Power system load modeling is a mature and generally well researched area which, as many other in electrical power engineering at the present time, is going through a period of renewed interest in both industry and academia. This interest is fueled by the appearance of new non-conventional types of loads (power electronic-based, or interfaced through power electronics) and requirements to operate modern electric power systems with increased penetration of non-conventional and mostly intermittent types of generation in a safe and secure manner. As a response to this renewed interest, in February 2010 CIGRE established working group C4.605: “Modelling and aggregation of loads in flexible power networks”. One of the first tasks of the working group was to identify current international industry practice on load modeling for static and dynamic power system studies. For that purpose, a questionnaire was developed and distributed during the summer/autumn of 2010 to more than 160 utilities and system operators in over 50 countries on five continents. This paper summarizes some of the key findings from about 100 responses to the questionnaire received by September 2011 and identifies prevalent types of load models used as well as typical values of their parameters.

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.

Load disaggregation for the identification of specific load types in the total demands (e.g., demand-manageable loads, such as heating or cooling loads) is becoming increasingly important for the operation of existing and future power supply systems. This paper introduces an approach in which periodical changes in the total demands (e.g., daily, weekly, and seasonal variations) are disaggregated into corresponding frequency components and correlated with the same frequency components in the meteorological variables (e.g., temperature and solar irradiance), allowing to select combinations of frequency components with the strongest correlations as the additional explanatory variables. The paper first presents a novel Fourier series regression method for obtaining target frequency components, which is illustrated on two household-level datasets and one substation-level dataset. These results show that correlations between selected disaggregated frequency components are stronger than the correlations between the original non-disaggregated data. Afterwards, convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) methods are used to represent dependencies among multiple dimensions and to output the estimated disaggregated time series of specific types of loads, where Bayesian optimisation is applied to select hyperparameters of CNN-BiLSTM model. The CNN-BiLSTM and other deep learning models are reported to have excellent performance in many regression problems, but they are often applied as “black box” models without further exploration or analysis of the modelled processes. Therefore, the paper compares CNN-BiLSTM model in which correlated frequency components are used as the additional explanatory variables with a naïve CNN-BiLSTM model (without frequency components). The presented case studies, related to the identification of electrical heating load and lighting load from the total demands, show that the accuracy of disaggregation improves after specific frequency components of the total demand are correlated with the corresponding frequency components of temperature and solar irradiance, i.e., that frequency component-based CNN-BiLSTM model provides a more accurate load disaggregation. Obtained results are also compared/benchmarked against the two other commonly used models, confirming the benefits of the presented load disaggregation methodology.

The paper presents the results of load modelling at low voltage level of transformer station (TS) 10/0.4 kV/kV supplying predominantly the residential load. The necessary data is obtained from a recording device and measuring information system for continuous measurements permanently installed in the concerned distribution network. The identified parameters of static exponential load model are classified according to day periods and days of the week, and statistically analysed in order to obtain reliable parameter estimates. These are compared with literature data for the same residential load class. Possibilities for further application of the described load modelling procedure are listed.

This paper analyzes the effects of shunt capacitors installed on the low voltage sides of 10/0.4 kV distribution transformers on the operation of these transformers. Using the results of an extensive measurement campaign, this paper compares: real and reactive power losses, secondary-side current, and primary-side apparent power before and after the installation of transformer capacitors. The following four cases of reactive power compensation are considered and discussed: adequate, conditionally adequate, and total compensation, as well as overcompensation. The presented analysis helps to identify transformer substations where the initial compensation should be changed or adjusted, in order to improve transformers' operational performance. Voltage dependencies of real and reactive power demands of the transformer-supplied loads are taken into account, based on the previously identified load characteristics in the considered networks as well as voltage dependence of reactive power of shunt capacitors. The result with and without including these voltage dependencies in the analysis is compared and discussed.