Title :
A new GA-approach for optimizing bidding strategy viewpoint of profit maximization of a GENCO
Author :
Azadeh, A. ; Ghadrei, S.F. ; Nokhandan, B. Pourvalikhan
Author_Institution :
Dept. of Ind. Eng., Univ. of Tehran, Tehran
fDate :
March 30 2009-April 2 2009
Abstract :
Generation companies (GENCO) wish to maximize their profit while participate in the electricity market. In this paper, a GA approach is developed for solving GENCO profit maximization problem to determine optimal bidding strategy for GENCO in the day-ahead market. It is assumed that Each GENCO submit its own bid as pairs of price and quantity. Also, it was assumed that the sealed auction with a pay-as-bid MCP would be employed. Since, there are some complex constraints for GENCO to be taken into account; this is a non-convex problem which is difficult to solve by traditional optimization techniques. In this paper, problem is solved from view point of profit maximization of GENCO that consider both rivals´ bid and profit functions. Therefore, there is a multi-objective function to solve. A simple example is designed and illustrated how GA-approach can tackle this problem efficiently.
Keywords :
genetic algorithms; power markets; pricing; bidding strategy viewpoint optimization; day-ahead market; electricity market; generation companies; genetic algorithm; nonconvex problem; profit maximization; Constraint optimization; Costs; Economic forecasting; Educational institutions; Electricity supply industry; Genetic algorithms; Industrial engineering; Load forecasting; Mathematical model; Power generation; Day-ahead market; GENCO; Genetic Algorithm; bidding strategy; profit maximization;
Conference_Titel :
Hybrid Intelligent Models and Applications, 2009. HIMA '09. IEEE Workshop on
Conference_Location :
Nashville, TN
Print_ISBN :
978-1-4244-2758-1
DOI :
10.1109/HIMA.2009.4937829