Optimal Defense Resource Allocation Considering Nonlinear Attack Cost in Power Systems

In recent years, numerous studies have explored how to allocate limited defense resource among meters to increase difficulties for launching attacks in power systems. However, existing studies often simplify this problem by assuming the linearity of attack cost functions, which creates an inevitable gap between theoretical analysis and practical scenarios. In this article, general nonlinear attack cost functions are considered in the formulation of the optimal defense resource problem, resulting in a mixed-integer nonlinear programming problem. This poses a challenging task for numerical methods or popular commercial solvers. To address this issue, an advanced slime mould algorithm with specialized initialization and evolutionary schemes is proposed. Specifically, a customized initialization strategy is designed such that the population can be easily initialized within the nonconvex feasible region aligning with the nonlinear constraints. Besides, in the evolutionary process, the fitness function is well-designed to encourage the individuals violating nonlinear constraints to evolve toward feasible directions. Comprehensive case studies verify that, compared to the state-of-the-art solvers, the proposed approach can achieve satisfactory defense resource allocation results in terms of feasibility, optimality, and real-time performance.