Business Models for Flexibility of Electric Vehicles: Evolutionary Computation for a Successful Implementation - Lezama, Fernando; Soares, Joao; Faia, Ricardo; Vale, Zita; Macedo, Leonardo H. [UNESP]; Romero, Ruben [UNESP]; ACM (Assoc Computing Machinery, 2019)
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.
Keywords: Business models; computational intelligence; electric vehicles; local markets