Development of bidding strategies using genetic algorithm in deregulated electricity market

For past recent years, electric power companies are shifting towards the major restructuring process and deregulated market scenario. Competition has been introduced in the power system all over the world with the aim of increasing the efficiency of the industrial sector and reducing the cost of electricity to the customers. In the competitive electricity markets, the power producers are required to submit their MW outputs and associated prices. The bids of all the suppliers are collected by Independent System Operator who determines the power output of every unit. ISO then solves the welfare maximization problem. When no consumer bidding is considered, ISO solves the total system cost minimization problem where it solves the optimal power flow algorithm that determines prices and quantities. The maximization of individual profit forms a two level optimization problem. In first level, individual maximizes its profit and in second level, the total system cost is minimized based on the bids in the market. This paper proposed conversion of bi-level bidding problem into a single level bidding minimization problem. The new single level minimization problem incorporating transmission constraints, operating limits and ISO market clearing function is solved using GA (Genetic Algorithm). Experimental investigation is carried out on IEEE 14 bus system and simulation results shows that the profits of strategic producer using proposed methodology are higher than that of non-strategic producer.