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An operational bidding framework for aggregated electric vehicles on the electricity spot market

Author

Listed:
  • Visser, L.R.
  • Kootte, M.E.
  • Ferreira, A.C.
  • Sicurani, O.
  • Pauwels, E.J.
  • Vuik, C.
  • Van Sark, W.G.J.H.M.
  • AlSkaif, T.A.

Abstract

Fluctuating electricity prices offer potential economic savings for the consumption of electricity by flexible assets such as Electric Vehicles (EVs). This study proposes an operational bidding framework that minimizes the charging costs of an EV fleet by submitting an optimized bid to the day-ahead electricity market. The framework consists of a bidding module that determines the most cost-effective bid by considering an electricity price and an EV charging demand forecast module. In this study we develop and evaluate several regression and machine learning models that forecast the electricity price and EV charging demand. Furthermore, we examine the composition of a most optimal operational bidding framework by comparing the outcome of the bidding module when fed with each of the forecast models. This is determined by considering the day-ahead electricity price and imbalance costs due to forecast errors. The study demonstrates that the best performing self-contained forecast models with the objective of electricity price and EV charging demand forecasting, do not deliver the best overall results when included in the bidding framework. Additionally, the results show that the best performing framework obtains a 26% cost savings compared to a reference case where EVs are charged inflexibly. This corresponds to an achieved savings potential of 92%. Consequently, along with the developed bidding framework, these results provide a fundamental basis for effective electricity trading on the day-ahead market.

Suggested Citation

  • Visser, L.R. & Kootte, M.E. & Ferreira, A.C. & Sicurani, O. & Pauwels, E.J. & Vuik, C. & Van Sark, W.G.J.H.M. & AlSkaif, T.A., 2022. "An operational bidding framework for aggregated electric vehicles on the electricity spot market," Applied Energy, Elsevier, vol. 308(C).
    • Handle: RePEc:eee:appene:v:308:y:2022:i:c:s0306261921015403
    DOI: 10.1016/j.apenergy.2021.118280
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    References listed on IDEAS

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    Cited by:

    1. Zhang, Kaizhe & Xu, Yinliang & Sun, Hongbin, 2024. "Joint chance-constrained program based electric vehicles optimal dispatching strategy considering drivers' response uncertainty," Applied Energy, Elsevier, vol. 356(C).
    2. Zhu, Xu & Sun, Yuanzhang & Yang, Jun & Dou, Zhenlan & Li, Gaojunjie & Xu, Chengying & Wen, Yuxin, 2022. "Day-ahead energy pricing and management method for regional integrated energy systems considering multi-energy demand responses," Energy, Elsevier, vol. 251(C).
    3. Afentoulis, Konstantinos D. & Bampos, Zafeirios N. & Vagropoulos, Stylianos I. & Keranidis, Stratos D. & Biskas, Pantelis N., 2022. "Smart charging business model framework for electric vehicle aggregators," Applied Energy, Elsevier, vol. 328(C).

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