An Equivalent Time-Variant Storage Model to Harness EV Flexibility: Forecast and Aggregation
Abstract
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 onmore »
- Authors:
-
[1];
[1];
[4]
- Technical Univ. of Denmark, Roskilde (Denmark). Dept. of Electrical Engineering
- Marche Polytechnic Univ., Ancona (Italy). Dept. of Industrial Engineering and Mathematical Sciences (DIISM)
- Stanford Univ., CA (United States)
- SLAC National Accelerator Lab. (SLAC), Menlo Park, CA (United States)
- Publication Date:
- Research Org.:
- SLAC National Accelerator Lab., Menlo Park, CA (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1529174
- Grant/Contract Number:
- 0000-1756; AC02-76SF00515; 609687
- Resource Type:
- Accepted Manuscript
- Journal Name:
- IEEE Transactions on Industrial Informatics
- Additional Journal Information:
- Journal Volume: 15; Journal Issue: 4; Journal ID: ISSN 1551-3203
- Publisher:
- IEEE
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 24 POWER TRANSMISSION AND DISTRIBUTION; Aggregation; data analysis; demand response (DR); electric vehicles (EVs); power system flexibility
Citation Formats
Pertl, Michael, Carducci, Francesco, Tabone, Michaelangelo, Marinelli, Mattia, Kiliccote, Sila, and Kara, Emre C. An Equivalent Time-Variant Storage Model to Harness EV Flexibility: Forecast and Aggregation. United States: N. p., 2018.
Web. doi:10.1109/tii.2018.2865433.
Pertl, Michael, Carducci, Francesco, Tabone, Michaelangelo, Marinelli, Mattia, Kiliccote, Sila, & Kara, Emre C. An Equivalent Time-Variant Storage Model to Harness EV Flexibility: Forecast and Aggregation. United States. https://doi.org/10.1109/tii.2018.2865433
Pertl, Michael, Carducci, Francesco, Tabone, Michaelangelo, Marinelli, Mattia, Kiliccote, Sila, and Kara, Emre C. Thu .
"An Equivalent Time-Variant Storage Model to Harness EV Flexibility: Forecast and Aggregation". United States. https://doi.org/10.1109/tii.2018.2865433. https://www.osti.gov/servlets/purl/1529174.
@article{osti_1529174,
title = {An Equivalent Time-Variant Storage Model to Harness EV Flexibility: Forecast and Aggregation},
author = {Pertl, Michael and Carducci, Francesco and Tabone, Michaelangelo and Marinelli, Mattia and Kiliccote, Sila and Kara, Emre C.},
abstractNote = {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.},
doi = {10.1109/tii.2018.2865433},
journal = {IEEE Transactions on Industrial Informatics},
number = 4,
volume = 15,
place = {United States},
year = {Thu Aug 16 00:00:00 EDT 2018},
month = {Thu Aug 16 00:00:00 EDT 2018}
}
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Works referencing / citing this record:
Optimal Strategy to Exploit the Flexibility of an Electric Vehicle Charging Station
journal, October 2019
- Diaz-Londono, Cesar; Colangelo, Luigi; Ruiz, Fredy
- Energies, Vol. 12, Issue 20
System Planning of Grid-Connected Electric Vehicle Charging Stations and Key Technologies: A Review
journal, November 2019
- Ma, Chao-Tsung
- Energies, Vol. 12, Issue 21