Advanced hybrid intelligent model and algorithm for de-regulated electricity market

An advanced aggressive framework is developed in diverse electricity markets all over the world, which has altered the manner that electric companies yield benefits. Under such circumstance, the companies are supposed to implement required bidding models not only for the sake of attaining reasonable dispatch but also for enhancing benefits. Since the artificial intelligence has found successful in many applications, this paper intends to deploy the hybrid model with the combination of Group Search Optimisation (GSO) and Gravitational Search Algorithm (GSA) termed as Group Search with Gravitational Force (GSGF) model to meet demand-side management principles. By considering the parameters under deregulated environment, the proposed model solves the unit commitment problem. Accordingly, this paper designs the bidding model of IEEE-30 and IEEE-75 test bus system with appropriate bidding coefficient, which appears to attain a high profit. Further, it analyses the performance of both the test bus systems by evaluating total profit, consumed bidding power, statistical report of cost and Market Clearing Price (MCP). In the analysis, it compares the performance of proposed GSGF model with the traditional GA (Genetic Algorithm), ABC (Artificial Bee Colony), PSO (Particle Swarm Optimisation), GSA and GSO bidding models. From the experimental results, the profit of the proposed model is higher than the conventional model, thus attains maximum performance. © 2020, © 2020 Engineers Australia.