Rooftop Photovoltaic Potential Research Articles

Application of Satellite Data for Estimating Rooftop Solar Photovoltaic Potential

Rooftop solar photovoltaics can significantly contribute to global energy transitions by providing clean, decentralized energy without the need for new land, thereby avoiding land-use conflicts. It serves as a valuable complement to other renewable-energy sources and is expected to play a crucial role in future electricity systems. Due to the spatiotemporal variability in the solar radiation on roof surfaces, it is essential to determine the potentials of the rooftop photovoltaics and its variations in specific regions. In Germany, this potential was assessed in 5 km × 5 km zones, as well as at the federal-state and national levels. High-quality satellite and reanalysis data were used to determine the power output of the solar photovoltaics. Additionally, high-resolution (2 m × 2 m) European Settlement Map data, calibrated with solar cadastre data, were utilized to evaluate different scenarios. The results show that the potential is concentrated in larger urban areas and the southwestern part of Germany due to the availability of rooftop space and solar radiation. Overall, the national rooftop areas are substantial across all scenarios, ranging from 2100 to 4500 km2. The applied methods and scenarios provide a straightforward way to reveal the spatiotemporal variability and define realistic ranges of the solar photovoltaic potential without requiring detailed information about each building. However, assessing the rooftop photovoltaic potential remains challenging and uncertain due to the lack of large-scale, high-resolution data on building characteristics and the complexity of the solar radiation distribution in urban environments.

Evaluation Method for Hosting Capacity of Rooftop Photovoltaic Considering Photovoltaic Potential in Distribution System

Regarding the existing evaluation methods for photovoltaic (PV) hosting capacity in the distribution system that do not consider the spatial distribution of rooftop photovoltaic potential and are difficult to apply on the actual large-scale distribution systems, this paper proposes a PV hosting capacity evaluation method based on the improved PSPNet, grid multi-source data, and the CRITIC method. Firstly, an improved PSPNet is used to efficiently abstract the rooftop in satellite map images and then estimate the rooftop PV potential of each distribution substation supply area. Considering the safety, economy, and flexibility of distribution system operation, we establish a multi-level PV hosting capacity evaluation system. Finally, based on the rooftop PV potential estimation of each distribution substation supply area, we combine the multi-source data of the grid digitalization system to carry out security verification and indicator calculation and convert the indicator calculation results of each scenario into a comprehensive score through the CRITIC method. We estimate the rooftop photovoltaic potential and evaluate the PV hosting capacity of an actual 10 kV distribution system in Shantou, China. The results show that the improved PSPNet solves the hole problem of the original model and obtains a close-to-realistic rooftop photovoltaic potential estimation value. In addition, the proposed method considering the photovoltaic potential in this paper can more accurately evaluate the rooftop PV hosting capacity of the distribution system compared with the traditional method, which provides data support for the power grid corporation to formulate a reasonable PV development and hosting capacity enhancement program.

ETHOS.PASSION: An open-source workflow for rooftop photovoltaic potential assessments from satellite imagery

The assessment of rooftop photovoltaic potential has become increasingly accurate due to the expanding availability of satellite imagery and improvements in computer vision methods. However, the analysis of satellite imagery is impeded by a lack of transparency, reproducibility, and standardized description of the methods employed. Studies are heterogeneous, target different types of potential with redundant efforts, and are mostly not open source or use private datasets for training. With respect to the estimation of photovoltaic potential, this study proposes a conceptual frame of reference for clearly identifying tasks, their relationships, and their data. Additionally, the open-source workflow ETHOS.PASSION is introduced, which integrates the assessment of geographical, technical and economic potentials of regions under consideration along with the calculation of surface areas, orientations and slopes of individual rooftop sections. ETHOS.PASSION also includes the detection of superstructures, i.e., obstacles such as windows or existing photovoltaic installations. The novel two-look approach combines two deep learning models identifying rooftops and sections, and an additional model for the identification of superstructures. The three models show a mean Intersection Over Union between classes of 0.8478, 0.7531 and 0.4927 respectively, and more importantly display consistent results amongst randomly sampled real world images. The final results are evaluated for multiple datasets and compared against other studies, with a case study in the Aachen region of Germany being presented.

Estimation of Rooftop Solar Photovoltaic Potential Based on High-Resolution Images and Digital Surface Models

Buildings are important components of urban areas, and the construction of rooftop photovoltaic systems plays a critical role in the transition to renewable energy generation. With rooftop solar photovoltaics receiving increased attention, the problem of how to estimate rooftop photovoltaics is under discussion; building detection from remote sensing images is one way to address it. In this study, we presented an available approach to estimate a building’s rooftop solar photovoltaic potential. A rapid and accurate rooftop extraction method was developed using object-based image classification combining normalized difference vegetation index (NDVI) and digital surface models (DSMs), and a method for the identification of suitable rooftops for solar panel installation by analysing the geographical restrictions was proposed. The approach was validated using six scenes from Beijing that were taken using Chinese Gaofen-2 (GF-2) satellite imagery and Pleiades imagery. A total of 176 roofs in six scenarios were suitable for PV installation, and the estimated photovoltaic panel area was 205,827 m2. The rooftop photovoltaic potential was estimated to total 22,551 GWh. The results indicated that the rooftop photovoltaic potential estimation method performs well.

Assessing urban rooftop PV economics for regional deployment by integrating local socioeconomic, technological, and policy conditions

Assessing the urban rooftop photovoltaic (PV) economics is important for scaling up rooftop PVs for rapid decarbonization. In this study, socioeconomic, technological, and policy factors were integrated into a rooftop PV economic assessment. This comprehensive method was applied to 21 cities in the Guangdong province of China, in combination with local real parameters. The results show that, with higher urbanization levels, electricity tariffs, and PV self-consumption rates, cities in the Pearl River Delta area generally had better economic performance in terms of both economic scale and profitability. They accounted for 75% of the total rooftop PV potential in Guangdong Province and had an internal rate of return (IRR) as high as 14.6–19.2% for Commercial and Industrial buildings (C&I), and 9.9–15.9% for residential buildings (R). Socioeconomic, technological, and policy factors jointly affect the economic performance and create a promising future for Guangdong rooftop PVs. It is projected that the IRR of current worst performing cities will grow by 4.9–5.8, and 4.4–5.9 percentage points, and the payback period will be shortened by 5.8–6.7 and 3.4–4.1 years by 2030 for R and C&I buildings, respectively. Among the three factors, technological advancement had the most significant long-term effect, followed by socioeconomic development. Policy factors play an important role in alleviating short-term cash flow pressure. Integrating the three factors in the assessment provides us with a more comprehensive picture of how urban rooftop PV economics will evolve and a better idea of how to prioritize rooftop PV development. Suggestions for prioritizing the deployment of rooftop PV systems in Guangdong by 2030 were provided.

Different photovoltaic power potential variations in East and West China

Photovoltaic (PV) technology can help reduce carbon emissions significantly, but its benefits may be affected by climate change. Few studies have reported on the impact of climate change on the spatial and temporal distribution of solar energy in China based on the latest Coupled Model Intercomparison Project Phase 6 (CMIP6) models, and few have explored the rooftop PV potential. In this study, 16 CMIP6 models are used for evaluation of the inter-annual and seasonal changes of solar irradiance and PV capacity factor under low (SSP1–2.6), medium (SSP2–4.5) and high (SSP5–8.5) greenhouse gas emission scenarios. The effects on the variation of solar irradiance of three factors are examined: clouds, aerosol optical thickness (AOT) and specific humidity. Furthermore, the future power generation potential of rooftop PV is investigated. It is found that the solar irradiance exhibits an upward trend on national level under all the future scenarios, especially for the SSP1–2.6 scenario, with an increasing rate of 1.4 W m−2 decade−1, mainly due to a reduction in AOT. Solar irradiance increases notably in southeast China while increases little in west China, and the level for 2051–2060 remains lower than that for 1960–2014. The PV capacity factor increases in southeast China with increasing solar irradiance, with a maximum increase of about 4% compared to the average PV CF for 1960–2014 and the highest increasing rate being 0.37% decade−1. In addition, to achieve the projected national distributed PV power generation level, >70% of the effective rooftop area needs to be utilized in 2050. This study hopes to enhance understanding of the impact of climate change on solar energy and provide recommendations for future PV planning to better achieve the long-term planetary temperature goal set by the Paris Agreement.

The importance of battery storage systems in reducing grid issues in sector-coupled and renewable low-voltage grids

Transforming the energy system towards renewable energies and the electrification of the transport and heating sectors is necessary. A substantial part of this transformation occurs in the low-voltage grids. The increasing number of photovoltaic systems, electric vehicles and heat pumps poses new challenges for low-voltage grids. The load on lines and transformers is increasing due to higher electricity demand. Battery storage systems and the flexible operation of consumers can increase photovoltaic self-consumption and relieve low-voltage grids by using a grid-serving mode of operation and thus supplement grid expansion.We conducted time series-based load flow calculations using five representative low-voltage grids for four weeks of the year. We determined the loads and the need for curtailment caused by a high expansion of roof-mounted photovoltaic systems and new sector-coupled consumers and how these loads and curtailment can be reduced with the help of battery storage systems and flexible consumers.Our analyses show that consumption-related overloads can be completely avoided, and the share of locally consumed photovoltaic energy can be doubled. Digitalising the low-voltage grids is required to detect overloads and allow battery storage systems and consumers to react specifically to these grid overloads. Rural low-voltage grids are particularly affected by overloads, as they have greater rooftop photovoltaic potential and tend to be weaker. Battery storage systems and smart consumers are thus becoming essential cornerstones for the transformation of the energy system.

High-resolution analysis of rooftop photovoltaic potential based on hourly generation simulations and load profiles

Rooftop photovoltaics (PV) are playing an increasingly important role in building a clean and decarbonized energy system. For such distributed resources, formulating scientific development plans and incentives tailored to local conditions requires a comprehensive potential assessment at high spatial and temporal resolutions. Here, we evaluate the resource volume, power generation potential, economic feasibility, and market returns on electricity sales of rooftop PV in Jiangsu Province, China at hourly and 500-m resolutions by combining remote sensing survey, PV output simulation and load dispatching. The province's annual rooftop PV generation meets approximately 30% of the total social electricity consumption, and the entire region has reached both plant-side and user-side grid parity. Based on the case study, we investigate the suitable development scale of rooftop PV subject to different owners, as well as the impact of grid's system flexibility and energy storage on rooftop PV curtailment. For household use, the installation of a 3-kW rooftop PV is suitable, while for grid power supply, rooftop PV development needs to be sized to accommodate the grid. We find that increasing system flexibility significantly reduces PV curtailment for the same penetration rate, and that the use of energy storage helps increasing PV penetration under the curtailment constraint. This study represents a relatively extreme case of high penetration of variable solar PV generations, high uncertainty of PV outputs, and high variability in user-side loads, providing a reliable reference for rooftop PV development in other China's provinces.

Comparative analysis on the effectiveness of green roofs and photovoltaic panels as sustainable rooftop technologies.

Photovoltaic (PV) panels and green roofs are considered as the most effective sustainable rooftop technologies at present, which utilizes the effective rooftop area of a building in a sustainable manner. To assess the most suitable rooftop technology out of the two, it is vital to have an idea on the energy savings potential of these sustainable rooftop technologies, alongside a financial feasibility analysis considering their overall life spans and additional ecosystem services. To achieve this objective, ten selected rooftops located in a tropical city were retrofitted with hypothetical PV panels and semi-intensive green roof scenarios to perform the present analysis. The energy-saving potential for PV panels was estimated with the assistance of PVsyst software, and green roof ecosystem services were evaluated through a range of empirical formulas. The financial feasibility of the two technologies was assessed by Payback Period and Net Present Value (NPV), through data obtained by local information sources such as solar panels and green roof manufacturers. The results indicate that PV panels achieve a rooftop PV potential of 244.39 KWh/yr/m2 during their 20-year life span. Furthermore, green roofs reach an energy-saving potential of 22.29 KWh/yr/m2 during a 50-year life span. Moreover, based on the financial feasibility analysis, PV panels demonstrated an average payback period of 3-4 years. Green roofs exemplified 17-18 years to recover their total investment for the selected case studies in Colombo, Sri Lanka. Although green roofs do not provide comparatively significant energy savings, these sustainable rooftop technologies aid in energy saving under different response intensities. In addition, green roofs offer several other ecosystem services that improve urban areas' quality of life. Collectively, these findings highlight the particular importance of each rooftop technology promoting building energy savings.

Assessing Carbon Reduction Potential of Rooftop PV in China through Remote Sensing Data-Driven Simulations

Developing rooftop photovoltaic (PV) has become an important initiative for achieving carbon neutrality in China, but the carbon reduction potential assessment has not properly considered the spatial and temporal variability of PV generation and the curtailment in electricity dispatch. In this study, we propose a technical framework to fill the gap in assessing carbon reduction potential through remote sensing data-driven simulations. The spatio-temporal variations in rooftop PV generations were simulated on an hourly basis, and a dispatch analysis was then performed in combination with hourly load profiles to quantify the PV curtailment in different scenarios. Our results showed that the total rooftop PV potential in China reached 6.5 PWh yr−1, mainly concentrated in the eastern region where PV generation showed high variability. The carbon reduction from 100% flexible grids with 12 h of storage capacity is close to the theoretical maximum, while without storage, the potential may be halved. To maximize the carbon reduction potential, rooftop PV development should consider grid characteristics and regional differences. This study has important implications for the development of rooftop PV and the design of carbon-neutral pathways based on it.

A Complete and High-Resolution Estimate of Sardinia’s Rooftop Photovoltaic Potential

The implementation of the energy transition and the building of energy communities are driving forward the exploitation of the potential for rooftop photovoltaic power generation. Estimating rooftop PV generation potential requires the processing of different types of data, such as the cadastral information of buildings, a detailed description of available rooftop areas, and solar irradiance data. High-resolution estimation based on GIS data is normally limited to small survey areas. Instead, by using an algorithm for the efficient calculation of shadows over rooftops, and the integration of solar irradiance over time, we developed a procedure that allows for the rapid full census assessment of rooftop photovoltaic potential with a spatial resolution of 1 m, applicable at the regional scale and requiring minimal computational resources. We applied this approach to the rooftops of buildings in Sardinia, an island and region of Italy of particular interest for the energy transition. In addition to estimating the geographic potential, we carried out a preliminary assessment of the technical and economic potential, yielding a maximal photovoltaic rooftop generation potential of 22 TWh for the entire region.

Assessing the Potential of Rooftop Photovoltaics by Processing High-Resolution Irradiation Data, as Applied to Giessen, Germany

In recent years, prices for photovoltaics have fallen steadily and the demand for sustainable energy has increased. Consequentially, the assessment of roof surfaces in terms of their suitability for PV (Photovoltaic) installations has continuously gained in importance. Several types of assessment approaches have been established, ranging from sampling to complete census or aerial image analysis methodologies. Assessments of rooftop photovoltaic potential are multi-stage processes. The sub-task of examining the photovoltaic potential of individual rooftops is crucial for exact case study results. However, this step is often time-consuming and requires lots of computational effort especially when some form of intelligent classification algorithm needs to be trained. This often leads to the use of sampled rooftop utilization factors when investigating large-scale areas of interest, as data-driven approaches usually are not well-scalable. In this paper, a novel neighbourhood-based filtering approach is introduced that can analyse large amounts of irradiation data in a vectorised manner. It is tested in an application to the city of Giessen, Germany, and its surrounding area. The results show that it outperforms state-of-the-art image filtering techniques. The algorithm is able to process high-resolution data covering 1 km2 within roughly 2.5 s. It successfully classifies rooftop segments which are feasible for PV installations while omitting small, obstructed or insufficiently exposed segments. Apart from minor shortcomings, the approach presented in this work is capable of generating per-rooftop PV potential assessments at low computational cost and is well scalable to large scale areas.

Potentials of Renewable Energy Sources in Germany and the Influence of Land Use Datasets

Potential analyses identify possible locations for renewable energy installations, such as wind turbines and photovoltaic arrays. The results of previous potential studies for Germany, however, are not consistent due to different assumptions, methods, and datasets being used. For example, different land-use datasets are applied in the literature to identify suitable areas for technologies requiring open land. For the first time, commonly used datasets are compared regarding the area and position of identified features to analyze their impact on potential analyses. It is shown that the use of Corine Land Cover is not recommended as it leads to potential area overestimation in a typical wind potential analyses by a factor of 4.7 and 5.2 in comparison to Basis-DLM and Open Street Map, respectively. Furthermore, we develop scenarios for onshore wind, offshore wind, and open-field photovoltaic potential estimations based on land-eligibility analyses using the land-use datasets that were proven to be best by our pre-analysis. Moreover, we calculate the rooftop photovoltaic potential using 3D building data nationwide for the first time. The potentials have a high sensitivity towards exclusion conditions, which are also currently discussed in public. For example, if restrictive exclusions are chosen for the onshore wind analysis the necessary potential for climate neutrality cannot be met. The potential capacities and possible locations are published for all administrative levels in Germany in the freely accessible database (Tool for Renewable Energy Potentials—Database), for example, to be incorporated into energy system models.

An estimation framework of regional rooftop photovoltaic potential based on satellite remote sensing images

This paper proposes a comprehensive framework for estimating the regional rooftop photovoltaic (PV) potential. The required rooftop information is extracted from Gao Fen-7 satellite images. In particular, the rooftop area is obtained using a semantic segmentation network. The azimuth and inclination angles are calculated based on the digital surface model. In addition, to improve the accuracy of the economic evaluation, buildings are divided into commercial and industrial buildings and residential buildings. Based on the difference in the roof inclination, the rooftops can be divided into flat roofs, on which the PV panels are installed with the optimal inclination angle, and sloped rooftops, on which the PV panels are installed in a lay-flat manner. The solar irradiation on the plane-of-array is calculated using the isotropic sky translocation model. Then, the available installed capacity and generation potential of the rooftop PV is obtained. Finally, the net present value, dynamic payback period, and internal rate of return are used to evaluate the economic efficiency of the rooftop PV project. The proposed framework is applied in the Da Xing district of Beijing, China, with a total area of 546.84 km2. The results show that the rooftop area and available installed capacity of PV are 25.63 km2 and 1487.45 MWp, respectively. The annual rooftop PV generation potential is 2832.23 GWh, with significant economic returns.

An integrated technical, economic, and environmental framework for evaluating the rooftop photovoltaic potential of old residential buildings

Energy-saving reconstruction of old residential buildings is a vital way to achieve sustainable development, but the potential of rooftop photovoltaic (PV) energy-saving in old residential buildings has not been studied. This study established a basic framework for the estimation of rooftop PV technical, economic and environmental potential in the old residential buildings of Nanjing City, and provide the prediction results for the development of rooftop PV development plan in Nanjing. We estimated the available area, maximum electricity generation, environmental and economic benefits of rooftop PV in five districts of Nanjing by using building roof profile data, calculation method of maximum solar radiation at optimal tilt angle and GIS method. We find that the electricity generation potential of installing rooftop PV in the old residential buildings in the study area would meet about 17.7–20% of the residential electricity demand under three scenarios of the PV performance ratios (PR). Additionally, the carbon reduction potential of the life cycle rooftop PV reaches 13912874.12t (PR = 0.85), 13094469.76t (PR = 0.8), and 12276065.4t (PR = 0.75), respectively; and the result of economic potential shows that the life cycle of rooftop PV cannot generate economic benefits with an NPV value less than 0. The feasibility analysis framework provided in this paper can be applied to the assessment of rooftop PV potential in other cities in China, and provide valuable advice for the rational planning of rooftop PV.

Rooftop photovoltaic potential in Istanbul: Calculations based on LiDAR data, measurements and verifications

Usage of solar energy is increasing steadily especially in the rooftop installations of buildings in large cities. This study contains calculations of electrical energy produced by photovoltaic panels placed on roofs of buildings for city of Istanbul using building data and verify calculated results by a mobile measurement system. Three dimensional city model utilizes light detection and ranging data that covers an area of 5400 km2 for the whole city. The main object classes such as ground, building, vegetation were derived, buildings are vectorized and a digital elevation model are used for the generation of a second level of detail. Using the geometric data of the buildings in Istanbul as input to the developed software, the electricity production of panels is calculated on the suitable roofs of the buildings considering the observed climatic conditions and solar radiation for the city. A photovoltaic weather mobile vehicle measurement system was designed and applied for verification of developed model in eight different areas of Istanbul for stationing 50 days at each location. Results of difference between measurements and calculations of solar irradiation and electrical energy production are 2.44% and 14.70%, respectively. Batch processing was performed on 39 districts of Istanbul with 1.3 million buildings and annual electrical energy production from the roofs of all buildings is calculated to be 30.8 TWh at the point of common coupling to the utility. Total rooftop electricity production of Istanbul has a potential to meet 67% of the total electricity consumption for the year 2019.

Determination of the urban rooftop photovoltaic potential: A state of the art

In today’s world, the necessity of reducing greenhouse gas emissions to meet the global warming regulations has increased the demand for renewable energy sources. A notable portion of energy consumption is dedicated to urban environments. While solar energy is the most promising sustainable energy, urban environments can be considered as high-potential electricity producers by using rooftop-mounted photovoltaic systems. However, effective guidelines for optimal installation of solar photovoltaics remains a challenge. Although in recent years there has been a vast development of methods as well as improvement in availability of data sources. Since it is not always possible to apply the same techniques, specific approaches are needed for local, regional, or continental scales. It still remains to develop a uniform accurate multi-factor method that uses uniform open data sources to determine urban rooftop’s photovoltaic potential. The aim of this paper is to make a complete systematic review of various developed methodologies published in the current state of the art, and identify vital factors for urban rooftop solar photovoltaic potential assessment as well as to detect the best available methods to create a complete global basis for future studies.

Deep learning method for evaluating photovoltaic potential of urban land-use: A case study of Wuhan, China

To strengthen the synergy between urban photovoltaic development and urban planning, which can help to promote photovoltaic and renewable energy development in cities, a workflow based on a deep-learning method by using neural networks and urban satellite images is constructed, which is applied to study the relationship between urban rooftop photovoltaic potential and urban land use. A Chinese city, Wuhan, has been considered the subject and divided into 5184 units, which is a 1201.2 km2 area in central urban space. The result shows that, three types of urban land use type have the highest rooftop photovoltaic potential, which are Continuous Urban area, Discontinuous Dense Urban area and Industrial, commercial, public and education unit. The annual photovoltaic potential of these three land use types have reached 1818.41 GWh/year, 1957.32 GWh/year and 2022.71 GWh/year, and the average photovoltaic power generation per unit area of these three types have reached 11.23 GWh/km2·year, 9.99 GWh/km2·year and 13.07 GWh/km2·year. In addition, these three land use types contributed 71.4% of the city’s total rooftop photovoltaic potential. When considering the coordination of roof photovoltaic development and urban planning, these three types of land use should be given priority. The method and findings can provide urban planning authorities with tools and data to reference when developing urban master plan and PV development plans.

Big data mining for the estimation of hourly rooftop photovoltaic potential and its uncertainty

The large-scale deployment of photovoltaics (PV) on building rooftops can play a significant role in the transition to a low-carbon energy system. To date, the lack of high-resolution building and environmental data and the large uncertainties related to existing processing methods impede the accurate estimation of large-scale rooftop PV potentials. To address this gap, we developed a methodology that combines Machine Learning algorithms, Geographic Information Systems and physical models to estimate the technical PV potential for individual roof surfaces at hourly temporal resolution. We further estimate the uncertainties related to each step of the potential assessment and combine them to quantify the uncertainty on the final PV potential. The methodology is applied to 9.6 million rooftops in Switzerland and can be transferred to any large region or country with sufficient available data. Our results suggest that 55% of the total Swiss roof surface is available for the installation of PV panels, yielding an annual technical rooftop PV potential of 24±9TWh. This could meet more than 40% of Switzerland’s current annual electricity demand. The presented method for an hourly rooftop PV potential and uncertainty estimation can be applied to the large-scale assessment of future energy systems with decentralised electricity grids. The results can be used to propose effective policies for the integration of rooftop photovoltaics in the built environment.

Simplified Method for Analyzing the Availability of Rooftop Photovoltaic Potential

This paper presents a new simplified method for analyzing the availability of photovoltaic potential on roofs. Photovoltaic systems on roofs are widespread as they represent a sustainable and safe investment and, therefore, a means of energy self-sufficiency. With the growth of photovoltaic systems, it is also crucial to correctly evaluate their global efficiency. Thus, this paper presents a comparison between known methods for estimating the photovoltaic potential (as physical, geographic and technical contributions) on a roof and proposes a new simplified method, that takes into account the economic potential of a building that already has installed a photovoltaic system. The measured values of generated electricity of the photovoltaic system were compared with calculated photovoltaic potential. In general, the annual physical, geographic, technical and economic potentials were 1273.7, 1253.8, 14.2 MWh, and 279.1 Wh, respectively. The analysis of all four potentials is essential for further understanding of the sustainable and safe investment in photovoltaic systems.