Photovoltaic Production Research Articles

The Advancement of Silicon as A Photovoltaic Material: Historical Accomplishments, Present Efficiency and Future Development

In today's society where energy and environmental issues are increasingly gaining attention, research, development, and utilization of renewable and clean energy continuously attract the interest of academia, environmental protection, and business alike. Its application has already entered people's lives and is gradually expanding its market share. As a result, there is a growing demand for more efficiently designed, environmentally friendly and less costly new energy devices. Among various energy resources such as tidal, wind or geothermal, solar energy stands out as the most widely used clean and renewable resource. Given that solar energy is virtually limitless for humans, the function of solar cells in converting solar energy to electricity demonstrates significant social, environmental, and economic benefits. For the development of solar energy and new research, photovoltaic materials are crucial. Non-toxic and with excellent photovoltaic properties, silicon is the second most abundant element in the Earth's crust and the most abundant in water. Since crystalline silicon solar cells produced for the first time in 1954, silicon has held the most important material position in photovoltaic cell manufacturing and maintained market dominance. The importance of silicon solar cell research, its development and current status are briefly outlined in this paper. It reviews the advantages and disadvantages of existing silicon-based photovoltaic materials and potential future developments. As a photovoltaic material, silicon holds significant advantages compared to others, suggesting a broad scope for both research and commercial prospects in the long term.

Slashing the surplus – how prosumers with smart metering respond to regulatory restrictions on self-consumption in Croatia

With the diffusion of prosumerism, where households act both as producers and consumers of energy, policy makers must strike a balance between encouraging microgeneration and regulating this new prosumer segment on the energy market. However, effective policy implementation depends on prosumers’ behavioural reactions. This paper provides evidence on the interplay between digital real-time information and regulation of self-consumption for rooftop photovoltaics (PV) in Croatia. Croatian households that produce more annual electricity than they consume are automatically re-classified as renewable traders, which means additional administrative duties and less favorable tax treatment. This creates perverse incentives to reduce PV generation or increase energy consumption by year-end. We document the behavioural reactions to this policy design, indicating that energy production and consumption are highly elastic regarding regulatory incentives, but only if these incentives are made transparent and accessible with timely information. We collected two survey waves (n = 54 and n = 80) and smart meter data (n = 39), which illustrate the behavioural reaction before and after year-end. According to the survey wave before year-end, almost half of the participants considered curtailing their PV output. According to the smart meter data, a sizable share did indeed take action by shutting down PV production or by powering additional devices to reduce the surplus near year-end. In a second survey wave in the new year, prosumers provide ex-post insights on the specific measures taken to reduce surplus. We discuss research insights regarding the transparency and control offered by metering feedback, and how this can influence household behaviour within regulatory frameworks.

Dynamic assessment of photovoltaic waste streams in the EU-27 countries under the circular economy principles of ‘Reduce, Reuse and Recycle’

As the European Union (EU) has increased its targets for renewable energy development to 42.5 % by 2030, a much larger amount of photovoltaic (PV) waste is expected, that needs to be managed efficiently under circular economy (CE) strategies. This work provides quantitative estimations of PV waste flows under the CE paradigm of "Reduce, Reuse, Recycle". ‘Reduction’ is regarded by the changing composition of modules. The ‘Reuse’ stream only includes panels requiring ‘very light’ repair. Recycling is estimated under 2 scenarios, with and without reuse flow. A dynamic material flow analysis under three lifetime scenarios and a dynamic share of technologies is considered. By 2050, 21-35 MT will accumulate. If all waste is diverted to recycling, the gross value of recovered materials will reach €32-47 billion by 2050. As the EU intends to boost PV manufacturing by 2030, the materials amassed can cover a significant part of the required resources.

A Computationally Efficient Rule-Based Scheduling Algorithm for Battery Energy Storage Systems

This paper presents a rule-based control strategy for the Battery Management System (BMS) of a prosumer connected to a low-voltage distribution network. The main objective of this work is to propose a computationally efficient algorithm capable of managing energy flows between the distribution network and a prosumer equipped with a photovoltaic (PV) energy production system. The goal of the BMS is to maximize the prosumer’s economic revenue by optimizing the use, storage, sale, and purchase of PV energy based on electricity market information and daily production/consumption curves. To achieve this goal, the method proposed in this paper consists of developing a rule-based algorithm that manages the prosumer’s Battery Energy Storage System (BESS). The rule-based approach in this type of problem allows for the reduction of computational costs, which is of fundamental importance in contexts where many users will be coordinated simultaneously. This means that the BMS presented in this work could play a vital role in emerging Renewable Energy Communities (RECs). From a general point of view, the method requires an algorithm to process the load and generation profiles of the prosumer for the following three days, together with the hourly price curve. The output is a battery scheduling plan for the timeframe, which is updated every hour. In this paper, the algorithm is validated in terms of economic performance achieved and computational times on two experimental datasets with different scenarios characterized by real productions and loads of prosumers for over a year. The annual economic results are presented in this work, and the proposed rule-based approach is compared with a linear programming optimization algorithm. The comparison highlights similar performance in terms of economic revenue, but the rule-based approach guarantees 30 times lower processing time.

Photovoltaic power forecasting: A Transformer based framework

The accurate prediction of photovoltaic (PV) energy production is a crucial task to optimise the integration of solar energy into the power grid and maximise the benefit of renewable source trading in the energy market. This paper systematically and quantitatively analyses the literature by comparing different machine learning techniques and the impact of different meteorological forecast providers. The methodology consists of an irradiance model coupled with a meteorological provider; this combination removes the constraint of a local irradiance measurement. The result is a Transformer Neural Network architecture, trained and tested using OpenMeteo data, whose performance is superior to other combinations, providing a MAE of 1.22 kW (0.95%), and a MAPE of 2.21%. The implications of our study suggest that adopting a comprehensive approach, integrating local weather data, modelled irradiance, and PV plant configuration data, can significantly improve the accuracy of PV power forecasting, thus contributing to more effective technological and economic integration.

Unit commitment in solar‐based integrated energy distribution systems with electrical, thermal and natural gas flexibilities: Application of information gap decision theory

AbstractThe depleting oil reserves, air pollution and increasing energy demand, have overturned the focus of the scientific community to renewable energy sources. Among which the photovoltaic (PV) systems occupy more than half of the market share and are generally installed at the distribution level. The volatile and uncertain nature of these PV productions necessitates flexible resources in energy systems. To this end, the district heating systems have an outstanding flexibility on account of their high thermal inertia. This study investigates the optimal unit commitment scheduling for gas‐fired and non‐gas‐fired distributed generation units (NGU) in an integrated energy distribution system (IEDS) within the physical constraints of the electrical, natural gas and thermal energy distribution networks. Moreover, a planning‐based optimization framework is proposed to investigate the investment of battery storage systems in the electric distribution network under the high penetration of PV systems with the aim of enhancing flexibility and reducing the operating costs of the IEDS. In this framework, the information gap decision theory is deployed under risk‐averse and risk‐seeker strategies to deal with uncertain PV energy production. Additionally, the environmental emissions are considered in a multi‐objective approach. The IEDS is embodied through IEEE 33‐bus EDS, 20‐node natural gas network and an 8‐node district heating systems. Eventually, The proposed approach makes a noteworthy contribution to the advancement of solar energy systems in IEDS.

A deep dive into the factors affecting household solar photovoltaic adoption: An extended perspective of the value-based adoption model

Solar photovoltaic (PV) products have emerged as a promising solution to address environmental and green energy issues in light of increasing concerns about carbon emissions, environmental awareness, and renewable energy. This research uses the value-based adoption model (VAM) to examine how perceived benefits and sacrifices impact the adoption intention of PV technology. It additionally explores the role of familiarity with the service as a moderator and its impact on perceived harm risk, perceived value, and adoption intention. The results reveal that economic and environmental benefits significantly influence perceived value, while perceived financial and harm risks also play substantial roles in shaping perceived value. Moreover, the study shows a positive relationship between perceived value and the intention to adopt solar PV and also suggests that familiarity with solar services moderates the relationship between perceived harm risk and perceived value. These insights are valuable for policymakers and energy companies seeking to understand consumers’ perspectives better and increase their intentions to adopt PV technology.

Energy Flexibility in Aluminium Smelting: A Long-Term Feasibility Study Based on the Prospects of Electricity Load and Photovoltaic Production

This paper investigates the economic feasibility of utilising energy flexibility in aluminium production as a viable solution to leverage electricity surpluses arising from the increasing number of photovoltaic (PV) system installations. Future trends suggest that the generation capacity of PV systems will soon surpass consumption, leading to significant electricity surpluses, particularly during the summer. This surplus electricity, which is anticipated to be available at low prices, offers a unique opportunity to evaluate different investment and utilisation scenarios for aluminium production while simultaneously decreasing its environmental impact. The results demonstrate that, despite their high initial investment cost, large-scale PV power plants can potentially deliver maximum economic gains over a ten-year period. Conversely, the direct utilisation of surpluses without substantial investment can yield savings of up to EUR 17 million within the same time frame for Slovenia’s case with an aluminium smelter, which has a maximum power usage of 60 MW. The findings of this study have significant implications in terms of shaping future energy strategies and policies, emphasizing the value of integrating renewable energy sources and industrial processes for enhanced economic and environmental outcomes.

Dual-objective optimization of solar driven alkaline electrolyzer system for on-site hydrogen production and storage: Current and future scenarios

Achieving cost competitiveness of renewable hydrogen could accelerate the transition to the deeply decarbonized energy system. In this article, we develop and apply a dual-objective optimization model to explore the photovoltaic (PV) hydrogen production pathway and costs development from the present through 2050. The model is applied for optimal capacity allocation of the megawatt-scale off-grid PV-Hydrogen system to achieve maximum production at the minimal levelized cost of hydrogen (LCOH). Methodology includes a smoothing control strategy. Simulation is performed utilizing measured meteorological data for one year with hourly resolution and considering electrolyzer's load flexibility constraint. It has been found that the smoothing control strategy is indispensable for maximizing PV energy utilization, enhancing the electrolyzer's capacity factor and reducing the power curtailments. The analysis shows that the off-grid solar hydrogen in Algeria lacks economic competitiveness currently. Components CAPEX reduction turns out to be the fundamental condition towards the future LCOH decrease. LCOH could decline from 4.2 $/kg in 2025 to 2.24 $/kg in 2050 under central assumptions and to roughly 1.4 $/kg under optimistic assumptions. Alkaline electrolysis step cost could reduce by 0.28 $/kg every decade. Hydrogen storage autonomy could rise the LCOH by 7.1 c$ per day of autonomy in 2050.

Developing an energy storage systems selection methodology for peak shaving purposes in photovoltaic micro-installations

This study investigates the selection methodology for peak shaving purposes in photovoltaic micro-installations. Due to the increasing share of photovoltaic micro-installations in the national electricity production and the consequent changes in the country's energy policy, the need for energy storage is becoming increasingly apparent. Therefore, based on data collected throughout the study of a photovoltaic installation conducted in years 2020-2024 and data on market electricity prices, the correlation between production, market electricity prices, and energy value was analyzed. The analysis established interconnections between these values and identified factors influencing their changes. It was observed that the market price of electricity is relatively low during hours of increased photovoltaic energy production. By comparing the correlations between energy production and electricity prices over different years, an increasing impact of the increasing share of photovoltaic energy production in national electricity production on the market price of electricity was noted, highlighting the importance of energy storage. For the described household, an energy storage system was selected with a view to meeting the energy demand during hours of higher market electricity prices. The selected model was Lithium-Iron-Phosphate battery with a capacity of 5 kWh. Furthermore, the cost effectiveness of household-scale energy storage systems was examined. Implementing the proposed solutions could result in increased auto-consumption and presumably improve cost-effectiveness of photovoltaic installations in the future.

Effect of Solar Irradiation Inter-Annual Variability on PV and CSP Power Plants Production Capacity: Portugal Case-Study

The sizing of solar energy power plants is usually made using typical meteorological years, which disregards the inter-annual variability of the solar resource. Nevertheless, such variability is crucial for the bankability of these projects because it impacts on the production goals set at the time of the supply agreement. For that reason, this study aims to fill the gap in the existing literature and analyse the impact that solar resource variability has on solar power plant production as applied to the case of Portugal (southern Europe). To that end, 17 years (2003–2019) of meteorological data from a network of 90 ground stations hosted by the Portuguese Meteorological Service is examined. Annual capacity factor regarding photovoltaic (PV) and concentrating solar power (CSP) plants is computed using the System Advisor Model, used here for solar power performance simulations. In terms of results, while a long-term trend for increase in annual irradiation is found for Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI), 0.4148 and 3.2711 kWh/m2/year, respectively, consistent with a solar brightening period, no corresponding trend is found for PV or CSP production. The latter is attributed to the long-term upward trend of 0.0231 °C/year in annual average ambient temperature, which contributes to PV and CSP efficiency reduction. Spatial analysis of inter-annual relative variability for GHI and DNI shows a reduction in variability from the north to the south of the country, as well as for the respective power plant productions. Particularly, for PV, inter-annual variability ranges between 2.45% and 12.07% in Faro and Santarém, respectively, while higher values are generally found for CSP, 3.71% in Faro and 16.04% in São Pedro de Moel. These results are a contribution to future instalments of PV and CSP systems in southern Portugal, a region with very favourable conditions for solar energy harvesting, due to the combination of high production capacity and low inter-annual variability.

Optimal operation control strategy for off-grid photovoltaic hydrogen production system considering hydrogen production efficiency

Off-grid photovoltaic hydrogen production is an effective solution for improving photovoltaic (PV) utilization and obtaining green hydrogen. The main challenge faced by off-grid photovoltaic hydrogen production systems (OGPHPS) is how to deal with the randomness, intermittency, and volatility of PV generation. Therefore, this paper proposes an optimal operation control strategy (OOCS). First, a model of OGPHPS is established, comprising photovoltaics, batteries, and an electrolytic array (ELA). Second, a two-step optimization strategy (TSOS) that integrates both long-term and short-term time scales is proposed. In the first step of TSOS, the day-ahead optimization of the operation state of ELA is conducted according to day-ahead power prediction. A constraint was proposed to balance the operation time of the electrolyzer. In the second step of TSOS, according to the ultra-short time power prediction and the electrolyzer efficiency curve, the rolling optimization of the operating power distribution of ELA is carried out. Through TSOS, PV generation consumption is maximized by ELA, the continuous operational stability of OGPHPS is enhanced, and the lifespan of electrolyzer is extended. Third, to alleviate the influence of instantaneous power fluctuation on the system, an energy management strategy (EMS) for OGPHPS is proposed. Finally, a simulation model of OGPHPS was established, and the simulation results showed that the direct current bus voltage fluctuation was within 3%. Compared to the chain allocation strategy, the OOCS achieves higher hydrogen production under the same solar irradiance conditions. Particularly in low irradiance, OOCS has more sufficient consumption of PV generation and higher hydrogen production efficiency.

High-Precision Defect Detection in Solar Cells Using YOLOv10 Deep Learning Model

This study presents an advanced defect detection approach for solar cells using the YOLOv10 deep learning model. Leveraging a comprehensive dataset of 10,500 solar cell images annotated with 12 distinct defect types, our model integrates Compact Inverted Blocks (CIBs) and Partial Self-Attention (PSA) modules to enhance feature extraction and classification accuracy. Training on the Viking cluster with state-of-the-art GPUs, our model achieved remarkable results, including a mean Average Precision (mAP@0.5) of 98.5%. Detailed analysis of the model’s performance revealed exceptional precision and recall rates for most defect classes, notably achieving 100% accuracy in detecting black core, corner, fragment, scratch, and short circuit defects. Even for challenging defect types such as a thick line and star crack, the model maintained high performance, with accuracies of 94% and 96%, respectively. The Recall–Confidence and Precision–Recall curves further demonstrate the model’s robustness and reliability across varying confidence thresholds. This research not only advances the state of automated defect detection in photovoltaic manufacturing but also underscores the potential of YOLOv10 for real-time applications. Our findings suggest significant implications for improving the quality control process in solar cell production. Although the model demonstrates high accuracy across most defect types, certain subtle defects, such as thick lines and star cracks, remain challenging, indicating potential areas for further optimization in future work.

Optimization of PV benefits for electric vehicles charging station using a deterministic method and fuzzy inference system method

Utilizing renewable energy, specifically Photovoltaic (PV), for Electric Vehicle (EV) charging presents diverse technical and economic opportunities, reflecting a recent trend in innovation and research to address global transportation challenges while highlighting the value of renewable sources. In this context, this paper aims to devise an optimal power management strategy for an EV charging station powered by a PV-based microgrid (MG). The main purpose is to improve the benefits of PV production in recharging EVs to increase self-consumption, decrease dependency on the power grid, and reduce energy costs. Production data from a real-scale MG at the Catholic University in France were used as a case study. This MG comprises a heterogeneous fleet of EVs, PV sources, stationary storage, EV charging stations, and a connection to the power grid. The optimization of PV benefits relies on leveraging the knowledge of EV characteristics to modulate their charge profile. In this context, two methods are used and compared for the modulation of the EVs' charging profile: a deterministic method and a Fuzzy Inference System (FIS)-based method. For each method, different scenarios emerge based on the knowledge or lack thereof of the EVs' characteristics. This methodology permits the optimal distribution of energy among multiple EVs and regulates the necessary power to achieve the desired charge level upon departure. It encourages charging through PV production, resulting in reduced energy costs. The comparison of total costs reveals notable differences between the deterministic and FIS approaches for both slow and fast charging modes. In the case of slow charging, the deterministic approach achieves savings of −863.4c€/day, whereas the FIS method delivers greater savings of −898.7c€/day. For fast charging, the deterministic approach yields savings of −524.6c€/day, while the FIS method results in even higher savings of −540.7c€/day. These results highlight that the FIS approach enables better cost management, primarily due to more efficient utilization of PV energy. Additionally, simulation results from MATLAB/Simulink confirm the effectiveness of the FIS-based method, which improves PV energy utilization to 98.20 %, compared to 81.60 % with the deterministic approach. This highlights the superior efficiency of the proposed FIS.

A novel approach for identification of PV cell model parameters with Grey Wolf Election-Based Optimization algorithm

In the past few years, photovoltaic production has significantly increased worldwide and has become a necessary element for achieving global agreements to minimize carbon dioxide emissions. Therefore, a precise and reliable simulation of PV panels is of increasing interest to researchers. However, this task is predicated on determining the PV model parameters; thus, it continues to be a highly challenging task. To overcome this challenge, we propose a swarm-based human optimization algorithm, i.e. the Grey Wolf Election-Based Optimization algorithm. This new approach combines inventive human behavior in the election process with the powerful search ability of grey wolves. The root-mean-square deviations predicted by the Grey Wolf Election-Based Optimization algorithm for a RTC France cell are 7.7386E-04, 7.5644E-04, and 7.4965E-04 for single, double, and triple-diode models, respectively. A total of thirteen of the most cited and newest meta-heuristic algorithms were employed as competitors to adequately assess the performance of the proposed algorithm. The experimental results demonstrate that the proposed algorithm outperformed the compared algorithms and provided more efficient solutions.

Modification of New Semiconductor Materials by Doping

In the era of information, people’s lives are inseparable from electronic equipments. Semiconductors are key materials for manufacturing electronic equipment, which can be applied in multiple fields such as integrated circuits, solar cells, and so on. However, pure semiconductors have a single property, which are difficult to meet the design and usage requirements of different electronic devices. To solve the problem, introducing heteroatoms for modification to make them have more suitable performance will be a good way. Good semiconductor performance generally relies on precise control of the doping process. Different types of semiconductor materials typically require specific doping techniques to improve its performance. Adding small amounts of heteroatoms to pure semiconductors can significantly improve their conductivity by forming p/n junctions or achieving specific functions. Doping technology has been widely used in the semiconductor manufacturing industry. It is of great significance for the manufacturing of chips, photovoltaics and other devices. At present, there are still many shortcomings in doping technology. It may come across the semiconductor structure failure caused by doping, or difficulty in meeting performance requirements for use. Thereofore, continuous research and improvement are still needed. This paper reviewed the applications and the doping techniques of various semiconductor materials including two-dimensional semiconductors and organic semiconductors. In addition, it also provides prospects for their related applications and development.

Effect of Sulfurization Temperature on Properties of Cu2ZnSnS4 Thin Films and Diffusion of Ti Substrate Elements

The addition of flexible Cu2ZnSnS4 (CZTS) thin film solar cells to titanium (Ti) substrates is an attractive way to achieve the low-cost manufacturing of photovoltaics. Prior research has indicated that the appropriate diffusion of Ti elements can enhance the crystalline growth of CZTS films. However, the excessive diffusion of Ti has been shown to adversely affect the photovoltaic performance of CZTS photovoltaic devices. Therefore, it is essential to regulate the diffusion of Ti elements within CZTS thin films to optimize their photovoltaic properties. The tendency for Ti substrate elements to diffuse into CZTS films is also influenced by the activation energy associated with these Ti elements. The sulfurization temperature is posited to be a critical factor in modulating the diffusion and activation energy of Ti elements within CZTS thin films. Consequently, this research investigates the alteration of the sulfurization temperature of CZTS thin films in order to enhance the properties of these thin films and to examine the diffusion behavior of titanium elements. The results reveal that as the sulfurization temperature increases, the diffusion of Ti elements within the CZTS thin films initially increases, then decreases, and subsequently increases again. This pattern suggests that the diffusion of Ti elements is affected not only by the activation energy of the Ti elements but also by the defect hopping distance within the CZTS thin films. Notably, at a sulfurization temperature of 550 °C, the grains at the base of the CZTS thin film demonstrate an increased density, which is associated with a reduced defect hopping distance, thereby hindering the diffusion of Ti elements within the CZTS thin films. Furthermore, at this specific sulfurization temperature, the slope of the current–voltage (I–V) curve for the CZTS/Ti structure reaches its maximum, indicating optimal ohmic contact characteristics.

Climate Change Impact on Photovoltaic Production From the Perspective of Climate Models: a Systematic Review

Objective: This study investigates how the researchers approached the theme the climate change impacts on photovoltaic production (PVP) from the perspective of simulations using climate models. Methodology: A comprehensive literature review was conducted to establish correlations between future PV production and climate change susceptibility on regional and global scales. Keywords related to the theme were used in the Web of Science database. Inclusion criteria selected only complete, free, English scientific articles related to the research theme, published between 1965 and 2023. Results and analysis: As a result, it generated a sample consisted of 58 articles on the impact of climate change on photovoltaic production, with 14 included in the final quantitative synthesis. A wide range of results was presented, stemming from both climate change models and models assessing the influence of weather-related factors on PV production. Patterns in the impacts of climate change on photovoltaic production were observed, and the main climate models used to estimate PVP were recognized, grouped, and discussed. Key databases, commonly used simulation periods, and equations linking meteorological variables to PVP were identified and examined. Conclusion: Numerous publications have emerged on the impacts of climate change on PVP, focusing on empirical equations that relate meteorological variables, cell temperature, and cell efficiency. Most studies adopted a forecast period until the end of the 21st century. The majority of the studies focused on Europe and Asia, with little research on impacts of climate changes on PVP in South and Central America.

A Versatile Platform for PV System Integration into Microgrids

Advancing decarbonization critically depends on the integration of PV systems into microgrids. However, this integration faces challenges, including the variability of photovoltaic solar energy production, the demands of energy management, and the complexities of grid synchronization and communication. To address these challenges, a PV emulator platform is an essential tool. This paper presents a novel four-layer PV emulator platform that seamlessly integrates power systems, control systems, measurement instrumentation, and communication processes. The proposed platform enables the emulation of I-V curves and the dynamic adjustment of operating points—including both the maximum power point (MPP) and power reserve point (PRP)—as well as temperature and irradiance while providing sufficient power capacity for microgrid integration. To validate its effectiveness, the platform was assessed for its capability to adjust operating points, such as MPPs or PRPs, under varying irradiance and temperature conditions. The results show that the platform effectively adjusts operating points with a deviation of less than 5% from theoretical values and successfully tracks a sequence of operating points. This performance underscores the platform’s potential in integrating and managing PV systems within microgrid environments, thereby advancing the path to decarbonization.

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.