Open AccessProbabilistic congestion driven network expansion planning using point estimate technique
Author(s) -
Saberi Hossein,
Monsef Hassan,
Amraee Turaj
Publication year - 2017
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.2016.2065
Subject(s) - probabilistic logic , computer science , network congestion , point (geometry) , mathematical optimization , artificial intelligence , mathematics , computer network , geometry , network packet
This study presents a probabilistic model to determine the optimal expansion of transmission network under uncertainties of demand, wind power generation, and energy price. The Benders decomposition approach and the point estimate method (PEM) are used to solve the developed mixed‐integer optimisation problem and tackle the uncertainties, respectively. The proposed market‐based expansion problem is a two‐stage model in which the master problem optimises the investment cost, and the sub‐problem (SP) includes the cost of energy not served, congestion cost and congestion rent. A new objective function is proposed to consider the congestion rent in the Benders algorithm. In the SP, a two‐step optimisation process is then proposed to generate proper Benders cuts. The proposed model has been applied to the Garver's, and modified IEEE 24‐bus and IEEE 118‐bus test systems. Furthermore, to validate the accuracy of the proposed PEM, a Monte Carlo simulation method (MCSM) has been used and the results of PEM have been compared to the MCSM.