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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 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]

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]

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]

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]

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 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]

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]

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]