Application of a genetic algorithm to n-K power system security assessment
This paper addresses the security assessment of power systems when the simultaneous loss of K components is considered. The problem is formulated as a bilevel program. The upper-level optimization identifies a set of simultaneous out-of-service components in the power system, whereas the lower-level optimization models the reaction of the system operator against the outages selected in the upper level. The system operator reacts by determining the optimal power system operation under contingency. Due to the inherent nonconvexity and nonlinearity of the resulting bilevel problem, efficient solution procedures are yet to be explored. A genetic algorithm is proposed in this paper to attain high-quality near-optimal solutions with moderate computational effort. The modeling flexibility provided by this evolution-inspired methodology makes it suitable for this kind of bilevel programming problems. Numerical results demonstrate the effectiveness of the proposed approach in the identification of critical power system components. âº We examine the n-K power system security assessment by a genetic algorithm. âº This problem is formulated as a mixed-integer nonlinear bilevel program. âº Corrective actions are extended to include discrete decisions such as line switching. âº The proposed genetic algorithm features a repair procedure to avoid infeasibilities. âº High-quality near-optimal solutions are attained in moderate computation times.