Revenue Maximization for Electric Vehicle Charging Service Providers Using Sequential Dynamic Pricing
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F18%3A00322134" target="_blank" >RIV/68407700:21230/18:00322134 - isvavai.cz</a>
Result on the web
<a href="https://dl.acm.org/citation.cfm?id=3237506" target="_blank" >https://dl.acm.org/citation.cfm?id=3237506</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Revenue Maximization for Electric Vehicle Charging Service Providers Using Sequential Dynamic Pricing
Original language description
With the increasing prevalence of electric vehicles (EVs), the provision of EV charging is becoming a standard commercial service. With this shift, EV charging service providers are looking for ways to make their business more profitable. Dynamic pricing is a proven technique to increase revenue in markets with time-variant, heterogeneous demand. In this paper, we propose a Markov Decision Process (MDP)-based approach to revenue-maximizing dynamic pricing for charging service providers. We implement the approach using an ensemble of policy iteration MDP solvers and evaluate it using a simulation based on real-world data. We show that our proposed method achieves significantly higher revenue than methods utilizing flat-based pricing. In addition to achieving higher revenue for charging service providers, the method also increases the efficiency of allocation measured in terms of the total utilization of the charging station.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems
ISBN
978-1-5108-6808-3
ISSN
1548-8403
e-ISSN
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Number of pages
9
Pages from-to
832-840
Publisher name
ACM
Place of publication
New York
Event location
Stockholm
Event date
Jul 10, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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