A statistical model for comparing future wind power scenarios with varying geographical distribution of installed generation capacity

M. Koivisto*, J. Ekström, J. Seppänen, I. Mellin, Robert Millar, L. Haarla

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

18 Citations (Scopus)

Abstract

As installed wind generation capacity increases, understanding the effect of wind power on the electric power system is becoming more important. This paper introduces a statistical model that can be used to estimate the variability in wind generation and assess the risk of wind generation contingencies over a large geographical area. The analysis of the installed wind generation capacities is separated from the analysis of the spatial and temporal dependency structures. This enables the study of different future wind power scenarios with varying generation capacities. The model is built on measured hourly wind generation data from Denmark, Estonia, Finland and Sweden. Three scenarios with different geographical distributions of wind power are compared to show the applicability of the model for power system planning. A method for finding the scenario with the minimum variance of the aggregate wind generation is introduced. As the geographical distribution of wind power can be affected by subsidies and other incentives, the presented results can have policy implications.

Original languageEnglish
Pages (from-to)665-679
Number of pages15
JournalWind Energy
Volume19
Issue number4
DOIs
Publication statusPublished - 1 Apr 2016
MoE publication typeA1 Journal article-refereed

Keywords

  • Gaussian copula
  • generalized Pareto distribution
  • Monte Carlo simulation
  • power system planning
  • vector autoregressive model
  • wind power

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