TY - JOUR
T1 - Optimum day-ahead bidding profiles of electrical vehicle charging stations in FCR markets
AU - Hasanpor Divshali, Poria
AU - Evens, Corentin
N1 - 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 .
Publisher Copyright:
© 2020
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2021/1/1
Y1 - 2021/1/1
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.
KW - Ancillary service
KW - Electrical vehicle charging stations
KW - Frequency containment reserves markets
UR - http://www.scopus.com/inward/record.url?scp=85089097672&partnerID=8YFLogxK
U2 - 10.1016/j.epsr.2020.106667
DO - 10.1016/j.epsr.2020.106667
M3 - Article
VL - 190
JO - Electric Power Systems Research
JF - Electric Power Systems Research
SN - 0378-7796
M1 - 106667
T2 - 21st Power Systems Computation Conference (PSCC 2020)
Y2 - 29 June 2020 through 3 July 2020
ER -