Optimization Control Strategy Of Air Source Heat Pump Chilled Water Return Temperature And Energy Saving Evaluate

Air-conditioning load is varied with weather and other factors dynamically air conditioning system is running under partial load most of time, and air conditioning equipment always has large spare capicaty, so air conditioning system regulation also should be flexable. Variable chilled water temperature adjustment is a simple but efficient way of air conditioning system regulation, and has many advantages such as improve chiller performance efficiently, need less investment on the secondary energy-saving reform. Existing research is focus on variable supply water temperature, the feasibility and energy-saving effect of increasing chilled water supply temperature is widely studied. And many practice verified variable chilled water temperature adjustment can save air conditioning system energy consumption efficiently.Air source heat pump(ASHP) is a common cool and heat source equipment widely used in medium and small buildings. Fixed frequency ASHP has no ability of load adjustment, hard to keep water supply temperature stable. Always take on-off control based on return water temperature, to adapt the varying of air conditioning system dynamic load. Existing research result is hard to guide air conditioning system optimization regulation of these control strategy which take return water temperature control. So ASHP's varying water temperature adjustment is always negelected, affect ASHP air conditioning system running efficient and energy-saving.To improve ASHP air conditioning system running efficient, research objuct of this study is the ASHP air conditioning system in Beijing University of Technology HVAC lab, research meathord combine test and simulation, developed ASHP chilled water return temperature optimization control strategy, and evaluated the energy-saving efficient. The main work of this thesis includes:1) Built ASHP air conditioning system simulation model. This article built ASHP air conditioning simulation model based on Modelica, the model is consist of four sunmodel like room, ASHP, fan coil unit(FCU) and cycle pump. Compare with test or design data the simulation data's relative error of submodel is less than 7%, the simulation model can simulation ASHP air conditioning running accurately and dynamicly.2) Revealed the influence of fixed frequency ASHP return water temperature setting to air conditioning system. Setting different return temperature in simulation model, got the influence of different return temperature setting to indoor environment, FCU energy cost, ASHP cooling capicaty and energy cost. According to the statistical analysis, return water temperature setting increase per 1oC, air conditioning COP enhance 1.7%, total energy consumption reduce 3.5%. And in cooling season, more than 75% time return water temperature can set 16oC, but some conditions temperature setting should be lower to satisfy indoor comfort.3) Developed ASHP chilled water return temperature optimization setting predict model. Firstly, using simulation get water return temperature optimization setting and running condition datas of satisfied indoor comfort. Then using generalized regression neural network(GRNN) built ASHP chilled water return temperature optimization setting predict model, which EEP is less than 4%, correlation coefficient is higher than 0.9, more than 93% datas' relative error is less than 10%.4) Developed varying return temperature optimization control based on ASHP chilled water return temperature optimization setting predict model. Verified in high, medium and low load rate typical day, indoor temperature is lower than 26.4oC, indoor PMV is less than 1.27, can satisfy man's comfort require.5) Analysised energy saving effect of varying return temperature optimization control. Select Beijing, Shijiazhuang, Shanghai and Guangzhou as typical urban. Compare varying return temperature optimization control with traditional fixed 12oC srtting control, energy consumption in cooling season is lower 12.93%, 12.31%, 12.10% and 10.29% respectivitely, energy-saving efficient is temarkable especially in Beijing, Shijiazhuang and Shanghai.