Publication: A self-adaptive differential evolution algorithm for transmission network expansion planning with system losses consideration
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Issued Date
2012
Resource Type
File Type
application/pdf
Other identifier(s)
2-s2.0-84874483133
Rights Holder(s)
มหาวิทยาลัยศรีนครินทรวิโรฒ
Bibliographic Citation
PECon 2012 - 2012 IEEE International Conference on Power and Energy. Vol , No. (2012), p.151-156
Suggested Citation
Sum-Im T., Ongsakul W. A self-adaptive differential evolution algorithm for transmission network expansion planning with system losses consideration. PECon 2012 - 2012 IEEE International Conference on Power and Energy. Vol , No. (2012), p.151-156. doi:10.1109/PECon.2012.6450196 Retrieved from: https://hdl.handle.net/20.500.14740/6882
Author(s)
Abstract
In this paper, a self-adaptive differential evolution algorithm (SaDEA) is applied directly to the DC power flow based model in order to efficiently solve transmission network expansion planning (TNEP) problem. The purpose of TNEP is to minimize the transmission investment cost associated with the technical operation and economical constraints. The TNEP problem is a large-scale, complex and nonlinear combinatorial problem of mixed integer nature where the number of candidate solutions to be evaluated increases exponentially with system size. In addition, the TNEP problem with system losses consideration is also investigated in this paper. The efficiency of the proposed method is initially demonstrated via the analysis of low and medium complexity transmission network test cases. A detailed comparative study among conventional genetic algorithm (CGA), tabu search (TS), artificial neural networks (ANNs), hybrid artificial intelligent techniques and the proposed method is presented. From the obtained experimental results, the proposed technique provides the accurate solution, the feature of robust computation, the simple implementation and the satisfactory computational time. © 2012 IEEE.
Subject(s)
Artificial intelligent techniques
Candidate solution
Combinatorial problem
Comparative studies
Computational time
DC power flow
Medium complexity
Mixed integer
Robust computation
Self-adaptive differential evolution algorithms
System loss
System size
Technical operations
Transmission investments
Transmission line loss
Transmission network expansion planning
Electric power transmission
Investments
Neural networks
Tabu search
Electric power transmission networks
Candidate solution
Combinatorial problem
Comparative studies
Computational time
DC power flow
Medium complexity
Mixed integer
Robust computation
Self-adaptive differential evolution algorithms
System loss
System size
Technical operations
Transmission investments
Transmission line loss
Transmission network expansion planning
Electric power transmission
Investments
Neural networks
Tabu search
Electric power transmission networks