Probabilistic-Interval Energy Flow Analysis of Regional Integrated Electricity and Gas System Considering Multiple Uncertainties and Correlations

Xiaoyun Hu,Xia Zhao,Xinxin Feng

doi:10.1109/access.2019.2958704

Abstract

This paper studies the uncertainty analysis of the regional integrated electricity and gas system (IEGS) composed of a three-phase unbalanced power distribution system and a gas distribution network. The well-known probabilistic energy flow (PEF) analysis of IEGS is extended to a comprehensive probabilistic-interval energy flow (PIEF) problem by modeling uncertainties in pipeline parameters as interval variables and spatial dependency between them as correlation angles. The polynomial chaos expansion (PCE) method is introduced to IEGS analysis and an improved PCE-based method combining the technique of dimension-reduction and the sparse-PCE is proposed to address the numerous uncertainties involved in a regional IEGS. A methodological framework considering probabilistic/interval uncertainties, linear/rank correlations among probabilistic variables, and dependency between interval variables is then developed for both PEF and PIEF analysis. Numerical simulations of a sample regional IEGS are used to verify the accuracy and efficiency of the proposed method, as well as the superiority of the proposed PIEF analysis over the existing PEF analysis.

Highlights

PARAMETERSBpikm system Associated conductance and susceptance between phase p and m of branch connecting busi and k
With the significant growth of gas-fired units, the electricity and natural gas networks are getting increasingly interdependent [1]
With the help of the pre-process of dimension-reduction, the proposed polynomial chaos expansion (PCE)-MIX method only needs to deal with 48 variables rather than the full-dimensional 135 variables feed to the PCE-orthogonal matching pursuit (OMP) method so that both TPCE and TPOST are significantly reduced compared with PCE-OMP