Open AccessDynamic optimal power flow model incorporating interval uncertainty applied to distribution network
Author(s) -
Chen Pengwei,
Xiao Xiangning,
Wang Xuhui
Publication year - 2018
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2017.1874
Subject(s) - interval (graph theory) , mathematical optimization , computer science , constraint (computer aided design) , affine transformation , affine arithmetic , power (physics) , taylor series , boundary (topology) , mathematics , mathematical analysis , physics , geometry , combinatorics , quantum mechanics , pure mathematics
Dynamic optimal power flow (DOPF) in active distribution networks generally relies on a perfect forecasting of uncertainties such as intermittent distributed generations and time‐varying loads, which is generally difficult to achieve in practise. To make DOPF possess the ability to deal with uncertainties, especially for the satisfaction of operating constraints in an uncertain environment, an interval DOPF (I‐DOPF) model is derived in this study, by using affine arithmetic and interval Taylor expansion. To solve the I‐DOPF problem efficiently, the solving method based on successive linear approximation (SLA) and distributed optimisation strategy is further discussed. The proposed I‐DOPF model and its solving method are subsequently applied to a modified IEEE 33‐bus network and a real 113‐bus distribution network. The simulation results demonstrate that the I‐DOPF model has a good performance on boundary constraint satisfaction under uncertainties; the SLA‐based solving method can be well integrated with distributed optimisation to meet the practical requirements of data exchange in large‐scale active distribution networks.