S
S. Nie
Researcher at University of Toronto
Publications - 18
Citations - 374
S. Nie is an academic researcher from University of Toronto. The author has contributed to research in topics: Water resources & Renewable energy. The author has an hindex of 10, co-authored 18 publications receiving 265 citations. Previous affiliations of S. Nie include University of Cambridge.
Papers
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A copula-based flexible-stochastic programming method for planning regional energy system under multiple uncertainties: A case study of the urban agglomeration of Beijing and Tianjin
TL;DR: Compared to joint-probabilistic chance-constrained programming (JCP), the CFSP method is more effective for handling multiple random parameters associated with different probability distributions in which their correlations are unknown.
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Analyzing climate change impacts on water resources under uncertainty using an integrated simulation-optimization approach
TL;DR: In this paper, an integrated simulation-optimization (ISO) approach is developed for assessing climate change impacts on water resources in watersheds, where uncertainties presented as both interval numbers and probability distributions can be reflected.
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Planning carbon dioxide mitigation of Qingdao's electric power systems under dual uncertainties
TL;DR: In this article, a two-stage interval-possibilistic programming (TIPP) method is developed for planning carbon emission trading (CET) in the electric power systems of Qingdao (China), where dual uncertainties expressed as interval-random variables and interval-Possibiliistic parameters can be handled.
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Analyzing the effects of economic development on the transition to cleaner production of China’s energy system under uncertainty
TL;DR: In this article, an ensemble energy system model for China (named as CN-EES model) is developed through incorporating computable general equilibrium model and interval-parameter programming method within an energy system optimization framework.
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Optimization of electric power systems with cost minimization and environmental-impact mitigation under multiple uncertainties
TL;DR: In this article, a multistage inexactfactorial fuzzy probability programming (MIFP) method is developed for optimizing electric power systems with cost minimization and environmental-impact mitigation.