Hybrid imperialist competitive algorithm — A meta-heuristic approach to solve security constrained optimal power flow

Optimal power flow (OPF) is an important tool used in the planning and operation stages of a power system, wherein a certain objective is optimized subject to specific system constraints. Security constrained optimal power flow (SCOPF) is an advanced OPF formulation wherein security constraints should be satisfied in case of contingencies, in addition to the normal system constraints. Possible contingencies that are likely to occur in the system such as line outages are considered and for vulnerable cases, SCOPF is solved to characterize secure operating conditions for the system. The imperialist competitive algorithm (ICA) is a relatively new evolutionary programming technique that has been successfully applied to many optimization problems. To facilitate fast convergence of the solution, teaching learning algorithm (TLA) is incorporated into ICA for improved local search. This paper presents the application of the hybrid ICA-TLA to solve SCOPF problem and arrive at the optimal setting of the control variables, while satisfying the system constraints. The system inequality constraints are handled using the classical penalty function method. The proposed approach is evaluated on IEEE 30 bus system and the results are compared against those obtained in literature. The analysis showcases the effectiveness of hybrid ICA in solving SCOPF problem.