Abstract
To minimize the total energy consumption of a cooling tower-assisted heat pump (CTAHP) system in cooling mode, a model-based control strategy with hybrid optimization algorithm for the system is presented in this paper. An existing experimental device, which mainly contains a closed wet cooling tower with counter flow construction, a condenser water loop and a water-to-water heat pump unit, is selected as the study object. Theoretical and empirical models of the related components and their interactions are developed. The four variables, viz. desired cooling load, ambient wet-bulb temperature, temperature and flow rate of chilled water at the inlet of evaporator, are set to independent variables. The system power consumption can be minimized by optimizing input powers of cooling tower fan, spray water pump, condenser water pump and compressor. The optimal input power of spray water pump is determined experimentally. Implemented on MATLAB, a hybrid optimization algorithm, which combines the Limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm with the greedy diffusion search (GDS) algorithm, is incorporated to solve the minimization problem of energy consumption and predict the system’s optimal set-points under quasi-steady-state conditions. The integrated simulation tool is validated against experimental data. The results obtained demonstrate the proposed operation strategy is reliable, and can save energy by 20.8% as compared to an uncontrolled system under certain testing conditions.
Highlights
With rapid economic development and improvement of living standard, energy demand in buildings has increased in China
The efficient operation of cooling tower-assisted heat pump (CTAHP) systems exerts significant implication on the energy-saving of central air conditioning systems
The significant energy saving potential of cooling tower-assisted chiller systems can be estimated by using different optimization strategies, such as an energy optimization methodology [2], multi-objective evolutionary algorithms [3], an effective and robust chiller sequence control strategy [4], a multivariable Newton-based extremum-seeking control (ESC) scheme [5], an optimal approach temperature (OAT) control strategy [6], a data-driven approach with a two-level intelligent algorithm [7], etc
Summary
With rapid economic development and improvement of living standard, energy demand in buildings has increased in China. Central air conditioning systems have been widely used, and their energy consumptions account for over 40% of the total energy consumption in buildings [1]. Numerous studies have highlighted the potential impact of optimization on energy consumption of cooling tower-assisted air conditioning systems. The significant energy saving potential of cooling tower-assisted chiller systems can be estimated by using different optimization strategies, such as an energy optimization methodology [2], multi-objective evolutionary algorithms [3], an effective and robust chiller sequence control strategy [4], a multivariable Newton-based extremum-seeking control (ESC) scheme [5], an optimal approach temperature (OAT) control strategy [6], a data-driven approach with a two-level intelligent algorithm [7], etc
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