This research paper proposes an advanced method for Power Flow Analysis (PFA) in unbalanced distribution networks considering high Renewable Energy Sources (RES) penetration with special attention to technical and environmental Key Performance Indicators (KPIs). The innovation of the proposed unbalanced power flow algorithm lies on the tree-like approach of the unbalanced network with the use of Backward/Forward Sweep (BFS) methods, which renders the algorithm significantly faster than the usual methodologies. The PFA is applied on the unbalanced IEEE-13 node test feeder including two-axis tracking Photovoltaics (PV) for three different load profiles, i.e., commercial, residential and mixed, for one representative day of the year. The aforementioned cases are compared to each other in terms of voltage deviation, distribution losses, self-consumption, emissions and reverse power flow. Finally, sensitivity analysis is carried out in order to highlight the effect of RES production on the unbalanced distribution network.

This study presents a Distribution System State Estimation (DSSE) algorithm for unbalanced smart grids and evaluates its performance with special focus on the effect of the unavailability of measurements, which is a common problem on smart grids. The proposed DSSE algorithm is based on the Weighted Least Squares (WLS) method and the unbalanced power flow analysis and addresses several challenges such as unbalanced consumption, various configurations and types of loads, line couplings and lack of measurements. To validate the proposed algorithm’s performance, the IEEE 4-node test feeder and the IEEE European Low Voltage test feeder, comprising 907 nodes, are used as benchmarks. A detailed analysis of the DSSE state results, their deviations from the IEEE test feeders, and their sensitivity regarding measurements’ availability, demonstrates the accuracy and robustness of the proposed solution.