An Intelligent Self Healing System for Congestion Management in Smart Grid Using Genetic Algorithm

Abstract

Power transfer to the load through transmission and distribution network is the major role of a power system operator. Transmission line congestion management is essential for satisfactory delivery of power to the customers. In this paper, a mathematical computational algorithm is developed using Genetic Algorithm (GA) based optimization which relieves congestion in the transmission lines. GA is able to remove congestion by proposing the appropriate change required in line reactance to relieve transmission line congestion. The proposed algorithm considers series capacitance compensation thereby increasing the line loading ability. The proposed method removes congestion automatically in any line, considering its line load limit and makes the grid smart through this self-healing property. This automated technique is able to propose the change in line reactance value required to remove congestion without changing the generation and load. To validate the effectiveness of the algorithm, it is tested in 5 bus IEEE, 14 bus IEEE, and 30 bus IEEE test systems. This algorithm is also verified in scaled down laboratory model of 5 bus IEEE system available in the laboratory. The testing results prove that the proposed GA based congestion management algorithm can relieve congestion in any line of any network automatically.