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Optimal power tracking for autonomous demand side management of electric vehicles

  • Increasing electric vehicle penetration leads to undesirable peaks in power if no proper coordination in charging is implemented. We tested the feasibility of electric vehicles acting as flexible demands responding to power signals to minimize the system peaks. The proposed hierarchical autonomous demand side management algorithm is formulated as an optimal power tracking problem. The distribution grid operator determines a power signal for filling the valleys in the non-electric vehicle load profile using the electric vehicle demand flexibility and sends it to all electric vehicle controllers. After receiving the control signal, each electric vehicle controller re-scales it to the expected individual electric vehicle energy demand and determines the optimal charging schedule to track the re-scaled signal. No information concerning the electric vehicles are reported back to the utility, hence the approach can be implemented using unidirectional communication with reduced infrastructural requirements. The achieved results show that the optimal power tracking approach has the potential to eliminate additional peak demands induced by electric vehicle charging and performs comparably to its central implementation. The reduced complexity and computational overhead permits also convenient deployment in practice.

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Author:Subodha Tharangi Ireshika Muhandiram ArachchigeORCiD, Klaus RheinbergerORCiD, Ruben Lliuyacc-Blas, Mohan Lal Kolhe, Markus PreißingerORCiD, Peter KepplingerORCiD
Parent Title (English):Journal of Energy Storage
Document Type:Article
Year of publication:2022
Release Date:2022/06/15
Tag:Demand side management; Distribution grids; Electric vehicle charging; Peak demand reduction; Power tracking
Issue:Bd. 52, Part B
Article Number:104917
Number of pages:11
Organisationseinheit:Forschung / Forschungszentrum Energie
DDC classes:600 Technik, Medizin, angewandte Wissenschaften
JEL-Classification:C Mathematical and Quantitative Methods
Open Access?:ja
Peer review:wiss. Beitrag, peer-reviewed
Publicationlist:Rheinberger, Klaus
Kepplinger, Peter
Preißinger, Markus
Muhandiram Arachchige, Subodha Ireshika
Lliuyacc Blas, Ruben Ronald
Licence (German):License LogoCreative Commons - CC BY - International - Attribution- Namensnennung 4.0