Uncertainty Analysis in Distribution Networks Integrated with Renewables by Probabilistic Collocation Method

Abstract

Increased penetration of distributed generation (DG) driven by Variable Renewable Energy (VRE) sources and integration of modern loads constituted by Electric Vehicles (EV) and behind-the-meter smart appliances pose operational challenges for traditional distribution systems. This paper introduces a framework based on probabilistic collocation method (PCM) to model and analyzes the effects of inherent uncertainties, both in generation, and load, on distribution systems. First, the uncertainties are modeled by statistical distributions that closely mimic their physical behavior and studied through Monte-Carlo (MC) simulations. Later, an analytical PCM based approach is formulated and designed on the modified IEEE 13-node test feeder including VRE. A comparative study demonstrates the effectiveness of the proposed PCM based uncertainty modeling in distribution feeders with lesser computational burden and improved accuracy.