research-article

Flexibility Reward Scheme for Grid-Friendly Electric Vehicle Charging in the Distribution Power Grid

Authors:

Philipp Danner,

Wolfgang Duschl,

Dominik Danner,

Ammar Alyousef,

Hermann de MeerAuthors Info & Claims

e-Energy '18: Proceedings of the Ninth International Conference on Future Energy Systems

Pages 564 - 569

https://doi.org/10.1145/3208903.3213893

Published: 12 June 2018 Publication History

Abstract

Due to the increasing number of Electric Vehicles (EV) entering the transportation sector, we need to take a closer look to the distribution power grid, which needs to support the increasing number of charging processes. Grid enhancement to peak load of EV charging processes is very expensive, hence an intelligent solution is preferred. This paper introduces a new concept, called the Reward Scheme, which main objective is to advertise grid-friendly charging processes in order to avoid power quality issues in the distribution grid. To achieve this, we distinguish between guaranteed and flexible power at a charging spot. Using the guaranteed power as a reference, we calculate the grid-friendliness factor of different flexible charging rates, which in turn are proposed to the grid user, e.g. the Charging Service Providers (CSP). This paper describes a simulation-driven approach to obtain the grid-friendliness factor, as well as the required communication between the CSP and the Distribution System Operator (DSO).

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Cited By

Teawnarong AAngaphiwatchawal PChaitusaney S(2024)Optimal Operations of Local Energy Market With Electric Vehicle Charging and Incentives for Local Grid ServicesIEEE Access10.1109/ACCESS.2024.345577412(128375-128391)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3455774
Sharif MHeendeniya CMuhammad ALückemeyer G(2020)Context-Aware Optimal Charging Distribution using Deep Reinforcement LearningProceedings of the 2020 4th International Conference on Big Data and Internet of Things10.1145/3421537.3421547(64-68)Online publication date: 22-Aug-2020
https://dl.acm.org/doi/10.1145/3421537.3421547

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cover image ACM Conferences

e-Energy '18: Proceedings of the Ninth International Conference on Future Energy Systems

June 2018

657 pages

ISBN:9781450357678

DOI:10.1145/3208903

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGCOMM: ACM Special Interest Group on Data Communication

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 June 2018

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Refereed limited

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Horizon 2020

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e-Energy '18

Sponsor:

SIGCOMM

e-Energy '18: The Ninth International Conference on Future Energy Systems

June 12 - 15, 2018

Karlsruhe, Germany

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Overall Acceptance Rate 160 of 446 submissions, 36%

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E-Energy '25

Sponsor:
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The 16th ACM International Conference on Future and Sustainable Energy Systems

June 17 - 20, 2025

Rotterdam , Netherlands

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Cited By

Teawnarong AAngaphiwatchawal PChaitusaney S(2024)Optimal Operations of Local Energy Market With Electric Vehicle Charging and Incentives for Local Grid ServicesIEEE Access10.1109/ACCESS.2024.345577412(128375-128391)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3455774
Sharif MHeendeniya CMuhammad ALückemeyer G(2020)Context-Aware Optimal Charging Distribution using Deep Reinforcement LearningProceedings of the 2020 4th International Conference on Big Data and Internet of Things10.1145/3421537.3421547(64-68)Online publication date: 22-Aug-2020
https://dl.acm.org/doi/10.1145/3421537.3421547

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