Fuzzy adaptive particle swarm optimization for bidding strategy in uniform price spot market

被引：95

作者：

Bajpai, P. ^{[1
]}

Singh, S. N. ^{[1
]}

机构：

[1] Indian Inst Technol, Dept Elect Engn, Kanpur 208016, Uttar Pradesh, India

来源：

IEEE TRANSACTIONS ON POWER SYSTEMS | 2007年 / 22卷 / 04期

关键词：

bidding strategies; electricity market; fuzzy inference; Monte Carlo simulation; normal probability distribution; particle swarm optimization;

D O I：

10.1109/TPWRS.2007.907445

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In a deregulated electricity market, generators have to optimally bid to maximize their profit under incomplete information of other competing generators. This paper addresses an optimal bidding strategy of a thermal generator in a uniform price spot market considering a precise model of nonlinear operating cost function and minimum up/down constraints of unit commitment. The bidding behaviors of other competing generators are described using normal probability distribution function. Bidding strategy of a generator for each trading period in a day-ahead market is solved by fuzzy adaptive particle swarm optimization (FAPSO), where inertia weight is dynamically adjusted using fuzzy evaluation. FAPSO can dynamically follow the frequently changing market demand and supply in each trading interval. The effectiveness of the proposed approach is tested with examples and the results are compared with the solutions obtained using genetic algorithm (GA) approach and other versions of PSO.

引用

页码：2152 / 2160

页数：9

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