Probabilistic Available Delivery Capability Assessment of General Distribution Network with Renewables

2017/10/26 by Hao Sheng, Sheng, Hao, Xiaozhe Wang +1
Decision Sciences · Engineering · #FOS: Electrical engineering #Optimal Power Flow Distribution #Power System Reliability and Maintenance #Probabilistic and Robust Engineering Design #Signal Processing (eess.SP) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.1710.09915

openalex publication_date 2017/10/26 · openalex created_date 2017/11/10 · openalex updated_date 2026/07/28

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.

Citations

• Rejoinder to "Least angle regression" by Efron et al
• The Homogeneous Chaos

Related

• Applying Polynomial Chaos Expansion to Assess Probabilistic Available Delivery Capability for Distribution Networks with Renewables
• A Data-Driven Sparse Polynomial Chaos Expansion Method to Assess Probabilistic Total Transfer Capability for Power Systems with Renewables
• A Non-Intrusive Low-Rank Approximation Method for Assessing the Probabilistic Available Transfer Capability
• Robust VAR Capability Curve of DER with Uncertain Renewable Generation
• Fast Probabilistic Hosting Capacity Analysis for Active Distribution Systems