Scientific and technological progress and future perspectives of the solar assisted heat pump (SAHP) system

被引:67
作者
Fan, Yi [2 ]
Zhao, Xudong [1 ,2 ]
Han, Zhonghe [1 ]
Li, Jing [2 ]
Badiei, Ali [2 ]
Akhlaghi, Yousef Golizadeh [2 ]
Liu, Zhijian [1 ]
机构
[1] North China Elect Power Univ, Baoding 071000, Peoples R China
[2] Univ Hull, Ctr Sustainable Energy Technol, Kingston Upon Hull HU6 7RX, N Humberside, England
关键词
Solar-assisted heat pump; Solar efficiency; Coefficient of performance; Heat storage and exchanging unit; Multi-source heat pump; Machine learning; THERMAL-ENERGY STORAGE; PHASE-CHANGE MATERIAL; WATER-HEATER; EXPERIMENTAL VALIDATION; PERFORMANCE ANALYSIS; THERMODYNAMIC ANALYSIS; DEFROSTING METHOD; FIELD-TEST; OPTIMIZATION; COLLECTOR;
D O I
10.1016/j.energy.2021.120719
中图分类号
O414.1 [热力学];
学科分类号
摘要
Among all available solar technologies, solar-assisted heat pump (SAHP) is the most popular technology for low-and medium-temperature applications, i.e., drying, space heating, and water heating. This paper presents a critical review of the SAHP, leading to identification of the key technological challenges with the current SAHP technology, including (1) poor energy performance at low ambient temperatures; (2) difficulty in coupling solar thermal panels with heat pumps to achieve increased operational time and reduced electrical energy usage; (3) mismatch between heat demand of the building and heat supply of the SAHP system; and (4) lack of multi-functional modelling tool. A recently developed SAHP technology is introduced, which can effectively tackle the challenges with the current SAHPs, thus achieving enhanced solar efficiency, improved heat pump energy efficiency, and fast responsive time to the heat demand of buildings. The future research directions of the SAHP were identified: (1) multi-source heat pump; (2) heat pump defrosting capacity using waste heat and renewable energy; (3) advanced heat storage and exchange unit (HSEU) technology; and (4) advanced machine learning and multi-objective evolutionary optimisation models for performance prediction and optimisation of the SAHP using big datasets. (c) 2021 Elsevier Ltd. All rights reserved.
引用
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页数:12
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