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Journal articles
A novel method for decomposing electricity feeder load into elementary profiles from customer information
Abstract : To plan a distribution grid involves making a long-term forecast of sub-hourly demand, which requires modeling the demand and its dynamics with aggregated measurement data. Distribution system operators (DSOs) have been recording electricity sub-hourly demand delivered by their medium-voltage feeders (around 1000—10,000 customers) for several years. Demand profiles differ widely among the various considered feeders. This is partly due to the varying mix of customer categories from one feeder to another. To overcome this issue, elementary demand profiles are often associated with customer categories and then combined according to a mix description. This paper presents a novel method to estimate elementary profiles that only requires several feeder demand curves and a description of customers. The method relies on a statistical blind source model and a new estimation procedure based on the augmented Lagrangian method. The use of feeders to estimate elementary profiles means that measurements are fully representative and continuously updated. We illustrate the proposed method through a case study comprising around 1000 feeder demand curves operated by the main French DSO Enedis. We propose an application o that uses the obtained profiles to evaluate the contribution of any set of new customers to a feeder peak load. We show that profiles enable a simulation of new unmeasured areas with errors of around 20%. We also show how our method can be used to evaluate the relevancy of different customer categorizations.
Cited literature [23 references]
https://hal-mines-paristech.archives-ouvertes.fr/hal-01558385
Contributor : Magalie Prudon <>
Submitted on : Friday, July 7, 2017 - 3:35:40 PM
Last modification on : Monday, December 7, 2020 - 9:38:06 AM
Long-term archiving on: : Wednesday, January 24, 2018 - 7:34:15 AM
Contributor : Magalie Prudon <>
Submitted on : Friday, July 7, 2017 - 3:35:40 PM
Last modification on : Monday, December 7, 2020 - 9:38:06 AM
Long-term archiving on: : Wednesday, January 24, 2018 - 7:34:15 AM
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