The security constrained optimal power flow (SCOPF) is an extension of the standard OPF which takes into account line outages that have an effect on the line flows. The SCOPF problem is modelled as a nonconvex mixed-integer non-linear, large-scale optimization problem, with both continuous and discrete variables. The optimization problem determines a generation dispatch with lowest costs while respecting the constraints, both under normal operating conditions and for specified disturbances, such as outages or equipment failures. A number of issues make the SCOPF much more challenging than the OPF problem: the significantly larger problem size, the need to handle discrete variables describing control actions (e.g. the start up of generating units and network switching) and the variety of corrective control strategies in the post-contingency states. Similar to OPF problems, different solution approaches have been proposed to solve the SCOPF problem such as linear programming approximations and heuristics in addition to non-linear programming based methods.

1) Model: DC & AC SCOPF

Class: MILP + NLP

Software: CPLEX + IPM solver

- A. Marano-Marcolini, F. Capitanescu, J. L. Martinez-Ramos and L. Wehenkel, "Exploiting the Use of DC SCOPF Approximation to Improve Iterative AC SCOPF Algorithms," in *IEEE Transactions on Power Systems*, vol. 27, no. 3, pp. 1459-1466, 2012.

2) Model: two-stage stochastic

Class: MILP

Software: CPLEX

- Y. Wen, C. Guo, D. S. Kirschen and S. Dong, "Enhanced Security-Constrained OPF With Distributed Battery Energy Storage," in IEEE Transactions on Power Systems, vol. 30, no. 1, pp. 98-108, Jan. 2015.

**N-k contingency planning**:

1) Model: Two-stage robust approach

Class: MILP

Software: CPLEX

- W. Yuan; J. Wang; F. Qiu; C. Chen; C. Kang; B. Zeng, "Robust Optimization-Based Resilient Distribution Network Planning Against Natural Disasters," in IEEE Transactions on Smart Grid , vol.PP, no.99, pp.1-10.

**Contributors**:

Dr Cedric Josz, Laboratory for Analysis and Architecture of Systems LAAS CNRS

Dr Martin Mevissen, IBM

Dr Bissan Ghaddar, University of Waterloo

Dr Alireza Soroudi, University College Dublin