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Nguyen, Hieu T.

Publications and source records attributed to Nguyen, Hieu T..

Electrifying Education: Insights into Charging Electric School Buses in the United States

To combat climate change, the United States joined 193 Parties in committing to the Paris Agreement, which aims to limit global warming to 2 degrees C or less, and decarbonizing transportation will be a key requirement for achieving this goal. School buses (SBs) are a common form of student transport in the U.S. with nearly all SBs today powered by fossil fuels (primarily diesel). As a result, SBs have historically been a concerning source of both greenhouse gas (GHG) emissions and local air pollutants with negative health impacts for students and others living nearby. Electric SBs (ESBs) are a promising emerging technology for decarbonizing student transport, however, ESB adoption in the U.S. remains at an early stage (approximate 1.1%) with many outstanding uncertainties. This study aims to elucidate several of these by taking inventory of the total SB stock within U.S. states and studying real-world SB operating profiles to infer potential battery range requirements, daily charging opportunities, and charging infrastructure requirements for ESBs. In addition, we observe the geographic trends of early-stage ESB adoption, which can be used to better understand early adopter patterns and train vehicle technology adoption models.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗

Solving differential‐algebraic equations in power system dynamic analysis with quantum computing

Abstract Power system dynamics are generally modeled by high dimensional non‐linear differential‐algebraic equations (DAEs) given a large number of components forming the network. These DAEs' complexity can grow exponentially due to the increasing penetration of distributed energy resources, whereas their computation time becomes sensitive due to the increasing interconnection of the power grid with other energy systems. This paper demonstrates the use of quantum computing algorithms to solve DAEs for power system dynamic analysis. We leverage a symbolic programming framework to equivalently convert the power system's DAEs into ordinary differential equations (ODEs) using index reduction methods and then encode their data into qubits using amplitude encoding. The system non‐linearity is captured by Hamiltonian simulation with truncated Taylor expansion so that state variables can be updated by a quantum linear equation solver. Our results show that quantum computing can solve the power system's DAEs accurately with a computational complexity polynomial in the logarithm of the system dimension. We also illustrate the use of recent advanced tools in scientific machine learning for implementing complex computing concepts, that is, Taylor expansion, DAEs/ODEs transformation, and quantum computing solver with abstract representation for power engineering applications.

computational complexity↗