Abstract
The EUniversal project, funded by the European Union, aims to establish a universal approach to the utilization of flexibility by Distribution System Operators (DSOs) and their engagement with new flexibility markets. To achieve this objective, the project team has focused on developing the Universal Market Enabling Interface (UMEI) concept. This paper presents an overview of the process of adapting grid core systems to interact with different market platforms and agents, which is a key aspect of the real-world demonstration set to take place in Portugal. © The Institution of Engineering and Technology 2023.

Abstract
This paper describes the work done in the 3PHASE project, regarding the development of a state estimator for distribution networks handling substantial integration of DER (Distributed Energy Resources), AMI (Advanced Metering Infrastructure) data and unbalanced and asymmetrical configurations. The load and DER power allocation presented here, as part of a DMS system, constitutes a first estimation of the network, assuming an extreme importance for other studies as it helps solve the lack of measurements problem.

Abstract
The rapid proliferation of uncontrolled resources poses significant voltage regulation challenges in low-voltage (LV) distribution grids. In this condition, conventional charging strategies, often based on fixed or static schedules, may lead to adverse voltage deviations under unpredictable load conditions and variable renewable generation. To address these challenges, this paper studies a hybrid deep reinforcement learning (DRL) framework based on a Proximal Policy Optimization (PPO) policy network enriched by a Graph Convolution Variation (GCV) feature extractor to improve voltage regulation issues in LV grids. In addition to ensuring that electric vehicles (EVs) achieve their required state-of-charge (SoC), the framework dynamically adjusts charging rates in real time to maintain LV-grid voltage within acceptable limits. Extensive simulation results, including detailed analysis and comparisons with the static charging method, demonstrate significant improvements in voltage regulation, and enhanced overall grid performance. The obtained results demonstrate the effectiveness of controlling EVs' charging controls in an intelligent manner to address the voltage regulation issue in low-voltage grids. © 2025 Elsevier B.V., All rights reserved.