RISK-CONSTRAINED OPTIMAL BIDDING STRATEGY FOR A GENERATION COMPANY USING SELF-ORGANIZING HIERARCHICAL PARTICLE SWARM OPTIMIZATION

被引：5

作者：

Boonchuay, Chanwit ^{[2
]}

Ongsakul, Weerakorn ^{[1
]}

机构：

[1] Asian Inst Technol, Energy Field Study, Sch Environm Resources & Dev, Klongluang 12120, Pathumthani, Thailand

[2] Rajamangala Univ Technol Rattanakosin, Dept Elect Engn Technol, Fac Ind & Technol, Hua Hin, Prachuap Khiri, Thailand

来源：

APPLIED ARTIFICIAL INTELLIGENCE | 2012年 / 26卷 / 03期

关键词：

D O I：

10.1080/08839514.2012.646162

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This article proposes optimal bidding strategies for a generation company (GenCo) considering risk of profit variation by self-organizing hierarchical particle swarm optimization with time-varying acceleration coefficients (SPSO-TVAC). Based on a trade-off technique, the expected profit maximization and risk minimization are achieved. Nonconvex operating cost functions of thermal generation units and minimum up/ down time constraints are cooperated to provide the optimal bid prices in a day-ahead uniform price spot market. The rivals' bidding behavior is estimated by Monte Carlo simulation. Test results indicate that SPSO-TVAC is superior to inertia weight approach particle swarm optimization (IWAPSO) and genetic algorithm (GA) in searching the optimal bidding strategy solutions.

引用

页码：246 / 260

页数：15

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