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 - Publisher Copyright:
© 2020 The Authors
PY - 2021/1/15
Y1 - 2021/1/15
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 - http://www.scopus.com/inward/record.url?scp=85094813427&partnerID=8YFLogxK
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 -
