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@ARTICLE{Tepe:1024779,
author = {Tepe, Benedikt and Haberschusz, David and Figgener, Jan and
Hesse, Holger and Sauer, Dirk Uwe and Jossen, Andreas},
title = {{F}eature-conserving gradual anonymization of load profiles
and the impact on battery storage systems},
journal = {Applied energy},
volume = {343},
issn = {0306-2619},
address = {Amsterdam [u.a.]},
publisher = {Elsevier Science},
reportid = {FZJ-2024-02445},
pages = {121191 -},
year = {2023},
note = {Additional Grants: BMBF within the SimBAS project (Grant
No. 03XP0338A)},
abstract = {Electric load profiles are highly relevant for battery
storage research and industry as they determine system
design and operation strategies. However, data obtained from
electrical load measurements often cannot be shared or
published due to privacy concerns. This paper presents a
methodology to gradually anonymize load profiles while
conforming to various degrees of anonymity. It segregates
the original load profile into base and peak sequences and
extracts features from each of the sequences. With the help
of the features, a synthetic, anonymized load profile is
created. Different levels of anonymization can be selected,
which transform the original profile to the desired extent.
A random permutation of the peak sequences or base sequences
is used to achieve this transformation. Exemplary profiles
from a household and an electric vehicle charging station
are used to demonstrate the functionality of the
anonymization. The indicators of the anonymized load
profiles are compared with the original ones in both time
and frequency domains, and the effects of load profile
anonymization on the operation of battery storage systems in
two scenarios are analyzed. While the anonymized load
profiles retain the time-invariant indicators from the
original profile, the permutation causes a loss of
regularity in the load profiles. As a result, relevant
indicators of battery storage systems subjected to these
anonymized profiles deviate to a greater extent in
time-dependent applications such as self-consumption
increase. This is reflected in the overestimation of
equivalent full cycles by up to $6\%$ and underestimation of
self-sufficiency by up to 9 percentage points. In
time-independent applications such as peak shaving, however,
the indicators can be well reproduced with deviations of up
to $3\%$ despite the lost regularity. In order to make the
anonymization methodology usable for everyone, we present
the open-source tool LoadPAT, in which users can anonymize
their load profiles and choose their desired level of
anonymization. This work is intended to further encourage
the dissemination of open-source data.},
cin = {IEK-12 / JARA-ENERGY},
ddc = {620},
cid = {I:(DE-Juel1)IEK-12-20141217 / $I:(DE-82)080011_20140620$},
pnm = {1223 - Batteries in Application (POF4-122) / BMWi-0325666 -
Wissenschaftliches Mess- und Evaluierungsprogramm
Solarstromspeicher (BMWi-0325666) / BMWi-03ET6117 -
Wissenschaftliches Mess- und Evaluierungsprogramm
Solarstromspeicher 2.0 (BMWi-03ET6117)},
pid = {G:(DE-HGF)POF4-1223 / G:(DE-82)BMWi-0325666 /
G:(DE-82)BMWi-03ET6117},
typ = {PUB:(DE-HGF)16},
UT = {WOS:001001351900001},
doi = {10.1016/j.apenergy.2023.121191},
url = {https://juser.fz-juelich.de/record/1024779},
}