Heuristic Dispatch Based on Price Signals for Behind-the-Meter PV-Battery Systems in the System Advisor Model

Mirletz, Brian T.; Guittet, Darice

doi:10.1109/pvsc43889.2021.9519013

Cited by 4 publications

(1 citation statement)

References 21 publications

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“…14 Non-linear or physics-based models may be used to optimize dispatch using an algorithmic or gradientfree approach. 10,23,24 Accessibility of battery degradation models, and their use in applications such as those cited above, is improved by recent publication of open-access battery models, including physics-based models such as SLIDE 25 and PyBAMM, 26 as well as technoeconomic modeling tools using the reduced-order battery lifetime models studied in this work, such as SimSES developed by Technical University of Munich, 27 and the System Advisor Model developed by the National Renewable Energy Lab. 28 One of the primary challenges for utilizing battery lifetime models is the relationship between accelerated aging data and realworld use.…”

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confidence: 99%

Machine-Learning Assisted Identification of Accurate Battery Lifetime Models with Uncertainty

Gasper

Collath²,

Hesse³

et al. 2022

J. Electrochem. Soc.

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Reduced-order battery lifetime models, which consist of algebraic expressions for various aging modes, are widely utilized for extrapolating degradation trends from accelerated aging tests to real-world aging scenarios. Identifying models with high accuracy and low uncertainty is crucial for ensuring that model extrapolations are believable, however, it is difficult to compose expressions that accurately predict multivariate data trends; a review of cycling degradation models from literature reveals a wide variety of functional relationships. Here, a machine-learning assisted model identification method is utilized to fit degradation in a stand-out LFP-Gr aging data set, with uncertainty quantified by bootstrap resampling. The model identified in this work results in approximately half the mean absolute error of a human expert model. Models are validated by converting to a state-equation form and comparing predictions against cells aging under varying loads. Parameter uncertainty is carried forward into an energy storage system simulation to estimate the impact of aging model uncertainty on system lifetime. The new model identification method used here reduces life-prediction uncertainty by more than a factor of three (86% ± 5% relative capacity at 10 years for human-expert model, 88.5% ± 1.5% for machine-learning assisted model), empowering more confident estimates of energy storage system lifetime.

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