MINLP-based hybrid strategy for operating mode selection of TES-backed-up refrigeration systems
<|reference_start|>MINLP-based hybrid strategy for operating mode selection of TES-backed-up refrigeration systems: This brief deals with the satisfaction of the daily cooling demand by a hybrid system that consists of a vapour-compression refrigeration cycle and a thermal energy storage (TES) unit, based on phase change materials. The addition of the TES tank to the original refrigeration plant allows to schedule the cooling production regardless of the instantaneous demand, given that the TES tank can store cold energy and release it whenever deemed appropriate. The scheduling problem is posed as an optimization problem based on mixed-integer non-linear programming (MINLP), since it includes both discrete and continuous variables. The latter corresponds to the references on the main cooling powers involved in the problem (cooling production at the evaporator and TES charging/discharging), whereas the discrete variables define the operating mode scheduling. Therefore, in addition to the hybrid features of the physical plant, a hybrid optimal control strategy is also proposed. A receding horizon approach is applied, similar to model predictive control (MPC) strategies, while economic criteria are imposed in the objective function, as well as feasibility issues. The TES state estimation is also addressed, since its instantaneous charge ratio is not measurable. The proposed strategy is applied in simulation to a challenging cooling demand profile and the main advantages of the MINLP-based strategy over a non-linear MPC-based scheduling strategy previously developed are highlighted, regarding operating cost, ease of tuning, and ability to adapt to cooling demand variations.<|reference_end|>
@article{bejarano2024minlp-based,
    title={MINLP-based hybrid strategy for operating mode selection of
  TES-backed-up refrigeration systems},
    author={G. Bejarano, D. Rodr'iguez, J. M. Lemos, M. Vargas, M. G. Ortega},
    journal={International Journal of Robust and Nonlinear Control (2020), 30,
  6091-6111},
    year={2024},
    doi={10.1002/rnc.4674},
    archivePrefix={arXiv},
    eprint={2402.03580},
    primaryClass={math.OC cs.SY eess.SY}
}