A novel solar photovoltaic/thermal assisted gas engine driven energy storage heat pump system (SESGEHPs) and its performance analysis. (15th March 2019)

• Record Type: Journal Article
• Authors: Zhang, Qiang; Yang, Zhao; Li, Ning; Feng, Rui; Shi, Peifeng
• Is Part Of: Energy conversion and management. Volume 184(2019)
• Page Start: 301
• Page End: 314
• Publication Date: 2019-03-15
• Subjects: Solar photovoltaic/thermal -- Gas engine driven energy storage heat pump system -- The bilateral load demands -- Generalized regression neural network; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2019.01.039
• Languages: English
• ISSNs: 0196-8904

Abstract

Highlights: A novel SESGEHPs is presented. The SESGEHPs is better matched to the building and evaporator load. A new generalized regression neural network process and prediction model is built. The nonlinear relationship between the SESGEHPs and building load is obtained. Abstract: In order to study the most economical way of heating the buildings, considering the effect of the bilateral load demands on the way of heating the buildings, a novel solar photovoltaic/thermal assisted gas engine driven energy storage heat pump system (SESGEHPs) is presented which can meet the demands of the real-time building heating load and lower temperature heat source of evaporator to ensure the PV/T system works in the optimum temperature range. A test bench of SESGEHPs is set up and the experiments are carried out to test the system heating performance, including the characteristics of the PV/T system, ESGEHPs, building heating load and environmental parameters. The experimental results show that the average COP and PER of the SESGEHPs is nearly 24.5% and 20.4% higher than the ESGEHPs, respectively. The fuel-consumption and the average compressor power consumption of SESGEHPs is nearly 7.5% and 24.0% lower than the ESGEHPs, respectively. Finally, the experimental data are processed using the generalized regression neural network (GRNN) to get the relationship between the independent variable (solar radiation, outdoor dry bulb temperature and wind speed) and dependent variable (the building heating load, heating capacity of the SESGEHPs, heat-obtained quantity and power generation of the PV/T system). It is found that the operating characteristics of the SESGEHPs are greatly affected by the solar radiation and the outdoor temperature, and less affected by the wind speed.