L. Yu

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.

TL;DR: In this paper, a robust flexible probabilistic programming (RFPP) method is developed for planning municipal energy system (MES) with considering peak electricity prices (PEPs) and electric vehicles (EVs), where multiple uncertainties regarded as intervals, probability distributions and flexibilities as well as their combinations can be effectively reflected.

TL;DR: In this paper, a fuzzy-stochastic simulation-optimization model (FSSOM) is developed for planning EPS (electric power systems) with considering peak demand under uncertainty, which integrates techniques of SVR (support vector regression), Monte Carlo simulation, and FICMP (fractile interval chance-constrained mixed integer programming).

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.

TL;DR: Compared to the conventional stochastic programming, the developed CSFP method can more effectively analyze individual and interactive effects of multiple random variables, so that the loss of uncertain information can be mitigated and the robustness of solution can be enhanced.