Algorithmic Analysis on Hybrid-Intelligent Deregulated Electricity Market

Abstract

Over the last decades, the demand for electricity is increasing due to the growth of population as well as the advancement of technology. This sharp increment in the consumption of electrical energy has kept the required demand unfulfilled and the deregulation of the electricity market has introduced more competition among private players in the electricity industry. This often leads to issues like higher congestion, Price volatility, Voltage limit, Stability limit and so on. Thus, with the intention of overriding these issues, this paper develops a hybrid optimization model referred as Group Search with Gravitational Force (GSGF) model, which is formed by hybridizing the concepts of Group Search Optimization (GSO) as well as Gravitational Search Algorithm (GSA). The unit commitment problem is solved by the proposed model with the consideration of system parameters under a deregulated environment. Further, with appropriate bidding coefficient $ac_{j}$ and $bc_{j}$, this research work designs the bidding model of IEEE 30 and IEEE 75 test bus system. Further, the proposed GSGF model is algorithmically evaluated in terms of Market Clearing Price (MCP), total profit and consumed bidding power, respectively by varying the maximum pursuit distance $wi_{\max}$.