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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]

The paper presents accurate and simple dynamic model of a supercapacitor bank system for power system dynamics studies. The proposed model is derived from a detailed RC circuit representation. Furthermore, a complete control system of the supercapacitor bank is also presented. The proposed model is easy to integrate in any power system simulation software and consists of only up to four standard datasheet parameters. The performance of the proposed model in grid frequency control and low-voltage ride through is illustrated on IEEE 14-bus test system in DIgSILENT PowerFactory. It is shown that in case of transient stability simulations the ideal (simplified) model of the supercapacitor can be used while in case of frequency control the ideal representation may not always be appropriate. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of ‘quickest possible’ online transient stability assessment, by minimizing the decision time of combined event detection that might lead to a system split and unstable generator group prediction, from real-time wide area power system measurements. More importantly it does so by respecting predefined probabilistic error constraints for the prediction. The statistical theory of optimal detection is applied, firstly to choose the detection threshold and secondly to select a flexible assessment time, after using probabilistic neural networks to provide a temporal representation of the data. On simulated wide area measurements from the interconnected New England test system and New York power system this approach is between two and three times faster on average than strategies based on fixed assessment times, despite having comparable error rates. [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]

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]

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]

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]

This paper introduces a two-step methodology for online identification of the participation of generators in power system oscillatory modes, based on measured responses. The dominant modes in generator measured responses are initially identified using a mode identification technique and then introduced, in the next step, as input into a clustering algorithm. Critical groups of generators that exhibit poorly or negatively damped oscillations are identified, in order to enable corrective control actions and stabilize the system. The uncertainties associated with the operation of modern power systems with renewable energy sources (RESs) are investigated as well as the impact of the dynamic behavior of power electronic interfaced RESs. [ABSTRACT FROM AUTHOR]

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 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]

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]

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]

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 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 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]

This paper presents comparative analysis of the performance of three efficient estimation methods when applied to the probabilistic assessment of small-disturbance stability of uncertain power systems. The presence of uncertainty in system operating conditions and parameters results in variations in the damping of critical modes and makes probabilistic assessment of system stability necessary. The conventional Monte Carlo (MC) approach, typically applied in such cases, becomes very computationally demanding for very large power systems with numerous uncertain parameters. Three different efficient estimation techniques are therefore compared in this paper—point estimation methods, an analytical cumulant-based approach, and the probabilistic collocation method—to assess their feasibility for use with probabilistic small disturbance stability analysis of large uncertain power systems. All techniques are compared with each other and with a traditional numerical MC approach, and their performance illustrated on a multi-area meshed power system. [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]

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]

This paper explores the feasibility of using the Probabilistic Collocation Method (PCM) in power-system studies in the presence of uncertainties. The application of the PCM is illustrated on small disturbance stability studies and voltage stability studies. First, the PCM is used to determine the effects of a supplementary power oscillation damping (POD) controller installed on a VSC-HVDC line in the presences of parameter and operational uncertainties. Second, it is used to calculate the probabilistic distance to voltage collapse in a distribution system based on measurement errors. The PCM is shown to produce accurate statistical results whilst drastically reducing the computational time of the studies in both cases. Additionally, a ranking process based on eigenvalue sensitivity is presented alongside the small disturbance analysis as an appropriate method for reduction of system complexity for extension to large power systems. This technique is validated through the application of the PCM on a large power-system model, yielding accurate probabilistic results. [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 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]