A basic study on autonomous decentralized operation for distributed energy systems

Author

Yasuda, Keiichiro ; Oya, Shunsuke ; Tamura, Kenichi

Author_Institution

Dept. of Electr. & Electron. Eng., Tokyo Metropolitan Univ., Hachioji, Japan

fYear

2012

fDate

14-17 Oct. 2012

Firstpage

3185

Lastpage

3190

Abstract

This paper deals with autonomous decentralized operation for a distributed energy system composed of a number of customers with dispersed generation and storage systems. The distributed energy system studied in this paper has the following operation structures: (1) Operation for customer´s energy storage which is decided by production rules with learning function, and (2) Operation for power flow on distribution network which is determined by a successive approximation method. In this paper, we especially study the autonomous decentralized operation in (1). To enhance the autonomy and effectiveness of the autonomous decentralized operation a distributed energy system, Genetic Algorithm is applied to learning of production rules.

Keywords

approximation theory; decentralised control; distributed control; distributed power generation; genetic algorithms; learning systems; power distribution control; power generation control; autonomous decentralized operation; customer energy storage; dispersed generation system; dispersed storage system; distributed energy system; distribution network; genetic algorithm; learning function; power flow; production rule learning; successive approximation method; Approximation methods; Electricity; Energy storage; Load modeling; Numerical simulation; Power systems; Production; Autonomous Decentralized Operation; Dispersed Generation; Distributed Energy Systems; Genetic Algorithm; Learning Function; Rule-Based;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics (SMC), 2012 IEEE International Conference on

Conference_Location

Seoul

Print_ISBN

978-1-4673-1713-9

Electronic_ISBN

978-1-4673-1712-2

Type

conf

DOI

10.1109/ICSMC.2012.6378281

Filename

6378281