An advanced control strategy for optimizing the operation state of chillers with cold storage technology in data center

Abstract

Data centers facing huge energy consumption challenges, the chiller is one of the main energy expenditure equipment. This study proposed a novel efficient operation strategy for chillers integrated with cold water storage technology. An advanced model predictive control (MPC) was developed to regulate the running parameters of the chillers and cold water storage device, targeting the maximum energy efficiency of cooling system. Specifically, the mixed integer linear programming (MILP) algorithm was constructed in MPC with low computer calculation cost to solve the optimization problem. The performance of the MPC strategy was validated through an actual data center test located at Guangzhou city and further comprehensively assessed through annual simulations. The relative error in terms of refrigeration capacity and cooling capacity of cold water storage device between simulation and field test was less than 5 %. During the on-site testing, compared with a Baseline strategy, the coefficient of performance (COP) of the MPC strategy increased by 1.96 on average with the cooling system energy consumption reduced by 5.8 %, and the power usage effectiveness (PUE) was reduced by 0.013. The annual PUE decreased by 0.018 and the annual electricity cost decreased by 21 % when the IT power was 4570 kW based on the simulation. In addition, the effect of model mismatch was quantified by setting the deviation degree of the chiller partial load rate (PLR).