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Energy consumption and its associated consequences can be reduced by implementing district cooling strategies that supply low temperature water to a wide range of end users through chillers and distribution networks. Adequate understanding, performance prediction and further optimization of vapor compression chillers used widely in district cooling plants have been a subject of intense research through model-based approaches. In this context, we perform an extensive review of different modeling techniques used for predicting steady-state or dynamic performance of vapor compression liquid chillers. The explored modeling techniques include physical and empirical models. Different physical models used for vapor compression chillers, based on physics laws, are discussed in detail. Furthermore, empirical models (based on artificial neural networks, regression analysis) are elaborated along with their advantages and drawbacks. The physical models can depict both steady- and unsteady-state performance of the vapor compression chiller; however, their accuracy and physical realism can be enhanced by considering the geometrical arrangement of the condenser and evaporator and validating them for various ecofriendly refrigerants and large system size (i.e., cooling capacity). Apparently, empirical models are easy to develop but do not provide the necessary physical realism of the process of vapor compression chiller. It is further observed that DC plants/networks have been modeled from the point of view of optimization or integration but no efforts have been made to model the chillers with multiple VCR cycles. The development of such models will facilitate to optimize the DC plant and provide improved control strategies for effective and efficient operation.

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