Photovoltaic Potential Research Articles

A novel method for assessment rooftop PV potential based on remote sensing images

The assessment of rooftop photovoltaic (PV) potential is highly significant for energy policy formulation. With the rapid development of computer vision (CV) and remote sensing imagery, utilizing CV to extract rooftop information is an ideal approach. However, deep learning requires a large amount of accurately annotated data, and annotating remote sensing images is a labor-intensive task. This limitation hinders the application of deep learning in rooftop PV potential assessment. To address this issue, this paper proposes a semi-supervised learning (SSL)-based segmentation model to extract rooftop information from remote sensing images. Subsequently, a rooftop classification method is proposed to categorize rooftops into several classes and estimate their rooftop PV available area ratios. Finally, the total available rooftop PV area in urban areas is evaluated, and the potential rooftop PV installed capacity and power generation are calculated. This method is applied in the Longhu District of Shantou City, Guangdong Province. The evaluation results show that the total rooftop area in Longhu District is 17.2 km2, with a rooftop PV available area of 12.7 km2. It is estimated that the rooftop PV installed capacity in Longhu District is 1849.4 MW, with an annual power generation of 2219.3 GWh.

GIS-based solar radiation modelling for photovoltaic potential in cities: A sensitivity analysis for the evaluation of output variability range

Currently, there is increasing attention to matching the buildings’ energy demand with energy produced by renewable sources. This approach serves to both reduce the environmental footprint, the target of several European policies and initiatives and ensure access to energy for everyone worldwide, aligning with one of the aims of the United Nations Agenda 2030. Urban environments are suitable for the installation of photovoltaic panels, which can be easily integrated within roofs to minimise land use and installation costs. Multiple studies focused on solar radiation as the most critical element affecting the estimation of the photovoltaic potential, in particular by resorting to Geographic Information Systems (GIS). Among different tools, which are useful for integrating layers with different spatial resolutions, the r.sun function embedded in GRASS GIS has been selected as the most suitable to capture complex variations resulting from weather and geographical parameters. However, a gap in the literature has emerged as no study is available to define the precision degree of the tool when inputting parameters derived from various data sources based on different model assumptions. The scope of this research is to collect open-access weather inputs and quantify the solar radiation output along with its variability range. Based on 28 test plans, resulting from the combination of input parameters from alternative data sources, this research quantified the output variability range to be ±20 %, thus requiring additional processing to increase the precision. The paper concludes with a critical assessment of the strengths and weaknesses of the current research, emphasizing the importance of validating the results with measured values in forthcoming research, to move from precision to accuracy evaluation.

Future photovoltaic potential in India: navigating the interplay between air pollution control and climate change mitigation

Abstract India has set ambitious solar energy targets to meet its climate commitments. However, climate change, already evident in the country, poses significant challenges to solar power generation. Therefore, assessing the impact of climate change on future photovoltaic potential in India is essential. This study evaluates the mid-century (2041-2050) solar photovoltaic potential across Indian power grids using CMIP6 models under two scenarios: SSP2-4.5 (moderate climate action with intermediate air pollution) and SSP5-8.5 (weak climate action with strong air pollution control). The results indicate that the nationally averaged photovoltaic potential is projected to decrease by -2.3±0.6% (SSP5-8.5) to -3.3±0.9% (SSP2-4.5) compared to the 1985-2014 baseline, primarily due to reduced radiation and increased temperatures. Additionally, cell temperatures are expected to rise by 1.5±0.13°C (SSP2-4.5) to 2±0.11°C (SSP5-8.5), leading to efficiency losses and additional 18±5 days under SSP2-4.5 (26±3 days under SSP5-8.5) of efficiency de-rating, particularly in solar-rich regions. This translates to a loss of 600±160 GWh under SSP2-4.5 and 840±100 GWh under SSP5-8.5, based on the solar generation status of 2023-24. Overall, SSP5-8.5 projects a smaller reduction in photovoltaic potential, it also predicts greater temperature-induced efficiency losses compared to SSP2-4.5, due to aerosol direct effect and weak climate action. Finally, this assessment highlights the need for combined climate and pollution mitigation efforts to boost India’s photovoltaic potential and secure a sustainable, resilient energy future.

Assessing the adoption level of solar PV installations, on district scale of urban environment

This study addresses a research gap by implementing a novel methodology that combines GIS and MCDM techniques to evaluate photovoltaic (PV) potential at an urban scale, specifically within Riga, Latvia. Utilizing the AHP and MABAC methods, the research integrates technical, social, and economic criteria to rank urban districts based on their suitability for PV installations. 58 different districts were evaluated according to technical and socioeconomic criteria and were ranked through the MABAC MCDM methodology, from most preferable to less, on a PV adoption level. This comprehensive approach not only identifies the most promising areas for rooftop PV deployment but also emphasizes the importance of considering socioeconomic factors to enhance equity in renewable energy adoption. The findings provide valuable insights for urban planners and policymakers, demonstrating a systematic, data-driven method to promote sustainable energy initiatives in urban environments.

Assessing Photo-Voltaic Potential in Urban Environments: A Comparative Study between Aerial Photogrammetry and LiDAR Technologies

Abstract. Urban development and population growth have significantly increased energy demands, predominantly met by non-renewable resources, negatively impacting nature and climate. Consequently, there has been a global shift towards renewable energy sources, with solar energy being widely adopted. Photovoltaic (PV) potential measures the usable electricity from solar energy using PV technology. With conventional methods, estimating PV potential involves converting 3D point cloud data to 2.5D elevation models, which can affect the accuracy of the estimation. A research gap exists in using adequate 3D point cloud datasets for PV potential estimation considering roof surface area and the surface normal vectors. This study compares aerial photogrammetry and aerial LiDAR point clouds for PV potential estimation against the DSM-based approach. Accurate PV potential estimation must consider solar incidence, roof area, azimuth, tilt angles, and PV efficiency. Traditional 2.5D methods often overlook crucial azimuth and tilt data, limiting the accuracy of the PV estimation. Converting 3D data to 2.5D may result in information loss, while 3D analyses offer higher accuracy. To investigate the mentioned gaps, this research aims to evaluate the capabilities of photogrammetry and LiDAR for urban PV potential estimation, highlighting their feasibility and accuracy over 2.5D methods.

Possibility of Substitution of LOD1 Data for LOD2 Data on Photovoltaic Potential Estimation at City Districts Level Using a 3D City Model

Abstract. We developed a system to estimate the photovoltaic (PV) potential of urban buildings using a 3D city model at level of detail 2 (LOD2), considering an arrangement of solar panels. Although the PV potential estimation of buildings at the level of city districts prefers LOD2 data, many urban city districts with buildings capable of installing solar panels on façades do not have LOD2 data owing to the high production cost of LOD2 data. Therefore, we investigated the possibility of utilising LOD1 data instead of LOD2 data in the PV potential estimation of buildings at the level of city districts. We conducted an experiment in the investigation using 3D city model data from 24 city districts, which are most of the large cities with LOD2 data in Japan. The experiment results suggest that LOD1 would not be a substitute for LOD2 data in the PV potential estimation of building roofs. The cause of the inadaptability is that most roofs in LOD2 buildings have more small polygons than LOD1 building roofs. Roofs of a LOD2 building with many small polygons have fewer solar panels than roofs of a LOD1 building with a large polygon. On the contrary, the experiment results indicate that LOD1 may be a substitute directly for LOD2 in the PV potential estimation of building façades that are expected to be used as a mounting platform for solar panels in urban buildings from now on. The average of the relative differences in the calculated PV potential of building façades between using LOD1 data and using LOD2 data is approximately 10% in the experiment.

Energy, environmental, economic, and social assessment of photovoltaic potential on expressway slopes: A case in Fujian Province, China

Expressway slopes within the boundary of expressway construction land have the potential to be transformed from conventional land use to valuable utilization through the installation of photovoltaic (PV) systems. Considering the continuous increase in expressway mileage, the PV potential could be enormous; therefore, a method to quantify such potential is urgently needed. This study proposes a potential assessment method for expressway slope photovoltaics. First, the suitable locations of expressway slopes for installing PV systems are identified on the basis of publicly available digital maps, combined with the principles of PV site selection and geographic information systems. Then, the PVsyst model is used to simulate the power generation efficiency and power output of the PV systems and to evaluate their PV potential, contribution, and economic benefits. The implementation of the proposed method in Fujian Province, China, indicates that the installed capacity of PV systems could reach 16.3 GW, with an annual power output of 17,940 GWh, contributing to the peak shaving of power grids and regional energy supply. The reductions in CO2, SOx, and NOx emissions are 16.65 Mt, 8970 t, and 3947 t per year, respectively. Considering carbon trading, the payback period is shortened to 11.7 years, with the return on investment of 65.38 % and the net present value of 52.6 billion RMB over a 25-year operational period. Additionally, this project could create 19.88 % of jobs in the energy sector in Fujian Province, providing 27,861 jobs during the construction phase and 18,248 jobs during the operation and maintenance phase. Thus, this project is economically viable and generates positive energy, environmental, and social benefits, contributing to the promotion of local energy transition and sustainable development.

Visible-Light Photodetection by Zero-Dimensional Hybrid Indium-Halide with Minimal Structural Distortion and Reduced Band Gap.

Perovskite-inspired zero-dimensional (0D) hybrid halides exhibit impressive light emission properties; however, their potential in photovoltaics is hindered by the absence of interconnection between the inorganic polyhedra, leading to acute radiative recombination and insufficient charge separation. We demonstrate that incorporating closely-spaced dissimilar polyhedral units with minimal structural distortion leads to a remarkable enhancement in visible-light photodetection capability. We designed 0D C24H72N8In2Br14 (HIB) with a tetragonal crystal system, replacing the Cs+ of Cs2InBr5.H2O (CIB) with 1,6-hexanediammonium (HDA) cation. HIB comprises [InBr6]3- octahedra, and [InBr4]- tetrahedra units spaced 3.9 Å apart by the HDA linker. The [InBr4]- unit is additionally linked to HDA via intercalated bromine through hydrogen and halogen bonding interactions, respectively. This structural arrangement lowers the dielectric confinement, thereby enhancing carrier density and mobility, and increasing the diffusion coefficient compared to CIB. With 3.6 % bromine vacancy within the [InBr4]- block, mid-gap states are created, reducing the direct band gap to 2.19 eV. HIB demonstrates an unprecedently high responsivity of 9975.9±201.6 mA W-1 under 3 V potential bias at 485 nm wavelength, among low-dimensional hybrid halides. In the absence of potential bias, the self-powered photodetection parameters are 81.2±3.0 mA W-1 and (6.98±0.21)×109 Jones.

Visible‐Light Photodetection by Zero‐Dimensional Hybrid Indium‐Halide with Minimal Structural Distortion and Reduced Band Gap

AbstractPerovskite‐inspired zero‐dimensional (0D) hybrid halides exhibit impressive light emission properties; however, their potential in photovoltaics is hindered by the absence of interconnection between the inorganic polyhedra, leading to acute radiative recombination and insufficient charge separation. We demonstrate that incorporating closely‐spaced dissimilar polyhedral units with minimal structural distortion leads to a remarkable enhancement in visible‐light photodetection capability. We designed 0D C24H72N8In2Br14 (HIB) with a tetragonal crystal system, replacing the Cs+ of Cs2InBr5.H2O (CIB) with 1,6‐hexanediammonium (HDA) cation. HIB comprises [InBr6]3− octahedra, and [InBr4]− tetrahedra units spaced 3.9 Å apart by the HDA linker. The [InBr4]− unit is additionally linked to HDA via intercalated bromine through hydrogen and halogen bonding interactions, respectively. This structural arrangement lowers the dielectric confinement, thereby enhancing carrier density and mobility, and increasing the diffusion coefficient compared to CIB. With 3.6 % bromine vacancy within the [InBr4]− block, mid‐gap states are created, reducing the direct band gap to 2.19 eV. HIB demonstrates an unprecedently high responsivity of 9975.9±201.6 mA W−1 under 3 V potential bias at 485 nm wavelength, among low‐dimensional hybrid halides. In the absence of potential bias, the self‐powered photodetection parameters are 81.2±3.0 mA W−1 and (6.98±0.21)×109 Jones.

The prospect of floating photovoltaic in clean energy provision and net-zero-emissions

AbstractThis study emphasizes the critical role of renewable energy in addressing climate change challenges, particularly in reducing greenhouse gas emissions. It highlights the central importance of solar photovoltaic systems. While recognizing South Africa’s progress in renewable energy deployment, the study notes that substantial efforts are still needed to meet the country’s renewable energy targets. The study’s primary aim is to enhance the understanding and deployment of solar photovoltaic systems by critically examining the photovoltaic potential and performance of different sites. To achieve this, two scenarios—land photovoltaic and floating photovoltaic systems—were considered, focusing on hypothetical 10 MWp-installed capacity systems. The results indicate that the 10-MWp land photovoltaic system has a global tilted irradiance of 2184.7 kWh/m2, an annual total output of 18 GWh, and an average yearly performance ratio of 81%. The economic parameters for the land photovoltaic system include a capital expenditure of $9.0 million, an operational expenditure of $157,500 annually, and a levelised cost of energy of $0.04089/kWh. In contrast, the 10-MWp floating photovoltaic system reports a Global Tilted Irradiance of 1797 kWh/m2, an annual total output of 16 GWh, and a performance ratio of 76%. The economic parameters for the floating photovoltaic system include a capital expenditure of $10.6 million, an operational expenditure of $176,250 annually, and a levelised cost of energy of $0.04936/kWh. Both scenarios’ econo-technical parameters fall within acceptable ranges. However, the land photovoltaic system shows better performance, indicating some efficiency loss in the floating photovoltaic system possibly due to harsher offshore conditions. Based on the data from the reports, the study concludes that the econo-technical feasibility of floating photovoltaic in the Western Cape Province of South Africa is high. Graphical abstract

Design and Simulation for Minimizing Non-Radiative Recombination Losses in CsGeI2Br Perovskite Solar Cells.

CsGeI2Br-based perovskites, with their favorable band gap and high absorption coefficient, are promising candidates for the development of efficient lead-free perovskite solar cells (PSCs). However, bulk and interfacial carrier non-radiative recombination losses hinder the further improvement of power conversion efficiency and stability in PSCs. To overcome this challenge, the photovoltaic potential of the device is unlocked by optimizing the optical and electronic parameters through rigorous numerical simulation, which include tuning perovskite thickness, bulk defect density, and series and shunt resistance. Additionally, to make the simulation data as realistic as possible, recombination processes, such as Auger recombination, must be considered. In this simulation, when the Auger capture coefficient is increased to 10-29 cm6 s-1, the efficiency drops from 31.62% (without taking Auger recombination into account) to 29.10%. Since Auger recombination is unavoidable in experiments, carrier losses due to Auger recombination should be included in the analysis of the efficiency limit to avoid significantly overestimating the simulated device performance. Therefore, this paper provides valuable insights for designing realistic and efficient lead-free PSCs.

New absorbent layer through employing semi-transparent Cs0.05FA0.95PbI2.55Br0.45 in perovskite solar cells for solar windows: Balancing efficiency and transparency

Perovskite materials are gaining attention due to their superior photovoltaic performance and potential for transparent solar cell applications, such as solar windows and building-integrated systems. This study focuses on designing thinner Cs0.05FA0.95PbI2.55Br0.45 perovskite layers to balance transparency and power conversion efficiency (PCE). The solar cell performance improves with increased absorber layer thickness, resulting in a current density (JSC) rise from 9.98 to 21.60 mA/cm2. The optimized device achieves an open-circuit voltage (VOC) of 1.24 V, a fill factor (FF) of 80.70 %, and PCE of 20 %. For absorber thicknesses of 100–500 nm, the PCE ranges from 10.01 % to 19.45 %, while the average visible transmittance (AVT) decreases from 51.05 % to 2.63 %. These results provide insights into designing semi-transparent perovskite solar cells with a balance between efficiency and transparency.

Advanced photovoltaic technology can reduce land requirements and climate impact on energy generation

Future changes in solar radiation and rising temperatures will likely reduce global solar photovoltaic potential, but advancing photovoltaic technologies could counteract these effects. We investigate the potential of photovoltaic to satisfy energy demands given climate change and technological development. We find that conventional photovoltaic will require 0.5 to 1.2% of global land area to meet projected energy demands by 2085 without accounting for climate change effects. When considering climate impacts, this requirement increases to 0.7–1.5% of the global land area. However, utilising advanced photovoltaic technologies can reduce this area to 0.3–1.2%, effectively mitigating climate impacts. Regional climate change impacts vary substantially, resulting in photovoltaic potential decreases of up to 3% in Latin America and the Caribbean, and by up to 8% in South Asia. Our results suggest that technology-driven increases in future global photovoltaic energy production can more than compensate for the climate related reductions.

Mapping development potential and priority zones for utility-scale photovoltaic on the Qinghai-Tibet Plateau

Sustainable renewable energy is a critical goal of the 2030 Sustainable Development Agenda. The scientific and rational development of solar power in the Qinghai-Tibet Plateau (QTP) is vital for China's carbon peak and carbon neutrality goals. However, more accurate, high spatial resolution assessments are needed to evaluate the utility-scale photovoltaic (PV) development potential on the QTP. To address this, we integrated geographical and technical potentials into a unified framework for assessing PV development potential. Using the spatial multi-criteria decision analysis technique, supported by the Bayesian Best-Worst method, we generated a 30-m suitability map for assessing geographical and technical potentials, along with priority zones. We found that approximately 35.22 % of the study area is suitable for utility-scale PV development, mainly concentrated in the Qaidam Basin in Qinghai and the northern Tibet Plateau in Xizang. The estimated PV technical potential for highly suitable and suitable classes is approximately 42.34 million GWh/a, which is equivalent to reduction of approximately 31.43 billion t/a of carbon emissions. We also formulated four priority development strategies using a spatially explicit model to meet the urgent 2030 target for utility-scale PV installations. Moreover, validation of existing PV projects and sensitivity analysis were conducted, confirming the accuracy and robustness of the results. This study provides practical guidance for informed decision-making by the government, investors, and stakeholders.

Enhancing microgrid energy management through solar power uncertainty mitigation using supervised machine learning

This study addresses the inherent challenges associated with the limited flexibility of power systems, specifically emphasizing uncertainties in solar power due to dynamic regional and seasonal fluctuations in photovoltaic (PV) potential. The research introduces a novel supervised machine learning model that focuses on regression methods specifically tailored for advanced microgrid energy management within a 100% PV microgrid, i.e. a microgrid system that is powered entirely by solar energy, with no reliance on other energy sources such as fossil fuels or grid electricity. In this context, “PV” specifically denotes photovoltaic solar panels that convert sunlight into electricity. A distinctive feature of the model is its exclusive reliance on current solar radiation as an input parameter to minimize prediction errors, justified by the unique advantages of supervised learning. The performance of four well-established supervised machine learning models—Neural Networks (NN), Gaussian Process Regression (GPR), Support Vector Machines (SVM), and Linear Regression (LR)—known for effectively addressing short-term uncertainty in solar radiation, is thoroughly evaluated. Results underscore the superiority of the NN approach in accurately predicting solar irradiance across diverse geographical sites, including Cairo, Egypt; Riyadh, Saudi Arabia; Yuseong-gu, Daejeon, South Korea; and Berlin, Germany. The comprehensive analysis covers both Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI), demonstrating the model’s efficacy in various solar environments. Additionally, the study emphasizes the practical implementation of the model within an Energy Management System (EMS) using Hybrid Optimization of Multiple Electric Renewables (HOMER) software, showcasing high accuracy in microgrid energy management. This validation attests to the economic efficiency and reliability of the proposed model. The calculated range of error, as the median error for cost analysis, varies from 2 to 6%, affirming the high accuracy of the proposed model.

Enhancement of household photovoltaic consumption potential in village microgrid considering electric vehicles scheduling and energy storage system configuration

The large-scale development of household photovoltaic in rural areas increases grid operation challenges and leads to higher costs for its access to the grid. To promote self-generation and self-consumption of photovoltaic, this paper investigates methods to enhance the photovoltaic consumption potential in village microgrid. An electric vehicles orderly charging scheduling model and an energy storage configuration model are proposed respectively, solved by CPLEX tool and genetic algorithm, and the photovoltaic consumption under different charging scenarios is compared with disordered or orderly charging and with or without energy storage. The results show that electric vehicles orderly charging scheduling not only reduces the load peak-valley difference, but also increases the photovoltaic consumption, and the configuration of energy storage enhances the photovoltaic consumption potential higher than electric vehicles charging scheduling, but its investment cost is larger, and it needs to be reasonably planned for its capacity. Comprehensive analysis suggests that load management and energy storage configuration co-optimization is the optimal choice to increase photovoltaic consumption. This study verifies the potential of load management and energy storage configuration to enhance household photovoltaic consumption, which can provide an application reference for the sustainable development of household photovoltaic and village microgrid.

Modeling building energy self-sufficiency of using rooftop photovoltaics on an urban scale

The significant contribution of buildings to global energy-related CO2 emissions and climate change has led to projections of a carbon–neutral building stock by 2050. This study evaluates the potential contribution of rooftop photovoltaics to urban energy self-sufficiency by developing an enhanced CityBEM framework, our in-house urban building energy model (UBEM). This methodology enables city-scale, simultaneous simulations of building energy use and rooftop photovoltaic retrofitting with low computational time. CityBEM’s robustness and high spatiotemporal resolution facilitate transient simulations of individual buildings with diverse usage types across large urban areas, addressing common computational constraints and input data limitations in UBEM applications. The rooftop photovoltaic model in CityBEM utilizes a comprehensive approach with physics-based modeling of crystalline PVs, model validation, and a proper design of array networks to manage self-shading effects. More than 57,000 buildings with a combined footprint of 32.37 million square meters are located in the simulation test case covering downtown Montreal (Quebec, Canada) and the surrounding areas. Results indicate that rooftop PV adoption in these buildings could potentially generate 5.9 TWh of electricity annually, reduce energy demand by 27.3%, and achieve an average annual energy saving of 40%. This local generation could also reduce operational CO2 equivalent emissions by over 0.2 megatons. Overall, this study highlights the significant potential of rooftop photovoltaics for a cleaner, more energy self-sufficient urban future.

Towards decarbonizing large-scale industries: A decision support framework for optimizing grid-connected PV-battery energy systems planning – Case study of an OCP mining site, Morocco, North Africa

Incorporating renewable energy into forthcoming grid-connected or decentralized energy systems assumes an escalating significance and can potentially enhance endeavors toward accomplishing Sustainable Development Goal 7 (SDG7). Nevertheless, deploying sustainable renewable energy-intensive systems may present challenges related to their intermittency and cost instability. This paper proposes the development of a decision support model aimed at planning an optimal on-grid PV battery system. The model builds upon the Open Energy Modeling Framework (OEMOF) and defines asset capacities, energy dispatch, operational strategies, and optimal costs for the designated system. Key factors considered include energy demand profile, photovoltaic potential, electricity tariffs, and the availability of the National Grid. Furthermore, the model evaluation incorporates the computation of pertinent performance, feasibility, and viability indicators, notably the Levelized Cost of Energy (LCOE). To validate the framework, the article conducts a case study to determine the optimal sizing and planning of a grid-connected PV battery energy system. The objective is to cater to the electricity needs of an OCP (Office Chérifien des Phosphates) mining site in Morocco. The study considers the characteristics of the national power grid, incorporating time-varying electricity tariffs based on a Demand-Response program taking into account various rates according to the time of use (ToU). The case study includes a sensitivity analysis that examines different factors, namely the proportion of renewable energy, the investment costs of photovoltaic systems and batteries, and the financial parameter known as the weighted average cost of capital (WACC). Through this analysis, the study assesses the impact of these variables on the calculated cost of energy. Experimental results revealed a range of LCOE values from $0.07111/kWh to $0.11847/kWh for high renewable energies integration.

Direction Dependent Ferroelectricity and Conductivity in a Single Crystal 2D Halide Double Perovskite.

Halide ferroelectric materials have garnered a lot of interest because of their distinctive electrical and structural characteristics. In this study, the design and development of a new non-centrosymmetric 2D layered halide double perovskite material, Cl1.14Br2.86PA4AgInBr8 (CPAIn) is reported. This material shows ferroelectric properties above room temperature, with a Curie temperature of 190 °C. This behavior is achieved through the substitution of the halogenated A-site organic linker, 3-chloropropylammonium. CPAIn exhibits anisotropic ferroelectric behavior with higher spontaneous polarization of 6.25 µC cm-2 along the perpendicular direction to the octahedral layers, whereas the value decreases to 0.174 µC cm-2 between sheets. While using bottom contact to study the nature of polarity within a sheet, the P-E loop displays capacitive loop. The nature and value of polarization is highly direction dependent, and to further understand the mechanism of conduction, a combination of temperature-dependent impedance studies and poling dependent conductivity techniques are employed. These directional dependent properties hold immense potential in memory devices, sensors and photovoltaics, piezoelectric devices and energy storage.

Evaluation of photovoltaic installation potential in industrial complexes around metropolitan areas: Regulatory obstacles and geographical considerations

The assessment of photovoltaic (PV) installation potential in industrial complexes is critical for advancing renewable energy objectives, particularly in urbanized settings like Gyeonggi Province, South Korea. This study examines the complex interaction of geographical, regulatory, and environmental factors affecting PV feasibility across 193 industrial complexes. Employing a GIS-based methodology, the research evaluates available rooftop and general land areas totaling 47.3 km2, estimating a PV capacity potential of 3.72 GW. Despite significant rooftop spaces predominantly on concrete structures (10 km2), stringent setback regulations present substantial obstacles, particularly in areas such as Ansan. General land sites covering 16.6 km2 offer promising prospects, notably in Pyeongtaek. Current regulatory challenges, including setback regulations, impede the realization of the targeted 3.8 GW PV capacity, highlighting the potential benefits of policy reforms to facilitate deployment. This study underscores PV's pivotal role in sustainable energy transitions and advocates for strategic reforms to align regulatory frameworks with renewable energy aspirations. By integrating GIS analyses with regulatory insights, the study informs spatial planning initiatives essential for scaling up solar energy contributions in industrial contexts, supporting informed decision-making toward achieving a low-carbon future.