Discrete pricing optimization for dedicated electric vehicle slow-charging in a behind-the-meter community

Liu, Zhan, Wen, Fushuan and Zhou, Yue ORCID: https://orcid.org/0000-0002-6698-4714 2026. Discrete pricing optimization for dedicated electric vehicle slow-charging in a behind-the-meter community. Presented at: 5th International Symposium on Electrical, Electronics and Information Engineering, Taizhou, China, 21-23 November 2025. Published in: Deng, Wei and Wen, Fushuan eds. New Frontiers in Electrical, Electronics and Information Engineering: Conference Proceedings of the 2025 5th International Symposium on Electrical, Electronics and Information Engineering. Lecture Notes in Electrical Engineering. , vol.1677 Springer Science Business Media, pp. 183-194. 10.1007/978-981-92-4035-7_14

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Abstract

In a green power direct supply behind-the-meter (BTM) community, dedicated slow-charging facilities serve electric vehicle (EV) users and could purchase power from both a photovoltaic (PV) owner and the utility grid. The interaction between the charging facility operator (CFO) and EVs can be modeled as a Stackelberg game: the CFO sets charging prices to maximize net profit, while EV users adjust charging behavior to minimize charging cost. Then a discrete pricing approach is employed, where the spacing of price levels are considered and set as decision variables. To address the introduced bilinear terms by binary and continuous variables, inequality relaxations are applied. Simulation results of a sample example with four price levels illustrates the CFO’s pricing strategy and the corresponding EV power optimization results, demonstrate the accuracy of the linearization method and present pricing outcomes under different price level numbers, and show that the proposed pricing model could increase the revenue of the CFO compared with a single-price scheme.

Item Type:	Conference or Workshop Item - published (Paper)
Date Type:	Publication
Status:	Published
Schools:	Schools > Engineering
Publisher:	Springer Science Business Media
ISBN:	978-981-92-4034-0
ISSN:	1876-1100
Last Modified:	15 Sep 2026 08:46
URI:	https://orca.cardiff.ac.uk/id/eprint/189592

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