Transmission congestion control research in power system based on immune genetic algorithm

This paper investigates the cost control problem of congestion management model in the real-time power systems. An improved optimal congestion cost model is built by introducing the congestion factor in dealing with the cases: opening the generator side and load side simultaneously. The problem of real-time congestion management is transformed to a nonlinear programming problem. While the transmission congestion is maximum the adjustment cost is minimum based on the immune genetic algorithm, and the global optimal solution is obtained. Simulation results show that the improved optimal model can obviously reduce the adjustment cost and the designed algorithm is safe and easy to implement.