研究目的
To investigate and optimize a solar-assisted heat pump system driven by hybrid PV collectors for cogeneration of heating and electricity in building applications.
研究成果
The optimized cogeneration system with R32 working fluid achieves high performance, producing 4.331 kW of heating and 0.537 kW of net electricity in steady-state, with mean energy efficiency of 65.9% and exergy efficiency of 8.80% over the winter period. The multi-objective optimization approach effectively balances heating and electricity production, making it suitable for building applications. Future work could explore different climates, system scales, and additional renewable integrations.
研究不足
The study assumes all produced heating is utilized by the building, which may not account for real-world variations. The model is based on specific climate conditions (Athens, Greece) and may not be generalizable. The optimization is performed in steady-state, and dynamic effects might introduce uncertainties. The use of specific working fluids and system parameters could limit applicability to other configurations.
1：Experimental Design and Method Selection:
The study uses a developed mathematical model in Engineering Equation Solver (EES) to simulate and optimize the system. It involves steady-state and dynamic simulations based on energy and exergy analyses, with multi-objective optimization procedures focusing on heating and electricity production.
2：Sample Selection and Data Sources:
The system includes 10 m2 hybrid PV collectors, a 0.5 m3 storage tank, a heat pump, an inverter, and a building load. Meteorological data for Athens, Greece, are used for dynamic simulations, extracted from Ref. [20].
3：5 m3 storage tank, a heat pump, an inverter, and a building load. Meteorological data for Athens, Greece, are used for dynamic simulations, extracted from Ref. [20].
List of Experimental Equipment and Materials:
3. List of Experimental Equipment and Materials: Hybrid PV collectors, storage tank, heat pump, inverter, working fluids (R32, R1234yf, R245fa, R404A, R290, R600a, R152a), and building components. Specific parameters are defined in Table 1 of the paper.
4：Experimental Procedures and Operational Workflow:
For steady-state optimization, mass flow rate (0.1 to 0.6 kg/s) and evaporator temperature (5 to 20 °C) are varied for each working fluid. Multi-objective optimization is applied to find optimum points. Dynamic simulations are conducted for six typical winter days using hourly solar irradiation and ambient temperature data.
5：1 to 6 kg/s) and evaporator temperature (5 to 20 °C) are varied for each working fluid. Multi-objective optimization is applied to find optimum points. Dynamic simulations are conducted for six typical winter days using hourly solar irradiation and ambient temperature data.
Data Analysis Methods:
5. Data Analysis Methods: Results are evaluated based on heating production, net electricity production, energy efficiency, and exergy efficiency. Optimization uses a geometric distance criterion to the ideal point. Statistical analysis is performed using EES software.
独家科研数据包，助您复现前沿成果，加速创新突破
获取完整内容
