Controlling the Charging of Electric Vehicles with Neural Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F18%3A10388389" target="_blank" >RIV/00216208:11320/18:10388389 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/8489027" target="_blank" >https://ieeexplore.ieee.org/document/8489027</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/IJCNN.2018.8489027" target="_blank" >10.1109/IJCNN.2018.8489027</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Controlling the Charging of Electric Vehicles with Neural Networks

Popis výsledku v původním jazyce

We propose and evaluate controllers for the coordination of the charging of electric vehicles. The controllers are based on neural networks and are completely de-centralized, in the sense that the charging current is completely decided by the controller itself. One of the versions of the controllers does not require any outside communication at all. We test controllers based on two different architectures of neural networks-the feed-forward networks and the echo state networks. The networks are optimized by either an evolutionary algorithm (CMA-ES) or by a gradient-based method. The results of the different architectures and the different optimization algorithms are compared in a realistic scenario. We show that the controllers are able to charge the cars while keeping the peak consumptions almost the same as when no charging is performed. Moreover, the controllers fill the valleys of the consumption thus reducing the difference between the maximum and minimum consumption in the grid.

Název v anglickém jazyce

Controlling the Charging of Electric Vehicles with Neural Networks

Popis výsledku anglicky

We propose and evaluate controllers for the coordination of the charging of electric vehicles. The controllers are based on neural networks and are completely de-centralized, in the sense that the charging current is completely decided by the controller itself. One of the versions of the controllers does not require any outside communication at all. We test controllers based on two different architectures of neural networks-the feed-forward networks and the echo state networks. The networks are optimized by either an evolutionary algorithm (CMA-ES) or by a gradient-based method. The results of the different architectures and the different optimization algorithms are compared in a realistic scenario. We show that the controllers are able to charge the cars while keeping the peak consumptions almost the same as when no charging is performed. Moreover, the controllers fill the valleys of the consumption thus reducing the difference between the maximum and minimum consumption in the grid.

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

<a href="/cs/project/GJ17-10090Y" target="_blank" >GJ17-10090Y: Optimalizace sítí</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

2018 International Joint Conference on Neural Networks (IJCNN)

ISBN

978-1-5090-6014-6

ISSN

2161-4407

e-ISSN

neuvedeno

Počet stran výsledku

8

Strana od-do

1-8

Název nakladatele

Institute of Electrical and Electronics Engineers Inc.

Místo vydání

Neuveden

Místo konání akce

Rio de Janeiro, Brazílie

Datum konání akce

8. 7. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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