A genetic algorithm for power system vulnerability analysis under multiple contingencies

Abstract

This chapter examines the use of a genetic algorithm to analyze the vulnerability of power systems. Recent blackouts worldwide have revealed the vulnerability of power systems and the inability of current security standards to cope with multiple contingencies. The need for new approaches for power system vulnerability assessment has given rise to the development of attacker-defendermodels, which are particular instances of bilevel programming. The upper-level optimization identifies a set of simultaneous outages in the power system whereas the lower-level optimization models the reaction of the system operator against the outages obtained in the upper level. The system operator reacts by determining the optimal power system operation under contingency. In general, attacker-defendermodels are characterized as mixed-integer nonlinear bilevel programs for which efficient solution procedures are yet to be explored. A genetic algorithm is described in this chapter to assess power system vulnerability through an attacker-defender model. The modeling flexibility provided by genetic algorithms makes them 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 ©Springer-Verlag Berlin Heidelberg 2013.