An Equivalent Time-Variant Storage Model to Harness EV Flexibility: Forecast and Aggregation

Michael Gerold Pertl, Francesco Carducci, Michaelangelo Tabone, Mattia Marinelli, Sila Kiliccote, Emre Can Kara*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

The demand for vehicle charging will require 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 charging at thousands of stations. This paper addresses the problem of forecasting and management of the aggregate flexible demand from tens to thousands of electric vehicle 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 non-residential EVSEs located in Northern California. The median coefficient of variation (CV) 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.
Original languageEnglish
JournalI E E E Transactions on Industrial Informatics
Volume15
Issue number4
Pages (from-to)1899 - 1910
ISSN1551-3203
DOIs
Publication statusPublished - 2018

Keywords

  • Aggregation
  • Data Analysis
  • Demand Response
  • Electric Vehicles
  • Power System Flexibility

Cite this

Pertl, Michael Gerold ; Carducci, Francesco ; Tabone, Michaelangelo ; Marinelli, Mattia ; Kiliccote, Sila ; Kara, Emre Can. / An Equivalent Time-Variant Storage Model to Harness EV Flexibility: Forecast and Aggregation. In: I E E E Transactions on Industrial Informatics. 2018 ; Vol. 15, No. 4. pp. 1899 - 1910.
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abstract = "The demand for vehicle charging will require 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 charging at thousands of stations. This paper addresses the problem of forecasting and management of the aggregate flexible demand from tens to thousands of electric vehicle 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 non-residential EVSEs located in Northern California. The median coefficient of variation (CV) 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 ofcharging in comparison to an uncontrolled case by 60{\%} with respect to an oracle scenario.",
keywords = "Aggregation, Data Analysis, Demand Response, Electric Vehicles, Power System Flexibility",
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An Equivalent Time-Variant Storage Model to Harness EV Flexibility: Forecast and Aggregation. / Pertl, Michael Gerold; Carducci, Francesco ; Tabone, Michaelangelo; Marinelli, Mattia; Kiliccote, Sila; Kara, Emre Can.

In: I E E E Transactions on Industrial Informatics, Vol. 15, No. 4, 2018, p. 1899 - 1910.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - An Equivalent Time-Variant Storage Model to Harness EV Flexibility: Forecast and Aggregation

AU - Pertl, Michael Gerold

AU - Carducci, Francesco

AU - Tabone, Michaelangelo

AU - Marinelli, Mattia

AU - Kiliccote, Sila

AU - Kara, Emre Can

PY - 2018

Y1 - 2018

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AB - The demand for vehicle charging will require 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 charging at thousands of stations. This paper addresses the problem of forecasting and management of the aggregate flexible demand from tens to thousands of electric vehicle 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 non-residential EVSEs located in Northern California. The median coefficient of variation (CV) 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 ofcharging in comparison to an uncontrolled case by 60% with respect to an oracle scenario.

KW - Aggregation

KW - Data Analysis

KW - Demand Response

KW - Electric Vehicles

KW - Power System Flexibility

U2 - 10.1109/TII.2018.2865433

DO - 10.1109/TII.2018.2865433

M3 - Journal article

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JF - I E E E Transactions on Industrial Informatics

SN - 1551-3203

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ER -