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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F1: Electrical Power System".

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 31018

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Prof. Dr. Sasa Djokic

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School of Engineering, The University of Edinburgh, Edinburgh EH9 3DW, Scotland, UK
Interests: power systems; load modelling; demand response and demand side management schemes; power quality; reliability; security; illuminating engineering; time-domain and frequency-domain (harmonic) analysis; smart grids and microgrids; active network management; distributed/micro generation and storage technologies; PV and wind generation systems; renewable energy resource assessment and forecasting; markovian models and monte carlo analysis; probabilistic analysis and evaluation of uncertainties; implementation of dynamic thermal rating; electric vehicles; modelling of power electronic technologies; data monitoring, collecting and processing; regulation, legislation and standardisation; fault response and supply interruptions; asset management; metaheuristic methods; optimal power flow analysis
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Prof. Dr. Jan Desmet

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Department of Electromechanical Systems, and Metal Engineering, Faculty of Engineering and Architecture, Ghent University, Ghent, Belgium
Interests: photovoltaic power systems, distribution networks, power system harmonics, IEC standards, distributed power generation, electric current measurement, phasor measurement, power generation control, power quality, energy conservation, energy storage, environmental factors, finite element analysis, fluorescent lamps, data monitoring and big data handling, load profiling, aggregation techniques, predictive analysis, hosting capacity, probabilistic analysis, grid modelling, hybrid storage, EV charging
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Prof. Dr. Lidija M. Korunović

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Department of Power Engineering, Faculty of Electronic Engineering, University of Niš, Niš, Serbia
Interests: power quality; power system operation; distribution networks; load modelling; load profiling; aggregation techniques; power system harmonics; demand response and demand side management; reliability; smart grids; renewable energy resource assessment; evaluation of uncertainties; data monitoring, collecting and processing; optimal power flow analysis; distributed power generation; energy efficiency; environmental factors; grid modelling
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Prof. Dr. Matti Lehtonen

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Department of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland
Interests: power and energy systems; demand response; power grids; renewable energy sources; power system economics; electrical load modeling; electric vehicle charging; energy storages; heating system electrification
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Special Issue Information

Dear Colleagues,

Over the recent years, there has been a strong interest in load modelling due to significant changes in the types, characteristics and compositions of loads—which are now increasingly based on various power electronic circuits and interfaces for achieving higher efficiency and improved control—as well as to better on-site and remote regulation techniques for individual loads or groups of loads. The provision of accurate load models is crucial for the planning and implementation of various demand-side management and demand–response schemes, particularly in terms of the anticipated electrification of the road transportation sector and of heating. Furthermore, the successful deployment of a range of “smart grid” functionalities relies on the correct assessment and evaluation of the aggregate effects of the changes in load patterns and in locally connected distributed generation and energy storage resources on the overall system performance. While some of these changes are part of the specific “smart grid” control schemes, others are inherently stochastic: therefore, appropriate models of aggregated loads as well as directly connected and inverter-interfaced renewable distributed generation technologies are required for their correct representation and for the further implementation of optimal controls for managing bidirectional power flows and ensuring the highest possible reliability, security and power quality.

A Special Issue on the subject of "Load Modelling in Power Systems" is currently being prepared for the journal Energies, aimed at publishing recent advances, field studies, contributions to knowledge and research results in related areas. The general theme of this Special Issue is modelling of loads in the context of analysis, control and operation of existing electricity supply networks and future “smart grids”, at all voltage levels and in a variety of applications, including:

Modelling of recently introduced, new and emerging types of loads;
Load models of different classes of customers in modern power supply systems;
Measurement-based and component-based load modelling approaches;
Time-domain, frequency-domain and other load models and modelling techniques;
Static and dynamic load models and modelling methodologies;
Data analytics and data mining for load modelling purposes;
Load profiling, load decomposition and load disaggregation;
Modelling and representation of aggregate loads and evaluation of their impact;
Combined load–generation–storage–network models, e.g., models of “active distribution network cells”, microgrids and virtual power plants;
Load modelling in related “smart grid” applications, e.g., demand-side management and demand–response schemes, functionalities and services.

Prof. Dr. Sasa Djokic
Prof. Dr. Jan Desmet
Prof. Dr. Lidija M. Korunović
Prof. Dr. Matti Lehtonen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

Load Modelling
Power Systems
Aggregate Load Models
Load Profiling and Load Decomposition
Demand–Response Management
Load Modelling for Smart Grid Applications.

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Related Special Issue

Load Modelling of Power Systems II in Energies (4 articles)

Published Papers (10 papers)

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Research

29 pages, 8392 KiB

Open AccessArticle

Methodology for Generating Synthetic Load Profiles for Different Industry Types

by Anna Sandhaas, Hanhee Kim and Niklas Hartmann

Energies 2022, 15(10), 3683; https://doi.org/10.3390/en15103683 - 17 May 2022

Cited by 11 | Viewed by 4557

Abstract

To achieve its climate goals, the German industry has to undergo a transformation toward renewable energies. To analyze this transformation in energy system models, the industry’s electricity demands have to be provided in a high temporal and sectoral resolution, which, to date, is not the case due to a lack of open-source data. In this paper, a methodology for the generation of synthetic electricity load profiles is described; it was applied to 11 industry types. The modeling was based on the normalized daily load profiles for eight electrical end-use applications. The profiles were then further refined by using the mechanical processes of different branches. Finally, a fluctuation was applied to the profiles as a stochastic attribute. A quantitative RMSE comparison between real and synthetic load profiles showed that the developed method is especially accurate for the representation of loads from three-shift industrial plants. A procedure of how to apply the synthetic load profiles to a regional distribution of the industry sector completes the methodology. Full article

(This article belongs to the Special Issue Load Modelling of Power Systems)

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20 pages, 1809 KiB

Open AccessArticle

A Flexible Top-Down Data-Driven Stochastic Model for Synthetic Load Profiles Generation

by Enrico Dalla Maria, Mattia Secchi and David Macii

Energies 2022, 15(1), 269; https://doi.org/10.3390/en15010269 - 31 Dec 2021

Cited by 4 | Viewed by 2517

Abstract

The study of the behavior of smart distribution systems under increasingly dynamic operating conditions requires realistic and time-varying load profiles to run comprehensive and accurate simulations of power flow analysis, system state estimation and optimal control strategies. However, due to the limited availability of experimental data, synthetic load profiles with flexible duration and time resolution are often needed to this purpose. In this paper, a top-down stochastic model is proposed to generate an arbitrary amount of synthetic load profiles associated with different kinds of users exhibiting a common average daily pattern. The groups of users are identified through a preliminary Ward’s hierarchical clustering. For each cluster and each season of the year, a time-inhomogeneous Markov chain is built, and its parameters are estimated by using the available data. The states of the chain correspond to equiprobable intervals, which are then mapped to a time-varying power consumption range, depending on the statistical distribution of the load profiles at different times of the day. Such distributions are regarded as Gaussian Mixture Models (GMM). Compared with other top-down approaches reported in the scientific literature, the joint use of GMM models and time-inhomogeneous Markov chains is rather novel. Furthermore, it is flexible enough to be used in different contexts and with different temporal resolution, while keeping the number of states and the computational burden reasonable. The good agreement between synthetic and original load profiles in terms of both time series similarity and consistency of the respective probability density functions was validated by using three different data sets with different characteristics. In most cases, the median values of synthetic profiles’ mean and standard deviation differ from those of the original reference distributions by no more than

\pm 10 %

both within a typical day of each season and within the population of a given cluster, although with some significant outliers. Full article

(This article belongs to the Special Issue Load Modelling of Power Systems)

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17 pages, 1363 KiB

Open AccessArticle

Application of Meteorological Variables for the Estimation of Static Load Model Parameters

by Aleksandar S. Jović, Lidija M. Korunović and Sasa Z. Djokic

Energies 2021, 14(16), 4874; https://doi.org/10.3390/en14164874 - 10 Aug 2021

Cited by 1 | Viewed by 1475

Abstract

This paper presents a novel approach for estimating the parameters of the most frequently used static load model, which is based on the use of meteorological variables and is an alternative to the commonly used but time-consuming measurement-based approach. The presented model employs five frequently reported meteorological variables (ambient temperature, relative humidity, atmospheric pressure, wind speed, and wind direction) and the load model parameters as the independent and dependent variables, respectively. The analysis compared the load model parameters obtained by using all five meteorological variables and also when the meteorological variables with the lowest influence are omitted successively (one by one) from the model. It is recommended based on these results to use the model with the maximum accuracy, i.e., with five meteorological variables. The model was validated on a validation set of measurements, demonstrating its applicability for the estimation of load model parameters when the measurements of electrical variables for parameter identification are not available. Finally, load model parameters of the analyzed demand were estimated on the basis of only ambient temperature, and it was found that such a linear model can be used with a similar accuracy as the models with up to four meteorological variables. Full article

(This article belongs to the Special Issue Load Modelling of Power Systems)

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24 pages, 5920 KiB

Open AccessFeature PaperArticle

Heating and Lighting Load Disaggregation Using Frequency Components and Convolutional Bidirectional Long Short-Term Memory Method

by Mingzhe Zou, Shuyang Zhu, Jiacheng Gu, Lidija M. Korunovic and Sasa Z. Djokic

Energies 2021, 14(16), 4831; https://doi.org/10.3390/en14164831 - 8 Aug 2021

Cited by 8 | Viewed by 2347

Abstract

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. Full article

(This article belongs to the Special Issue Load Modelling of Power Systems)

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23 pages, 2649 KiB

Open AccessArticle

Comparison of Economical and Technical Photovoltaic Hosting Capacity Limits in Distribution Networks

by Samar Fatima, Verner Püvi, Ammar Arshad, Mahdi Pourakbari-Kasmaei and Matti Lehtonen

Energies 2021, 14(9), 2405; https://doi.org/10.3390/en14092405 - 23 Apr 2021

Cited by 16 | Viewed by 2357

Abstract

Power distribution networks are transitioning from passive towards active networks considering the incorporation of distributed generation. Traditional energy networks require possible system upgrades due to the exponential growth of non-conventional energy resources. Thus, the cost concerns of the electric utilities regarding financial models of renewable energy sources (RES) call for the cost and benefit analysis of the networks prone to unprecedented RES integration. This paper provides an evaluation of photovoltaic (PV) hosting capacity (HC) subject to economical constraint by a probabilistic analysis based on Monte Carlo (MC) simulations to consider the stochastic nature of loads. The losses carry significance in terms of cost parameters, and this article focuses on HC investigation in terms of losses and their associated cost. The network losses followed a U-shaped trajectory with increasing PV penetration in the distribution network. In the investigated case networks, increased PV penetration reduced network costs up to around 40%, defined as a ratio to the feeding secondary transformer rating. Above 40%, the losses started to increase again and at 76–87% level, the network costs were the same as in the base cases of no PVs. This point was defined as the economical PV HC of the network. In the case of networks, this level of PV penetration did not yet lead to violations of network technical limits. Full article

(This article belongs to the Special Issue Load Modelling of Power Systems)

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19 pages, 2727 KiB

Open AccessArticle

A Novel Multi-Area Distribution State Estimation Approach for Active Networks

by Mohammad Gholami, Ali Abbaspour Tehrani-Fard, Matti Lehtonen, Moein Moeini-Aghtaie and Mahmud Fotuhi-Firuzabad

Energies 2021, 14(6), 1772; https://doi.org/10.3390/en14061772 - 23 Mar 2021

Cited by 9 | Viewed by 1922

Abstract

This paper presents a hierarchically distributed algorithm for the execution of distribution state estimation function in active networks equipped with some phasor measurement units. The proposed algorithm employs voltage-based state estimation in rectangular form and is well-designed for large-scale active distribution networks. For this purpose, as the first step, the distribution network is supposed to be divided into some overlapped zones and local state estimations are executed in parallel for extracting operating states of these zones. Then, using coordinators in the feeders and the substation, the estimated local voltage profiles of all zones are coordinated with the local state estimation results of their neighboring zones. In this regard, each coordinator runs a state estimation process for the border buses (overlapped buses and buses with tie-lines) of its zones and based on the results for voltage phasor of border buses, the local voltage profiles in non-border buses of its zones are modified. The performance of the proposed algorithm is tested with an active distribution network, considering different combinations of operating conditions, network topologies, network decompositions, and measurement scenarios, and the results are presented and discussed. Full article

(This article belongs to the Special Issue Load Modelling of Power Systems)

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28 pages, 4787 KiB

Open AccessArticle

A Low-Voltage DC Backbone with Aggregated RES and BESS: Benefits Compared to a Traditional Low-Voltage AC System

by Hakim Azaioud, Robbert Claeys, Jos Knockaert, Lieven Vandevelde and Jan Desmet

Energies 2021, 14(5), 1420; https://doi.org/10.3390/en14051420 - 4 Mar 2021

Cited by 15 | Viewed by 4963

Abstract

The increasing penetration of PV into the distribution grid leads to congestion, causing detrimental power quality issues. Moreover, the multiple small photovoltaic (PV) systems and battery energy storage systems (BESSs) result in increasing conversion losses. A low-voltage DC (LVDC) backbone to interconnect these assets would decrease the conversion losses and is a promising solution for a more optimal integration of PV systems. The multiple small PV systems can be replaced by shared assets with large common PV installations and a large BESS. Sharing renewable energy and aggregation are activities that are stimulated by the European Commission and lead to a substantial benefit in terms of self-consumption index (SCI) and self-sufficiency index (SSI). In this study, the benefit of an LVDC backbone is investigated compared to using a low-voltage AC (LVAC) system. It is found that the cable losses increase by 0.9 percent points and the conversion losses decrease by 12 percent points compared to the traditional low-voltage AC (LVAC) system. The SCI increases by 2 percent points and the SSI increases by 6 percent points compared to using an LVAC system with shared meter. It is shown that an LVDC backbone is only beneficial with a PV penetration level of 65% and that the BESS can be reduced by 22% for the same SSI. Full article

(This article belongs to the Special Issue Load Modelling of Power Systems)

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24 pages, 5665 KiB

Open AccessArticle

A Novel Feature Set for Low-Voltage Consumers, Based on the Temporal Dependence of Consumption and Peak Demands

by Robbert Claeys, Hakim Azaioud, Rémy Cleenwerck, Jos Knockaert and Jan Desmet

Energies 2021, 14(1), 139; https://doi.org/10.3390/en14010139 - 29 Dec 2020

Cited by 10 | Viewed by 2159

Abstract

This paper proposes a novel feature construction methodology aiming at both clustering yearly load profiles of low-voltage consumers, as well as investigating the stochastic nature of their peak demands. These load profiles describe the electricity consumption over a one-year period, allowing the study of seasonal dependence. The clustering of load curves has been extensively studied in literature, where clustering of daily or weekly load curves based on temporal features has received the most research attention. The proposed feature construction aims at generating a new set of variables that can be used in machine learning applications, stepping away from traditional, high dimensional, chronological feature sets. This paper presents a novel feature set based on two types of features: respectively the consumption time window on a daily and weekly basis, and the time of occurrence of peak demands. An analytic expression for the load duration curve is validated and leveraged in order to define the the region that has to be considered as peak demand region. The clustering results using the proposed set of features on a dataset of measured Flemish consumers at 15-min resolution are evaluated and interpreted, where special attention is given to the stochastic nature of the peak demands. Full article

(This article belongs to the Special Issue Load Modelling of Power Systems)

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14 pages, 600 KiB

Open AccessArticle

Use of Available Daylight to Improve Short-Term Load Forecasting Accuracy

by Miguel López, Sergio Valero, Carlos Sans and Carolina Senabre

Energies 2021, 14(1), 95; https://doi.org/10.3390/en14010095 - 26 Dec 2020

Cited by 6 | Viewed by 2073

Abstract

This paper introduces a new methodology to include daylight information in short-term load forecasting (STLF) models. The relation between daylight and power consumption is obvious due to the use of electricity in lighting in general. Nevertheless, very few STLF systems include this variable as an input. In addition, an analysis of one of the current STLF models at the Spanish Transmission System Operator (TSO), shows two humps in its error profile, occurring at sunrise and sunset times. The new methodology includes properly treated daylight information in STLF models in order to reduce the forecasting error during sunrise and sunset, especially when daylight savings time (DST) one-hour time shifts occur. This paper describes the raw information and the linearization method needed. The forecasting model used as the benchmark is currently used at the TSO’s headquarters and it uses both autoregressive (AR) and neural network (NN) components. The method has been designed with data from the Spanish electric system from 2011 to 2017 and tested over 2018 data. The results include a justification to use the proposed linearization over other techniques as well as a thorough analysis of the forecast results yielding an error reduction in sunset hours from 1.56% to 1.38% for the AR model and from 1.37% to 1.30% for the combined forecast. In addition, during the weeks in which DST shifts are implemented, sunset error drops from 2.53% to 2.09%. Full article

(This article belongs to the Special Issue Load Modelling of Power Systems)

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16 pages, 2895 KiB

Open AccessArticle

Simulation of Electric Vehicle Charging Stations Load Profiles in Office Buildings Based on Occupancy Data

by Semen Uimonen and Matti Lehtonen

Energies 2020, 13(21), 5700; https://doi.org/10.3390/en13215700 - 31 Oct 2020

Cited by 25 | Viewed by 4436

Abstract

Transportation vehicles are a large contributor of the carbon dioxide emissions to the atmosphere. Electric Vehicles (EVs) are a promising solution to reduce the CO₂ emissions which, however, requires the right electric power production mix for the largest impact. The increase in the electric power consumption caused by the EV charging demand could be matched by the growing share of Renewable Energy Sources (RES) in the power production. EVs are becoming a popular sustainable mean of transportation and the expansion of EV units due to the stochastic nature of charging behavior and increasing share of RES creates additional challenges to the stability in the power systems. Modeling of EV charging fleets allows understanding EV charging capacity and demand response (DR) potential of EV in the power systems. This article focuses on modeling of daily EV charging profiles for buildings with various number of chargers and daily events. The article presents a modeling approach based on the charger occupancy data from the local charging sites. The approach allows one to simulate load profiles and to find how many chargers are necessary to suffice the approximate demand of EV charging from the traffic characteristics, such as arrival time, duration of charging, and maximum charging power. Additionally, to better understand the potential impact of demand response, the modeling approach allows one to compare charging profiles, while adjusting the maximum power consumption of chargers. Full article

(This article belongs to the Special Issue Load Modelling of Power Systems)

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