A genetic algorithm for AC optimal transmission switching

Abstract

Optimal transmission switching (OTS) is a new practice in power systems and can improve the economics of electric power systems integrated with renewable resources such as wind. In OTS modeling binary decision variables are added to the optimal power flow (OPF) problem to represent on and off switching status of lines. This extension to alternative current optimal power flow (ACOPF) problem results in a mixed integer nonlinear program (MINLP) which is not guaranteed to be solved optimally by existing solution methods and also requires excessive computation times for large real systems. In this paper we develop a genetic algorithm (GA) for ACOPF based OTS problem. In our GA approach we benefit from the structure of power transmission network and develop a line scoring method and a graphical distance based local improvement technique to better search the solution space. We compare our proposed genetic algorithm with two greedy heuristics on test power systems with renewable resources of energy. The results show that our proposed approach finds more economic solutions especially in larger power systems.