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Papers by Zhangshuan (Jason) Hou

OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information), Sep 11, 2023

2021 IEEE Power & Energy Society General Meeting (PESGM), Jul 26, 2021

This report documents the development of a set of detailed facies-based geologic conceptual model... more This report documents the development of a set of detailed facies-based geologic conceptual models of the subsurface beneath the Waste Management Area (WMA) C, which is located in the 200 East Area of the Central Plateau at Hanford Site in southeast Washington.

OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information), Jun 22, 2021

AGU Fall Meeting 2020, Dec 8, 2020

AGU Fall Meeting Abstracts, Dec 17, 2015

Solar Energy, Sep 1, 2021

Abstract A 5-year, 1-minute resolution observational dataset of clouds and solar radiation was pr... more Abstract A 5-year, 1-minute resolution observational dataset of clouds and solar radiation was produced that includes two metrics of the variability in surface solar irradiance due to cloud type and fractional sky cover. Multiple regression models were trained to fit observations of surface solar irradiance variability from those two cloud property predictors. We found that ensemble tree-based methods, Random Forest and Gradient Boosting Machine, have the least overfitting issues and showed the best performance with an R2 of 0.42. While the observational data trained in this study was only from one site, the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site in Oklahoma, initial comparisons of the seasonality of the statistics suggest that these results are relatively weather regime independent; the generality of such a finding across sites will be tested in future work. The observational data and developed machine learning model are being used to create a numerical weather prediction model parameterization to enable day-ahead solar variability prediction in a computationally efficient way. This is a first step towards creating a new paradigm of predicting day-ahead variability with the potential to provide a new tool to improve grid operation, planning, and resilience.

OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information), Dec 1, 2014

OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information), May 1, 2016

AGUFM, Dec 1, 2012

This study demonstrates the possibility of inverting hydrologic parameters using surface flux and... more This study demonstrates the possibility of inverting hydrologic parameters using surface flux and runoff observations in version 4 of the Community Land Model (CLM4). Previous studies showed that surface flux and runoff calculations are sensitive to major hydrologic parameters in CLM4 over different watersheds, and illustrated the necessity and possibility of parameter calibration. Both deterministic least-square fitting and stochastic Markov-chain Monte Carlo (MCMC)-Bayesian inversion approaches are evaluated by applying them to CLM4 at selected sites with different climate and soil conditions. The unknowns to be estimated include surface and subsurface runoff generation parameters and vadose zone soil water parameters. We find that using model parameters calibrated by the samplingbased stochastic inversion approaches provides significant improvements in the model simulations compared to using default CLM4 parameter values, and that as more information comes in, the predictive intervals (ranges of posterior distributions) of the calibrated parameters become narrower. In general, parameters that are identified to be significant through sensitivity analyses and statistical tests are better calibrated than those with weak or nonlinear impacts on flux or runoff observations. Temporal resolution of observations has larger impacts on the results of inverse modeling using heat flux data than runoff data. Soil and vegetation cover have important impacts on parameter sensitivities, leading to different patterns of posterior distributions of parameters at different sites. Overall, the MCMC-Bayesian inversion approach effectively and reliably improves the simulation of CLM under different climates and environmental conditions. Bayesian model averaging of the posterior estimates with different reference acceptance probabilities can smooth the posterior distribution and provide more reliable parameter estimates, but at the expense of wider uncertainty bounds.

Developing accurate and efficient modeling techniques for streamflow at the tens-kilometer spatia... more Developing accurate and efficient modeling techniques for streamflow at the tens-kilometer spatial scale and multi-year temporal scale is critical for evaluating and predicting the impact of climate-and human-induced discharge variations on river hydrodynamics. However, achieving such a goal is challenging because of limited surveys of streambed hydraulic roughness, uncertain boundary condition specifications, and high computational costs. We demonstrate that accurate and efficient threedimensional (3D) hydrodynamic modeling of natural rivers at 30-kilometer and 5-year scales is feasible using the following three techniques within OpenFOAM, an open-source computational fluid dynamics platform: 1) generating a distributed hydraulic roughness field for the streambed by integrating water stage observation data, a rough wall theory, and a local roughness optimization and adjustment strategy; 2) prescribing the boundary condition for the inflow and outflow by integrating precomputed results of a one-dimensional (1D) hydraulic model with the 3D model; and 3) reducing computational time using multiple parallel runs constrained by 1D inflow and outflow boundary conditions. Streamflow modeling for a 30-kilometerlong reach in the Columbia River (CR) over 58 months can be achieved in less than six days using 1.1 million CPU hours. The mean error between the modeled and the observed water stages for our simulated CR reach ranges from-16 cm to 9 cm (equivalent to ca. ±7% relative to the average water depth) at seven locations during most of the years between 2011 and 2019. We can reproduce the velocity distribution measured by the acoustic Doppler current profiler (ADCP). The correlation coefficients of the depth-averaged velocity between the model and ADCP measurements are in the range between 0.71 and 0.83 at 75% of the survey cross-sections. With the validated model, we further show that the relative importance of dynamic pressure versus hydrostatic pressure varies with discharge variations and topography heterogeneity. Given the model's high accuracy and computational efficiency, the model framework provides a generic approach to evaluate and predict the impact of climate-and human-induced discharge variations on river hydrodynamics at tens kilometer and decade scales.

Frontiers in water, Nov 26, 2020

Proceedings of the ... Annual Hawaii International Conference on System Sciences, 2018

Real-time monitoring of power system dynamics using phasor measurement units (PMUs) data improves... more Real-time monitoring of power system dynamics using phasor measurement units (PMUs) data improves situational awareness and system reliability, and helps prevent electric grid blackouts due to early anomaly detection. The study presented in this paper is based on real PMU measurements of the U.S. Western Interconnection system. Given the nonlinear and non-stationary PMU data, we developed a robust anomaly detection framework that uses wavelet-based multi-resolution analysis with moving-window-based outlier detection and anomaly scoring to identify potential PMU events. Candidate events were evaluated via spatiotemporal correlation analysis and classified for a better understanding of event types, resulting in successful anomaly detection and classification of the recorded events.

Stochastic Environmental Research and Risk Assessment, Jun 19, 2018

In this study, we focus on a hydrogeological inverse problem specifically targeting monitoring so... more In this study, we focus on a hydrogeological inverse problem specifically targeting monitoring soil moisture variations using tomographic ground penetrating radar (GPR) travel time data. Technical challenges exist in the inversion of GPR tomographic data for handling non-uniqueness, nonlinearity and high-dimensionality of unknowns. We have developed a new method for estimating soil moisture fields from crosshole GPR data. It uses a pilot-point method to provide a lowdimensional representation of the relative dielectric permittivity field of the soil, which is the primary object of inference: the field can be converted to soil moisture using a petrophysical model. We integrate a multi-chain Markov chain Monte Carlo (MCMC)-Bayesian inversion framework with the pilot point concept, a curved-ray GPR travel time model, and a sequential Gaussian simulation algorithm, for estimating the dielectric permittivity at pilot point locations distributed within the tomogram, as well as the corresponding geostatistical parameters (i.e., spatial correlation range). We infer the dielectric permittivity as a probability density function, thus capturing the uncertainty in the inference. The multi-chain MCMC enables addressing high-dimensional inverse problems as required in the inversion setup. The method is scalable in terms of number of chains and processors, and is useful for computationally demanding Bayesian model calibration in scientific and engineering problems. The proposed inversion approach can successfully approximate the posterior density distributions of the pilot points, and capture the true values. The computational efficiency, accuracy, and convergence behaviors of the inversion approach were also systematically evaluated, by comparing the inversion results obtained with different levels of noises in the observations, increased observational data, as well as increased number of pilot points. Keywords Tomographic ground penetrating radar Á Soil moisture Á Multi-chain Markov chain Monte Carlo Á Bayesian & Jie Bao

The report presents the results of the development of the open-source suite of applications for s... more The report presents the results of the development of the open-source suite of applications for synchrophasor analysis. The suite includes several software tools for oscillation analysis, power plant model validation, and frequency response analysis using synchrophasor measurements. All tools are based on the common framework and data sources. The developed tools have been used by different electrical utilities for synchrophasor analysis. The report includes several use cases based on the actual system PMU data.

This paper presents a comprehensive approach to predict Balancing Authority (BA) regulation and l... more This paper presents a comprehensive approach to predict Balancing Authority (BA) regulation and load following requirements in order to improve BA control performance. In this paper the Pacific Northwest National Laboratory's (PNNL) “ramp and uncertainty prediction tool (RUT) and day-ahead regulation prediction (DARP) tool” were upgraded to incorporate advanced probabilistic forecast information provided by AWS Truepower. The proposed methodology has been tested and validated using actual California Independent System Operator (CAISO) data. Simulation confirmed that integration probabilistic forecast information can reduce the predicted regulation range by about 12-31%. This means that BAs can procure fewer balancing resources without compromising their reliability and control performance requirements.

International Journal of Greenhouse Gas Control, Nov 1, 2016

The impact of temperature variations of the injected CO 2 on the mechanical integrity of a reserv... more The impact of temperature variations of the injected CO 2 on the mechanical integrity of a reservoir is an important problem but rarely addressed in the design of a CO 2 storage site. In this study, a three-dimensional (3D) thermo-geomechanical approach was developed to evaluate the possibility of fracturing the FutureGen 2.0 site due to injection of CO 2 at different temperatures. The approach sequentially coupled the STOMP-CO2 code for flow and thermal analyses to the ABAQUS ® finite element package for performing thermo-geomechanical analyses of this site. The 3D STOMP-CO2 model of the FutureGen 2.0 site contains four horizontal wells and variable layer thickness, flow and thermal properties. The 3D ABAQUS ® finite element (FE) model for thermo-geomechanical analysis which exactly maps the STOMP-CO2 model contains variable thermo-geomechanical properties. Boundary conditions were prescribed to the FE model to achieve the strike-slip faulting stress regime observed at the FutureGen 2.0 site. The STOMP-CO2 model takes into account the results from modeling the heat exchange between the environment and CO 2 during its transport in the pipeline and injection wells before reaching the reservoir, as well as its interaction with the reservoir host rock. Injection temperature in the reservoir, whose initial temperature was 36°C, was varied, and two cases were simulated and modeled: 28°C, the minimum possible temperature considered as an extreme case since it corresponds to winter conditions maintained during the 20 years of the injection, and 47°C that represents the annual average injection temperature. The STOMP-CO2/ABAQUS ® analyses indicate lower injection temperatures approaching 28°C could locally induce shear slip activation close to the wells and confined to the reservoir. Thermally induced hydraulic fracture is not expected for the 28°C-47°C injection temperature range or higher.

OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information), Sep 11, 2023

2021 IEEE Power & Energy Society General Meeting (PESGM), Jul 26, 2021

This report documents the development of a set of detailed facies-based geologic conceptual model... more This report documents the development of a set of detailed facies-based geologic conceptual models of the subsurface beneath the Waste Management Area (WMA) C, which is located in the 200 East Area of the Central Plateau at Hanford Site in southeast Washington.

OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information), Jun 22, 2021

AGU Fall Meeting 2020, Dec 8, 2020

AGU Fall Meeting Abstracts, Dec 17, 2015

Solar Energy, Sep 1, 2021

Abstract A 5-year, 1-minute resolution observational dataset of clouds and solar radiation was pr... more Abstract A 5-year, 1-minute resolution observational dataset of clouds and solar radiation was produced that includes two metrics of the variability in surface solar irradiance due to cloud type and fractional sky cover. Multiple regression models were trained to fit observations of surface solar irradiance variability from those two cloud property predictors. We found that ensemble tree-based methods, Random Forest and Gradient Boosting Machine, have the least overfitting issues and showed the best performance with an R2 of 0.42. While the observational data trained in this study was only from one site, the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site in Oklahoma, initial comparisons of the seasonality of the statistics suggest that these results are relatively weather regime independent; the generality of such a finding across sites will be tested in future work. The observational data and developed machine learning model are being used to create a numerical weather prediction model parameterization to enable day-ahead solar variability prediction in a computationally efficient way. This is a first step towards creating a new paradigm of predicting day-ahead variability with the potential to provide a new tool to improve grid operation, planning, and resilience.

OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information), Dec 1, 2014

OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information), May 1, 2016

AGUFM, Dec 1, 2012

This study demonstrates the possibility of inverting hydrologic parameters using surface flux and... more This study demonstrates the possibility of inverting hydrologic parameters using surface flux and runoff observations in version 4 of the Community Land Model (CLM4). Previous studies showed that surface flux and runoff calculations are sensitive to major hydrologic parameters in CLM4 over different watersheds, and illustrated the necessity and possibility of parameter calibration. Both deterministic least-square fitting and stochastic Markov-chain Monte Carlo (MCMC)-Bayesian inversion approaches are evaluated by applying them to CLM4 at selected sites with different climate and soil conditions. The unknowns to be estimated include surface and subsurface runoff generation parameters and vadose zone soil water parameters. We find that using model parameters calibrated by the samplingbased stochastic inversion approaches provides significant improvements in the model simulations compared to using default CLM4 parameter values, and that as more information comes in, the predictive intervals (ranges of posterior distributions) of the calibrated parameters become narrower. In general, parameters that are identified to be significant through sensitivity analyses and statistical tests are better calibrated than those with weak or nonlinear impacts on flux or runoff observations. Temporal resolution of observations has larger impacts on the results of inverse modeling using heat flux data than runoff data. Soil and vegetation cover have important impacts on parameter sensitivities, leading to different patterns of posterior distributions of parameters at different sites. Overall, the MCMC-Bayesian inversion approach effectively and reliably improves the simulation of CLM under different climates and environmental conditions. Bayesian model averaging of the posterior estimates with different reference acceptance probabilities can smooth the posterior distribution and provide more reliable parameter estimates, but at the expense of wider uncertainty bounds.

Developing accurate and efficient modeling techniques for streamflow at the tens-kilometer spatia... more Developing accurate and efficient modeling techniques for streamflow at the tens-kilometer spatial scale and multi-year temporal scale is critical for evaluating and predicting the impact of climate-and human-induced discharge variations on river hydrodynamics. However, achieving such a goal is challenging because of limited surveys of streambed hydraulic roughness, uncertain boundary condition specifications, and high computational costs. We demonstrate that accurate and efficient threedimensional (3D) hydrodynamic modeling of natural rivers at 30-kilometer and 5-year scales is feasible using the following three techniques within OpenFOAM, an open-source computational fluid dynamics platform: 1) generating a distributed hydraulic roughness field for the streambed by integrating water stage observation data, a rough wall theory, and a local roughness optimization and adjustment strategy; 2) prescribing the boundary condition for the inflow and outflow by integrating precomputed results of a one-dimensional (1D) hydraulic model with the 3D model; and 3) reducing computational time using multiple parallel runs constrained by 1D inflow and outflow boundary conditions. Streamflow modeling for a 30-kilometerlong reach in the Columbia River (CR) over 58 months can be achieved in less than six days using 1.1 million CPU hours. The mean error between the modeled and the observed water stages for our simulated CR reach ranges from-16 cm to 9 cm (equivalent to ca. ±7% relative to the average water depth) at seven locations during most of the years between 2011 and 2019. We can reproduce the velocity distribution measured by the acoustic Doppler current profiler (ADCP). The correlation coefficients of the depth-averaged velocity between the model and ADCP measurements are in the range between 0.71 and 0.83 at 75% of the survey cross-sections. With the validated model, we further show that the relative importance of dynamic pressure versus hydrostatic pressure varies with discharge variations and topography heterogeneity. Given the model's high accuracy and computational efficiency, the model framework provides a generic approach to evaluate and predict the impact of climate-and human-induced discharge variations on river hydrodynamics at tens kilometer and decade scales.

Frontiers in water, Nov 26, 2020

Proceedings of the ... Annual Hawaii International Conference on System Sciences, 2018

Real-time monitoring of power system dynamics using phasor measurement units (PMUs) data improves... more Real-time monitoring of power system dynamics using phasor measurement units (PMUs) data improves situational awareness and system reliability, and helps prevent electric grid blackouts due to early anomaly detection. The study presented in this paper is based on real PMU measurements of the U.S. Western Interconnection system. Given the nonlinear and non-stationary PMU data, we developed a robust anomaly detection framework that uses wavelet-based multi-resolution analysis with moving-window-based outlier detection and anomaly scoring to identify potential PMU events. Candidate events were evaluated via spatiotemporal correlation analysis and classified for a better understanding of event types, resulting in successful anomaly detection and classification of the recorded events.

Stochastic Environmental Research and Risk Assessment, Jun 19, 2018

In this study, we focus on a hydrogeological inverse problem specifically targeting monitoring so... more In this study, we focus on a hydrogeological inverse problem specifically targeting monitoring soil moisture variations using tomographic ground penetrating radar (GPR) travel time data. Technical challenges exist in the inversion of GPR tomographic data for handling non-uniqueness, nonlinearity and high-dimensionality of unknowns. We have developed a new method for estimating soil moisture fields from crosshole GPR data. It uses a pilot-point method to provide a lowdimensional representation of the relative dielectric permittivity field of the soil, which is the primary object of inference: the field can be converted to soil moisture using a petrophysical model. We integrate a multi-chain Markov chain Monte Carlo (MCMC)-Bayesian inversion framework with the pilot point concept, a curved-ray GPR travel time model, and a sequential Gaussian simulation algorithm, for estimating the dielectric permittivity at pilot point locations distributed within the tomogram, as well as the corresponding geostatistical parameters (i.e., spatial correlation range). We infer the dielectric permittivity as a probability density function, thus capturing the uncertainty in the inference. The multi-chain MCMC enables addressing high-dimensional inverse problems as required in the inversion setup. The method is scalable in terms of number of chains and processors, and is useful for computationally demanding Bayesian model calibration in scientific and engineering problems. The proposed inversion approach can successfully approximate the posterior density distributions of the pilot points, and capture the true values. The computational efficiency, accuracy, and convergence behaviors of the inversion approach were also systematically evaluated, by comparing the inversion results obtained with different levels of noises in the observations, increased observational data, as well as increased number of pilot points. Keywords Tomographic ground penetrating radar Á Soil moisture Á Multi-chain Markov chain Monte Carlo Á Bayesian & Jie Bao

The report presents the results of the development of the open-source suite of applications for s... more The report presents the results of the development of the open-source suite of applications for synchrophasor analysis. The suite includes several software tools for oscillation analysis, power plant model validation, and frequency response analysis using synchrophasor measurements. All tools are based on the common framework and data sources. The developed tools have been used by different electrical utilities for synchrophasor analysis. The report includes several use cases based on the actual system PMU data.

This paper presents a comprehensive approach to predict Balancing Authority (BA) regulation and l... more This paper presents a comprehensive approach to predict Balancing Authority (BA) regulation and load following requirements in order to improve BA control performance. In this paper the Pacific Northwest National Laboratory's (PNNL) “ramp and uncertainty prediction tool (RUT) and day-ahead regulation prediction (DARP) tool” were upgraded to incorporate advanced probabilistic forecast information provided by AWS Truepower. The proposed methodology has been tested and validated using actual California Independent System Operator (CAISO) data. Simulation confirmed that integration probabilistic forecast information can reduce the predicted regulation range by about 12-31%. This means that BAs can procure fewer balancing resources without compromising their reliability and control performance requirements.

International Journal of Greenhouse Gas Control, Nov 1, 2016

The impact of temperature variations of the injected CO 2 on the mechanical integrity of a reserv... more The impact of temperature variations of the injected CO 2 on the mechanical integrity of a reservoir is an important problem but rarely addressed in the design of a CO 2 storage site. In this study, a three-dimensional (3D) thermo-geomechanical approach was developed to evaluate the possibility of fracturing the FutureGen 2.0 site due to injection of CO 2 at different temperatures. The approach sequentially coupled the STOMP-CO2 code for flow and thermal analyses to the ABAQUS ® finite element package for performing thermo-geomechanical analyses of this site. The 3D STOMP-CO2 model of the FutureGen 2.0 site contains four horizontal wells and variable layer thickness, flow and thermal properties. The 3D ABAQUS ® finite element (FE) model for thermo-geomechanical analysis which exactly maps the STOMP-CO2 model contains variable thermo-geomechanical properties. Boundary conditions were prescribed to the FE model to achieve the strike-slip faulting stress regime observed at the FutureGen 2.0 site. The STOMP-CO2 model takes into account the results from modeling the heat exchange between the environment and CO 2 during its transport in the pipeline and injection wells before reaching the reservoir, as well as its interaction with the reservoir host rock. Injection temperature in the reservoir, whose initial temperature was 36°C, was varied, and two cases were simulated and modeled: 28°C, the minimum possible temperature considered as an extreme case since it corresponds to winter conditions maintained during the 20 years of the injection, and 47°C that represents the annual average injection temperature. The STOMP-CO2/ABAQUS ® analyses indicate lower injection temperatures approaching 28°C could locally induce shear slip activation close to the wells and confined to the reservoir. Thermally induced hydraulic fracture is not expected for the 28°C-47°C injection temperature range or higher.