Title :
Study on Generation Companies´ Bidding Strategy Based on Hybrid Intelligent Method
Author :
Da-Wei, Huang ; Xue-Shan, Han
Author_Institution :
Electr. Eng. Dept., Northeast Dianli Univ., Jilin, China
Abstract :
In the competitive electricity market environment, the bidding strategy of the generation companies (GenCos) is made based on the incomplete information. Thus, risk will be introduced inevitably. In this paper, a new risk evaluation method is presented considering fuzzy uncertainty of GenCospsila competitive bidding behaviors, the creditability of the real profit less than the fuzzy expected profit is taken as risk index.On this basis, the chance-constrained programming model of the GenCospsila optimal bidding strategy is presented. A hybrid intelligent algorithm of fuzzy simulation and neural network combined with GA is used to solve this problem. In the chance constrained programming model the object function and the chance-constrained formulas are uncertain functions, therefore the fuzzy simulation technique is used to obtain the function value and neural network is used to approach the uncertain function. In the end the feasibility of the model and solving method is tested by an IEEE-5 system case.
Keywords :
fuzzy neural nets; fuzzy set theory; game theory; genetic algorithms; minimisation; power engineering computing; power generation economics; power markets; profitability; risk analysis; GA algorithm; GenCos competitive bidding strategy; IEEE-5 system case; chance-constrained programming model; electricity market; fuzzy expected profit; fuzzy game theory; fuzzy simulation technique; fuzzy uncertain function; generation companies optimal bidding strategy; genetic algorithm; hybrid intelligent method; neural network; risk index level; risk minimization; Cities and towns; Electricity supply industry; Electronic mail; Fuzzy neural networks; History; Hybrid power systems; Neural networks; Power generation; Stochastic processes; Uncertainty; bidding strategy; chance-constrained program; electricity market; hybrid intelligent method; risk level;
Conference_Titel :
Hybrid Intelligent Systems, 2009. HIS '09. Ninth International Conference on
Conference_Location :
Shenyang
Print_ISBN :
978-0-7695-3745-0
DOI :
10.1109/HIS.2009.295