Mathematical Programs with Equilibrium Constraints (MPECs) are natural ways to address complementarity problems that arise in real-time optimization (RTO). Examples include disappearing phases in separators and heat exchangers, limits on flow, and discrete some decisions relating to cost structures. This talk explores three issues relating to MPECs. First, we discuss well-posed formulations of the MPEC problem through the exploration of convex inner problems. Second, we describe a number of NLP formulations that allows a straightforward approach to solve these problems under appropriate assumptions. Finally, we discuss the implementation of MPECs using state of the art NLP algorithms and modeling environments. This approach is illustrated with a number of realistic process examples, drawn from the RTO literature.