A solution method of unit commitment by artificial neural networks

Author

Sasaki, H. ; Watanabe, M. ; Kubokawa, D. ; Yorino, N. ; Yokoyama, R.

Author_Institution

Dept. of Electr. Eng., Hiroshima Univ., Japan

Volume

7

Issue

3

fYear

1992

fDate

8/1/1992 12:00:00 AM

Firstpage

974

Lastpage

981

Abstract

The authors explore 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 can be handled by dedicated neural networks. As an exact mapping of the problem onto the neural network is impossible with the state of the art, a two-step solution method was developed. First, generators to be stored 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 large-scale unit commitment problem with 30 generators over 24 periods, and results obtained were very encouraging

Keywords

electric generators; neural nets; power system analysis computing; Hopfield neural network; combinatorial optimization problems; electric generators; inequality constraints; power systems; unit commitment; Artificial neural networks; Biological neural networks; Hopfield neural networks; Linear programming; Neural networks; Neurons; Power engineering and energy; Power system interconnection; Power system planning; Power systems;

fLanguage

English

Journal_Title

Power Systems, IEEE Transactions on

Publisher

ieee

ISSN

0885-8950

Type

jour

DOI

10.1109/59.207310

Filename

207310