Each system operator (SO) controls the grid assets over the power network within its own geographical footprint (area). Multiple SOs coordinate to schedule the power flow over the tie-lines interconnecting such areas. In this work, we seek a tie-line schedule that minimizes the maximum aggregate cost of balancing demand and supply in real-time across multiple areas. It adds to the growing literature on multi-area optimal power flow under uncertainty — a paradigm for coordination motivated by the deepening penetration of renewable resources like wind and solar energy. We consider two different approaches. One draws from techniques in multi-parametric linear programming, and the other utilizes primal-dual iterative techniques for conic linear programs. Besides providing analytical convergence results, we illustrate the algorithms empirically.