Uncertainty quantification in low voltage distribution grids: Comparing Monte Carlo and general polynomial chaos approaches

Changes in load and distributed generation low voltage distribution systems (LVDS) have made the individual consumer offtake highly uncertain for system operator. In order to accurately determine hosting capacity of such systems, congestion related stochastic indices, e.g. probability undervoltage overvoltage, must be calculated. These require either too many deterministic calculations (simulation-based methods) or use analytical probabilistic power flow. Numerical simulation-based methods often a Monte Carlo (MC) based approach due its simplicity. Recently, as general polynomial chaos (gPC) expansion gained increasing interest. This paper develops non-intrusive gPC formulation determination LVDS illustrates effectiveness compared MC methods. Both are computational time, accuracy, set realistic feeders with high photovoltaic (PV) penetration. The PV injection uncertainty is characterized by univariate continuous distribution. merits using degree 2 Sobol sequences testing points assessment sources. • Carlo, Quasi-Monte Polynomial Chaos compared. comparisons applications. different variant tail mean events obtain best variant. influence number sources choosing right evaluated. Analysis helps select most appropriate flow tool systems.