This paper proposes a multivariate modeling approach to tackle spatio-temporal dependency of various variables accounted in electric power system operation and planning. The stochasticity of system load as well as power generation from renewable energy sources poses special challenges to power system planners. Increasing penetration levels of wind exacerbate the uncertainty and variability that must be addressed in coming years, and can be extremely relevant to power system planners. Inefficiency of univariate models and relying on correlation is seen as a future bottleneck. A joint multivariate modeling approach using vine copula is proposed in this work considering load and wind data from US utility.
Original languageEnglish
Title of host publication2018 IEEE Power & Energy Society General Meeting (PESGM)
Number of pages5
ISBN (Electronic)978-1-5386-7703-2
ISBN (Print)978-1-5386-7704-9
Publication statusPublished - 5 Aug 2018
Event2018 IEEE Power & Energy Society General Meeting (PESGM) - Portland, United States
Duration: 5 Aug 201810 Aug 2018


Conference2018 IEEE Power & Energy Society General Meeting (PESGM)
Abbreviated titlePESGM 2018
CountryUnited States

    Research areas

  • Dependency modeling, load, long-term horizon, multivariate model, wind power, stochastic modeling

ID: 41674725