David Lima

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.