Dynamic Pricing Strategy for Electromobility using Markov Decision Processes

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F18%3A00322131" target="_blank" >RIV/68407700:21230/18:00322131 - isvavai.cz</a>

Výsledek na webu

<a href="https://electrific.eu/wp-content/uploads/2018/04/ICAART_2018_88.pdf" target="_blank" >https://electrific.eu/wp-content/uploads/2018/04/ICAART_2018_88.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Dynamic Pricing Strategy for Electromobility using Markov Decision Processes

Popis výsledku v původním jazyce

Efficient allocation of charging capacity to electric vehicle (EV) users is a key prerequisite for large-scale adaption of electric vehicles. Dynamic pricing represents a flexible framework for balancing the supply and demand for limited resources. In this paper, we show how dynamic pricing can be employed for allocation of EV charging capacity. Our approach uses Markov Decision Process (MDP) to implement demand-response pricing which can take into account both revenue maximization at the side of the charging station provider and the minimization of cost of charging on the side of the EV driver. We experimentally evaluate our method on a real-world data set. We compare our dynamic pricing method with the flat rate time-of-use pricing that is used today by most paid charging stations and show significant benefits of dynamically allocating charging station capacity through dynamic pricing.

Název v anglickém jazyce

Dynamic Pricing Strategy for Electromobility using Markov Decision Processes

Popis výsledku anglicky

Efficient allocation of charging capacity to electric vehicle (EV) users is a key prerequisite for large-scale adaption of electric vehicles. Dynamic pricing represents a flexible framework for balancing the supply and demand for limited resources. In this paper, we show how dynamic pricing can be employed for allocation of EV charging capacity. Our approach uses Markov Decision Process (MDP) to implement demand-response pricing which can take into account both revenue maximization at the side of the charging station provider and the minimization of cost of charging on the side of the EV driver. We experimentally evaluate our method on a real-world data set. We compare our dynamic pricing method with the flat rate time-of-use pricing that is used today by most paid charging stations and show significant benefits of dynamically allocating charging station capacity through dynamic pricing.

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 10th International Conference on Agents and Artificial Intelligence

ISBN

978-989-758-275-2

ISSN

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e-ISSN

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Počet stran výsledku

8

Strana od-do

507-514

Název nakladatele

SciTePress

Místo vydání

Madeira

Místo konání akce

Funchal, Medeira, Portugal

Datum konání akce

16. 1. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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