Latest revision as of 16:46, 3 February 2021

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 beneits to the diferent 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 lexibility in distribution networks). Due to the complex mechanisms needed to fulill the interactions between stakeholders in such a scenario, computational intelligence (CI) techniques are envisaged as a viable option to provide eicient 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. This research has received funding from FEDER funds through the Operational Programme for Competitiveness and Internationalization (COMPETE 2020), under Project POCI-01-0145-FEDER-028983; by National Funds through the FCT Portuguese Foundation for Science and Technology, under Projects PTDC/EEI-EEE/28983/2017 (CENERGETIC), UID/EEA/00760/2019; and the São Paulo Research Foundation (FAPESP), under Projects 2018/08008-4 and 2018/20355- 1

Original document

The different versions of the original document can be found in:

• https://zenodo.org/record/3480331 under the license http://creativecommons.org/licenses/by/4.0/legalcode

• http://dl.acm.org/ft_gateway.cfm?id=3326807&ftid=2071084&dwn=1,

http://dx.doi.org/10.1145/3319619.3326807 under the license http://www.acm.org/publications/policies/copyright_policy#Background

• https://dblp.uni-trier.de/db/conf/gecco/gecco2019c.html#LezamaSFVMR19,

https://academic.microsoft.com/#/detail/2956342809