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- All Subjects: Lithium-ion battery
- Creators: Qin, Jiangchao
- Member of: ASU Electronic Theses and Dissertations
Description
Over the years, the growing penetration of renewable energy into the electricity market has resulted in a significant change in the electricity market price. This change makes the existing forecasting method prone to error, decreasing the economic benefits. Hence, more precise forecasting methods need to be developed. This paper starts with a survey and benchmark of existing machine learning approaches for forecasting the real-time market (RTM) price. While these methods provide sufficient modeling capability via supervised learning, their accuracy is still limited due to the single data source, e.g., historical price information only. In this paper, a novel two-stage supervised learning approach is proposed by diversifying the data sources such as highly correlated power data. This idea is inspired by the recent load forecasting methods that have shown extremely well performances. Specifically, the proposed two-stage method, namely the rerouted method, learns two types of mapping rules. The first one is the mapping between the historical wind power and the historical price. The second is the forecasting rule for wind generation. Based on the two rules, we forecast the price via the forecasted generation and the first learned mapping between power and price. Additionally, we observed that it is not the more training data the better, leading to our validation steps to quantify the best training intervals for different datasets. We conduct comparisons of numerical results between existing methods and the proposed methods based on datasets from the Electric Reliability Council of Texas (ERCOT). For each machine learning step, we examine different learning methods, such as polynomial regression, support vector regression, neural network, and deep neural network. The results show that the proposed method is significantly better than existing approaches when renewables are involved.
ContributorsLuo, Shuman (Author) / Weng, Yang (Thesis advisor) / Lei, Qin (Committee member) / Qin, Jiangchao (Committee member) / Arizona State University (Publisher)
Created2019
Description
With the status of nuclear proliferation around the world becoming more and more complex, nuclear forensics methods are needed to restrain the unlawful usage of nuclear devices. Lithium-ion batteries are present ubiquitously in consumer electronic devices nowadays. More importantly, the materials inside the batteries have the potential to be used as neutron detectors, just like the activation foils used in reactor experiments. Therefore, in a nuclear weapon detonation incident, these lithium-ion batteries can serve as sensors that are spatially distributed.
In order to validate the feasibility of such an approach, Monte Carlo N-Particle (MCNP) models are built for various lithium-ion batteries, as well as neutron transport from different fission nuclear weapons. To obtain the precise battery compositions for the MCNP models, a destructive inductively coupled plasma mass spectrometry (ICP-MS) analysis is utilized. The same battery types are irradiated in a series of reactor experiments to validate the MCNP models and the methodology. The MCNP nuclear weapon radiation transport simulations are used to mimic the nuclear detonation incident to study the correlation between the nuclear reactions inside the batteries and the neutron spectra. Subsequently, the irradiated battery activities are used in the SNL-SAND-IV code to reconstruct the neutron spectrum for both the reactor experiments and the weapon detonation simulations.
Based on this study, empirical data show that the lithium-ion batteries have the potential to serve as widely distributed neutron detectors in this simulated environment to (1) calculate the nuclear device yield, (2) differentiate between gun and implosion fission weapons, and (3) reconstruct the neutron spectrum of the device.
In order to validate the feasibility of such an approach, Monte Carlo N-Particle (MCNP) models are built for various lithium-ion batteries, as well as neutron transport from different fission nuclear weapons. To obtain the precise battery compositions for the MCNP models, a destructive inductively coupled plasma mass spectrometry (ICP-MS) analysis is utilized. The same battery types are irradiated in a series of reactor experiments to validate the MCNP models and the methodology. The MCNP nuclear weapon radiation transport simulations are used to mimic the nuclear detonation incident to study the correlation between the nuclear reactions inside the batteries and the neutron spectra. Subsequently, the irradiated battery activities are used in the SNL-SAND-IV code to reconstruct the neutron spectrum for both the reactor experiments and the weapon detonation simulations.
Based on this study, empirical data show that the lithium-ion batteries have the potential to serve as widely distributed neutron detectors in this simulated environment to (1) calculate the nuclear device yield, (2) differentiate between gun and implosion fission weapons, and (3) reconstruct the neutron spectrum of the device.
ContributorsZhang, Taipeng (Author) / Holbert, Keith E. (Thesis advisor) / Karady, George G. (Committee member) / Qin, Jiangchao (Committee member) / Metzger, Robert (Committee member) / Arizona State University (Publisher)
Created2017