Dynamic Scheduling: A Comparison of High-Fidelity Models with Local Optimization versus Surrogate Models with Global Optimization

Flexible process operation supports demand-side management under fluctuating electricity prices, with integrated scheduling and control enabling coordinated, real-time decisions. This work investigates the trade-off between model fidelity and optimization complexity in dynamic scheduling under integrated dynamic scheduling, using an air separation process in a day-ahead electricity market. We explore mechanistic and typical surrogate dynamic models yielding established optimization formulations combined with standard global and local solution strategies. (Non)linear surrogates are used in full-discretization, formulating (mixed-integer) linear and nonlinear scheduling problems. Local dynamic scheduling with full-order mechanistic models yields superior performance. Conversely, deterministic global dynamic optimization incurs infeasibilities for linear surrogates, and computational intractability for nonlinear surrogates due to branch-and-bound limitations. Instead, stochastic local optimization (multistart) with nonlinear models offers an alternative, delivering high-quality solutions. We conclude that local dynamic scheduling with mechanistic models, and multistart with Hammerstein-Wiener models are preferable for available detailed process models or operational data, respectively.