To continue reducing the levelized cost of solar energy, the photovoltaics (PV) industry is developing higher efficiency perovskite-based tandem solar cells. Among the various options, the two-terminal (2T) tandem has traditionally garnered the most interest and is expected to enter the market soon. However, the bifacial 2T perovskite-silicon (PVK-Si) tandem cell, constrained by current-matching requirements, would offer diminished energy gains in large-scale solar farms, especially when subjected to suboptimal albedo conditions. The 3/4T tandems obviate current matching and are expected to outperform 2T-tandem cells. However, the actual location-specific yield potential and relative gain of bifacial 3/4T tandems has not been reported in the literature. In this work, we use a novel end-to-end, multiscale simulation framework to carry out the first planet-scale simulation of single-axis-tracking solar farms employing bifacial PVK-Si 3/4T tandem in various ground albedo conditions. The analysis shows that the 3/4T cells offer up to 5% and 23% mean increase in annual energy yield compared with 2T-tandem and single-junction heterojunction solar cells in Earth's average albedo ( ${R}_A$ = 30%). Importantly, unlike the 2T tandem, the 3/4T tandem maintains its performance advantage across a wide range of albedo conditions, enabling flexible subcell design. The findings should encourage further research efforts aimed at tackling the recognized challenges associated with 3/4T technologies, such as minimizing optical losses and scaling up cell-to-module processes, to fully realize the potential of PVK-Si tandem technology.

The bifacial gain of various optimally-tilted, and tracking bifacial farms based on single-junction PERC and HIT technologies are well established. The solar module technology is, however, evolving rapidly with the commercial development of two, three, and four-terminal mono and bifacial HIT-Perovskite tandem cells underway. Given the complexity of current-matching in two-terminal tandem cells and significant variation of the weather conditions across the world, one wonders if the benefits of fixed-tilt and tracking cells obtained for single-junction solar cells would remain for tandem solar cells. In this paper, we use a detailed illumination and temperature-dependent bifacial solar farm model (supported by a detailed physical model for bifacial HIT-Perovskite tandem cells) to show that (a) row-to-row shading in solar arrays significantly suppresses the effective albedo collection and thereby the two-terminal (2T) tandem cell efficiency and relative gain compared to an optimal bifacial HIT cell, (b) the global energy yield potential of fixed-tilted and solar-tracking topologies would improve by adopting a 2T tandem design at optimal albedo, with maximum gain arising for tracking farms, (c) the 2T tandem cell/modules (subcell bandgaps, thickness) must be optimized for maximum benefit, and (d) even a relatively small deviation from the optimum will negate all benefits. Our results will broaden the scope and understanding of the emerging tandem bifacial technology by demonstrating global trends in energy gain for worldwide deployment and the need for location-specific tailoring of the module design.

Recent developments in monofacial multi-junction Perovskite-Si tandem technology have produced 30% efficient cells under controlled laboratory conditions. A bifacial tandem would further enhance the energy yield potential. However, recent studies have demonstrated that the current matching constraint erases the performance gain of two-terminal (2T) Tandem modules (over single junction HIT cells) due to time-dependent albedo associated with realistic solar farm configurations. Here, in this planet-scale study of the performance potential of tandem solar cells, we show that three or four-terminal modules (3/4T) would achieve the anticipated performance gain (17-23%) despite albedo variation. As such, 3/4T tandem will be the key to realizing/unlocking the next-generation leaps in yield performance and should be the key focus on research/development of next-generation solar modules and solar farms.

The photovoltaics (PV) technology landscape is evolving rapidly. To gauge the relative merit of emerging PV technologies and their scalable deployability, the global performance of these systems must be understood. Historically, most experimental and computational studies have focused on PV performance in specific regional climatic conditions; however, it has been difficult to translate these isolated regional studies to a global scale. Here, we present a physics-guided machine learning (PG-ML) scheme to demonstrate that: (a) analogous to Köppen–Geiger classification, the world can be divided into just a handful of PV-specific climate zones, and (b) the monthly energy yield (YM) data from a few locations (only 5!) is sufficient to predict the yearly energy yield (EY) of over 250,000 locations with a high spatial resolution ( $0.5^{\circ}\times 0.5^{\circ}$ ) and accuracy with root mean square error (RMSE) less than just 8 kW·h·m −2 . The map reveals that physically relevant meteorological conditions are shared across continents allowing pan-continental geographical extrapolation. Moreover, the scheme is agnostic to PV technology and farm topology, and thus, can be extended to novel PV technology/farm topology. Our results will lead to data-driven collaboration between national policymakers and research organizations to build efficient decision support systems for accelerated PV qualification and deployment across the world.

Lifetime prediction of the fielded c-Si solar modules due to location-specific weather conditions has been an important topic of photovoltaic research and the economic viability of solar energy. Data analytic techniques such as Performance Ratio method, Statistical Clear Sky model, and Suns-Vmp methods quantify the degradation from measured data of a solar farm, however, the non-linear time-dependence and correlated degradations make it difficult to use the empirical degradation rates for ultimate lifetime projection. In this paper, we propose a complementary physics-based model to predict the solder bond failure caused by mechanical stress associated with the daily or seasonal variations of the temperature. Given the worldwide weather information from NASA database, the proposed model predicts the location-specific output power degradation and the lifetime of a module due to solder bond failure. The model parameters are obtained by calibrating against qualification tests involving thermal cycling. This model will serve as a building block of a more comprehensive reliability model that can predict the lifetime of a module that experiences simultaneous and correlated degradation mechanisms involving yellowing, corrosion, and potential-induced degradations.

Photovoltaic (PV) cell technology has made great progress over the past few decades, bringing the PV energy cost down to a point where it is competitive to conventional electricity prices. While monofacial panels have historically dominated the market, recent developments in the manufacturing of bifacial panels (collecting light from both faces) have made them accessible for commercial applications. It is therefore imperative to define the design principles so that the steeply expanding market of bifacial modules and PV farms stays on an efficient path. In this paper, we will discuss physics-based models for these next-generation bifacial PV farms for analyzing yield and costs. Besides the conventional farm configurations, tracking systems are gaining market shares aiming to enhance the yield at a lower cost. Within ± 30 ∘ latitudes, we predict a 20%-30% energy gain for fixed-tilt bifacial over monofacial modules and an additional 20%–40% gain for single-axis bifacial tracking. Compound systems such as agrophotovoltaics and floating PV applications may be the possible future for a sustainable merger of food-water-energy systems. We show a competing relation between active light collection on crops and energy yield in an agrophotovoltaics system vs panel density—the final design will be decided by the crop yield or light usage efficacy constraint. The output reliability in terms of soiling and module degradation is also explained in this paper. Solar farms are expected to see 2%–5% loss in revenue in Asia and the Middle East even after optimal cleaning. Additionally, the bifacial modules degrade ∼0.5%–0.6%/year. While there have been several physics-based degradation analyses, the bifacial technology lacks a large enough data set of long-term degradation studies for accurate predictions. The combined economics of reliability against yield will decide the viability of the next generation bifacial PV industry.

The bifacial gain of East-West vertical and South-facing optimally-tilted bifacial solar farms are well established. One wonders if bifacial gain and the associated levelized cost of energy (LCOE) may be further improved by tracking the sun. Tracking bifacial photovoltaics (PV) system has advantages of improved temperature sensitivity, enhanced diffuse and albedo light collection, flattened energy-output, and reduced soiling. Monofacial tracking already provides many of these advantages, therefore the relative merits of bifacial tracking are not obvious. In this paper, we use a detailed illumination and temperature-dependent bifacial solar farm model to show that bifacial tracking PV delivers up to 45% energy gain when compared to fixed-tilt bifacial PV near the equator, and -10% bifacial energy gain over tracking monofacial farm with an albedo of 0.5. An optimum pitch further improves the gain of a tracking bifacial farm. Our results will broaden the scope and understanding of bifacial technology by demonstrating global trends in energy gain for worldwide deployment.

There has been an accelerated pace of installation variety of utility-scale solar farms across the world. Field data are streaming in from existing plants. Also, highly sophisticated physics-based numerical models and software are being developed to estimate the energy yield of a solar PV system over its lifetime. By necessity, the results are specific to a finite number of arbitrarily chosen geographical locations and require substantial computational resources for a worldwide energy estimation, making it difficult to extrapolate the results to new locations. In this paper, we demonstrate a novel machine learning-based tool that reduces the computational time by 4-orders of magnitude with an accuracy of R2 ~ 0.99. The tool utilizes the simulation-derived utility-scale solar PV energy yield to functionally interpolate the location-specific inputs and energy yields. This compact, efficient, and versatile representation will transform how large-scale modeling is used to predict the energy yield of various farm configurations for any location around the world.

Bifacial solar panels are perceived to be the technology of choice for next generation solar farms for their increased energy yield at marginally increased cost. As the bifacial farms proliferate around the world, it is important to investigate the role of temperature-dependent energy-yield and levelized cost of energy (LCOE) of bifacial solar farms relative to monofacial farms, stand-alone bifacial modules, and various competing bifacial technologies. In this work, we integrate irradiance and light collection models with experimentally validated, physics-based temperature-dependent efficiency models to compare the energy yield and LCOE reduction of various bifacial technologies across the world. We find that temperature-dependent efficiency changes the energy yield and LCOE by approximately -10 to 15%. Indeed, the results differ significantly depending on the location of the farm (which defines the illumination and ambient temperature), elevation of the module (increases incident energy), as well as the temperature-coefficients of various bifacial technologies. The analysis presented in this paper will allow us to realistically assess location-specific relative advantage and economic viability of the next generation bifacial solar farms.

The quest to increase the energy yield of solar PV farms has led to extensive research on bifacial modules and tracking systems. Previous studies have shown ~12% increase in power for single-axis tracking of standalone bifacial PV modules, but the corresponding gain for bifacial solar farms remains unknown. In this paper, we demonstrate the modeling and physics of single-axis tracking bifacial PV farms that include the essential aspect of mutual shading between the rows of PV panels. Our results show that single-axis tracking bifacial farms outperform fixed mono/bifacial for locations and times with higher direct light fraction. The worldwide maps show a range of yearly power gain from 10-20% for latitudes > 40°. Overall, a judicious deployment of single-axis tracking bifacial solar PV farms would offer immense potential in energy yield maximization and eventual LCOE minimization.

An ever-expanding photovoltaics (PV) community has been publishing enormous amounts of data regarding all aspects of PV technology. The data generated via these studies and reports range from experiments, simulation, and standard tests to policies and economics of PV. These data require organization, systematic storage, and analyses. Previous works have focused on research-specific data environments and repositories. However, a comprehensive discipline-neutral platform for preserving, sharing, and analyzing the data has not been built. Digital Environment for Enabling Data-Driven Science (DEEDS) provides a unique solution to this problem. DEEDS enables a user to create datasets (projects), cases, and tools; and store data which can be structured, compared, and numerically analyzed, all on a single holistic online platform. In this paper, we demonstrate the capabilities of DEEDS using an example research study called the Solar PV Diagnosis. DEEDS platform has the potential to be used by the entire PV community to preserve various PV projects, interpret their performance and reliability, and to facilitate worldwide collaboration.

The bifacial gain of East-West vertical and South-facing optimally-tilted bifacial farms are well established. One wonders if bifacial gain (and the associated LCOE) may be further improved by tracking the sun. Tracking bifacial PV has advantages of improved temperature sensitivity, enhanced diffuse and albedo light collection, flattened energy-output, reduced soiling, etc. Monofacial tracking already provides many of these advantages, therefore the relative merits of bifacial tracking are not obvious. In this paper, we use a detailed illumination and temperature-dependent bifacial solar farm model to show that bifacial tracking PV delivers up to 45% energy gain when compared to fixed-tilt bifacial PV near the equator, and ~10% bifacial energy gain over tracking monofacial farm with an albedo of 0.5. An optimum pitch further improves the gain of a tracking bifacial farm. Our results will broaden the scope and understanding of bifacial technology by demonstrating global trends in energy gain for worldwide deployment.

Solar industry is working towards reducing the levelized cost of energy (LCOE). The LCOE formulation includes various implicitly connected cost and physical design parameters. This entails complex cost analyses by economists, and electrical analysis and energy yield calculations of solar farms by device physicists/technologists. The standard formulation requires a constant exchange of information to optimize the design of a specific farm. In this paper, we present a fundamental reformulation of LCOE (in terms of LCOE*) to deconvolve the cost vs. energy yield analysis so that economists and technologists can work independently towards minimizing LCOE. We validate our LCOE* formulation by comparing LCOE calculated by the traditional vs. new method. We further provide a global analysis of LCOE* as an illustrative example. This LCOE* will accelerate new technology development (e.g. bifacial PV) by allowing a rapid and transparent analysis of its cost-performance trade-off.

The energy gain of a bifacial solar farm (compared to its monofacial counterpart) is primarily determined by the magnitude of the ground albedo. There is a persistent suspicion that the seasonal and spatial variability of albedo will lead to dramatic fluctuation and significant loss of overall bifacial energy output, and a high-precision albedo measurement is a prerequisite for bifacial LCOE calculations. In this paper, we assess the hypothesis by calculating the energy output of bifacial solar farms across the world based on satellite-derived seasonal/spatial albedo data. Our results show that energy output is actually not a sensitive function of seasonal albedo, a low-resolution time-averaged albedo would produce comparable (within 1%) results. We conclude that high-quality economic viability and LCOE-assessment is possible even with low-resolution albedo information.

The steady decrease in the levelized cost of solar energy (LCOE) has made it increasingly cost-competitive against fossil fuels. The cost reduction is supported by a combination of material, device, and system innovations:To this end, bifacial solar farms are expected to decrease LCOE further by increasing the energy yield; but given the rapid pace of design/manufacturing innovations, a cost-inclusive optimization of bifacial PV systems at the farm-level (including land costs) has not been reported. In our worldwide study, we use a fundamentally new approach to decouple energy yield from cost considerations by parameterizing the LCOE formula in terms of "land-related cost" and "module-related cost" to show that an interplay of these parameters defines the optimum design of bifacial farms. For ground-mounted solar panels, we observe that the panels must be oriented horizontally and packed densely for locations with high "land-related cost", whereas the panels should be optimally tilted for places with high "module-related cost". For systems with relatively high "module-related costs" and for locations with vertical bar latitude vertical bar > 30 degrees, the bifacial modules must be tilted similar to 10 degrees-45 degrees higher and will reduce LCOE by 2-6% compared to their monofacial counterparts. The results in this paper will guide the deployment of LCOE-minimized ground-mounted tilted bifacial farms around the world.

Solar industry is working towards reducing the levelized cost of energy (LCOE). The LCOE formulation includes various implicitly connected cost and physical design parameters. This entails complex cost analyses by economists, and electrical analysis and energy yield calculations of solar farms by device physicists/technologists. The standard formulation requires a constant exchange of information to optimize the design of a specific farm. In this paper, we present a fundamental reformulation of LCOE (in terms of LCOE*) to deconvolve the cost vs. energy yield analysis so that economists and technologists can work independently towards minimizing LCOE. We validate our LCOE* formulation by comparing LCOE calculated by the traditional vs. new method. We further provide a global analysis of LCOE* as an illustrative example. This LCOE* will accelerate new technology development (e.g. bifacial PV) by allowing a rapid and transparent analysis of its cost-performance trade-off.

Daily and seasonal variability of the solar irradiation poses a major hurdle to the widespread adoption of photovoltaic (PV) systems. An integrated photovoltaic-electrochemical (EC)storage system offers a solution, but the thermodynamic efficiency (eta(sys)) of the "ideal" integrated system and the optimum configuration needed to realize the limit is known only for a few simple cases. Moreover, these limits are often derived through complex numerical simulations. In this paper, we show that a simple, conceptually transparent and physically intuitive analytical formula can precisely describe the eta(sys) of a "generalized" PV-EC integrated system. An M-cell PV module of N-junction bifacial tandem cells is illuminated under S-suns and mounted over ground of albedo R. There are K-EC cells in series, each defined by their reaction potential, exchange current, and Tafel slope. We derive the optimum thermodynamic limit eta(sys)(N, M, K, R, S) for all possible combinations of a PV-EC design. For a setup with optimal-(M, K) and large N, under 1-sun illumination and albedo = 0, the ultimate limit is eta(sys) similar to 52%. A comparison of our results with experimental results published by various groups worldwide suggests opportunities for further progress toward the corresponding thermodynamic limit.

In spite of the excellent reported efficiencies, perovskite based solar cells are still plagued by concerns related to stability and hysteresis. While the major physical phenomena related to carrier generation and transport are fairly well understood through theoretical models and simulations, the crucial role of interfaces is not well explored in the community. In this manuscript we show, through detailed numerical simulations, that interface charges significantly affect the electrostatics of the device under illumination thus leading to sub-optimal performance, especially the fill factor. Indeed, this work highlights the need for proper interface passivation and provides a predictive framework towards exploring the effect of interface charges on the eventual device stability and performance degradation.