Electric vehicle flexibility; Multi-stage optimization; Continuous intraday market; Electric vehicle user uncertainty; Short-term electricity price forecasting; Smart charging; Electricity markets; Day-ahead market
Abstract :
[en] The rise in electric vehicles (EVs) challenges energy suppliers with unpredictable charging behavior, making demand forecasts less accurate and increasing financial risks from power imbalances. In Europe, retailers can trade these imbalances in short-term markets like the continuous intraday (CID) market. By controlling EV charging times, suppliers can shift charging to periods with lower prices, potentially benefiting financially. However, the financial gains from trading this flexibility in the CID market remain uncertain due to EV user behavior and price fluctuations. In this study, we develop and test trading strategies designed to manage the power needs of a fleet of 1000 EVs across different segments of short-term electricity markets, focusing on the day-ahead (DA) auction, and the CID market. To address EV-use uncertainty, we take an initial EV charging flexibility forecast for the DA auction, and an updated forecast for the CID market. We find that trading in the CID market reduces the overall cost of making power purchases by capitalizing on the flexibilities of EV charging times. Our results suggest that energy suppliers trading in the CID market significantly reduce their financial risk, even when there are high margins of error in EV flexibility forecasts. In our scenario with the highest deviation between the DA and intraday (ID) flexibility metrics, applying the best CID strategies yielded an average yearly profit of €37.52 and €4,840.63 in 2019 and 2022 respectively. In comparison to the baseline strategy, which clears volumes as imbalances, the corresponding financial savings amounted to €1978.52 and €16,632.25, respectively.
Research center :
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > FINATRAX - Digital Financial Services and Cross-organizational Digital Transformations
Disciplines :
Computer science Management information systems Energy
Fonds National de la Recherche Luxembourg Fondation Enovos Enovos Luxembourg S.A
Funding number :
13342933; 17886330
Funding text :
This research was funded in part by the Luxembourg National Research Fund (FNR) and PayPal, PEARL grant reference 13342933/ Gilbert Fridgen. This research was funded in part by the Luxembourg National Research Fund (FNR) , grant reference 17886330. For the purpose of open access, and in fulfillment of the obligations arising from the grant agreement , the author has applied a Creative Commons Attribution 4.0 International (CC BY 4.0) license to any Author Accepted Manuscript version arising from this submission.
The authors gratefully acknowledge the Fondation Enovos under the aegis of the Fondation de Luxembourg in the frame of the philanthropic funding for the research project INDUCTIVE which is the initiator of this applied research.
The research was carried out as part of a partnership with the energy retailer Enovos Luxembourg S.A.
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