research-article

Business models for flexibility of electric vehicles: evolutionary computation for a successful implementation

Authors:

Fernando Lezama,

Leonardo H. Macedo,

Rubén RomeroAuthors Info & Claims

GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 1873 - 1878

https://doi.org/10.1145/3319619.3326807

Published: 13 July 2019 Publication History

Abstract

The electrical grid is undergoing an unprecedented evolution driven mainly by the adoption of smart grid technologies. The high penetration of distributed energy resources, including renewables and electric vehicles, promises several benefits to the different market actors and consumers, but at the same time imposes grid integration challenges that must adequately be addressed. In this paper, we explore and propose potential business models (BMs) in the context of distribution networks with high penetration of electric vehicles (EVs). The analysis is linked to the CENERGETIC project (Coordinated ENErgy Resource manaGEment under uncerTainty considering electrIc vehiCles and demand flexibility in distribution networks). Due to the complex mechanisms needed to fulfill the interactions between stakeholders in such a scenario, computational intelligence (CI) techniques are envisaged as a viable option to provide efficient solutions to the optimization problems that might arise by the adoption of innovative BMs. After a brief review on evolutionary computation (EC) applied to the optimization problems in distribution networks with high penetration of EVs, we conclude that EC methods can be suited to implement the proposed business models in our future CENERGETIC project and beyond.

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Cited By

Freymann ATruöl GTrautwein ISchrodi TStetter D(2024)Simulation-based Flexibility Calculation of Electric Vehicle Fleets for Offering Vehicle-to-Grid Services based on Statistical Distributions2024 9th International Conference on Smart and Sustainable Technologies (SpliTech)10.23919/SpliTech61897.2024.10612456(1-8)Online publication date: 25-Jun-2024
https://doi.org/10.23919/SpliTech61897.2024.10612456
Anton EOesterreich TSchuir JProtz LTeuteberg F(2021)A Business Model Taxonomy for Start-Ups in the Electric Power Industry — The Electrifying Effect of Artificial Intelligence on Business Model InnovationInternational Journal of Innovation and Technology Management10.1142/S021987702150004818:03Online publication date: 5-May-2021
https://doi.org/10.1142/S0219877021500048
Almeida JSoares J(2021)Integration of electric vehicles in local energy marketsLocal Electricity Markets10.1016/B978-0-12-820074-2.00018-6(21-36)Online publication date: 2021
https://doi.org/10.1016/B978-0-12-820074-2.00018-6

Index Terms

Business models for flexibility of electric vehicles: evolutionary computation for a successful implementation
1. Applied computing
  1. Physical sciences and engineering
    1. Engineering
2. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies

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cover image ACM Conferences

GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2019

2161 pages

ISBN:9781450367486

DOI:10.1145/3319619

Editor:
Manuel López-Ibáñez
University of Manchester, UK
,
General Chairs:
Anne Auger
Inria and Ecole Polytechnique, France
,
Thomas Stützle
IRIDIA, Université libre de Bruxelles Belgium

Copyright © 2019 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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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 July 2019

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Fundação para a Ciência e a Tecnologia

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GECCO '19

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SIGEVO

GECCO '19: Genetic and Evolutionary Computation Conference

July 13 - 17, 2019

Prague, Czech Republic

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Freymann ATruöl GTrautwein ISchrodi TStetter D(2024)Simulation-based Flexibility Calculation of Electric Vehicle Fleets for Offering Vehicle-to-Grid Services based on Statistical Distributions2024 9th International Conference on Smart and Sustainable Technologies (SpliTech)10.23919/SpliTech61897.2024.10612456(1-8)Online publication date: 25-Jun-2024
https://doi.org/10.23919/SpliTech61897.2024.10612456
Anton EOesterreich TSchuir JProtz LTeuteberg F(2021)A Business Model Taxonomy for Start-Ups in the Electric Power Industry — The Electrifying Effect of Artificial Intelligence on Business Model InnovationInternational Journal of Innovation and Technology Management10.1142/S021987702150004818:03Online publication date: 5-May-2021
https://doi.org/10.1142/S0219877021500048
Almeida JSoares J(2021)Integration of electric vehicles in local energy marketsLocal Electricity Markets10.1016/B978-0-12-820074-2.00018-6(21-36)Online publication date: 2021
https://doi.org/10.1016/B978-0-12-820074-2.00018-6
Paukstadt UBecker J(2019)Uncovering the business value of the internet of things in the energy domain – a review of smart energy business modelsElectronic Markets10.1007/s12525-019-00381-8Online publication date: 16-Dec-2019
https://doi.org/10.1007/s12525-019-00381-8

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