As part of the Green Deal strategy, in 2020 the European Commission proposed to change the 40 percent emissions reduction target to 55 percent. In addition, reducing energy consumption is essential to decreasing both emissions and energy costs for consumers and industry. Indeed, European energy transition goals include building interconnected energy systems and better integrated networks to support renewable energy sources, increasing energy efficiency, eco-design of products, and decarbonizing the gas sector. In this context, the AISOLE project is an example of an initiative aimed at making the transition entailed by physical and regulatory changes in distribution networks feasible, sustainable and safe by defining and developing the technology needed to support their operation. AISOLE aims to extend the results obtained from the project “Podcast – Distribution Optimization Platform through the use of data from electronic meters and disTributed Accumulation systems,” enriching the functionality of the technological solution by revising the existing architecture, providing for the distribution of some specific control and management activities on field devices, through the introduction of operational algorithms based on Artificial Intelligence and advanced optimization techniques. The algorithms will be based on real-time data collected from dedicated smart meters and meters in order to ensure optimal management of: – storage systems from an operational balancing perspective with load forecasting and modeling – storage and charging utilities for electric vehicles with the aim of operating the networks in the most efficient manner – energy community according to logics that will minimize operating costs and maximize benefits due to shared energy incentives – grid forming inverters for the provision of state-of-the-art regulation services – interaction between Distribution System Operator (DSO) and Transmission System Operator (TSO). Advanced functionality will also be defined and implemented within the DMS (Distribution Management System) for distribution network planning always based on the use of field data, artificial intelligence techniques, and optimization problems. Following the requirements analysis activities and the definition of the functionalities to be included within the DMS, Abika will actively intervene in the implementation of the software layers and in the identification of communication computer protocols necessary for data exchange, through the implementation of edge computing algorithms. Local control logics will be implemented that will enable the management of subsystems, even in case of communication issues with the central analysis unit of the distribution network. The focus of Abika’s work will then be on defining the architecture of the platform and how data will be acquired and processed. Finally, Abika will take part in the testing phase of the platform, through tests that will assess robustness and robustness of the algorithms developed and through the identification of Key Performance Indicators (KPIs) against which the platform will be validated.