A Polynomial Chaos-based Approach to Quantify Uncertainty of Solar Energy in Electric Power Distribution Systems

This paper proposes a Generalized Polynomial Chaos (gPC)-based approach to quantify the impacts of uncertainties resulting from renewable energy resources (RES) on voltage magnitudes of distribution systems. Quantifying uncertainties resulting from RES and load variations has become a key factor for modern distribution system operation and control. Several probabilistic methods have been used for uncertainty quantification such as Monte Carlo simulation and Perturbation techniques. However, these techniques are not appropriate for short-term operation and planning because they are computationally involved especially for large systems. The gPC-based method is deemed efficient for uncertainty quantification. It transforms the behavior of a random variable into a series of orthogonal polynomials that are easy to evaluate. The proposed method is implemented on the IEEE-123 distribution system integrated with solar energy. The results are compared with Monte Carlo simulation and show that the proposed algorithm provides higher efficiency and significant reduction in computation time in comparison with Monte Carlo simulation.