Revenue Maximization for Electric Vehicle Charging Service Providers Using Sequential Dynamic Pricing

Kód výsledku v 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>

Výsledek na webu

<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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Jazyk výsledku

angličtina

Název v původním jazyce

Revenue Maximization for Electric Vehicle Charging Service Providers Using Sequential Dynamic Pricing

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Revenue Maximization for Electric Vehicle Charging Service Providers Using Sequential Dynamic Pricing

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

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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Počet stran výsledku

9

Strana od-do

832-840

Název nakladatele

ACM

Místo vydání

New York

Místo konání akce

Stockholm

Datum konání akce

10. 7. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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