An optimization method for preventive control using differential evolution with consecutive search space reduction

This paper proposes a new methodology to improve the population based optimization techniques applied for preventive control actions enhancing power system security. The preventive control studied includes both generation rescheduling and load curtailment. We first investigate how the size of the search space affects and improves the best solution obtained in the optimization process. Then, we develop a new methodology that involves a number of optimization algorithms running consecutively as the size of the search space of each algorithm is reduced according to the objective function. The extensive computational requirement for dynamic security assessment during the optimization processes is overcome by the application of neural networks. The methodology is successfully applied for solving the security constrained optimization problem of a 16-generator 68-bus test system with both continuous and discrete decision variables using consecutive differential evolution optimization algorithms.