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Heben, Michael J.

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

22 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↗