Journal Home > Volume 17 , Issue 2

Installing photovoltaic (PV) systems is an essential step for low-carbon development. The economics of PV systems are strongly impacted by the electricity price and the shadowing effect from neighboring buildings. This study evaluates the PV generation potential and economics of 20 cities in China under three shadowing conditions. First, the building geometry models under three shadowing conditions for the 20 cities were constructed using QGIS. Then, 60 building models with PV systems and shadows from surrounding buildings were generated by City Buildings, Energy, and Sustainability (CityBES), an open platform, to simulate the PV power generation. Finally, the study presented one economic analysis model to evaluate the profitability by combining the market cost of rooftop PV systems and electricity prices in China. The economic model included four indicators: payback period (static and dynamic), net present value (NPV), and internal rate of return (IRR). The results show that the reduction of PV power generation ranges from 8.29% to 16.01% under medium shadowing, and experiences a maximum decrease of up to 39.71% under high shadowing. Further economic analysis shows that almost all the regions show reliable potential, obtaining an IRR higher than the reference value (5%). Nenjiang has the highest economic profit, with the highest NPV (86,181.15 RMB) and IRR (30.14%) under no shadowing among 20 cities. It also should be mentioned that the alignment between electricity price distribution and the solar power generation curve will directly impact the economic potential of PV systems.

Publication history
Copyright
Acknowledgements

Publication history

Received: 16 July 2023
Revised: 11 September 2023
Accepted: 29 September 2023
Published: 17 October 2023
Issue date: February 2024

Copyright

© Tsinghua University Press 2023

Acknowledgements

Acknowledgements

This research was funded by Hunan University, China, through the start-up funds and the Course Development Program of "Artificial Intelligence in Built Environment".

Return