研究目的
Investigating the optimal design of a hybrid CSP-PV plant using genetic algorithm to achieve the lowest cost of electricity generation while ensuring stable output and manageability.
研究成果
The integration of CSP and PV systems can significantly reduce the cost of electricity generation while ensuring stable output. The genetic algorithm-based optimization method effectively identifies the optimal configuration of the hybrid system. The study demonstrates that the CSP-PV hybrid system can achieve lower LCOE and higher annual utilization rates compared to standalone systems.
研究不足
The study assumes constant efficiencies for some components (e.g., battery efficiency) and ignores some losses in the actual power generation process, which may affect the accuracy of the results. The optimization is based on specific meteorological data from Lhasa, which may not be representative of other locations.
1:Experimental Design and Method Selection:
The study employs genetic algorithm for optimizing the design of a hybrid CSP-PV system, focusing on PV-installed capacity, battery capacity, and storage capacity of CSP system. The operation strategy of the system is proposed for parabolic trough CSP system and PV system.
2:Sample Selection and Data Sources:
Meteorological data from Lhasa (91.13°E 29.67°N) is used, including direct normal irradiation (DNI), global horizontal irradiance (GHI), ambient temperature, and wind speed.
3:13°E 67°N) is used, including direct normal irradiation (DNI), global horizontal irradiance (GHI), ambient temperature, and wind speed.
List of Experimental Equipment and Materials:
3. List of Experimental Equipment and Materials: The system includes PV arrays, inverters, batteries, trough collector subsystem, two tanks thermal storage subsystem, and power block subsystem. Heat transfer oil and molten salt are used as heat transfer fluid and storage medium, respectively.
4:Experimental Procedures and Operational Workflow:
The system's performance is simulated in Matlab, considering the operation strategy that prioritizes PV and minimizes turbine shutdown. The genetic algorithm is used to optimize the system's design parameters.
5:Data Analysis Methods:
The levelized cost of energy (LCOE) is calculated as the fitness function for the genetic algorithm. The system's annual power generation and utilization hours are analyzed.
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