Bidding strategy for generators considering ramp rates in a day-ahead electricity market

Abstract

In a day-ahead electricity market, competitive bidding strategy plays a vital role for power suppliers tomaximize their profit. In this type of market, each power supplier submits a set of hourly production prices and offerscapacity for the next period. The market operator, after receiving this data along with forecasted hourly load from thedemand side, allocates production output to each unit. Power suppliers face the problem in trading their offers in themarket, due to the uncertain behavior of competitive power suppliers and power demand. Therefore, the power supplierrequires a suitable bidding strategy for handling uncertainty in the market to maximize their profits. Moreover, theconsiderations of ramp rates are necessary for the precise representation of practical power system. Thus, in the presentwork, a modified gravitational search algorithm based on oppositional learning concept is used to solve strategic biddingproblem for power suppliers considering six generators with ramp rates, 24-h load data and rivals behavior. The totalhourly profits of generators with and without considering ramp rates have been compared.