DocumentCode :
2531985
Title :
Supplier multi-trading strategy: A stochastic programming approach
Author :
Feng, D. ; Gan, D. ; Zhong, J.
Author_Institution :
Coll. of Electr. Eng., Zhejiang Univ., Hangzhou
fYear :
2008
fDate :
20-24 July 2008
Firstpage :
1
Lastpage :
6
Abstract :
A power supplier in deregulated environment needs to allocate its generation capacities to participate in contract and spot markets. The well-known mean-variance method is inappropriate to deal with assets whose price distribution is non-normal. In order to model the electricity assets with different distributions into portfolio optimization, this paper proposes a stochastic programming approach based on genetic algorithm and Monte-Carlo simulation. In the real market data based numerical study, the performances of the proposed method and the standard mean-variance method are compared. It was found that the proposed method can obtain significantly better portfolios in the situation that non-normally distributed assets exist for trading. The modeling capacity, flexibility and robustness will make the proposed method potentially useful in application.
Keywords :
Monte Carlo methods; power markets; power system economics; stochastic programming; Monte-Carlo simulation; genetic algorithm; mean-variance method; modeling capacity; portfolio optimization; spot markets; stochastic programming approach; supplier multitrading strategy; Electricity supply industry; Electricity supply industry deregulation; Forward contracts; Gallium nitride; Genetic algorithms; Instruments; Portfolios; Power generation; Risk management; Stochastic processes; Electricity market; Genetic Algorithm; Monte Carlo simulation; multi-trading strategy; portfolio optimization; risk management;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century, 2008 IEEE
Conference_Location :
Pittsburgh, PA
ISSN :
1932-5517
Print_ISBN :
978-1-4244-1905-0
Electronic_ISBN :
1932-5517
Type :
conf
DOI :
10.1109/PES.2008.4596119
Filename :
4596119
Link To Document :
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