Open AccessProbabilistic decomposition‐based security constrained transmission expansion planning incorporating distributed series reactor
Author(s) -
Yuan Zhi,
Wang Weiqing,
Wang Haiyun,
Ghadimi Noradin
Publication year - 2020
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.2019.1625
Subject(s) - mathematical optimization , probabilistic logic , series (stratigraphy) , monte carlo method , linear programming , computer science , integer programming , decomposition , transmission (telecommunications) , benders' decomposition , decomposition method (queueing theory) , electric power system , flexibility (engineering) , set (abstract data type) , algorithm , mathematics , power (physics) , paleontology , telecommunications , artificial intelligence , biology , ecology , statistics , physics , discrete mathematics , quantum mechanics , programming language
This study presents a probabilistic transmission expansion planning model incorporating distributed series reactors, which are aimed at improving network flexibility. Although the whole problem is a mixed‐integer non‐linear programming problem, this study proposes an approximation method to linearise it in the structure of the Benders decomposition (BD) algorithm. In the first stage of the BD algorithm, optimal number of new transmission lines and distributed series reactors are determined. In the second stage, the developed optimal power flow problem, as a linear sub‐problem, is performed for different scenarios of uncertainties and a set of probable contingencies. The Benders cuts are iteratively added to the first stage problem to decrease the optimality gap below a given threshold. The proposed model utilises the Monte Carlo simulation method to take into account uncertainty of wind generations and demands. Several case studies on three test systems are presented to validate the efficacy of the proposed approach.