Optimum day-ahead bidding profiles of electrical vehicle charging stations in FCR markets

Poria Astero; Corentin Evens

doi:10.1016/j.epsr.2020.106667

Optimum day-ahead bidding profiles of electrical vehicle charging stations in FCR markets

Poria Astero (Corresponding Author), Corentin Evens

BA4204 Smart grids

Research output: Contribution to journal › Article › Scientific › peer-review

12 Citations (Scopus)

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Abstract

This research developed an application for electrical vehicles charging stations (EVCS) to estimate the optimum day-ahead bidding profiles in frequency containment reserves (FCR) markets and this paper presents the stochastic methodology behind this application. To achieve this, first, deterministic models are developed to calculate the maximum FCR that could be provided by each charging event (cycle) of an electric vehicle (EV). These models are established based on the technical requirements of FCR in the Nordic flexibility market, namely the frequency containment reserve for normal operation (FCR-N) and frequency containment reserve for disturbances (FCR-D). In the next step, these deterministic models will be combined with historical data of charging records in EVCS to calculate the probability density functions of the FCR profiles. Finally, the proposed application estimates the optimum FCR profiles, which maximise the expected profit of EVCS from participating in the day-ahead flexibility market, by performing a stochastic optimisation. The performance of the proposed application is evaluated by using empirical charging data of public EVCS in Helsinki area.

Original language	English
Article number	106667
Journal	Electric Power Systems Research
Volume	190
DOIs	https://doi.org/10.1016/j.epsr.2020.106667
Publication status	Published - 1 Jan 2021
MoE publication type	A1 Journal article-refereed
Event	21st Power Systems Computation Conference, PSCC 2020 - Porto, Portugal Duration: 29 Jun 2020 → 3 Jul 2020

Keywords

Ancillary service
Electrical vehicle charging stations
Frequency containment reserves markets

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10.1016/j.epsr.2020.106667Licence: CC BY

Cite this

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title = "Optimum day-ahead bidding profiles of electrical vehicle charging stations in FCR markets",

abstract = "This research developed an application for electrical vehicles charging stations (EVCS) to estimate the optimum day-ahead bidding profiles in frequency containment reserves (FCR) markets and this paper presents the stochastic methodology behind this application. To achieve this, first, deterministic models are developed to calculate the maximum FCR that could be provided by each charging event (cycle) of an electric vehicle (EV). These models are established based on the technical requirements of FCR in the Nordic flexibility market, namely the frequency containment reserve for normal operation (FCR-N) and frequency containment reserve for disturbances (FCR-D). In the next step, these deterministic models will be combined with historical data of charging records in EVCS to calculate the probability density functions of the FCR profiles. Finally, the proposed application estimates the optimum FCR profiles, which maximise the expected profit of EVCS from participating in the day-ahead flexibility market, by performing a stochastic optimisation. The performance of the proposed application is evaluated by using empirical charging data of public EVCS in Helsinki area.",

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note = "Funding Information: The research leading to this work was being carried out as a part of the EU-SysFlex project (Pan-European system with an efficient coordinated use of flexibilities for the integration of a large share of RES), which received funding from the EU's Horizon 2020 programme under grant agreement No 773505. The authors would like to thank Suvi Takala and Antti Hyttinen, from Helen Ltd, who helped with providing EVCS data. Funding Information: The research leading to this work was being carried out as a part of the EU-SysFlex project (Pan-European system with an efficient coordinated use of flexibilities for the integration of a large share of RES), which received funding from the EU's Horizon 2020 programme under grant agreement No 773505 .; 21st Power Systems Computation Conference, PSCC 2020 ; Conference date: 29-06-2020 Through 03-07-2020",

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N2 - This research developed an application for electrical vehicles charging stations (EVCS) to estimate the optimum day-ahead bidding profiles in frequency containment reserves (FCR) markets and this paper presents the stochastic methodology behind this application. To achieve this, first, deterministic models are developed to calculate the maximum FCR that could be provided by each charging event (cycle) of an electric vehicle (EV). These models are established based on the technical requirements of FCR in the Nordic flexibility market, namely the frequency containment reserve for normal operation (FCR-N) and frequency containment reserve for disturbances (FCR-D). In the next step, these deterministic models will be combined with historical data of charging records in EVCS to calculate the probability density functions of the FCR profiles. Finally, the proposed application estimates the optimum FCR profiles, which maximise the expected profit of EVCS from participating in the day-ahead flexibility market, by performing a stochastic optimisation. The performance of the proposed application is evaluated by using empirical charging data of public EVCS in Helsinki area.

AB - This research developed an application for electrical vehicles charging stations (EVCS) to estimate the optimum day-ahead bidding profiles in frequency containment reserves (FCR) markets and this paper presents the stochastic methodology behind this application. To achieve this, first, deterministic models are developed to calculate the maximum FCR that could be provided by each charging event (cycle) of an electric vehicle (EV). These models are established based on the technical requirements of FCR in the Nordic flexibility market, namely the frequency containment reserve for normal operation (FCR-N) and frequency containment reserve for disturbances (FCR-D). In the next step, these deterministic models will be combined with historical data of charging records in EVCS to calculate the probability density functions of the FCR profiles. Finally, the proposed application estimates the optimum FCR profiles, which maximise the expected profit of EVCS from participating in the day-ahead flexibility market, by performing a stochastic optimisation. The performance of the proposed application is evaluated by using empirical charging data of public EVCS in Helsinki area.

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Optimum day-ahead bidding profiles of electrical vehicle charging stations in FCR markets

Abstract

Keywords

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EU-SysFlex: Pan-European system with an efficient coordinated use of flexibilities for the integration of a large share of RES

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Optimum day-ahead bidding profiles of electrical vehicle charging stations in FCR markets

Abstract

Keywords

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Projects

EU-SysFlex: Pan-European system with an efficient coordinated use of flexibilities for the integration of a large share of RES

Cite this