In this paper we present a model-based approach for designing efficient control strategies with the aim of increasing the performance of Heating, Ventilation and Air- Conditioning (HVAC) systems with ice Cold Thermal Energy Storage (ice CTES). The use of TES systems ensures reduced energy costs and energy consumption, increased flexibility of operation, reduced equipment size and pollutant emissions. A simulation environment based on Matlab/Simulink® is developed, where the thermal behaviour of the plant is analysed by a lumped formulation of the conservation equations. In particular, the ice CTES is modelled as a hybrid system, where the water phase transitions (solid-melting-liquid, liquidfreezing- solid) are described by combining continuous and discrete dynamics, thus considering both latent and sensible heat. Three standard control strategies and a model predictive control approach are developed and compared. Extensive simulations confirm that the MPC provides the best control in terms of energy efficiency and cooling load demand satisfaction with respect to standard control strategies.

Modeling and control of HVAC systems with ice-cold thermal energy storage

BEGHI, ALESSANDRO;RAMPAZZO, MIRCO;SIMMINI, FRANCESCO
2013

Abstract

In this paper we present a model-based approach for designing efficient control strategies with the aim of increasing the performance of Heating, Ventilation and Air- Conditioning (HVAC) systems with ice Cold Thermal Energy Storage (ice CTES). The use of TES systems ensures reduced energy costs and energy consumption, increased flexibility of operation, reduced equipment size and pollutant emissions. A simulation environment based on Matlab/Simulink® is developed, where the thermal behaviour of the plant is analysed by a lumped formulation of the conservation equations. In particular, the ice CTES is modelled as a hybrid system, where the water phase transitions (solid-melting-liquid, liquidfreezing- solid) are described by combining continuous and discrete dynamics, thus considering both latent and sensible heat. Three standard control strategies and a model predictive control approach are developed and compared. Extensive simulations confirm that the MPC provides the best control in terms of energy efficiency and cooling load demand satisfaction with respect to standard control strategies.
Proceedings of the IEEE 52nd Conference on Decision and Control, CDC 2013
9781467357173
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11577/2795763
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