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 vapor‐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...

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Veröffentlicht in:International journal of robust and nonlinear control Jg. 30; H. 15; S. 6091 - 6111
Hauptverfasser: Bejarano, Guillermo, Rodríguez, David, Lemos, João M., Vargas, Manuel, Ortega, Manuel G.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Bognor Regis Wiley Subscription Services, Inc 01.10.2020
Schlagworte:
Cold storage
Computer simulation
Continuity (mathematics)
Cooling
Economic models
Energy storage
Evaporators
Hybrid systems
mixed‐integer nonlinear programming
Modal choice
Nonlinear programming
Optimal control
Optimization
Phase change materials
Predictive control
Refrigeration
refrigeration system
Schedules
Scheduling
State estimation
Strategy
Thermal energy
thermal energy storage
ISSN:1049-8923, 1099-1239
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Zusammenfassung:This brief deals with the satisfaction of the daily cooling demand by a hybrid system that consists of a vapor‐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 nonlinear 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 nonlinear MPC‐based scheduling strategy previously developed are highlighted, regarding operating cost, ease of tuning, and ability to adapt to cooling demand variations.
Bibliographie:Guillermo Bejarano, Departamento de Ingeniería, Escuela Técnica Superior de Ingeniería, Universidad Loyola Andalucía, Campus de Palmas Altas, 41014 Sevilla, Spain
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ISSN:1049-8923
1099-1239
DOI:10.1002/rnc.4674