Efficient solving of strategic bidding issues under no Karush–Kuhn–Tucker optimality constraints

A typical bidding procedure is to maximize the profit of the strategic producers, in a manner that the offer curve yields solutions to the ISO marketing clearing problem. The literature portrays the procedure in the form of a searching problem with dual levels, wherein the minimization of the market clearing function along with profit maximization is yielded using the offer curve. But, the elevated levels of complexity in computing and solving the bi-level searching problem have forced the researchers to transform it as a one-level maximization problem through employing Karush–Kuhn–Tucker (KKT) optimality conditions. Yet, serious problems emerge with the profit maximization algorithms incorporating KKT optimality conditions (for instance, the classical optimization algorithms). The complexity grows in size, if the transmission constraints get vital in the deregulated environment. Our research contribution devises the profit maximization problem to be a minimization function, lacking consideration on KKT optimality conditions for the ISO market clearing functions, the operating limits and the transmission constraints. A modified form of group search optimizer (GSO), one of the currently popular optimization algorithms, provides solutions to the developed model. The conventional GSO, an optimization algorithm that operates using population, has its motivation from the animals’ searching activities. The modified GSO changes the maximum pursuit angle of the animal searching activity relative to the number of iterations. The testing of the proposed method employs both the IEEE 30 bus system as well as the IEEE 14 bus system. Performance comparison among the proposed method, the strategic bidding approaches that rely on the particle swarm optimization, the genetic algorithm and the GSO reveal excessive profit maximization and superiority for the proposed method.