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

Journal Article (2022)
Author(s)

L. R. Visser (Universiteit Utrecht)

M.E. Kootte (TU Delft - Numerical Analysis)

A. C. Ferreira (Universiteit van Amsterdam)

O. Sicurani (Sympower)

E. J. Pauwels (Centrum Wiskunde & Informatica (CWI))

Kees Vuik (TU Delft - Numerical Analysis)

Wilfried G.J.H.M. Van Sark (Universiteit Utrecht)

T. A. AlSkaif (Wageningen University & Research)

Research Group
Numerical Analysis
Copyright
© 2022 L. R. Visser, M.E. Kootte, A. C. Ferreira, O. Sicurani, E. J. Pauwels, Cornelis Vuik, W. G.J.H.M. Van Sark, T. A. AlSkaif
DOI related publication
https://doi.org/10.1016/j.apenergy.2021.118280
More Info
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Publication Year
2022
Language
English
Copyright
© 2022 L. R. Visser, M.E. Kootte, A. C. Ferreira, O. Sicurani, E. J. Pauwels, Cornelis Vuik, W. G.J.H.M. Van Sark, T. A. AlSkaif
Research Group
Numerical Analysis
Volume number
308
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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.