Disaggregation Model: A Novel Methodology to Estimate Customers’ Profiles in a Low-Voltage Distribution Grid Equipped with Smart Meters
In the context of increasingly necessary energy transition, the precise modeling of profiles for low-voltage (LV) network consumers is crucial to enhance hosting capacity. Typically, load curves for these consumers are estimated through measurement campaigns conducted by Distribution System Operator...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/15/3/142 |
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| author | Guilherme Ramos Milis Christophe Gay Marie-Cécile Alvarez-Herault Raphaël Caire |
| author_facet | Guilherme Ramos Milis Christophe Gay Marie-Cécile Alvarez-Herault Raphaël Caire |
| author_sort | Guilherme Ramos Milis |
| collection | DOAJ |
| description | In the context of increasingly necessary energy transition, the precise modeling of profiles for low-voltage (LV) network consumers is crucial to enhance hosting capacity. Typically, load curves for these consumers are estimated through measurement campaigns conducted by Distribution System Operators (DSOs) for a representative subset of customers or through the aggregation of load curves from household appliances within a residence. With the instrumentation of smart meters becoming more common, a new approach to modeling profiles for residential customers is proposed to make the most of the measurements from these meters. The disaggregation model estimates the load profile of customers on a low-voltage network by disaggregating the load curve measured at the secondary substation level. By utilizing only the maximum power measured by Linky smart meters, along with the load curve of the secondary substation, this model can estimate the daily profile of customers. For 48 secondary substations in our dataset, the model obtained an average symmetric mean average percentage error (SMAPE) error of 4.91% in reconstructing the load curve of the secondary substation from the curves disaggregated by the model. This methodology can allow for an estimation of the daily consumption behaviors of the low-voltage customers. In this way, we can safely envision solutions that enhance the grid hosting capacity. |
| first_indexed | 2024-04-24T18:10:30Z |
| format | Article |
| id | doaj.art-a619a9b79d604cf9916ac4b8fd908ac3 |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-04-24T18:10:30Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-a619a9b79d604cf9916ac4b8fd908ac32024-03-27T13:46:53ZengMDPI AGInformation2078-24892024-03-0115314210.3390/info15030142Disaggregation Model: A Novel Methodology to Estimate Customers’ Profiles in a Low-Voltage Distribution Grid Equipped with Smart MetersGuilherme Ramos Milis0Christophe Gay1Marie-Cécile Alvarez-Herault2Raphaël Caire3Grenoble Electrical Engineering Laboratory (G2Elab), CNRS, University Grenoble Alpes, 38000 Grenoble, FranceEnedis, 92400 Courbevoie, FranceGrenoble Electrical Engineering Laboratory (G2Elab), CNRS, University Grenoble Alpes, 38000 Grenoble, FranceGrenoble Electrical Engineering Laboratory (G2Elab), CNRS, University Grenoble Alpes, 38000 Grenoble, FranceIn the context of increasingly necessary energy transition, the precise modeling of profiles for low-voltage (LV) network consumers is crucial to enhance hosting capacity. Typically, load curves for these consumers are estimated through measurement campaigns conducted by Distribution System Operators (DSOs) for a representative subset of customers or through the aggregation of load curves from household appliances within a residence. With the instrumentation of smart meters becoming more common, a new approach to modeling profiles for residential customers is proposed to make the most of the measurements from these meters. The disaggregation model estimates the load profile of customers on a low-voltage network by disaggregating the load curve measured at the secondary substation level. By utilizing only the maximum power measured by Linky smart meters, along with the load curve of the secondary substation, this model can estimate the daily profile of customers. For 48 secondary substations in our dataset, the model obtained an average symmetric mean average percentage error (SMAPE) error of 4.91% in reconstructing the load curve of the secondary substation from the curves disaggregated by the model. This methodology can allow for an estimation of the daily consumption behaviors of the low-voltage customers. In this way, we can safely envision solutions that enhance the grid hosting capacity.https://www.mdpi.com/2078-2489/15/3/142load modelslow-voltage gridload curvedisaggregation modeloptimizationcurve fitting |
| spellingShingle | Guilherme Ramos Milis Christophe Gay Marie-Cécile Alvarez-Herault Raphaël Caire Disaggregation Model: A Novel Methodology to Estimate Customers’ Profiles in a Low-Voltage Distribution Grid Equipped with Smart Meters Information load models low-voltage grid load curve disaggregation model optimization curve fitting |
| title | Disaggregation Model: A Novel Methodology to Estimate Customers’ Profiles in a Low-Voltage Distribution Grid Equipped with Smart Meters |
| title_full | Disaggregation Model: A Novel Methodology to Estimate Customers’ Profiles in a Low-Voltage Distribution Grid Equipped with Smart Meters |
| title_fullStr | Disaggregation Model: A Novel Methodology to Estimate Customers’ Profiles in a Low-Voltage Distribution Grid Equipped with Smart Meters |
| title_full_unstemmed | Disaggregation Model: A Novel Methodology to Estimate Customers’ Profiles in a Low-Voltage Distribution Grid Equipped with Smart Meters |
| title_short | Disaggregation Model: A Novel Methodology to Estimate Customers’ Profiles in a Low-Voltage Distribution Grid Equipped with Smart Meters |
| title_sort | disaggregation model a novel methodology to estimate customers profiles in a low voltage distribution grid equipped with smart meters |
| topic | load models low-voltage grid load curve disaggregation model optimization curve fitting |
| url | https://www.mdpi.com/2078-2489/15/3/142 |
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