Open AccessSolving long time‐horizon dynamic optimal power flow of large‐scale power grids with direct solution method
Author(s) -
Qin Zhijun,
Hou Yunhe,
Lu En,
Luo Cheng,
Cheng Shijie
Publication year - 2014
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.2013.0659
Subject(s) - power flow , scale (ratio) , computer science , flow (mathematics) , power (physics) , electric power system , horizon , mathematical optimization , time horizon , control theory (sociology) , mathematics , physics , artificial intelligence , control (management) , quantum mechanics , geometry
Dynamic optimal power flow (DOPF) is an extension of optimal power flow for the optimal generation dispatch in a given time‐horizon. The dynamic constraints bring tremendous numerical difficulties in solving this model. With particular attention to handle dynamic constraints, an efficient method has been presented for directly solving the large‐scale DOPF Karush‐Kuhn‐Tucker (KKT) system arising from the primal–dual interior point method. First, the reduced KKT system is derived, showing that dynamic constraints lead to non‐zeros in off‐diagonal parts in the coefficient of KKT system. Then, the efficiency of the algorithm is improved by two measures: (i) to utilise the Cholesky factorisation algorithm, a constant diagonal perturbation is introduced in the positive‐indefinite KKT coefficient and (ii) efficient reordering algorithms are identified and integrated in the sparse direct solver to improve the efficiency. Case studies on the IEEE 118‐bus system over 24–96 time intervals are presented. These case studies show that the proposed method has a significant speed‐up than decomposed interior point methods. The proposed method has also been successfully applied in Chinese realistic large‐scale power grids. Two realistic case studies are reported. Both realistic cases have over 100 000 decision variables.