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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]

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 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]

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]

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]

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]

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]

In order to strengthen the onshore transmission network in many parts of the world, voltage-source current–high-voltage direct current (VSC–HVDC) will increasingly be utilized. The effect of the operating point of a VSC-HVDC link and the control strategies employed can substantially affect the electromechanical oscillatory behavior of the ac network as well as the dc-side dynamics. In order for the full, flexible capability of VSC-HVDC to be exploited, further study of the effects of these controllers and their interactions with ac system responses is necessary. This paper addresses this gap. Both modal analysis and transient stability analysis are used to highlight tradeoffs between candidate VSC-HVDC power controllers and to study the electromechanical performance of the integrated ac/dc model. Tests are carried out on both a generic two-area model and a large-scale realistic network with detailed ac generator and HVDC models. [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]

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]

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]

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]

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]