An “load forecasting - dispatching” integration system for multiple boilers in thermal power plants

Author

Yu, Chen-Long ; Lu, Yong-Zai ; Chu, Jian

Author_Institution

Res. Inst. of Cyber-Syst. & Control, Zhejiang Univ., Hangzhou, China

Volume

3

fYear

2011

fDate

8-9 Sept. 2011

Firstpage

5

Lastpage

10

Abstract

An improved integration system of multiple utility boilers is proposed in this study. Short-term load demand forecasting and load dispatching for multiple boilers are the two function blocks, they are modeled with Artificial Neural Network (ANN) and Multi-objective Optimization Problem (MOP) respectively. In particular, the MOP is solved by a novel hybrid multi-objective optimization algorithm with a combination of Particle Swarm Optimization (PSO) and Extremal Optimization (EO) solutions, called “PSO-EO-MO”. Both the two function blocks have been developed to a integration system software based on real production data from a thermal power plant and have being running online on the spot with the procedure of production. The results illustrate the efficiency and applicability of the software and the software has got Software Copyright in 2010.

Keywords

boilers; demand forecasting; load dispatching; neural nets; particle swarm optimisation; power engineering computing; thermal power stations; artificial neural network; extremal optimization solutions; hybrid multiobjective optimization; integration system software; load dispatching; multiobjective optimization problem; multiple utility boilers; particle swarm optimization; short-term load demand forecasting; software copyright; thermal power plants; Boilers; Dispatching; Energy consumption; Forecasting; Heuristic algorithms; Load modeling; Software; EO; Load Forecasting; Load dispatching; MOP; PSO;

fLanguage

English

Publisher

ieee

Conference_Titel

Power Engineering and Automation Conference (PEAM), 2011 IEEE

Conference_Location

Wuhan

Print_ISBN

978-1-4244-9691-4

Type

conf

DOI

10.1109/PEAM.2011.6135003

Filename

6135003