electricity_market

所属分类:能源行业(电力石油煤炭)
开发工具:matlab
文件大小:13612KB
下载次数:410
上传日期:2010-11-18 16:06:46
上 传 者lorin
说明:  该文件是优秀论文并带有相应的源码。在Matlab软件平台上,对所提出的基于遗传算法的电力系统电力市场分段竞价交易算法进行了阶梯型报价曲线、线性报价曲线这两种常用报价曲线情况下的算例验证,计算结果显示算法的寻优效果因报价曲线性质的不同而略有区别,但两种情况均可得到优化的购电费用。实验结果证明了该算法应用于分段竞价模型的有效性和可行性,且不受报价曲线形式的限制。
(The document is an excellent paper with a corresponding source code. In the Matlab software platform for the proposed genetic algorithm-based power systems market segment auction algorithms offer ladder-type curve, the two commonly used linear bidding curve bidding curve example in case of validation, the results show algorithm optimization results for the different nature of bidding curve slightly different, but both cases can be optimized for power purchase costs. Experimental results show that the algorithm is applied to sub-effectiveness and feasibility of bidding model, and is not limited form of bidding curve.)

文件列表:
基于遗传算法的电力市场分段竞价交易决策研究.doc (668160, 2010-11-02)
uniformXover.m (1823, 1998-04-28)
Untitled.m (114, 2010-04-26)
data\all_lab.mat (1491272, 2008-01-06)
data\bestself.mat (66960, 2007-12-13)
data\bestself1.mat (46952, 2007-12-13)
data\bestself_Tr.mat (151216, 2007-12-17)
data\bestself_Tr1.mat (71136, 2007-12-17)
data\bestshortgen-nich-self.mat (96944, 2007-12-15)
data\bestshortgen_nich_self_TR.mat (137032, 2007-12-17)
data\best_nich_self1-Tr.mat (133040, 2007-12-17)
data\best_nich_self2-Tr.mat (133040, 2007-12-17)
data\best_nich_self3-Tr.mat (137016, 2007-12-17)
data\B_Nich_Tr.mat (132760, 2007-12-16)
data\copybest-nich-self1.mat (92968, 2007-12-15)
data\copybest-nich-self2.mat (92968, 2007-12-15)
data\copybest-nich-self3.mat (96944, 2007-12-15)
data\copyTr-BSgen-N-S.mat (80248, 2007-12-15)
data\lab.mat (2477048, 2008-01-12)
data\labNNich.mat (773640, 2007-12-27)
data\labSelf.mat (1615056, 2008-01-02)
data\labSGA.mat (1665768, 2007-12-28)
data\labSNGA.mat (1895440, 2008-01-02)
data\labSSGA.mat (1174688, 2007-12-27)
data\nich-self2.mat (92560, 2007-12-15)
data\nich-self3.mat (97360, 2007-12-15)
data\nich-self4.mat (96264, 2007-12-15)
data\nich-self5.mat (155072, 2007-12-16)
data\nich_self_Tr2.mat (136744, 2007-12-17)
data\nich_self_Tr3.mat (137432, 2007-12-17)
data\nich_self_Tr4.mat (136336, 2007-12-17)
data\nich_self_Tr5.mat (235216, 2007-12-17)
data\Nich_Tr1.mat (132352, 2007-12-16)
data\Nich_Tr2.mat (132616, 2007-12-16)
data\Nich_Tr3.mat (132624, 2007-12-16)
data\self1.mat (66952, 2007-12-13)
data\self2.mat (66816, 2007-12-13)
data\selfnofirstP.mat (92968, 2007-12-14)
data\SELF_Tr1.mat (133024, 2007-12-16)
data\SELF_Tr2.mat (132888, 2007-12-16)
... ...

This directory contains the Genetic Algorithm Optimization Toolbox for Matlab 5. To use this, if you are local to NCSU and have AFS access to this directory, simply extend the matlab path using the following command. You can also place this command in a file called startup.m. Everytime you start Matlab in the directory containing this file, the path will always be extended. >>path(path,'/afs/eos/service/ie/research/kay_res/GAToolBox/gaot'); Otherwise, install the .m files into a directory named gaot and extend the matlab path to that directory. The compressed tar archive and the zip file should automatically create the gaot directory for you. The companion paper describing this toolbox is included here as gaotv5.ps. The paper, the three demo files, gademo1.m gademo2.m gademo3.m, and four example scripts, binaryExample, floatExample, floatGradExample, and orderbasedExample, should be sufficient explanation of this toolbox. For any questions, comments, suggestions send mail to jjoine@eos.ncsu.edu. For a list of the files in the tool box get help on gaot. Thanks for trying the toolbox.

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