A systematic and probabilistic approach for optimal design and on-site adaptive balancing of building central cooling systems concerning uncertainties

Abstract

In current design practice, chillers and pumps are often oversized due to conservative consideration of uncertainties using safety factors to avoid the risk of undersizing, which often results in significant energy waste in operation. In recent years, probabilistic optimal design methods have been proposed for the components of cooling systems, enabling risk-based decision-making rather than sizing systems with safety margins to consider uncertainties. However, approaches for probabilistic optimal design and balancing of entire cooling systems are still absent. This article therefore presents a systematic approach of probabilistic optimal design and adaptive balancing for central cooling systems of buildings to minimize the impacts (energy waste and increased life-cycle cost) of oversizing in operation. The probabilistic optimal design considers both the uncertainties of design inputs and the flexibility of on-site adaptive balancing, while adaptive balancing enables flexible balancing to maximize energy saving according to characteristics of constructed systems. A case study is conducted to test and validate the proposed approach. Results show that significant cost reduction and energy saving were achieved for chillers and pumps, respectively, through the systematic approach of probabilistic design and adaptive balancing. Energy consumption of pumps was reduced by 41% through coordinating pump design with probabilistic chiller design.