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Heben, Michael J.

Publications and source records attributed to Heben, Michael J..

24 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

20%-efficient polycrystalline Cd(Se,Te) thin-film solar cells with compositional gradient near the front junction

Bandgap gradient is a proven approach for improving the open-circuit voltages (V OC s) in Cu(In,Ga)Se 2 and Cu(Zn,Sn)Se 2 thin-film solar cells, but has not been realized in Cd(Se,Te) thin-film solar cells, a leading thin-film solar cell technology in the photovoltaic market. Here, we demonstrate the realization of a bandgap gradient in Cd(Se,Te) thin-film solar cells by introducing a Cd(O,S,Se,Te) region with the same crystal structure of the absorber near the front junction. The formation of such a region is enabled by incorporating oxygenated CdS and CdSe layers. We show that the introduction of the bandgap gradient reduces the hole density in the front junction region and introduces a small spike in the band alignment between this and the absorber regions, effectively suppressing the nonradiative recombination therein and leading to improved VOCs in Cd(Se,Te) solar cells using commercial SnO 2 buffers. A champion device achieves an efficiency of 20.03% with a V OC of 0.863 V.

14 SOLAR ENERGY↗
DOE OSTI2022

Deep reinforcement learning based optimization for a tightly coupled nuclear renewable integrated energy system

New ways to integrate energy systems to maximize efficiency are being sought to meet carbon emissions goals. Nuclear-renewable integrated energy system (NR-IES) concepts are a leading solution that couples a nuclear power plant with renewable energy, hydrogen generation plants, and energy storage systems, such that thermal and electrical power are dispatchable to fulfill grid-flexibility requirements while also producing hydrogen and maximizing revenue. Here, this paper introduces a deep reinforcement learning (DRL)-based framework to address the complex decision-making tasks for NR-IES. The objective is to maximize revenue by generating and selling hydrogen and electricity simultaneously according to their time-varying prices while keeping the energy flow in the subsystems in balance. A Python-based simulator for a NR-IES concept has been developed to integrate with OpenAI Gym and Ray/RLlib to enable an efficient and flexible computational framework for DRL research and development. Three state-of-the-art DRL algorithms have been investigated, including two-delayed deep deterministic policy gradient (TD3), soft-actor critic (SAC), proximal policy optimization (PPO), to illustrate DRL’s superiority for controlling NR-IES by comparing it with a conventional control approach, particle swarm optimization (PSO). In this effort, PPO has shown more-stable performance and also better generalization capability than SAC and TD3. Comparisons with PSO have demonstrated that, on average, PPO can achieve 13.9% more mean episode returns from the training process and 29.4% more mean episode returns from the testing process when different hydrogen-production targets are applied.

08 HYDROGEN↗
DOE OSTI2022

Bifacial CdTe Solar Cells with Copper Chromium Oxide Back-Buffer Layer

Bifacial solar cells have the potential to maintain energy output higher than monofacial devices under unfavorable weather conditions. A transparent back-buffer layer which can passivate the interface and improve the minority carrier lifetime is critical in CdTe-based bifacial devices. Herein, solution-processed Cu x Cr y O z as a promising back-buffer for CdTe/CdS solar cells is demonstrated. The carrier lifetimes measured at the front and back of the device are 31.2 and 3.1 ns, respectively, which correspond to an increase of ≈38% and 138%, respectively, compared to the reference device. This dramatic improvement in lifetime results in a 100% increase in short-circuit current measured with backside illumination. The best bifacial device has efficiencies 7.6% and 12.5%, respectively, from back and front illumination, yielding a bifaciality factor of 0.60.

14 SOLAR ENERGY↗
DOE OSTI2022

Reduced Recombination and Improved Performance of CdSe/CdTe Solar Cells due to Cu Migration Induced by Light Soaking

The performance of CdTe solar cells has advanced impressively in recent years with the incorporation of Se. Instabilities associated with light soaking and copper reorganization have been extensively examined for the previous generation of CdS/CdTe solar cells, but instabilities in Cu-doped Se-alloyed CdTe devices remain relatively unexplored. In this work, we fabricated a range of CdSe/CdTe solar cells by sputtering CdSe layers with thicknesses of 100, 120, 150, 180, and 200 nm on transparent oxide-coated glass and then depositing CdTe by close-spaced sublimation. After CdCl 2 annealing, Cu-doping, and back metal deposition, a variety of analyses were performed both before and after light soaking to understand the changes in device performance. The device efficiency was degraded with light soaking in most cases, but devices fabricated with a CdSe layer thickness of 120 nm showed reasonably good efficiency initially (13.5%) and a dramatic improvement with light soaking (16.5%). Here, the efficiency improvement is examined within the context of Cu ion reorganization that is well known for CdS/CdTe devices. Low-temperature photoluminescence data and V oc versus temperature measurements indicate a reduction in nonradiative recombination due to the passivation of defects and defect complexes in the graded CdSe x Te 1–x layer.

CdSexTe1−x↗