PbSe Quantum Dot Solar Cells with More than 6% Efficiency Fabricated in Ambient Atmosphere
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bio-inspired↗
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Scalable fabrication of perovskite solar cells
Perovskite materials use earth-abundant elements, have low formation energies for deposition and are compatible with roll-to-roll and other high-volume manufacturing techniques. These features make perovskite solar cells (PSCs) suitable for terawatt-scale energy production with low production costs and low capital expenditure. Demonstrations of performance comparable to that of other thin-film photovoltaics (PVs) and improvements in laboratory-scale cell stability have recently made scale up of this PV technology an intense area of research focus. Here, we review recent progress and challenges in scaling up PSCs and related efforts to enable the terawatt-scale manufacturing and deployment of this PV technology. We discuss common device and module architectures, scalable deposition methods and progress in the scalable deposition of perovskite and charge-transport layers. We also provide an overview of device and module stability, module-level characterization techniques and techno-economic analyses of perovskite PV modules.
14 SOLAR ENERGY↗
The impact of policies and business models on income equity in rooftop solar adoption
Low- and moderate-income (LMI) households are less likely to adopt rooftop solar photovoltaics (PVs) than higher-income households in the United States. As the existing literature has shown, this dynamic can decelerate rooftop PV deployment and has potential energy justice implications, in light of the cost-shifting between PV and non-PV households that can occur under typical rate structures and incentive programmes. Here we show that some state policy interventions and business models have expanded PV adoption among LMI households. Additionally, we find evidence that LMI-specific financial incentives, PV leasing and property-assessed financing have increased the diffusion of PV adoption among LMI households in existing markets and have driven more installations into previously underserved low-income communities. By shifting deployment patterns, we posit that these interventions could catalyse peer effects to increase PV adoption in low-income communities even among households that do not directly benefit from the interventions.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Atomic and electronic structure of Lomer dislocations at CdTe bicrystal interface
Extended defects are of considerable importance in determining the electronic properties of semiconductors, especially in photovoltaics (PVs), due to their effects on electron-hole recombination. We employ model systems to study the effects of dislocations in CdTe by constructing grain boundaries using wafer bonding. Atomic-resolution scanning transmission electron microscopy (STEM) of a [1–10]/ (110) 4.8° tilt grain boundary reveals that the interface is composed of three distinct types of Lomer dislocations. Geometrical phase analysis is used to map strain fields, while STEM and density functional theory (DFT) modeling determine the atomic structure at the interface. The electronic structure of the dislocation cores calculated using DFT shows significant mid-gap states and different charge-channeling tendencies. Cl-doping is shown to reduce the midgap states, while maintaining the charge separation effects. In conclusion, this report offers novel avenues for exploring grain boundary effects in CdTe-based solar cells by fabricating controlled bicrystal interfaces and systematic atomic-scale analysis.
36 MATERIALS SCIENCE↗
Enhanced photovoltaic performance of ultrathin Si solar cells via semiconductor nanocrystal sensitization: Energy transfer vs. optical coupling effects
Excitonic energy transfer (ET) offers exciting opportunities for advances in optoelectronic devices such as solar cells. While recent experimental attempts have demonstrated its potential in both organic and inorganic photovoltaics (PVs), what remains to be addressed is quantitative understanding of how different ET modes contribute to PV performance and how ET contribution is differentiated from the classical optical coupling (OC) effects. In this study, we implement an ET scheme using a PV device platform, comprising CdSe/ZnS nanocrystal energy donor and 500 nm-thick ultrathin Si acceptor layers, and present the quantitative mechanistic description of how different ET modes, distinguished from the OC effects, increase the light absorption and PV efficiency. We find that nanocrystal sensitization enhances the short circuit current of ultrathin Si solar cells by up to 35%, of which the efficient ET, primarily driven by a long-range radiative mode, contributes to 38% of the total current enhancement. Lastly, these results not only confirm the positive impact of ET but also provide a guideline for rationally combining the ET and OC effects for improved light harvesting in PV and other optoelectronic devices.
14 SOLAR ENERGY↗
B-Site doped lead halide perovskites: synthesis, band engineering, photophysics, and light emission applications
Lead halide perovskite (LHP) semiconductors with the general chemical formula ABX 3 are now being widely investigated for a variety of applications including but not limited to high-efficiency photovoltaics (PVs) and light-emitting diodes (LEDs).
Luo, Binbin↗
Bio-inspired strategies for next-generation perovskite solar mobile power sources
Smart electronic devices are becoming ubiquitous due to many appealing attributes including portability, long operational time, rechargeability and compatibility with the user-desired form factor. Integration of mobile power sources (MPS) based on photovoltaic technologies with smart electronics will continue to drive improved sustainability and independence. With high efficiency, low cost, flexibility and lightweight features, halide perovskite photovoltaics have become promising candidates for MPS. Realization of these photovoltaic MPS (PV-MPS) with unconventionally extraordinary attributes requires new ‘out-of-box’ designs. Natural materials have provided promising designing solutions to engineer properties under a broad range of boundary conditions, ranging from molecules, proteins, cells, tissues, apparatus to systems in animals, plants, and humans optimized through billions of years of evolution. Applying bio-inspired strategies in PV-MPS could be biomolecular modification on crystallization at the atomic/meso-scale, bio-structural duplication at the device/system level and bio-mimicking at the functional level to render efficient charge delivery, energy transport/utilization, as well as stronger resistance against environmental stimuli (e.g., self-healing and self-cleaning). In this paper, we discuss the bio-inspired/-mimetic structures, experimental models, and working principles, with the goal of revealing physics and bio-microstructures relevant for PV-MPS. Here the emphasis is on identifying the strategies and material designs towards improvement of the performance of emerging halide perovskite PVs and strategizing their bridge to future MPS.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Modeling diurnal and annual ethylene generation from solar-driven electrochemical CO 2 reduction devices
Integrated solar fuels devices for CO 2 reduction (CO 2 R) are a promising technology class towards reducing carbon emissions. Designing integrated CO 2 R solar fuels devices requires careful co-design of electrochemical and photovoltaic components as well as consideration of the diurnal and seasonal effects of solar irradiance, temperature, and other meteorological factors expected for ‘on-sun’ deployment. Using a photovoltaic-electrochemical (PV-EC) platform, we developed a temperature and potential-dependent diurnal and annual model using experimentally-determined CO 2 R performance of Cu-based electrocatalysts, local meteorological data from the National Solar Radiation Database (NSRD), and modeled performance of commercial c-Si PVs. Here we simulated gaseous diurnal product outputs with and without the effects of ambient temperature. From these outputs, we observed seasonal variation in gaseous product generation, with up to two-fold increases in ethylene productivity between the Winter and Summer, analyzed the consequences of dynamic cloud coverage, and identified periods where device cooling/heating mechanisms could be implemented to maximize ethylene generation. Finally, we modeled the annual ethylene generation for a scaled 1 MW solar farm at three different locations (Beijing, CN; Sydney, AUS; Barstow, CA) to determine the consequences of local meteorological climates on PV-EC CO 2 R product output, recording a maximum ethylene output of 18.5 tonne per year at Barstow. Overall, this model presents a critical tool for streamlining the translation of experimental solar-driven electrochemical research to real-world implementation.
14 SOLAR ENERGY↗
Perspectives for sustainability analysis of scalable perovskite photovoltaics
We propose a multi-scale analytics and modeling framework to fill the gap in integrating circular solar economy principles with ecosystem and climate commitments, enabling a holistic sustainability analysis of perovskite PVs.
Tian, Xueyu [Systems Engineering, College of Engin↗
Forecast for a growth-rate measurement using peculiar velocities from LSST supernovae
We investigate whether the cosmic growth-rate parameter fσ 8 can be measured using peculiar velocities (PVs) derived from type Ia supernovae (SNe Ia) in the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST). We produced simulations of different SN types using a realistic LSST observing strategy that incorporated noise, a photometric detection from the difference-image analysis (DIA) pipeline, and a PV field modeled from the Uchuu universe machine simulations. We tested three different observational scenarios that ranged from ideal conditions with spectroscopic host galaxy redshifts and spectroscopic SN typing to realistic photometric typing that resulted in a contamination with non-Ia SNe. Using a maximum likelihood technique, we showed that the LSST can measure fσ 8 with a precision of 10% in the redshift range 0.02 < z < 0.14 for our most realistic scenario. In three tomographic bins, the LSST will be able to constrain the growth-rate parameter with errors below 18% up to redshift z = 0.14. We also tested the contamination effect on the maximum likelihood method and found that for a contamination fraction below ∼2%, we recovered unbiased measurements. The results of this analysis highlight that the LSST SN sample is expected to complement traditional redshift-space distortion measurements at high redshift. This will provide a novel avenue for testing general relativity and different dark energy models.
Rosselli, D↗
Band offsets of n-type electron-selective contacts on cuprous oxide (Cu{sub 2}O) for photovoltaics
The development of cuprous oxide (Cu{sub 2}O) photovoltaics (PVs) is limited by low device open-circuit voltages. A strong contributing factor to this underperformance is the conduction-band offset between Cu{sub 2}O and its n-type heterojunction partner or electron-selective contact. In the present work, a broad range of possible n-type materials is surveyed, including ZnO, ZnS, Zn(O,S), (Mg,Zn)O, TiO{sub 2}, CdS, and Ga{sub 2}O{sub 3}. Band offsets are determined through X-ray photoelectron spectroscopy and optical bandgap measurements. A majority of these materials is identified as having a negative conduction-band offset with respect to Cu{sub 2}O; the detrimental impact of this on open-circuit voltage (V{sub OC}) is evaluated through 1-D device simulation. These results suggest that doping density of the n-type material is important as well, and that a poorly optimized heterojunction can easily mask changes in bulk minority carrier lifetime. Promising heterojunction candidates identified here include Zn(O,S) with [S]/[Zn] ratios >70%, and Ga{sub 2}O{sub 3}, which both demonstrate slightly positive conduction-band offsets and high V{sub OC} potential. This experimental protocol and modeling may be generalized to evaluate the efficiency potential of candidate heterojunction partners for other PV absorbers, and the materials identified herein may be promising for other absorbers with low electron affinities.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Development of simple band-spectral pyranometer and quantum meter using photovoltaic cells and bandpass filters
In recent years, greenhouse automatic-control, based on the measurement of solar irradiance, has been attracting attention. This control is an effective method for improving crop production. In the agricultural field, it is necessary to measure Photon Flux Density (PFD), which is an important parameter in the promotion of plant growth. In particular, the PFD of Photosynthetically Active Radiation (PAR, 400-700 nm) and Plant Biologically Active Radiation (PBAR, 300-800 nm) have been discussed in agricultural plant science. The commercial quantum meter (QM, PAR meter) can only measure Photosynthetically Photon Flux Density (PPFD) which is the integrated PFD quantity on the PAR wavelength. In this research, a band-spectral pyranometer or quantum meter using PVs with optical bandpass filters for dividing the PBAR wavelength into 100 nm bands (five independent channels) was developed. Before field testing, calibration of the instruments was carried out using a solar simulator. Next, a field test was conducted in three differing weather conditions such as clear, partly cloudy and cloudy skies. As a result, it was found that the response rate of the developed pyranometer was faster by four seconds compared with the response rate of the commercial pyranometer. Moreover, the outputs of each channel in the developed pyranometer were very similar to the integrated outputs of the commercial spectroradiometer. It was confirmed that the solar irradiance could be measured in each band separately using the developed band-spectral pyranometer. It was indicated that the developed band-spectral pyranometer could also be used as a PV band-spectral quantum meter which is obtained by converting the band irradiance into band PFD.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Distinguishing bulk and surface recombination in CdTe thin films and solar cells using time-resolved terahertz and photoluminescence spectroscopies
Understanding the nature of recombination and its dependence on defects and interfaces is essential for engineering materials and contacts for higher open circuit voltage (V oc ) and power conversion efficiency in photovoltaic (PV) devices. Time-resolved photoluminescence (TRPL) has conventionally been used to evaluate recombination, but carrier redistribution often dominates the response at short times. In this work, we report on the quantification of carrier dynamics and recombination mechanisms by complementary use of both time-resolved terahertz spectroscopy (TRTS) and TRPL combined with numerical modeling of the continuity equations and Poisson’s equation. We have demonstrated this approach using CdTe thin films. A thin film stack with CdTe fabricated by vapor transport deposition and treated with CdCl 2 exhibited a bulk lifetime of 1.7 ± 0.1 ns, negligible CdTe/CdS interface recombination velocity, and back surface recombination velocity of 6.3 ± 1.3 x10 4 cm/s. In contrast, a film stack without CdCl 2 treatment had a bulk lifetime of only 68 ± 12 ps and a higher interface recombination velocity of 4 ± 2 x10 8 cm/s. By determining the locus and mechanisms of performance-limiting recombination, we can accelerate the development of thin-film PVs with higher V oc and efficiency. While the method has been demonstrated here using CdTe, it is also applicable to perovskites, Cu(InGa)Se 2 , Cu2ZnSn(S,Se) 4, and emerging technologies.
14 SOLAR ENERGY↗
Farm-level Interactions Study of a Novel Tri-port Soft-switching Medium-Voltage String Inverter (MVSI) based Large-scale PV-Plus-Storage Farms
A medium-voltage photovoltaic power conversion unit supported by battery based on a novel topology of soft switching solid state transformer is presented in this paper. It can reduce the Levelized Cost of Energy (LCOE) of PVS farm in an extended degree. However, its capability to provide grid-support services, such as frequency response, reactive power support, etc, have not been demonstrated. A series/parallel combination of these modules form a large 20 MVA solar farm. To validate them with reduced computation burden, a small-signal model of MVSI is firstly derived and validated in this paper. The model has been validated to match the full-switch model in both steady-state and dynamic conditions. Next, a farm-level simulation based on the proved small-signal model was implemented to demonstrate grid-support services. Real irradiation date from NREL is used to show the operational capabilities of the system. As a result, the overall farm is simulated based on MATLAB/Simulink domain to check the performance for various cases namely: power transfer from dc-ac side, reactive power support during grid voltage sag and utilization of battery during grid voltage disturbance or partial shading over PV panels.
14 SOLAR ENERGY↗
Convolutional Neural Network-Based Protection-Zone Classification of Faults in Distribution Feeders with Photovoltaics.
Fault detection and isolation is critical for reliable operation of distribution systems. The ride-through requirements for the distributed energy resources (DER), mandated by the IEEE 1547-2018 standard, makes it challenging to use undervoltage (UV) conditions for fault detection. In addition, with low fault current contribution from these inverter-based DERs, the time-overcurrent relays are also less effective. Thus motivated, this paper presents a learning-based approach for fault detection and localization. A convolutional neural network (CNN)-based model is proposed which uses local voltage and current waveforms from DER locations and feeder substations, for training a zonal classifier. The classifier can be adopted into any relay-like device for discriminating between faults originating from different protection zones. The performance of the proposed approach was tested on publicly available test feeders with distributed photovoltaics (PVs).
Fault localization, convolutional neural networks,↗
NO-LESS: Near optimal curtailment strategy selection for net load balancing in micro grids
The integration of renewable energy sources such as solar PVs into micro grids has increased in recent years. However, the intermittent and unpredictable nature of renewable energy generation makes supply uncertain, in turn requiring continuous net load balancing to ensure grid stability. One technique to achieve net load balancing is curtailment, however, selection of the optimal set of curtailment strategies to achieve a targeted curtailment is NP-hard. State-of-the-art curtailment selection techniques approach this problem by either developing computationally expensive optimal curtailment strategies or reduce accuracy to generate sub-optimal solutions quickly. In this work, we develop NO-LESS: a Near OptimaL CurtailmEnt Strategy Selection algorithm. NO-LESS is a novel Fully Polynomial Time Approximation Scheme (FPTAS) which determines bounded (near optimal) curtailment strategies in a small amount of time while simultaneously satisfying practical constraints on strategy switching overhead and curtailment fairness. We perform both theoretical analysis and practical evaluation to show that NO-LESS is a scalable solution for performing net load balancing through curtailment strategy selection in Micro Grids.
14 SOLAR ENERGY↗
Data-Driven Distribution System Coordinated PV Inverter Control Using Deep Reinforcement Learning
The deployment of distributed solar photovoltaic (PV) systems has increased consistently over the past decades. High penetrations of PVs could cause a series of adverse grid impacts, such as voltage violations. The recent development of smart inverter technologies rises the incentives of developing PV control solutions that regulate the inverter output power and seeking the optimization on system operational objectives. This paper proposes a data-driven control solution based on deep reinforcement learning (DRL) to optimize PV inverters for voltage regulation. The proposed solution can minimize PV real power curtailment while maintaining network voltage at an acceptable range. Comparison results between the proposed DRL control algorithms with deep deterministic policy gradient (DDPG) and volt-var control on a real feeder in west Colorado highlight the advantage of the proposed framework in controlling the system voltage while minimizing the PV real power curtailment.
deep reinforcement learning↗
Phasor-Based Adaptive Control of a Test-Feeder Distribution Network
Control in the power distribution system is needed to match the generation of electricity (that is, supply) with the demand for power (that is, load) at all times while maintaining a high level of power quality [1] (see "Summary"). The complexity of controlling the electric distribution system increases when distributed intermittent energy sources, such as solar photovoltaics (PVs), displace large-scale dispatchable generation [2]-[10]. Furthermore, if the supply from distributed resources exceeds the time-varying demand across a feeder, then the feeder-level voltage will increase. In extreme cases, large voltage increases will trigger the disconnection of distributed generation to protect electrical equipment and ensure customer safety. Consequently, coordinating renewable energy sources that are distributed throughout the distribution system-to match real-time demand across an entire power network-brings the additional challenge of maintaining a high level of supply reliability.
24 POWER TRANSMISSION AND DISTRIBUTION↗