Statistical modelling of load profiles incorporating correlations using Copula

Raoul Bernards; Johan Morren; Han Slootweg

doi:10.1109/ISGTEurope.2017.8260181

Statistical modelling of load profiles incorporating correlations using Copula

Raoul Bernards, Johan Morren, Han Slootweg

Electrical Energy Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

9 Citations (Scopus)

3 Downloads (Pure)

Abstract

The growth in stochastic local renewable generation and the increase in new high peak power loads such as electric vehicles and heat pumps warrants the use of more detailed load models to properly assess grid adequacy. In this paper a method is proposed to develop load and generation models based on statistical analyses of measurement data using Gaussian Mixture Models and Copula functions. The two stage approach enables separate modelling of the marginal distributions and correlation structures and is shown to be able to provide a good representation of the statistical behaviour of measured electric household load. The fitted models are then used to stochastically generate load profiles and assess the peak shaving potential and required size of an energy storage unit. Taking into account the correlations is shown to be important for a proper representation of the profiles and may have a substantial effect on the results of the analysis.

Original language	English
Title of host publication	2017 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2017 - Proceedings
Place of Publication	Piscataway
Publisher	Institute of Electrical and Electronics Engineers
Pages	1-6
Number of pages	6
Volume	2018-January
ISBN (Electronic)	978-1-5386-1953-7
ISBN (Print)	978-1-5386-1954-4
DOIs	https://doi.org/10.1109/ISGTEurope.2017.8260181
Publication status	Published - 26 Sept 2017
Event	7th IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT Europe 2017 - Politecnico di Torino, Torino, Italy Duration: 26 Sept 2017 → 29 Sept 2017 Conference number: 7 http://sites.ieee.org/isgt-europe-2017/

Conference

Conference	7th IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT Europe 2017
Abbreviated title	ISGT Europe 2017
Country/Territory	Italy
City	Torino
Period	26/09/17 → 29/09/17
Internet address	http://sites.ieee.org/isgt-europe-2017/

Keywords

Copula
Gaussian mixture model
Load modelling
Smart grid planning
Statistical learning

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/ISGTEurope.2017.8260181

Cite this

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title = "Statistical modelling of load profiles incorporating correlations using Copula",

abstract = "The growth in stochastic local renewable generation and the increase in new high peak power loads such as electric vehicles and heat pumps warrants the use of more detailed load models to properly assess grid adequacy. In this paper a method is proposed to develop load and generation models based on statistical analyses of measurement data using Gaussian Mixture Models and Copula functions. The two stage approach enables separate modelling of the marginal distributions and correlation structures and is shown to be able to provide a good representation of the statistical behaviour of measured electric household load. The fitted models are then used to stochastically generate load profiles and assess the peak shaving potential and required size of an energy storage unit. Taking into account the correlations is shown to be important for a proper representation of the profiles and may have a substantial effect on the results of the analysis.",

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note = "7th IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT Europe 2017, ISGT Europe 2017 ; Conference date: 26-09-2017 Through 29-09-2017",

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Bernards, R, Morren, J & Slootweg, H 2017, Statistical modelling of load profiles incorporating correlations using Copula. in 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2017 - Proceedings. vol. 2018-January, Institute of Electrical and Electronics Engineers, Piscataway, pp. 1-6, 7th IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT Europe 2017, Torino, Italy, 26/09/17. https://doi.org/10.1109/ISGTEurope.2017.8260181

Statistical modelling of load profiles incorporating correlations using Copula. / Bernards, Raoul; Morren, Johan ; Slootweg, Han.
2017 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2017 - Proceedings. Vol. 2018-January Piscataway: Institute of Electrical and Electronics Engineers, 2017. p. 1-6.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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N2 - The growth in stochastic local renewable generation and the increase in new high peak power loads such as electric vehicles and heat pumps warrants the use of more detailed load models to properly assess grid adequacy. In this paper a method is proposed to develop load and generation models based on statistical analyses of measurement data using Gaussian Mixture Models and Copula functions. The two stage approach enables separate modelling of the marginal distributions and correlation structures and is shown to be able to provide a good representation of the statistical behaviour of measured electric household load. The fitted models are then used to stochastically generate load profiles and assess the peak shaving potential and required size of an energy storage unit. Taking into account the correlations is shown to be important for a proper representation of the profiles and may have a substantial effect on the results of the analysis.

AB - The growth in stochastic local renewable generation and the increase in new high peak power loads such as electric vehicles and heat pumps warrants the use of more detailed load models to properly assess grid adequacy. In this paper a method is proposed to develop load and generation models based on statistical analyses of measurement data using Gaussian Mixture Models and Copula functions. The two stage approach enables separate modelling of the marginal distributions and correlation structures and is shown to be able to provide a good representation of the statistical behaviour of measured electric household load. The fitted models are then used to stochastically generate load profiles and assess the peak shaving potential and required size of an energy storage unit. Taking into account the correlations is shown to be important for a proper representation of the profiles and may have a substantial effect on the results of the analysis.

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KW - Statistical learning

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Statistical modelling of load profiles incorporating correlations using Copula

Abstract

Conference

Keywords

UN SDGs

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