A Flexible Top-Down Data-Driven Stochastic Model for Synthetic Load Profiles Generation
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/1/269 |
| _version_ | 1797499097348833280 |
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| author | Enrico Dalla Maria Mattia Secchi David Macii |
| author_facet | Enrico Dalla Maria Mattia Secchi David Macii |
| author_sort | Enrico Dalla Maria |
| collection | DOAJ |
| description | 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>±</mo><mn>10</mn><mo>%</mo></mrow></semantics></math></inline-formula> both within a typical day of each season and within the population of a given cluster, although with some significant outliers. |
| first_indexed | 2024-03-10T03:42:34Z |
| format | Article |
| id | doaj.art-7e06e5741d154cee971986162eabfb5a |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T03:42:34Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-7e06e5741d154cee971986162eabfb5a2023-11-23T11:28:11ZengMDPI AGEnergies1996-10732021-12-0115126910.3390/en15010269A Flexible Top-Down Data-Driven Stochastic Model for Synthetic Load Profiles GenerationEnrico Dalla Maria0Mattia Secchi1David Macii2Institute for Renewable Energy, Eurac Research, Via Alessandro Volta, 13/A, 39100 Bozen-Bolzano, ItalyInstitute for Renewable Energy, Eurac Research, Via Alessandro Volta, 13/A, 39100 Bozen-Bolzano, Italy Department of Industrial Engineering, University of Trento, Via Sommarive, 9, 38123 Trento, ItalyThe 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>±</mo><mn>10</mn><mo>%</mo></mrow></semantics></math></inline-formula> both within a typical day of each season and within the population of a given cluster, although with some significant outliers.https://www.mdpi.com/1996-1073/15/1/269load modeling for smart grid applicationstime series clusteringAggregate Load ModelsGaussian Mixture Modelstime-inhomogeneous Markov chainpower systems |
| spellingShingle | Enrico Dalla Maria Mattia Secchi David Macii A Flexible Top-Down Data-Driven Stochastic Model for Synthetic Load Profiles Generation Energies load modeling for smart grid applications time series clustering Aggregate Load Models Gaussian Mixture Models time-inhomogeneous Markov chain power systems |
| title | A Flexible Top-Down Data-Driven Stochastic Model for Synthetic Load Profiles Generation |
| title_full | A Flexible Top-Down Data-Driven Stochastic Model for Synthetic Load Profiles Generation |
| title_fullStr | A Flexible Top-Down Data-Driven Stochastic Model for Synthetic Load Profiles Generation |
| title_full_unstemmed | A Flexible Top-Down Data-Driven Stochastic Model for Synthetic Load Profiles Generation |
| title_short | A Flexible Top-Down Data-Driven Stochastic Model for Synthetic Load Profiles Generation |
| title_sort | flexible top down data driven stochastic model for synthetic load profiles generation |
| topic | load modeling for smart grid applications time series clustering Aggregate Load Models Gaussian Mixture Models time-inhomogeneous Markov chain power systems |
| url | https://www.mdpi.com/1996-1073/15/1/269 |
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