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Time Series Aggregation Module (TSAM)
Hannes Kachel
Technische Universität Berlin | Department of Energy and Resource Management | 05.12.2019
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Idea TSAM – without storages
Idea TSAM – with storages
Application and first results
Motivation 4
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14
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Outline
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Idea TSAM – without storages
Idea TSAM – with storages
Application and first results
Motivation 4
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Page
Outline
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Why do we use TSAM?
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Bildquelle: Iks-gmbh (2017).
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Idea TSAM – without storages
Idea TSAM – with storages
Application and first results
Motivation 4
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Outline
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Sequence of time series reduction
pre-processing of
time series
aggregation
algorithm
add extreme periods
back-scaling
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Quelle: Kotzur et al. (2018).
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1. Pre-processing of time series
normalisation
-> ∈ (0, 1)
choose length and number of typical periods
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Quelle: Kotzur et al. (2018).
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Example pre-processing:
typical day for one application
• one application -> a = 1
• one year with 365 days
-> i = 365 candiates for
typDays
• one day with 24 hours
-> g = 24
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Quelle: Kotzur et al. (2018).
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2. Clustering days of two applications
• # cluster a priori
• clustering similar days
• -> k typDays
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Quelle: Kotzur et al. (2018).
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3. There are 4 methods for extreme periods
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Quelle: Kotzur et al. (2018).
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4. Back scaling of aggregated time series
• goal: no systematic errors
average aggregated time series = average
full time series
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Quelle: Kotzur et al. (2018).
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Idea TSAM – without storages
Idea TSAM – with storages
Application and first results
Motivation 4
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Outline
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There are two time layers:
intra and inter-periods
1. intra-periods
– As before
2. inter-perioden
– Storage level between periods
3. Superposition
– sum of sub storage levels equals storage level
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Quelle: Kotzur et al. (2018).
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Storage level is deconstructed
• i: days
• g: hours
• blue: intra
• red: inter
superposition:
𝑥𝑖𝑛𝑡𝑟𝑎 + 𝑥𝑖𝑛𝑡𝑒𝑟 = 𝑠𝑡𝑎𝑡𝑒 𝑥
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Quelle: Kotzur et al. (2018).
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Calculation of a storage level delta
for each typical period
• i: days
• g: hours
• red: intra
• blue: inter
• k: typical
Day
Superposition:
𝑥𝑖𝑛𝑡𝑟𝑎 + 𝑥𝑖𝑛𝑡𝑒𝑟 = 𝑠𝑡𝑎𝑡𝑒 𝑥
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Quelle: Kotzur et al. (2018).
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Idea TSAM – without storages
Idea TSAM – with storages
Application and first results
Motivation 4
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14
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Outline
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Application in a dummy dispatch model
1. Easy application
• great documentation
• many examples
2. Special features
• Pre aggregation with chosen data
• Disaggregation
• RMSE, RMSE duration, MAE
3. Chances
• Cluster Indices only for hierarchical clustering
• Idea and calculation of storage level deltas
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Results with dummy model
Variation of
• number typical periods
• length of typical periods (not shown)
• 1 year horizon
-35
-30
-25
-20
-15
-10
-5
0
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0 10 20 30 40 50
diffe
rence
in s
olu
tion
tim
e
number of typical periods
Absolute difference of solution time to no TSA
-0,02
-0,015
-0,01
-0,005
0
0,005
0,01
0,015
0,02
0,025
0 10 20 30 40 50
rela
tive e
rror
number of typical periods
Relative error of objective to no TSA
Quelle: Eigene Darstellung.
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Thank you
very much
for your
attention
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Literaturverzeichnis
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Kotzur, L., Markewitz, P., Robinius, M., Stolten, D. 2017: Impact of different time series aggregation methods on optimal
energy system design. In: Renewable Energy Vol. 117, S. 474 – 487.
Kotzur, L., Markewitz, P., Robinius, M., Stolten, D. 2017: Time series aggregation for energy system design: Modeling
seasonal storage. In: Applied Energy Vol. 213, S. 123 – 135.
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Bildquellenverzeichnis
Seite 21
Iks-gmbh.com. 2017: Anmeldung zum IKS-Thementag "Komplexität beherrschen". https://www.iks-
gmbh.com/unternehmen/news/einladung-zum-iks-thementag-komplexitaet-beherrschen-agiles-arbeiten-und-
microservices-architekturen/online-anmeldung-zum-iks-thementag-komplexitaet-beherrschen-am-28-03-217/, 30.
12.2019.