Optimal Design Method of a Hybrid CSP-PV Plant Based on Genetic Algorithm Considering the Operation Strategy

Abstract

Solar energy is the most abundant renewable energy and it has a great potential for development. There are two ways to transfer solar energy to electricity: photovoltaic power generation (PV) and concentrated solar power (CSP). CSP-PV hybrid system can be fully integrated with the advantages of the two systems to achieve low cost, stable output, and manageable to generate electricity. In this paper, the operation strategy of the CSP-PV system is proposed for parabolic trough CSP system and PV system which are now commercially operated. Genetic algorithm is used to optimize the design of the system and calculate PV-installed capacity, battery capacity, and storage capacity of CSP system, making the system to achieve the lowest cost of electricity generation. The results show that the introduction of the CSP system makes it possible to ensure the stability of the output power of hybrid system when the battery capacity is small, which greatly improves the annual utilization time of the PV and reduces solar abandonment. When the system is optimized by operation characteristics of Spring Equinox, the lowest LCOE is 0.0627 $/kWh, the rated capacity of PV and CSP system are 222.462 MW and 30 MW, respectively, and the capacity of heat storage and battery are 356.562 MWh and 14.687 MWh. When the system is optimized by the operation characteristics of the whole year, the lowest LCOE is 0.0555 $/kWh, the rated capacity of PV and CSP system are 242.954 MW and 30 MW, respectively, and the capacity of heat storage and battery are 136.059 MWh and 8.977 MWh. The comparison shows that the power generation curves of the hybrid system are similar in the two optimization-based methods—Spring Equinox based and annual based, but LCOE is lower when optimized by the annual operation characteristic, and the annual utilization rate of the system is higher when optimized by Spring Equinox based.