% immediately following the fault clearance and can increase to almost 100% if the prediction is made 2.5 s after the fault clearance. [ABSTRACT FROM PUBLISHER]

The paper presents a methodology to identify generators at risk of subsynchronous resonance (SSR) in meshed AC/DC networks. Two risk indices are developed to rank generators based on the level of exposure to SSR. One of them measures the severity of subsynchronous torsional interactions, and the other indicates the gravity of transient torque problem. It is shown that proposed indices can be used to identify generators in the network that are at the risk of experiencing high mechanical torques due to SSR and to rank them based on severity of potential exposure to SSR. The methodology is demonstrated on meshed 16-machine, 68-bus IEEE test network with different series compensation levels of AC lines, asymmetrical compensation and voltage source converter (VSC) based HVDC lines. Finally, electromagnetic transient simulations are used to verify the ranking of generators obtained using developed indices. [ABSTRACT FROM PUBLISHER]

This paper presents the development of the dynamic equivalent model of an active distributed network (ADN) based on the grey-box approach. The equivalent model of an ADN comprises a converter-connected generator and a composite load model in parallel. The grey-box approach was chosen for model development as it incorporates prior knowledge about the ADN structure into the model, makes the model more physically relevant and intuitive than black-box or white-box models, and potentially improves the accuracy of the model. The dynamic equivalent model is presented in the seventh-order nonlinear state space format. It was initially loosely developed from the algebraic and differential equations describing assumed typical components of an ADN. Various static load models, dynamic load compositions, fault locations and a diverse range of distributed generation types and scenarios are considered in order to establish the generic range of model parameters for an ADN. The model is intended for the use in large power system stability studies. [ABSTRACT FROM PUBLISHER]

Paper presents an equivalent model of an active distribution network cell (ADNC) with distributed generation for transmission system stability studies. The equivalent model of ADNC comprises a converter-connected generator and a composite load model in parallel. The gray-box approach was chosen as it enables inclusion of prior knowledge about the ADNC structure into the model development, hence making the model more physically relevant and intuitive than a black-box or white-box model. The dynamic equivalent model is presented in a seventh-order nonlinear quasi state space format, developed from the algebraic and differential equations describing assumed typical components of the ADNC. The developed equivalent model of ADNC was validated through small and large disturbance studies using the modified IEEE nine-bus transmission system model. [ABSTRACT FROM PUBLISHER]

The paper presents a supplementary VSC-HVDC Power Oscillation Damping (POD) controller based on wide area measurement signals (WAMS). The controller is designed as Multi Input Single Output (MISO) using a Modal Linear Quadratic Gaussian (MLQG) methodology in order to target critical inter-area electromechanical modes. The approach has been tested on a large (16 machine, 68 bus) test network incorporating parallel HVDC/AC transmission and has shown improved damping compared to a traditional Power System Stabilizer (PSS) based controller structure utilizing local signals. The design process has incorporated the effects of wide area signal transmission delays. Variation in these signal delays and the complete loss of signals has been also investigated to establish the robustness of the WAMS based controller and its sensitivity to loss of signals. Extension of the controller to incorporate reactive power modulation has been investigated, as has variation in available active power modulation capacity. The proposed controller performance has been assessed through small and large disturbance analysis. [ABSTRACT FROM PUBLISHER]

The paper presents a robust probabilistic methodology for assessing the performance of power system controllers. In this study, the proposed probabilistic technique is applied to a novel power oscillation damping (POD) controller structure implemented through active power modulation of multiple voltage source converter based high voltage direct current (VSC-HVDC) links within a large heavily meshed network exhibiting multiple inter-area modes. The modal linear quadratic Gaussian (MLQG) controller, as an example of advanced POD, is implemented in both a centralized and a decentralized form for comparison. Following the development and demonstration of the effectiveness of the controller designs, the robust probabilistic performance evaluation is carried out incorporating outage contingencies for generators, lines, and VSC-HVDC lines. The results obtained demonstrate that both controller structures are largely robust to wide ranging operating conditions. For those conditions where controller performance is unsatisfactory, various mitigation techniques are discussed, including the use of classification tools to guide operational limits. A contingency specific controller design is shown to significantly improve performance when robustness cannot be achieved. [ABSTRACT FROM PUBLISHER]

Voltage unbalance is a costly and potentially damaging phenomenon. It affects both distribution network operators (DNOs) and customers. As part of a move toward greater network visibility, DNOs are increasingly motivated to monitor their networks in finer detail using an increasingly diverse range of monitoring devices. If the information from monitoring devices can be collated intelligently, the impact of unbalance and other power-quality issues can be accurately estimated throughout the network. In this paper, a new methodology is presented which utilizes distribution system state estimation (DSSE) to estimate the level, location, and impact of voltage unbalance on a real distribution network. The developed methodology is novel, pulling together and advancing existing research on DSSE and unbalance. The methodology is validated using data from a real U.K. distribution network with significant unbalance. The methodology is shown to be capable of statistically estimating the level, location, and effects of unbalance within the network, even when some areas of the network are unobservable. [ABSTRACT FROM AUTHOR]

The paper proposes a new methodology for ranking synchronous generators in a power system according to their contribution to angular and voltage stability. The method is based on sensitivity analysis of electromechanical modes and takes into account the location of generators, their inertia, active and reactive power outputs, and control functions. Though uses of such a ranking may vary, its application in large-scale wind power integration is presented in this study. The Generator Ranking Index (GRI) was able to identify the group of generators which are most suitable for manipulation in order to make way for new energy source, e.g., wind generators that do not provide similar support to the system. [ABSTRACT FROM PUBLISHER]

This paper investigates the factors that influence the outcome of financial-loss analysis in voltage sag studies. Focusing on industrial processes, this paper considers the effects of voltage sag profile estimation, plant-load profile modelling, and process cycle modelling on the financial risk associated with process failure. Based on voltage sag monitoring records, sag profile is probabilistically modeled for the estimation of future sag characteristic. Variations in financial risk due to varying process activities in different stages of a process (process cycle) and load profile are also investigated. The effects of the individual factors are analyzed through Monte Carlo simulation. The stochastic net present value method is introduced as a financial analysis tool. [ABSTRACT FROM AUTHOR]

This paper describes a methodology to estimate post process failure cost of downtime (COD). COD can be estimated using either historical or direct online costs (direct and restart costs) as input to the model. Application of the proposed COD estimation model is illustrated on an actual industrial process that experienced several downtime events due to voltage sags in its electric supply. Raw financial data obtained from industrial collaborators are normalized and used as an input to developed software for COD estimation. Several case studies, including influence of simultaneous manufacturing of different product variants and optimization (minimization) of COD are presented and discussed in detail. [ABSTRACT FROM AUTHOR]

This paper presents an approach to optimally select and allocate flexible ac transmission (FACTS) devices in a distribution network in order to minimize the number of voltage sags at network buses. The method proposed is based on the optimization of a preselected objective function using simple and niching genetic algorithms (GA). The objective of the optimization is to achieve the improvement in overall system sag performance of the network. Using proposed GA-based optimization, the location, the type and the rating of six (in total) FACTS devices are optimized simultaneously. Three types of FACTS devices are implemented in this study, namely, static var compensator, static compensator, and dynamic voltage restorer. The performance of the proposed algorithm is tested and illustrated on 295-bus generic distribution system. [ABSTRACT FROM PUBLISHER]

Flexible ac transmission system (FACTS) devices or their subderivative custom power devices are efficient and often used and recommended power-electronics-based devices for mitigation of voltage sags in electrical power system. With FACTS devices installed, the overall system (and individual bus) sag performance could significantly change depending on the type of the device used. In order to assess this change in sag performance in realistic large power systems, the classical (essentially static) fault calculation procedure should be amended to incorporate the effects of these devices on bus voltages. This paper presents a new approach of quasistatic sag analysis using a system impedance (Z bus ) matrix that incorporates FACTS devices. Three types of FACTS devices which are most often used in practical applications are considered in this study: static compensator, static var compensator, and the dynamic voltage restorer. The case studies based on the 295-bus generic distribution system are used to illustrate the modeling method and the effectiveness of these devices in sag mitigation. [ABSTRACT FROM PUBLISHER]

FACTS based devices are proven to be an efficient mitigation solution for voltage sag prevention. The high cost of FACTS based devices often prohibits their wider deployment within power networks. This paper presents an approach for comprehensively assessing the financial benefits to the network resulting from their use. The annual financial losses of the entire network due to voltage sags are used as a savings target. The three most widely used FACTS based devices for voltage sag mitigation, namely, Static VA Compensator (SVC), Static Compensator (STATCOM) and Dynamic Voltage Restorer (DVR) are then optimally placed using a Niching Genetic Algorithm (NGA). The aim is to reduce overall financial losses in the network due to voltage sags. The cost of the individual devices along with their installation costs and annual maintenance are taken into account in the optimization procedure. Since this methodology is largely based on an economic evaluation of the solution, several conventional economic analysis methods are utilized. Simulations are performed on a 295-bus generic distribution network (GDN). [ABSTRACT FROM AUTHOR]

Describes a method of load ranking in power systems using sensitivity factors and limited number of points from a hyperspace of uncertain parameters. Features of the proposed method; Details of load ranking procedures; Interpretation of ranking results.

In this paper a distributed control strategy for coordinating multiple battery energy storage systems to support frequency regulation in power systems with high penetration of renewable generation is proposed. The approach is based on an online convex optimisation framework that considers both the operating costs of storage systems and the frequency regulation requirements. The proposed framework uses a dynamic regret based on the last available information up to the current point in time of renewable generation and demand, which are uncertainty sources. The approach provides more accurate control than the currently employed prediction-based algorithms and it can be implemented in a distributed manner using a multi-agent system framework that facilitates plug-and-play functionality. The effectiveness and scalability of the proposed strategy is assessed through several case studies. [ABSTRACT FROM AUTHOR]

The renewable energy source based generating technologies and flexible demand and storage devices exhibit significant temporal and spatial uncertainties in generating and loading profiles and introduce additional level of uncertainty in network operation. The dynamic behaviors of such a network can be affected and the stable operation may be compromised. This paper proposes a probabilistic analysis approach for the evaluation of the effect of uncertain parameters on power system voltage and angular stability. Load margin, the damping of critical eigenvalues and the transient stability index have been chosen as the relevant stability indices for voltage stability, small-disturbance stability, and transient stability analysis, respectively. The Morris screening sensitivity analysis method coupled with a multivariate Gaussian copula to account for parameter correlations is used for the priority ranking of uncertain parameters. The approach is illustrated on a number of case studies using modified IEEE 68-bus NETS-NYPS test system. The results obtained in this paper reveal that the critical parameters appear as groups if the input dataset is correlated, and, hence even a parameter (which may be uninfluential individually) can have a significant impact on system dynamic behavior due to its correlation with other influential parameters. [ABSTRACT FROM AUTHOR]

Load modelling plays a key role in assessing peak load reduction and has attracted renewed interest recently due to increasing diversity and uncertainty in load characteristic resulted by emergence of new types of loads and more penetrations of distributed renewable generations onto electricity networks. This paper presents a methodology for the development of a static load model based load characteristic profile in medium voltage distribution networks from year-long field measurements. High-resolution load monitoring devices with the 1 s sampling rate installed at 60 primary substations in the U.K. distribution network are used to collect the data for the purpose of load modelling. Load models at these 60 substations comprising domestic, commercial/industrial, and mixed-type load demand are presented in the paper together with relevant model parameters. Field trials (which involve triggering transformer taps to initiate voltage changes) were performed throughout the year on 15 selected substations aimed at load profile validation. The developed load profiles take into account seasonal, weekly, and daily variations of P–V and Q–V characteristics. Representative 24 h (0.5 h) load matrices are developed for all 60 substations and provide an insight into the operational flexibility and network resilience to voltage variations. [ABSTRACT FROM AUTHOR]

Nowadays, modern industrial processes are controlled by equipment highly sensitive to voltage sags and interruptions. Although the proposed methods in the literature allow utilities to estimate annual equipment trips due to voltage sags and permanent interruptions, they do not assess explicitly momentary interruptions. This paper proposes a probabilistic methodology that includes the attempts of reclosers to clear nonpermanent faults during the estimation of annual voltage sags and momentary interruptions at each busbar. Furthermore, voltage-tolerance ranges for each type of equipment are taken into account to quantify annual equipment trips due to voltage sags and momentary interruptions for each customer in the distribution network. The results allow the utility to identify a geographic area where sets of customers face high equipment trips during the year. The proposed methodology can be used by utilities to prioritize corrective measures to improve the quality of service to those customers most affected by annual equipment trips. [ABSTRACT FROM PUBLISHER]

Significant financial losses due to voltage sags, short duration interruptions (SDIs), and long duration interruptions (LDIs) have been reported over the past years. The methods proposed in the literature allow us to estimate the annual indices related to process trips, though, as they are confined to voltage sags and LDIs, they do not take into account SDIs. This paper proposes to estimate annual financial losses related to process trip (FLPT) for each customer in the distribution system. The methodology correlates the attempts of reclosers to clear nonpermanent faults along with the concept of process immunity time in order to separately estimate the annual FLPT due to voltage sags, SDIs, and LDIs. This division allows the utility to quantify the severity of each of them and identify geographic areas where mitigation solutions should be deployed to avoid customer complaints and penalties by regulatory agencies. [ABSTRACT FROM PUBLISHER]

This paper establishes a generic severity function that can be used to produce power system security risk profiles. It is illustrated by analyzing the impacts of system load attributes on the small-disturbance rotor angle stability of a power system. The load attributes contributing to the oscillatory modes can be considered as inherent uncertain variables within power systems and include load power variations, load composition, and load model parameters. Uncertainty in the renewable power generation is also incorporated in the probabilistic modelling and risk assessment to demonstrate the flexibility of the approach. A novel approach is proposed to select the severity functions to logically represent small-disturbance security margin. The risk profile of a power system has been presented considering the probability density functions of power system critical modal damping and a selected set of severity functions. The analysis techniques developed are illustrated with a modified version of the 68-bus NETS-NYPS power system with a high amount of renewable power penetration. The relative importance of the load attributes and the impact of these attributes on stability boundaries have been identified at varying risk levels with respect to their contribution to small-disturbance stability. [ABSTRACT FROM PUBLISHER]

The paper introduces a probabilistic framework for online identification of post fault dynamic behavior of power systems with renewable generation. The framework is based on decision trees and hierarchical clustering and incorporates uncertainties associated with network operating conditions, topology changes, faults, and renewable generation. In addition to identifying unstable generator groups, the developed clustering methodology also facilitates identification of the sequence in which the groups lose synchronism. The framework is illustrated on a modified version of the IEEE 68 bus test network incorporating significant portion of renewable generation. [ABSTRACT FROM PUBLISHER]

This paper presents the concept of provision of differentiated quality of electricity supply based on customers' requirements in distribution networks. To fulfill this concept, five new gap indices are proposed to reflect the satisfaction of the received power-quality (PQ) performance compared to the thresholds which are set based on customers' requirements regarding the performance of individual PQ phenomenon or the aggregated PQ performance. Using these new indices as objective functions, an optimization-based mitigation strategy is proposed to carry out the strategic placement of different flexible ac transmission systems devices based on the analysis of PQ performance and sensitivity analysis. In this methodology, a greedy algorithm is applied to search the optimal mitigation scheme in order to enable the provision of differentiated PQ levels. The feasibility of the proposed mitigation methodology is demonstrated using a large-scale generic distribution network. The advantages and disadvantages of using the proposed indices as the optimization objective functions are also analyzed in this paper. [ABSTRACT FROM AUTHOR]

This paper critically evaluates a number of sensitivity analysis (SA) techniques to identify the most influential parameters affecting power system small-disturbance stability. SA of uncertain parameters has attracted increased attention with the adoption of deregulated market structure, intermittent energy resources, and new types of loads. Identification of the most influential parameters affecting system stability using SA techniques will facilitate better operation and control with reduced monitoring (only of the parameters of interest) by system operators and stakeholders. In total, nine SA techniques have been described, implemented, and compared in this paper. These can be categorized into three different types: local, screening, and global SA. This comparative analysis highlights their computational complexity and simulation time. The methods have been illustrated using a two-area power system and 68 bus NETS-NYPS test system. The priority ranking of all uncertain parameters has been evaluated, identifying the most critical parameters with respect to the small-signal stability of the test systems. It is shown that for many applications, the Morris screening approach is most suitable, providing a good balance between accuracy and efficiency. [ABSTRACT FROM AUTHOR]