Pohang University of Science and Technology · 工学
Professor Dong Gu Choi's research lab specializes in energy systems modeling, sustainability assessment, and decision-making under uncertainty, with a strong focus on integrating stochastic optimization and multi-criteria analysis into energy policy and infrastructure planning. The lab investigates the economic, environmental, and operational dimensions of energy transitions, particularly in electricity systems and transportation, using advanced modeling techniques such as Markov decision processes, discrete-time Markov chains, and system optimization across multiple time scales. Key research directions include energy storage valuation, electric vehicle integration, time-of-use tariff design, and wind power grid integration. The lab’s work bridges theoretical modeling with real-world applications to support sustainable and efficient energy system development.
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The electricity sector in Korea is facing complex sustainability issues with recent government energy policy reprioritizing social and environmental concerns over economics. As a response, we developed a multi-criteria decision making (MCDM) model linked to an energy-system model to assess the sustainability of different policy scenarios in the Korean electricity sector. Our analysis shows that, while the new transition policy is not an attractive option in total cost and emissions, it can be de
We investigate the causes behind the underwhelming adoption of voluntary Time‐of‐Use (TOU) tariffs in the residential electricity market. TOU tariffs are deployed by utilities to better match electricity generation capacity with market demand by giving consumers price incentives to reduce their consumption when electricity demand is at its peak. However, consumers in residential electricity markets are heterogeneous in their consumption preferences. Hence, utilities face a trade‐off when deployi
Adoption of electric vehicles (EVs) would affect the costs and sources of electricity and the United States efficiency requirements for conventional vehicles (CVs). We model EV adoption scenarios in each of six regions of the Eastern Interconnection, containing 70% of the United States population. We develop electricity system optimization models at the multidecade, day-ahead, and hour-ahead time scales, incorporating spatial wind energy modeling, endogenous modeling of CV efficiencies, projecti
<b>Purpose</b>: This study builds a stochastic model of a discrete-time Markov chain (DTMC) that fits well with a dataset of professional playing records. <b>Methods</b>: The point-by-point dataset of Men's single matches played in the Association of Tennis Professionals (ATP) tour from 2011 to 2015 is analyzed. A long-debated assumption on the <i>iid</i>-ness in the point winning probability of the server is statistically tested. A DTMC model is then developed to analyze the dataset further. <b
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