Article

Differential evolution algorithm for static and multistage transmission expansion planning. IET Gener Transm Distrib

Sch. of Eng. & Design, Brunei Univ., Uxbridge
IET Generation Transmission & Distribution (Impact Factor: 1.35). 05/2009; 3(4):365 - 384. DOI: 10.1049/iet-gtd.2008.0446
Source: IEEE Xplore

ABSTRACT

A novel differential evolution algorithm (DEA) is applied directly to the DC power flow-based model in order to efficiently solve the problems of static and multistage transmission expansion planning (TEP). The purpose of TEP is to minimise the transmission investment cost associated with the technical operation and economical constraints. Mathematically, long-term TEP using the DC model is a mixed integer nonlinear programming problem that is difficult to solve for large-scale real-world transmission networks. In addition, the static TEP problem is considered both with and without the resizing of power generation in this research. The efficiency of the proposed method is initially demonstrated via the analysis of low, medium and high complexity transmission network test cases. The analysis is performed within the mathematical programming environment of MATLAB using both DEA and conventional genetic algorithm and a detailed comparative study is presented.

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    • "Another work done by T. Sum-Im et al [5] employed the differential evolution algorithm (DEA) as an optimizing tool for the transmission expansion planning. It was applied directly to the DC power flow-based model in order to find a solution for static and multistage transmission expansion planning (TEP). "
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    ABSTRACT: Transmission expansion planning has become a complicated process in the recent era. The fast growth of the transmission networks and the deregulation has introduced more factors to be considered in transmission expansion planning to the transmission network planners. The main goal of this process is to locate the additional transmission lines that must be added to meet the forecasted load in the system adequately with minimum cost. This work solves the transmission expansion planning using meta-heuristic algorithm by the means of differential evolution as an optimization tool. An AC load flow model is used in solving the TEP problem. DEA produced a high quality solution for the TEP problem.
    Full-text · Conference Paper · Oct 2014
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    • "Although the plan was not robust enough due to several simplifications; for instance, ignoring the security constraint and inability in handling the uncertainties in the deregulated environment, DE could show its ability to handle the integer variables and non-linear constrained multi-objective optimization problem. Another work done by T. Sum-Im et al [10] employed the differential evolution algorithm (DEA) as an optimizing tool for the transmission expansion planning. It was applied directly to the DC power flow-based model in order to find a solution for static and multistage transmission expansion planning (TEP). "
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    ABSTRACT: Transmission expansion planning has become a complicated procedure more than it was. The rapid growth of the transmission networks and the deregulation has introduced more objectives and uncertainties to the transmission network planners. As a result of that, new approach and criteria that can replace the old ones are needed for TEP problem. The main goal of this process is to locate the additional transmission lines that must be added to meet the forecasted load in the system adequately with minimum cost. There have been several methods applied for this purpose; mathematical optimization methods, heuristic and Meta heuristic methods. This paper reviews the use of Meta heuristic method by the means of differential evolution algorithm (DEA) to solve this multi objective optimization problem. In addition, some suggestions have been made by the author that can make the DEA more efficient and applicable in the real world networks.
    Full-text · Conference Paper · Sep 2013
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    • "Several restrictions have to be modeled in a mathematical representation to ensure that the mathematical solutions are in line with the planning requirements. These constraints are as follows (Sum-Im et al, 2009): "

    Full-text · Article · Jul 2012
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