Time series aggregation for energy system design: Modeling seasonal storage
• Comprehensive mathematical derivation for the superposition of system states on different time grids. • A novel approach linking the states between typical operating periods in energy system design models. • Method validation with different energy system configurations for typical days aggregated...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Kotzur, Leander [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Umfang: |
13 |
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| Übergeordnetes Werk: |
Enthalten in: Risky business: Psychopathy, framing effects, and financial outcomes - Costello, Thomas H. ELSEVIER, 2018, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:213 ; year:2018 ; day:1 ; month:03 ; pages:123-135 ; extent:13 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.apenergy.2018.01.023 |
|---|
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ELV04186297X |
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| 520 | |a • Comprehensive mathematical derivation for the superposition of system states on different time grids. • A novel approach linking the states between typical operating periods in energy system design models. • Method validation with different energy system configurations for typical days aggregated with k-medoid clustering. • Reduction of computational load by 90% for renewable-based energy system optimization, including seasonal storage. | ||
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| 650 | 7 | |a Renewable energy |2 Elsevier | |
| 650 | 7 | |a Typical periods |2 Elsevier | |
| 650 | 7 | |a Mixed integer linear programming |2 Elsevier | |
| 650 | 7 | |a Clustering |2 Elsevier | |
| 650 | 7 | |a Seasonal storage |2 Elsevier | |
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• Comprehensive mathematical derivation for the superposition of system states on different time grids. • A novel approach linking the states between typical operating periods in energy system design models. • Method validation with different energy system configurations for typical days aggregated with k-medoid clustering. • Reduction of computational load by 90% for renewable-based energy system optimization, including seasonal storage. |
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• Comprehensive mathematical derivation for the superposition of system states on different time grids. • A novel approach linking the states between typical operating periods in energy system design models. • Method validation with different energy system configurations for typical days aggregated with k-medoid clustering. • Reduction of computational load by 90% for renewable-based energy system optimization, including seasonal storage. |
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Time series aggregation for energy system design: Modeling seasonal storage |
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