Probabilistic Available Delivery Capability Assessment of General Distribution Network with Renewables

TL;DR

This paper introduces a probabilistic framework for assessing the available delivery capability of distribution networks with renewables and electric vehicles, using sparse polynomial chaos expansion and continuation methods for efficiency.

Contribution

It proposes a novel probabilistic ADC formulation that accounts for renewable and load uncertainties, along with an efficient computational solution combining PCE and continuation methods.

Findings

Demonstrates high accuracy of the proposed method on IEEE 13 node test feeder.

Shows significant impact of renewables and load variations on ADC.

Provides a computationally efficient approach for probabilistic ADC assessment.

Abstract

Rapid increase of renewable energy sources and electric vehicles in utility distribution feeders introduces more and more uncertainties. To investigate how such uncertainties may affect the available delivery capability (ADC) of the distribution network, it is imperative to employ a probabilistic analysis framework. In this paper, a formulation for probabilistic ADC incorporating renewable generators and load variations is proposed; a computationally efficient method to solve the probabilistic ADC is presented, which combines the up-to-date sparse polynomial chaos expansion (PCE) and the continuation method. A numerical example in the IEEE 13 node test feeder is given to demonstrate the accuracy and efficiency of the proposed method.