Decision support system for the management of electricity consumption contracts for Smart Grids environment using Differential Evolution and Artificial Neural Network

Author

Matte Freitas, Daniel ; Pereira Pinto, Joao Onofre ; Godoy, R.B. ; Galotto, Luigi ; Ribeiro, P.E.M.J. ; Pinto, Alexandra M. A. C.

Author_Institution

BATLAB, Fed. Univ. of Mato Grosso do Sul, Campo Grande, Brazil

fYear

2013

fDate

10-13 Nov. 2013

Firstpage

7592

Lastpage

7597

Abstract

The objective of this paper is to present a support system to manage electricity consumption contracts for Smart Grid environment. The system modeling uses historical data consumption and energy trading rules to find the optimal contract structure. Focused Time Lagged Feed forward Network was used to model the historical data. The global search tool Differential Evolution was used to find the best contract structure. This paper presents the use of the tool with current Brazilian pricing rules. However, to change the rules for a dynamic scenario of Smart Grid can be easily implemented. The results are satisfactory and indicate the feasibility of the system for different cases.

Keywords

decision support systems; electrical contracting; energy consumption; evolutionary computation; feedforward neural nets; power engineering computing; power system management; search problems; smart power grids; Brazilian pricing rules; artificial neural network; best contract structure; decision support system; differential evolution; electricity consumption contracts; energy trading rules; focused time lagged feed forward network; global search tool; historical data consumption; optimal contract structure; smart grids environment; system modeling; Contracts; Electricity; Neurons; Smart grids; Sociology; Statistics; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Electronics Society, IECON 2013 - 39th Annual Conference of the IEEE

Conference_Location

Vienna

ISSN

1553-572X

Type

conf

DOI

10.1109/IECON.2013.6700398

Filename

6700398