An Opposition Theory inspired BAT Algorithm(OTIBA) for profit maximization of GENCOs in Day-ahead Power Market

Energy market is the place where power producers can sell the energy to the consumers. To do so, they require prior information of rival bidding behaviors to achieve higher profit. Many times, company has to frame a policy regarding fixation of bid price to gain more profits in the dynamically changing electricity markets. Uncertainty modelling of rival generating company behavior, anticipating of market clearing mechanism and employment of revenue maximizing tool are some promising research potentials. An application of opposition theory inspired bat algorithm for designing intelligent bidding simulator is presented in this paper. The proposed simulator is employed by a generating company that bids in dynamic energy market for seeking profit enhancing prepositions. A Monte Carlo (MC) simulation based strategy is employed to handle uncertainty in energy bids of rivals. A method with amalgamation Opposition Theory Inspired Bat Algorithm (OTIBA) and Monte Carlo simulation is explained in lucid manner. The explained methodology is tested over two different test power systems. Different analyses are conducted to express the positive implications of this approach. Analyses reveal that applied method can be a proven tool for maximizing the profit prepositions of a generating company.