Transmission Augmentation With Mathematical Modeling of Market Power and Strategic Generation Expansion-Part II

被引:6
作者
Hesamzadeh, Mohammad R. [1 ]
Biggar, Darryl R. [2 ,3 ]
Hosseinzadeh, Nasser [4 ]
Wolfs, Peter J. [5 ]
机构
[1] Royal Inst Technol KTH, Stockholm, Sweden
[2] Australian Competit & Consumer Commiss, Melbourne, Vic, Australia
[3] Australian Energy Regulator, Melbourne, Vic, Australia
[4] Swinburne Univ Technol, Melbourne, Vic, Australia
[5] Curtin Univ Technol, Perth, WA, Australia
关键词
Heuristic optimization techniques; high performance computing techniques; transmission system augmentation; MULTIMODAL OPTIMIZATION; ALGORITHM;
D O I
10.1109/TPWRS.2011.2145009
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This paper describes a numerical approach to solving the mathematical structure proposed in the first part of this paper. The numerical approach employs a standard genetic algorithm (GA) embedded with an island parallel genetic algorithm (IPGA). The GA handles the decision variables of the transmission network service provider, (TNSP) while the IPGA module finds the equilibrium of the electricity market. The IPGA module uses the concept of parallel islands with limited communication. The islands evolve in parallel and communicate with each other at a specific rate and frequency. The communication pattern helps the IPGA module to spread the best-found genes across all isolated islands. The isolated evolution removes the fitness pressure of the already-found optima from the chromosomes in other islands. A stability operator has been developed which detects stabilized islands and through a strong mutation process re-employs them in exploring the search space. To improve the efficiency of the proposed numerical solution, two high performance computing (HPC) techniques are used-shared-memory architecture and distributed-memory architecture. The application of the proposed approach to the assessment of transmission augmentation is illustrated using an IEEE 14-bus example system.
引用
收藏
页码:2049 / 2057
页数:9
相关论文
共 20 条
[1]  
[Anonymous], GAMS User Guide
[2]   A Sequential Niche Technique for Multimodal Function Optimization [J].
Beasley, David ;
Bull, David R. ;
Martin, Ralph R. .
EVOLUTIONARY COMPUTATION, 1993, 1 (02) :101-125
[3]   LEADER-FOLLOWER STRATEGIES FOR MULTILEVEL SYSTEMS [J].
CRUZ, JB .
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 1978, 23 (02) :244-255
[4]  
Falcao DM, 1997, LECT NOTES COMPUT SC, V1215, P1
[5]  
Glover F., 1989, ORSA Journal on Computing, V1, P190, DOI [10.1287/ijoc.2.1.4, 10.1287/ijoc.1.3.190]
[6]  
Goldberg D. E., P 2 INT C GEN ALG LA, P41
[7]  
Goldberg D.E, 1975, GENETIC ALGORITHMS S
[8]   An automated hybrid genetic-conjugate gradient algorithm for multimodal optimization problems [J].
Gudla, PK ;
Ganguli, R .
APPLIED MATHEMATICS AND COMPUTATION, 2005, 167 (02) :1457-1474
[9]  
Hesamzadeh MR, 2011, IEEE T POWER SYST, V26, P2040, DOI 10.1109/TPWRS.2011.2145008
[10]   Multi-objective optimization using genetic algorithms: A tutorial [J].
Konak, Abdullah ;
Coit, David W. ;
Smith, Alice E. .
RELIABILITY ENGINEERING & SYSTEM SAFETY, 2006, 91 (09) :992-1007