Dong Gu Choi
포항공과대학교 공과대학 기계공학과 · 공학
Dong Gu Choi 교수의 연구실은 에너지 전환, 전력 시스템 최적화 및 지속가능성 평가를 중심으로 한 다학제적 연구를 수행합니다. 특히 전력 수요 관리, 전기차 및 에너지 저장장치(ESS)의 통합적 운영, 시간대별 요금제의 소비자 수용성 등 실생활 에너지 정책과 소비자 행동을 고려한 모델링 기반 분석에 특화되어 있습니다. 복잡한 에너지 시장 환경에서의 의사결정 지원을 위한 다기준 의사결정모형(MCDM), 마코프 결정과정(MDP), 확률적 시뮬레이션 등 수리적·통계적 모델링 기법을 적극 활용합니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
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
