Abstract
According to the European Clean Energy Package (2019) Distribution System Operators (DSOs) shall effectively use flexibility services from local and regional assets to safely host more renewable energy sources in the electricity grid. Electricity prosumers become crucial players due to their potential to provide flexibility by adapting their production and consumption behaviour. Yet, integrating new types of assets into the distribution grid to use flexibility creates complexity and hardly predictable power flows in the distribution networks. The European H2020 demonstration project EUniversal aims to overcome the existing limitations in the use of flexibility. For that purpose, smart grid tools for grid state assessment and active system management are developed. A demonstration pilot is set up to test the flexibility value chain from congestion detection to market-based flexibility procurement via a local flexibility market. The pilot is conducted in the LV grids of the German DSO MITNETZ STROM, examining the use of flexible resources in the LV grid for congestion management. The article describes the set-up of the flexibility value chain and shows how all individual parts are integrated into the complete process. © The Institution of Engineering and Technology 2023.

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
Low voltage (LV) grids face a challenge of effectively managing the growing presence of new loads like electric vehicles and heat pumps, along with the equally growing installation of rooftop photovoltaic panels. This paper describes practical applications of sensitivity factors, extracted from smart meter data (i.e., without resorting to grid models), to i) link voltage problems to different costumers/devices and their location in the grid, ii) manage the flexibility provided by distributed energy resources (DERs) to regulate voltage, and iii) assess favorable locations for DER capacity extensions, all with the aim of supporting the decision-making process of distribution system operators (DSOs) and the design of incentives for customers to invest in DERs. The methods are tested by running simulations based on historical meter data on six grid models provided by the EU-Joint Research Center. The results prove that it is feasible to implement advanced LV grid analysis and management tools despite the typical limitations in its electrical and topological characterisation, while avoiding the use of computationally heavy tools such as optimal power flows.

Abstract
The integration of Distributed Energy Resources (DERs) into low-voltage (LV) distribution grids poses significant challenges for grid management, particularly regarding the need for accurate information on the connection phases of installations to ensure proper load balancing and to enhance hosting capacity. This paper presents a novel voltage-based phase mapping approach using the Mahalanobis-Wasserstein (MW) distance - a metric that exploits voltage time series data to accurately assign users to their corresponding phases without requiring additional hardware or prior knowledge of the grid's topology. The proposed method demonstrates strong resilience to missing data, a frequent issue in real-world deployments, and incorporates a confidence score to quantify the reliability of the phase assignments. © 2025 IEEE.

Abstract
The topology of low-voltage (LV) distribution grids is often partially known or inaccurately documented by grid operators, including line and cable characteristics, hindering the effective integration and management of Distributed Energy Resources (DERs). This paper presents a data-driven method to reconstruct LV grid topologies using only voltage measurements from customers' smart meters. The approach relies on an adapted genetic algorithm (GA) that iteratively explores candidate configurations, guided by a score function that evaluates both the physical plausibility of estimated line impedances and their consistency with noisy voltage data, which is progressively corrected throughout the process, i.e., the method also filters out errors affecting the initial measurements. The method requires no prior information on grid connectivity and demonstrates robustness to measurement noise, making it well suited for real-world deployment. © 2025 IEEE.