Analyzing electric vehicle, load and photovoltaic generation uncertainty using publicly available datasets
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866915021414465536 |
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| author | Hashmi, Md Umar Gioffrè, Domenico Nagels, Simon Van Hertem, Dirk |
| author_facet | Hashmi, Md Umar Gioffrè, Domenico Nagels, Simon Van Hertem, Dirk |
| contents | This paper aims to analyze three publicly available datasets for quantifying seasonal and annual uncertainty for efficient scenario creation. The datasets from Elaad, Elia and Fluvius are utilized to statistically analyze electric vehicle charging, normalized solar generation and low-voltage consumer load profiles, respectively. Frameworks for scenario generation are also provided for these datasets. The datasets for load profiles and solar generation analyzed are for the year 2022, thus embedding seasonal information. An online repository is created for the wider applicability of this work. Finally, the extreme load week(s) are identified and linked to the weather data measured at EnergyVille in Belgium. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_01284 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Analyzing electric vehicle, load and photovoltaic generation uncertainty using publicly available datasets Hashmi, Md Umar Gioffrè, Domenico Nagels, Simon Van Hertem, Dirk Systems and Control This paper aims to analyze three publicly available datasets for quantifying seasonal and annual uncertainty for efficient scenario creation. The datasets from Elaad, Elia and Fluvius are utilized to statistically analyze electric vehicle charging, normalized solar generation and low-voltage consumer load profiles, respectively. Frameworks for scenario generation are also provided for these datasets. The datasets for load profiles and solar generation analyzed are for the year 2022, thus embedding seasonal information. An online repository is created for the wider applicability of this work. Finally, the extreme load week(s) are identified and linked to the weather data measured at EnergyVille in Belgium. |
| title | Analyzing electric vehicle, load and photovoltaic generation uncertainty using publicly available datasets |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2409.01284 |