A SOLUTION METHOD OF UNIT COMMITMENT BY ARTIFICIAL NEURAL NETWORKS

被引：136

作者：

SASAKI, H ^{[1
]}

WATANABE, M ^{[1
]}

KUBOKAWA, J ^{[1
]}

YORINO, N ^{[1
]}

YOKOYAMA, R ^{[1
]}

OUYANG, Z ^{[1
]}

SHAHIDEHPOUR, SM ^{[1
]}

机构：

[1] TOKYO METROPOLITAN UNIV,DEPT ELECT ENGN,SETAGAYA KU,TOKYO 159,JAPAN

来源：

IEEE TRANSACTIONS ON POWER SYSTEMS | 1992年 / 7卷 / 03期

关键词：

D O I：

10.1109/59.207310

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 explores the possibility of applying the Hopfield neural network to combinatorial optimization problems in power systems, in particular to unit commitment. A large number of inequality constraints included in unit commitment are handled by dedicated neural networks. As an exact mapping of the problem onto the neural network is impossible with the state of the art, we have developed a two step solution method: firstly, generators to start up at each period are determined by the network and then their outputs are adjusted by a conventional algorithm. The proposed neural network could solve a unit commitment of 30 units over 24 periods, and results obtained are very encouraging.

引用

页码：974 / 981

页数：8