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Peters, Ian Marius

Publications and source records attributed to Peters, Ian Marius.

10 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Strategic styles of hardware product development could accelerate commercialization in cleantech startups

Hardware-based startups risk having longer times-to-market, deterring investment in the clean technologies that are critical to a sustainable future. We interviewed 55 leaders at hardware startups, 20 of which are cleantech, mapped their development timelines, and found prototyping to be the longest development step (median of 19 weeks per prototype) regardless of prototype complexity or iteration. Qualitative interview analysis reveals the prototyping team’s choice of development style is a major factor affecting timeline. We define two development styles: natural and structured, typified by free-form exploration and rule-based execution, respectively. On average, natural development takes 35% less time than structured, and is thus preferred for early iterations, but adopting structure at strategic points is needed for timely commercialization. Critical points of transition to a structured style include adding new team members or engaging external partners, which demand clear communication and expectations. When pivoting to a new product or market, returning to a natural style is beneficial.

Looney, Erin (ORCID:0000000168959312)↗
DOE OSTI2023

Optimizing Perovskite Thin‐Film Parameter Spaces with Machine Learning‐Guided Robotic Platform for High‐Performance Perovskite Solar Cells

Abstract Simultaneously optimizing the processing parameters of functional thin films remains a challenge. The design and utilization of a fully automated platform called SPINBOT is presented for the engineering of solution‐processed functional thin films. The SPINBOT is capable of performing experiments with high sampling variability through the unsupervised processing of hundreds of substrates with exceptional experimental control. Through the iterative optimization process enabled by the Bayesian optimization (BO) algorithm, the SPINBOT explores an intricate parameter space, continuously improving the quality and reproducibility of the produced thin films. This machine learning (ML)‐guided reliable SPINBOT platform enables the acceleration of the optimization process of perovskite solar cells via a simple photoluminescence characterization of films. As a result, this study arrives at an optimal film that, when processed into a solar cell in an ambient atmosphere, immediately yields a champion power conversion efficiency (PCE) of 21.6% with satisfactory performance reproducibility. The unsealed devices retain 90% of their initial efficiency after 1100 h of continuous operation at 60–65 °C under metal‐halide lamps. It is anticipated that the integration of robotic platforms with the intelligent algorithm will facilitate the widespread adoption of effective autonomous experimentation to address the evolving needs and constraints within the materials science research community.

14 SOLAR ENERGY↗
DOE OSTI2023

Photovoltaics at multi-terawatt scale: Waiting is not an option

We report a major renewable-energy milestone occurred in 2022: Photovoltaics (PV) exceeded a global installed capacity of 1 TW dc . But despite considerable growth and cost reduction over time, PV is still a small part of global electricity generation (4 to 5% for 2022), and the window is increasingly closing to take action at scale to cut greenhouse gas (GHG) emissions while meeting global energy needs for the future. PV is one of very few options that can be dispatched relatively quickly, but discussions of TW-scale growth at the global level may not be clearly communicating the needed size and speed for renewable-energy installation. A major global risk would be to make poor assumptions or mistakes in modeling and promoting the required PV deployment and industry growth and then realize by 2035 that we were profoundly wrong on the low side and need to ramp up manufacturing and deployment to unrealistic or unsustainable levels.

14 SOLAR ENERGY↗
DOE OSTI2021

Solar photovoltaics is ready to power a sustainable future

Thanks to fast learning and sustained growth, solar photovoltaics (PV) is today a highly cost-competitive technology, ready to contribute substantially to CO 2 emissions mitigation. However, many scenarios assessing global decarbonization pathways, either based on integrated assessment models or partial-equilibrium models, fail to identify the key role that this technology could play, including far lower future PV capacity than that projected by the PV community. In this perspective, we review the factors that lie behind the historical cost reductions of solar PV and identify innovations in the pipeline that could contribute to maintaining a high learning rate. We also aim at opening a constructive discussion among PV experts, modelers, and policymakers regarding how to improve the representation of this technology in the models and how to ensure that manufacturing and installation of solar PV- can ramp up on time, which will be crucial to remain in a decarbonization path compatible with the Paris Agreement.

14 SOLAR ENERGY↗
DOE OSTI2020

Analysis of CdTe photovoltaic cells for ambient light energy harvesting

This paper investigates the suitability of CdTe photovoltaic cells to be used as power sources for wireless sensors located in buildings. We fabricate and test a CdTe photovoltaic cell with a transparent conducting oxide front contact that provides for high photocurrents and low series resistance at low light intensities and measures the photovoltaic response of this cell across five orders of magnitude of AM1.5G light intensity. Efficiencies of 10% and 17.1% are measured under ~1 W m -2 AM1.5G and LED irradiance respectively, the highest values for a CdTe device under ambient lighting measured to date. We use our results to assess the potential of CdTe for internet of things devices from an optoelectronic, as well as a techno-economic perspective, considering its established manufacturing know-how, potential for low-cost, proven long-term stability and issues around the use of cadmium.

14 SOLAR ENERGY↗
DOE OSTI2020

Global Techno-Economic Performance of Bifacial and Tracking Photovoltaic Systems

Although most of the current photovoltaic (PV) system installations use monofacial modules with fixed-tilt mounting structures, bifacial modules, trackers, and the combination of them are also getting more attention as potential candidates to further reduce the PV levelized cost of electricity (LCOE). This work is then the first to present a worldwide analysis on the yield potential and cost-effectiveness of PV farms composed of monofacial fixed-tilt and single/dual (1T/2T) tracker installations, as well as their bifacial counterparts. Our approach starts by estimating the irradiance reaching the front and rear surface of the modules for the different system designs (validated based on data from real PV systems and results from the literature) to estimate their energy production (row-row shading is neglected). Subsequently, the overall system cost during their 25-year lifetime is factored in, and LCOE is obtained. The results reveal that bifacial-1T installations increase energy yield by 35% and reach the lowest LCOE for the majority of the world (93.1% of the land area). Although dual axis trackers achieve the highest energy generation — especially for bifacial modules — their cost is still too high, and are therefore not as cost-effective. Sensitivity analyses based on the Monte Carlo and region sensitivity approach are performed to analyze the impact of input assumptions on the calculated LCOE. These reveal that our conclusions are robust in general but exact configuration to choose depends on specific site conditions. This investigation is not only of interest to the scientific community, but also can be used as a guide for PV installation companies and investors to determine the most suitable technology for a particular location.

14 SOLAR ENERGY↗
DOE OSTI2020

Revisiting the Terawatt Challenge

Richard E. Smalley, in 2003, defined the Terawatt (TW) Challenge as “Adapting our energy infrastructure to simultaneously address diminishing oil resources and rising levels of atmospheric CO 2 .” Smalley, best known for the discovery of C 60 , for which he received the 1996 Nobel Prize in Chemistry, continued to address the challenges of anthropomorphic and natural global energy flows until he passed away in 2005. Smalley challenged the world to transform the energy sector. He envisioned electricity transmitted by high-voltage direct current (DC) lines from massively deployed solar plants in sunny areas and remotely sited nuclear plants. He also envisioned using advanced batteries for local storage of energy. To meet the needs of ~10 people in a world with a dwindling oil supply, Smalley asserted that the world would need to transform its fossil-fuel-driven 14-TW (average power) energy used in 2003 to a largely renewable-energy-driven 30–60 TW (average power) in 2050. This would be possible only if solar-electricity costs could be drastically reduced. The challenges associated with this transition have been called the “Terawatt Challenge.” Fifteen years later, solar-module costs have been reduced by tenfold and annual deployment of solar photovoltaic (PV) modules has grown by a factor of 100,from ~1 gigawatt (GW) in 2004 to ~100 GW in 2018, with a total of 500 GW installed worldwide, producing 2% of the planet’s electricity. As global installed solar generating capacity approaches1 TW, we revisit Smalley’s TW challenge to identify what has changed and quantify the TW Challenge for a baseline scenario and for two scenarios designed as upper and lower bounds determined by the degree we implement electrification and storage. In this paper, we show that the energy choices we make today will dramatically affect the magnitude of future global energy requirements.

SOLAR ENERGY↗
DOE OSTI2020

Technoeconomic Analysis of Photovoltaics Module Manufacturing with Thin Silicon Wafers

Reducing silicon usage by adopting thinner wafers can significantly reduce capital expenditure (capex) and cost, and thus accelerate the growth of manufacturing capacity and deployment. In this work, we evaluated potential benefits of thin Si wafers for current and future PV modules. We apply a technoeconomic framework that couples bottom-up cost model, and a cash-flow growth model to analyze PV modules with thin wafers. First, we show that, comparing the current PERC with 160 um thick wafers to the high-efficiency concept with 50 um thin wafers, the capex is reduced from 0.39 to 0.2 $/(W/year) while the cost is from 0.32 to 0.2 $/W. Second, the potential of accelerated deployment is analyzed for the highefficiency thin wafer concept. We found the significant advantages of higher growth rate (~20% relative) and higher deployment plateau (~3 times) than the current PERC modules. modules with thin wafers. First, we show that, comparing the current PERC with 160 um thick wafers to the high-efficiency concept with 50 um thin wafers, the capex is reduced from 0.39 to 0.2 $/(W/year) while the cost is from 0.32 to 0.2 $/W. Second, the potential of accelerated deployment is analyzed for the highefficiency thin wafer concept. We found the significant advantages of higher growth rate (~20% relative) and higher deployment plateau (~3 times) than the current PERC modules.

14 SOLAR ENERGY↗
DOE OSTI2020

Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics

Process optimization of photovoltaic devices is a time-intensive, trial-and-error endeavor, which lacks full transparency of the underlying physics and relies on user-imposed constraints that may or may not lead to a global optimum. Herein, we demonstrate that embedding physics domain knowledge into a Bayesian network enables an optimization approach for gallium arsenide (GaAs) solar cells that identifies the root cause(s) of underperformance with layer-by-layer resolution and reveals alternative optimal process windows beyond traditional black-box optimization. Our Bayesian network approach links a key GaAs process variable (growth temperature) to material descriptors (bulk and interface properties, e.g., bulk lifetime, doping, and surface recombination) and device performance parameters (e.g., cell efficiency). For this purpose, we combine a Bayesian inference framework with a neural network surrogate device-physics model that is 100× faster than numerical solvers. With the trained surrogate model and only a small number of experimental samples, our approach reduces significantly the time-consuming intervention and characterization required by the experimentalist. As a demonstration of our method, in only five metal organic chemical vapor depositions, we identify a superior growth temperature profile for the window, bulk, and back surface field layer of a GaAs solar cell, without any secondary measurements, and demonstrate a 6.5% relative AM1.5G efficiency improvement above traditional grid search methods.

14 SOLAR ENERGY↗