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Register a new userRevisiting Thin Silicon for Photovoltaics: A Technoeconomic Perspective
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Adopting thin Si wafers for PV reduces capital expenditure (capex) and manufacturing cost, and accelerates the growth of PV manufacturing. There are two key questions about thin Si today: (a) how much can we still benefit economically from thinning wafers? (b) what are the technological challenges to transition to thin wafers? In this work, we re-evaluate the benefits and challenges of thin Si for current and future PV modules using a comprehensive techno-economic framework that couples device simulation, bottom-up cost modeling, and a cash-flow growth model. When adopting an advanced technology concept that features sufficiently good surface passivation, similarly high efficiencies are achievable for 50-um wafers as for 160-um ones. We then quantify the economic benefits for thin Si wafers in terms of poly-Si-to-module manufacturing capex, module cost, and levelized cost of electricity (LCOE) for utility PV systems. Particularly, LCOE favors thinner wafers for all investigated device architectures, and can potentially be reduced by more than 5% from the value of 160-um wafers. With further improvements in module efficiency, an advanced thin-wafer device concept with 50-um wafers could reduce manufacturing capex by 48%, module cost by 28%, and LCOE by 24%. Furthermore, we apply a sustainable growth model to investigate PV deployment scenarios in 2030. It is found that the state-of-the-art industry concept could not achieve the climate targets even with very aggressive financial scenarios, therefore the capex reduction benefit of thin wafers is needed to facilitate more rapid PV growth. Lastly, we discuss the remaining technological challenges and areas for innovation to enable high-yield manufacturing of high-efficiency PV modules with thin Si wafers.
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The radiative cooling of objects during daytime under direct sunlight has recently been shown to be significantly enhanced by utilizing nanophotonic coatings. Multilayer thin film stacks, 2D photonic crystals, etc. as coating structures improved the thermal emission rate of a device in the infrared atmospheric transparency window reducing considerably devices temperature. Due to the increased heating in photovoltaic (PV) devices, that has significant adverse consequences on both their efficiency and life-time, and inspired by the recent advances in daytime radiative cooling, we developed a coupled thermal-electrical modeling to examine the physical mechanisms on how a radiative cooler affects the overall efficiency of commercial photovoltaic modules. Employing this modeling, which takes into account all the major processes affected by the temperature variation in a PV device, we evaluated the relative impact of the main radiative cooling approaches proposed so far on the PV efficiency, and we established required conditions for optimized radiative cooling. Moreover, we identified the validity regimes of the currently existing PV-cooling models which treat the PV coolers as simple thermal emitters. Finally, we assessed some realistic photonic coolers from the literature, compatible with photovoltaics, to implement the radiative cooling requirements, and demonstrated their associated impact on the temperature reduction and PV efficiency. Providing the physical mechanisms and requirements for cooling radiatively solar cells, our study provides guidelines for utilizing suitable photonic structures as radiative coolers, enhancing the efficiency and the lifetime of PV devices.
Since thin-film silicon solar cells have limited optical absorption, we explore the effect of a nanostructured back reflector to recycle the unabsorbed light. As a back reflector we investigate a 3D photonic band gap crystal made from silicon that is readily integrated with the thin films. We numerically obtain the optical properties by solving the 3D time-harmonic Maxwell equations using the finite-element method, and model silicon with experimentally determined optical constants. The absorption enhancement relevant for photovoltaics is obtained by weighting the absorption spectra with the AM 1.5 standard solar spectrum. We study thin films either thicker ($L_{Si} = 2400$ nm) or much thinner ($L_{Si} = 80$ nm) than the wavelength of light. At $L_{Si} = 2400$ nm, the 3D photonic band gap crystal enhances the spectrally averaged ($lambda = 680$ nm to $880$ nm) silicon absorption by $2.22$x (s-pol.) to $2.45$x (p-pol.), which exceeds the enhancement of a perfect metal back reflector ($1.47$ to $1.56$x). The absorption is enhanced by the (i) broadband angle and polarization-independent reflectivity in the 3D photonic band gap, and (ii) the excitation of many guided modes in the film by the crystals surface diffraction leading to enhanced path lengths. At $L_{Si} = 80$ nm, the photonic crystal back reflector yields a striking average absorption enhancement of $9.15$x, much more than $0.83$x for a perfect metal, which is due to a remarkable guided mode confined within the combined thickness of the thin film and the photonic crystals Bragg attenuation length. The broad bandwidth of the 3D photonic band gap leads to the back reflectors Bragg attenuation length being much shorter than the silicon absorption length. Consequently, light is confined inside the thin film and the absorption enhancements are not due to the additional thickness of the photonic crystal back reflector.
As the cost of renewable energy falls below fossil fuels, the most important challenge to enable widespread sustainable power generation has become making renewables dispatchable. Low cost energy storage can provide this dispatchability, but there is no clear technology that can meet the need. Pumped hydroelectric and compressed air storage have low costs, but they are geographically constrained. Similarly, lithium-ion batteries are becoming ubiquitous, but even their lower bounding asymptote cost is too high to enable cost-competitive dispatchable renewables. Here, we introduce a concept based on thermal energy grid storage (TEGS) using a multijunction photovoltaic heat engine (MPV) with promising initial experimental results that could meet the low cost required to enable cost competitive dispatchable renewables. The approach exploits an important tradeoff between the accession of an extremely low cost per unit energy stored, by storing heat instead of electricity directly, while paying the penalty of a lower round trip efficiency. To understand why this tradeoff is advantageous, we first introduce a framework for evaluating storage technologies that treats round trip efficiency (RTE) as a variable, in addition to cost per unit energy stored (CPE) and cost per unit power (CPP). It is from this perspective that the TEGS-MPV concept offers a compelling economic proposition.
Process optimization of photovoltaic devices is a time-intensive, trial and error endeavor, without full transparency of the underlying physics, and with 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 that identifies the root cause(s) of underperformance with layer by-layer resolution and reveals alternative optimal process windows beyond global black-box optimization. Our Bayesian-network approach links process conditions to materials descriptors (bulk and interface properties, e.g., bulk lifetime, doping, and surface recombination) and device performance parameters (e.g., cell efficiency), using a Bayesian inference framework with an autoencoder-based surrogate device-physics model that is 100x faster than numerical solvers. With the trained surrogate model, our approach is robust and reduces significantly the time consuming experimentalist intervention, even with small numbers of fabricated samples. To demonstrate our method, we perform layer-by-layer optimization of GaAs solar cells. In a single cycle of learning, we find an improved growth temperature for the GaAs solar cells without any secondary measurements, and demonstrate a 6.5% relative AM1.5G efficiency improvement above baseline and traditional black-box optimization methods.
Experience replay enables off-policy reinforcement learning (RL) agents to utilize past experiences to maximize the cumulative reward. Prioritized experience replay that weighs experiences by the magnitude of their temporal-difference error ($|text{TD}|$) significantly improves the learning efficiency. But how $|text{TD}|$ is related to the importance of experience is not well understood. We address this problem from an economic perspective, by linking $|text{TD}|$ to value of experience, which is defined as the value added to the cumulative reward by accessing the experience. We theoretically show the value metrics of experience are upper-bounded by $|text{TD}|$ for Q-learning. Furthermore, we successfully extend our theoretical framework to maximum-entropy RL by deriving the lower and upper bounds of these value metrics for soft Q-learning, which turn out to be the product of $|text{TD}|$ and on-policyness of the experiences. Our framework links two important quantities in RL: $|text{TD}|$ and value of experience. We empirically show that the bounds hold in practice, and experience replay using the upper bound as priority improves maximum-entropy RL in Atari games.
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