On convergence of conventional and meta-heuristic methods for security-constrained OPF analysis

Security-constrained optimal power flow (SCOPF) studies are used for assessing network performance during both planning and operational stages. The requirements for increased flexibility and improved security necessitate to use robust and computationally efficient SCOPF methods, which are crucial for "smart grid" applications requiring (close to) real-time network control. Conventional SCOPF methods solve the corresponding nonlinear power flow equations using gradient-based iterative approaches and are computationally efficient, but sensitive to selection of initial values and might suffer from convergence problems. Metaheuristic SCOPF methods are based on various approaches that search over the system state space and do not suffer from convergence problems, but are more computationally demanding. While network planners and operators regularly use conventional SCOPF methods, meta-heuristic methods are rarely implemented in practice, even for off-line analysis during the planning stage. Using as an example the IEEE 30-bus test network, this paper analyses and compares conventional and meta-heuristic methods for security-constrained OPF studies, showing that meta-heuristic methods can be used when conventional methods fail to converge and/or to provide a global optimum solution.