Abstract
Digitalization has begun as a transformative force within the energy sector, reforming traditional practices and paving the way for enhanced operational efficiency and sustainability. Enabled by key technologies such as smart meters, digitalization embodies a paradigm shift in energy management. Nonetheless, it is crucial to recognize that these enabling technologies are only the catalysts and not the end goal. This paper presents a comprehensive overview of digital services and products in the energy sector, with a specific focus on emerging technologies like AI and Connected Data Spaces. The objective of this review paper is to assess the maturity and adoption levels of these digital solutions, seeking to draw insights into the factors influencing their varying levels of success. This maturity and adoption assessment was carried out by applying a Fuzzy logic approach which allowed us to compensate for the lack of detailed information in current literature. By analyzing the reasons behind high maturity-low adoption and vice-versa, this study seeks to cast light on the dynamics shaping the digital transformation of the energy sector.

Abstract
This research study presents an optimized approach for charging electric vehicles (EVs) in existing residential multi dwelling buildings. The proposed solution tackles the problem in two distinct, but complementary ways. First it takes advantage, in a novel way, of the existing electrical infrastructure by taping directly into the main feeder of the building, second it distributes the power in real time by leveraging in an optimized methodology. The aim of this methodology is to minimize the discrepancy between the desired and final state of charge (SOC) of EVs by the end of each charging session. To achieve this, the method leverages on commuting and charging preferences of EV owners, as well as the electrical infrastructure of residential buildings. To dynamically adjust the charging power for each EV in real-time, an optimized charging management system is employed. This system solves a non-linear minimization optimization problem that considers various parameters, including the initial SOC of each EV, the desired final SOC, the available charging time, and the available charging power. To assess the effectiveness of the proposed methodology, comparative analysis was conducted against a baseline methodology commonly used in practice. The results show that the optimized approach significantly outperforms the non-optimized methods, particularly in high demand scenarios. In these scenarios, the optimized methodology allows for a 200% increase in the supplied energy to the buildings' EV fleet, as well as more than doubling the range made available to users when compared to traditional approaches. In conclusion, this research work offers a robust and effective solution for charging EVs in residential buildings. © 2013 IEEE.