An Equivalent Time-Variant Storage Model to Harness EV Flexibility: Forecast and Aggregation Article Swipe
Michael Pertl
,
Francesco Carducci
,
Michaelangelo D. Tabone
,
Mattia Marinelli
,
Sila Kiliccote
,
Emre Can Kara
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1109/tii.2018.2865433
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1109/tii.2018.2865433
The demand for vehicle charging will necessitate large investments in power distribution, transmission, and generation. However, this demand is often also flexible in time, and can be actively managed to reduce the needed investments, and to better integrate renewable electricity. Harnessing this flexibility requires forecasting and controlling electric vehicle (EV) charging at thousands of stations. In this work, we address the problem of forecasting and management of the aggregate flexible demand from tens to thousands of EV supply equipment (EVSEs). First, it presents an equivalent time-variant storage model for flexible demand at an aggregation of EVSEs. The proposed model is generalizable to different markets, and also to different flexible loads. Model parameters representing multiple EVSEs can be easily aggregated by summation, and forecasted using autoregressive models. The forecastability of uncontrolled demand and storage parameters is evaluated using data from 1341 nonresidential EVSEs located in Northern California. The median coefficient of variation is as low as 24% for the forecast of uncontrolled demand at the highest aggregation and 10-15% for the storage parameters. The benefits of aggregation and forecastability are demonstrated using an energy arbitrage scenario. Purchasing energy day ahead is less expensive than in the real-time market, but relies on a uncertain forecast of charging availability. The results show that the forecastability significantly improves for larger aggregations. This helps the aggregator make a better forecast, and decreases the cost of charging in comparison to an uncontrolled case by 60% with respect to an oracle scenario.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tii.2018.2865433
- OA Status
- green
- Cited By
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All OpenAlex metadata
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https://openalex.org/W2887611898Canonical identifier for this work in OpenAlex
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https://doi.org/10.1109/tii.2018.2865433Digital Object Identifier
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An Equivalent Time-Variant Storage Model to Harness EV Flexibility: Forecast and AggregationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2018Year of publication
- Publication date
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2018-08-16Full publication date if available
- Authors
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Michael Pertl, Francesco Carducci, Michaelangelo D. Tabone, Mattia Marinelli, Sila Kiliccote, Emre Can KaraList of authors in order
- Landing page
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https://doi.org/10.1109/tii.2018.2865433Publisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://www.osti.gov/biblio/1529174Direct OA link when available
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Flexibility (engineering), Computer science, Data modeling, Statistics, Mathematics, DatabaseTop concepts (fields/topics) attached by OpenAlex
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57Total citation count in OpenAlex
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2025: 11, 2024: 6, 2023: 11, 2022: 7, 2021: 12Per-year citation counts (last 5 years)
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32Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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