Improved Monte Carlo techniques for distributed generation impact evaluation

The integration of emerging technologies associated with renewable sources of electrical energy introduces new uncertainties into the operation and planning of the electrical power system (EPS). In this context, the Monte Carlo method (MCM) is a widely used technique for addressing uncertainties in distributed generation (DG) impact analysis. However, this method demands a high computational effort, which tends to increase with the dimensionality of the problem and the correlations among random variables. For this reason, this work investigates some of the main Monte Carlo-based techniques for improving computational efficiency in DG impact assessment, namely Latin Hypercube Sampling (LHS), Quasi-Monte Carlo (QMC) and Importance Sampling (IS). These methods are applied in a case study involving an IEEE feeder connected to DG, and the simulations are conducted via the OpenDSS software. The results show enhancements in both precision and convergence when compared to Crude Monte Carlo (CMC). Additionally, the implementation of the hybrid technique IS-QMC demonstrates that the combination of these methods can lead to superior outcomes.