  Determining contingency aware dispatch decisions by solving a
security-constrained optimal power flow (SCOPF) is challenging for real-world
power systems, as the high problem dimensionality often leads to impractical
computational requirements. This problem becomes more severe when the SCOPF has
to be solved not only for a single instance, but for multiple periods, e.g. in
the context of electricity market analyses. This paper proposes an algorithm
that identifies the minimal set of constraints that exactly define the space of
feasible nodal injections for a given network and contingency scenarios. By
internalizing the technical limits of the nodal injections and enforcing a
minimal worst-case impact of contingencies to line flows, computational effort
can be further improved. The case study applies and analyzes the methods on the
IEEE 118 and A&M 2000 bus systems, as well as the German and European
transmission systems. In all tested cases the proposed algorithm identifies at
least 95% of the network and security constraints as redundant, leading to
significant SCOPF solve time reductions. Scalability and practical
implementation are explicitly discussed. The code and input data of the case
study is published supplementary to the paper under an open-source license.
