Online Dynamic Pricing for Electric Vehicle Charging Stations with Reservations
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866918000504864768 |
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| author | Mrkos, Jan Komenda, Antonín Fiedler, David Vokřínek, Jiří |
| author_facet | Mrkos, Jan Komenda, Antonín Fiedler, David Vokřínek, Jiří |
| contents | This paper introduces a novel model for online dynamic pricing of electric vehicle charging services that integrates reservation, parking, and charging into a comprehensive bundle priced as a whole. Our approach focuses on the individual high-demand, fast-charging location, employing a Poisson process as a model of charging reservation arrivals, and develops an online dynamic pricing strategy optimized through a Markov Decision Process (MDP). A key contribution is the novel analysis of discretization error introduced when incorporating the continuous-time Poisson process into the discrete MDP framework. The MDP model's feasibility is demonstrated with a heuristic dynamic pricing method based on Monte-Carlo tree search, offering a viable path for real-world applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_05538 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Online Dynamic Pricing for Electric Vehicle Charging Stations with Reservations Mrkos, Jan Komenda, Antonín Fiedler, David Vokřínek, Jiří Multiagent Systems Artificial Intelligence This paper introduces a novel model for online dynamic pricing of electric vehicle charging services that integrates reservation, parking, and charging into a comprehensive bundle priced as a whole. Our approach focuses on the individual high-demand, fast-charging location, employing a Poisson process as a model of charging reservation arrivals, and develops an online dynamic pricing strategy optimized through a Markov Decision Process (MDP). A key contribution is the novel analysis of discretization error introduced when incorporating the continuous-time Poisson process into the discrete MDP framework. The MDP model's feasibility is demonstrated with a heuristic dynamic pricing method based on Monte-Carlo tree search, offering a viable path for real-world applications. |
| title | Online Dynamic Pricing for Electric Vehicle Charging Stations with Reservations |
| topic | Multiagent Systems Artificial Intelligence |
| url | https://arxiv.org/abs/2410.05538 |