Date of Award


Degree Name

Master of Science


Electrical Engineering


Yuanrui Sang


Greenhouse gas emissions have been increasing in concentration in the atmosphere, which poses risks to the environment. Since 2011, the ๐ถ๐ถ๐ถ๐ถ2 emissions reached an average of 410 parts per million (ppm). The burning of fossil fuels for energy production was identified as the primary source of these emissions, making up 25% of global emissions by sector in 2010. To promote sustainable practices, there is an increasing need to accurately track greenhouse gas emissions in power systems. To accomplish this, an emission tracking method that can provide emission information for each load point in real-time is needed. In addition, certain indices are needed to indicate how the reduction in emissions would impact the environment with reference to climate change reduction programs. The Global Warming Potential (GWP) and Human Toxicity Potential (HTP) are indices that have been widely used as reference measurements to assess the impact of emissions and are discussed in this thesis. In addition, the rapid increase of electric vehicles (EVs) in circulation guarantees a rise in consumption from the grid. This consumption is concentrated at specific locations where multiple electric vehicles utilize charging stations, potentially leading to a substantial increase in the load. The upsurge in generation to contribute to EV charging could result in more emissions being produced. The methodologies presented in this thesis will employ a system based on the DC optimal power flow (DCOPF) formulation. This system is designed to trace the origin of electricity generation, considering transmission topology and impedance. Identifying the source of generation used to fulfill demand is crucial for understanding the resulting emissions from this process. To fill these gaps, this thesis aims to provide a computationally efficient method of tracking greenhouse gas emissions and evaluate the environmental impact of power systems.The following are the key findings of this thesis. Chapter 3 contributes to the development of a marginal emission tracking system by using unit commitment (UC) and economic dispatch (ED) models to minimize the cost of meeting the demand in an RTS-96 test system. The results were used in a model based on linear optimization that (i) considers both location and time constraints; (ii) calculates the marginal emission factors (MEF) at each bus using the marginal generation of each generator that meets the load; (iii) considers transmission topology and impedance; (iv) considers binding transmission line constraints to ensure that alterations in power flow on any of these lines are restricted. The precise power contributions from each marginal generator are determined, and these are utilized to assess the emissions generated in meeting this demand. Chapter 4 outlines a method to assess the environmental impacts arising from increased demand at specific locations in the RTS-96 test system. The GWP and HTP serve as impact indicators, and an analysis is conducted to explore the relationship between these two factors. The MEF was calculated through the results of the UC and ED models. The greenhouse gases were adjusted for each location to calculate the GWP and HTP due to a marginal increase in load. It was found that locational marginal emissions can be utilized in the calculation of the GWP and HTP of certain buses which could aid in environmental impact assessments. Chapter 5 concentrates on formulating an algorithm for the optimal placement of EV charging stations within the RTS-96 test system. An analysis is conducted to identify the most suitable locations for this infrastructure, aiming to minimize both the carbon footprint and operational costs simultaneously. The outcomes derived from the UC and ED models are employed to determine the MEF for each location over an entire year. These results incorporate adjusted load factors to accommodate power fluctuations across different seasons. The results show that the optimal allocation of EV charging stations can be determined by analyzing locational marginal costs and emissions to minimize both factors. Finally, Chapter 6 presents a virtual carbon tracing algorithm to quantify emissions arising from the usage of EV charging stations. The algorithm uses the generation and power flow results from the UC and ED models to monitor the virtual carbon flow in the system. The simulation was executed on a test system modeled after the El Paso electric grid to yield realistic outcomes. The model identifies the source and magnitude of carbon emissions resulting from EV consumption, providing valuable insights for users and policymakers to understand their carbon footprint.




Recieved from ProQuest

File Size

132 p.

File Format


Rights Holder

Kenji Santacruz

Included in

Engineering Commons