Unsupervised grouping of industrial electricity demand profiles: Synthetic profiles for demand-side management applications

Javier Valdes; Yunesky Masip Macia; Wolfgang Dorner; Luis Ramirez Camargo

doi:10.1016/j.energy.2020.118962

Unsupervised grouping of industrial electricity demand profiles: Synthetic profiles for demand-side management applications

Javier Valdes
, Yunesky Masip Macia
, Wolfgang Dorner
, Luis Ramirez Camargo^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

Demand side management is a promising alternative to offer flexibility to power systems with high shares of variable renewable energy sources. Numerous industries possess large demand side management potentials but accounting for them in energy system analysis and modelling is restricted by the availability of their demand data, which are usually confidential. In this study, a methodology to synthetize anonymized hourly electricity consumption profiles for industries and to calculate their flexibility potential is proposed. This combines different partitioning and hierarchical clustering analysis techniques with regression analysis. The methodology is applied to three case studies in Chile: two pulp and paper industry plants and one food industry plant. A significant hourly, daily and annual flexibility potential is found for the three cases (15%–75%). Moreover, the resulting demand profiles share the same statistical characteristics as the measured profiles but can be used in modelling exercises without confidentiality issues.

Original language	English
Article number	118962
Journal	Energy
Volume	215
DOIs	https://doi.org/10.1016/j.energy.2020.118962
Publication status	Published - Jan 2021
Externally published	Yes

Bibliographical note

Funding Information:
The study was conducted under the auspices of the project INCREASE “Increasing renewable energy penetration in industrial production and grid integration through optimised CHP energy dispatch scheduling and demand-side management” (grant number BMBF150075 ) funded by the German Federal Ministry of Education and Research (BMBF) and the Chilean National Commission for Scientific Research and Technology (CONICYT) . The study was also supported by the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH through the Energy Program in Chile. We also gratefully acknowledge support from the European Research Council (“reFUEL” ERC-2017-STG 758149 ).

Funding Information:
The study was conducted under the auspices of the project INCREASE ?Increasing renewable energy penetration in industrial production and grid integration through optimised CHP energy dispatch scheduling and demand-side management? (grant number BMBF150075) funded by the German Federal Ministry of Education and Research (BMBF) and the Chilean National Commission for Scientific Research and Technology (CONICYT). The study was also supported by the Deutsche Gesellschaft f?r Internationale Zusammenarbeit (GIZ) GmbH through the Energy Program in Chile. We also gratefully acknowledge support from the European Research Council (?reFUEL? ERC-2017-STG 758149).

Publisher Copyright:
© 2020 The Authors

Funding

The study was conducted under the auspices of the project INCREASE “Increasing renewable energy penetration in industrial production and grid integration through optimised CHP energy dispatch scheduling and demand-side management” (grant number BMBF150075 ) funded by the German Federal Ministry of Education and Research (BMBF) and the Chilean National Commission for Scientific Research and Technology (CONICYT) . The study was also supported by the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH through the Energy Program in Chile. We also gratefully acknowledge support from the European Research Council (“reFUEL” ERC-2017-STG 758149 ). The study was conducted under the auspices of the project INCREASE ?Increasing renewable energy penetration in industrial production and grid integration through optimised CHP energy dispatch scheduling and demand-side management? (grant number BMBF150075) funded by the German Federal Ministry of Education and Research (BMBF) and the Chilean National Commission for Scientific Research and Technology (CONICYT). The study was also supported by the Deutsche Gesellschaft f?r Internationale Zusammenarbeit (GIZ) GmbH through the Energy Program in Chile. We also gratefully acknowledge support from the European Research Council (?reFUEL? ERC-2017-STG 758149).

Keywords

Chilean energy transition
Clustering
Demand response
Electricity load profiles
Time series analysis

UN SDGs

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

Access to Document

10.1016/j.energy.2020.118962

Cite this

@article{3a5cc475f82c4cb384d52ad794a2e852,

title = "Unsupervised grouping of industrial electricity demand profiles: Synthetic profiles for demand-side management applications",

abstract = "Demand side management is a promising alternative to offer flexibility to power systems with high shares of variable renewable energy sources. Numerous industries possess large demand side management potentials but accounting for them in energy system analysis and modelling is restricted by the availability of their demand data, which are usually confidential. In this study, a methodology to synthetize anonymized hourly electricity consumption profiles for industries and to calculate their flexibility potential is proposed. This combines different partitioning and hierarchical clustering analysis techniques with regression analysis. The methodology is applied to three case studies in Chile: two pulp and paper industry plants and one food industry plant. A significant hourly, daily and annual flexibility potential is found for the three cases (15\%–75\%). Moreover, the resulting demand profiles share the same statistical characteristics as the measured profiles but can be used in modelling exercises without confidentiality issues.",

keywords = "Chilean energy transition, Clustering, Demand response, Electricity load profiles, Time series analysis",

author = "Javier Valdes and \{Masip Macia\}, Yunesky and Wolfgang Dorner and \{Ramirez Camargo\}, Luis",

note = "Funding Information: The study was conducted under the auspices of the project INCREASE “Increasing renewable energy penetration in industrial production and grid integration through optimised CHP energy dispatch scheduling and demand-side management” (grant number BMBF150075 ) funded by the German Federal Ministry of Education and Research (BMBF) and the Chilean National Commission for Scientific Research and Technology (CONICYT) . The study was also supported by the Deutsche Gesellschaft f{\"u}r Internationale Zusammenarbeit (GIZ) GmbH through the Energy Program in Chile. We also gratefully acknowledge support from the European Research Council (“reFUEL” ERC-2017-STG 758149 ). Funding Information: The study was conducted under the auspices of the project INCREASE ?Increasing renewable energy penetration in industrial production and grid integration through optimised CHP energy dispatch scheduling and demand-side management? (grant number BMBF150075) funded by the German Federal Ministry of Education and Research (BMBF) and the Chilean National Commission for Scientific Research and Technology (CONICYT). The study was also supported by the Deutsche Gesellschaft f?r Internationale Zusammenarbeit (GIZ) GmbH through the Energy Program in Chile. We also gratefully acknowledge support from the European Research Council (?reFUEL? ERC-2017-STG 758149). Publisher Copyright: {\textcopyright} 2020 The Authors",

year = "2021",

month = jan,

doi = "10.1016/j.energy.2020.118962",

language = "English",

volume = "215",

journal = "Energy",

issn = "0360-5442",

publisher = "Elsevier Ltd",

}

TY - JOUR

T1 - Unsupervised grouping of industrial electricity demand profiles

T2 - Synthetic profiles for demand-side management applications

AU - Valdes, Javier

AU - Masip Macia, Yunesky

AU - Dorner, Wolfgang

AU - Ramirez Camargo, Luis

N1 - Funding Information: The study was conducted under the auspices of the project INCREASE “Increasing renewable energy penetration in industrial production and grid integration through optimised CHP energy dispatch scheduling and demand-side management” (grant number BMBF150075 ) funded by the German Federal Ministry of Education and Research (BMBF) and the Chilean National Commission for Scientific Research and Technology (CONICYT) . The study was also supported by the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH through the Energy Program in Chile. We also gratefully acknowledge support from the European Research Council (“reFUEL” ERC-2017-STG 758149 ). Funding Information: The study was conducted under the auspices of the project INCREASE ?Increasing renewable energy penetration in industrial production and grid integration through optimised CHP energy dispatch scheduling and demand-side management? (grant number BMBF150075) funded by the German Federal Ministry of Education and Research (BMBF) and the Chilean National Commission for Scientific Research and Technology (CONICYT). The study was also supported by the Deutsche Gesellschaft f?r Internationale Zusammenarbeit (GIZ) GmbH through the Energy Program in Chile. We also gratefully acknowledge support from the European Research Council (?reFUEL? ERC-2017-STG 758149). Publisher Copyright: © 2020 The Authors

PY - 2021/1

Y1 - 2021/1

N2 - Demand side management is a promising alternative to offer flexibility to power systems with high shares of variable renewable energy sources. Numerous industries possess large demand side management potentials but accounting for them in energy system analysis and modelling is restricted by the availability of their demand data, which are usually confidential. In this study, a methodology to synthetize anonymized hourly electricity consumption profiles for industries and to calculate their flexibility potential is proposed. This combines different partitioning and hierarchical clustering analysis techniques with regression analysis. The methodology is applied to three case studies in Chile: two pulp and paper industry plants and one food industry plant. A significant hourly, daily and annual flexibility potential is found for the three cases (15%–75%). Moreover, the resulting demand profiles share the same statistical characteristics as the measured profiles but can be used in modelling exercises without confidentiality issues.

AB - Demand side management is a promising alternative to offer flexibility to power systems with high shares of variable renewable energy sources. Numerous industries possess large demand side management potentials but accounting for them in energy system analysis and modelling is restricted by the availability of their demand data, which are usually confidential. In this study, a methodology to synthetize anonymized hourly electricity consumption profiles for industries and to calculate their flexibility potential is proposed. This combines different partitioning and hierarchical clustering analysis techniques with regression analysis. The methodology is applied to three case studies in Chile: two pulp and paper industry plants and one food industry plant. A significant hourly, daily and annual flexibility potential is found for the three cases (15%–75%). Moreover, the resulting demand profiles share the same statistical characteristics as the measured profiles but can be used in modelling exercises without confidentiality issues.

KW - Chilean energy transition

KW - Clustering

KW - Demand response

KW - Electricity load profiles

KW - Time series analysis

UR - https://www.scopus.com/pages/publications/85094813427

U2 - 10.1016/j.energy.2020.118962

DO - 10.1016/j.energy.2020.118962

M3 - Article

AN - SCOPUS:85094813427

SN - 0360-5442

VL - 215

JO - Energy

JF - Energy

M1 - 118962

ER -

Unsupervised grouping of industrial electricity demand profiles: Synthetic profiles for demand-side management applications

Abstract

Bibliographical note

Funding

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this