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Journal Article

Cost-Effective Reduction of Greenhouse Gas Emissions via Cross-Sector Purchases of Renewable Energy Certificates

Over half of the greenhouse gas (GHG) emissions in the United States come from the transportation and electricity generation sectors. To analyze the potential impact of cross-sector cooperation in reducing these emissions, we formulate a bi-level optimization model where the transportation sector can purchase renewable energy certificates (REC) from the electricity generation sector. These RECs are used to offset emissions from transportation in lieu of deploying high-cost fuel efficient technologies. The electricity generation sector creates RECs by producing additional energy from renewable sources. This additional renewable capacity is financed by the transportation sector and it does not impose additional cost on the electricity generation sector. Our results show that such a REC purchasing regime significantly reduces the cost to society of reducing GHG emissions. Additionally, our results indicate that a REC purchasing policy can create electricity beyond actual demand.
Technical Paper

Pricing of Renewable Gasoline and Its Impact on Greenhouse Gas Emission Reduction Planning for Automakers and Electricity Generators

With increasing evidence for climate change in response to greenhouse gasses (GHG) emitted by human activities, pressure is growing to reduce fuel consumption via increased vehicle efficiency and to replace fossil fuels with renewable fuels. While real-world experience with bio-ethanol and a growing body of research on many other renewable fuel pathways provide some guidance as to the cost of renewable transportation fuel, there has been little work comparing that cost to alternative means for achieving equivalent GHG reductions. In earlier work, we developed an optimization model that allowed the transportation and electricity generation sectors to work separately or jointly to achieve GHG reduction targets, and showed that cooperation can significantly reduce the society cost of GHG reductions.
Journal Article

Validation Metric for Dynamic System Responses under Uncertainty

To date, model validation metric is prominently designed for non-dynamic model responses. Though metrics for dynamic responses are also available, they are specifically designed for the vehicle impact application and uncertainties are not considered in the metric. This paper proposes the validation metric for general dynamic system responses under uncertainty. The metric makes use of the popular U-pooling approach and extends it for dynamic responses. Furthermore, shape deviation metric was proposed to be included in the validation metric with the capability of considering multiple dynamic test data. One vehicle impact model is presented to demonstrate the proposed validation metric.
Journal Article

Potential Natural Gas Impact on Cost Efficient Capacity Planning for Automakers and Electricity Generators in a Carbon Constrained World

Greenhouse gas (GHG) emission targets are becoming more stringent for both automakers and electricity generators. With the introduction of plug-in hybrid and electric vehicles, transportation and electricity generation sectors become connected. This provides an opportunity for both sectors to work together to achieve the cost efficient reduction of CO2 emission. In addition, the abundant natural gas (NG) in USA is drawing increased attention from both policy makers and various industries due to its low cost and low carbon content. NG has the potential to ease the pressure from CO2 emission constraints for both the light duty vehicle (LDV) and the electricity generation sectors while simultaneously reducing their fuel costs. To quantify the benefit of this collaboration, an analytical model is developed to evaluate the total societal cost and CO2 emission for both sectors.
Journal Article

A Copula-Based Approach for Model Bias Characterization

Available methodologies for model bias identification are mainly regression-based approaches, such as Gaussian process, Bayesian inference-based models and so on. Accuracy and efficiency of these methodologies may degrade for characterizing the model bias when more system inputs are considered in the prediction model due to the curse of dimensionality for regression-based approaches. This paper proposes a copula-based approach for model bias identification without suffering the curse of dimensionality. The main idea is to build general statistical relationships between the model bias and the model prediction including all system inputs using copulas so that possible model bias distributions can be effectively identified at any new design configurations of the system. Two engineering case studies whose dimensionalities range from medium to high will be employed to demonstrate the effectiveness of the copula-based approach.