Economic analysis of rooftop photovoltaics system under different shadowing conditions for 20 cities in China

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.

Unveiling the shadows: a qualitative exploration of barriers to rooftop solar photovoltaic adoption in residential sectors

In the face of global climate change,the urgent shift towards renewable energy sources such as solar power is vital for reducing greenhouse gas emissions and fostering a sustainable future,presenting a universal challenge and opportunity for energy policy worldwide.India’s adoption of rooftop solar photovoltaic is pivotal due to its vast solar potential,which aligns with national goals to increase renewable energy capacity,reduce carbon emissions,and achieve energy security.Kerala’s geographical location offers abundant solar potential,making it a prime candidate for the adoption of rooftop solar photovoltaic systems.Coupled with the state’s strong commitment to renewable energy initiatives such as the ambitious“SOURA”(solar subsidy program by the Kerala State Electricity Board)project and various incentives for solar adoption,Kerala stands at the forefront of India’s transition towards sustainable energy solutions.Understanding the barriers to rooftop solar photovoltaic adoption in Kerala is crucial for tailoring ef-fective policies and strategies that address specific hindrances from economic constraints to informational gaps.This study employs a qualitative research method to identify the barriers to rooftop solar photovoltaic adoption among households in Kerala.Through face-to-face interviews with a purposively selected sample of 52 households,the research aims to gain in-depth insights into the multifaceted challenges hindering the widespread adoption of solar energy in residential settings.The findings reveal several key barriers:financial barriers,informational barriers,technical barriers,regulatory barriers,social barriers,and psychological barriers.Sentiment analysis indicates that while there is a predominantly positive attitude towards solar photovoltaic adoption,there are sig-nificant concerns that still need to be addressed.Addressing these barriers with targeted policy interventions and public awareness campaigns could significantly enhance the adoption of rooftop solar photovoltaic systems in Kerala.

Geospatial assessment of rooftop solar photovoltaic potential using multi-source remote sensing data

Rooftop solar photovoltaics (PV) play increasing role in the global sustainable energy transition. This raises the challenge of accurate and high-resolution geospatial assessment of PV technical potential in policymaking and power system planning. To address the challenge, we propose a general framework that combines multi-resource satellite images and deep learning models to provide estimates of rooftop PV power generation. We apply deep learning based inversion model to estimate hourly solar radiation based on geostationary satellite images, and automatic segmentation model to extract building footprint from high-resolution satellite images. The framework enables precise survey of available rooftop resources and detailed simulation of power generation on an hourly basis with a spatial resolution of 100 m. The case study in Jiangsu Province demonstrates that the framework is applicable for large areas and scalable between precise locations and arbitrary regions across multiple temporal scales. Our estimates show that rooftop resources across the province have a potential installed capacity of 245.17 GW, corresponding to an annual power generation of 290.66 TWh. This highlights the huge space for carbon emissions reduction through developing rooftop PVs.