| Autor: |
Koivisto, Matti1 (AUTHOR) mkoi@dtu.dk, Jónsdóttir, Guðrún Margrét2 (AUTHOR), Sørensen, Poul1 (AUTHOR), Plakas, Konstantinos1 (AUTHOR), Cutululis, Nicolaos1 (AUTHOR) |
| Předmět: |
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| Zdroj: |
Renewable Energy: An International Journal. Oct2020, Vol. 159, p991-999. 9p. |
| Abstrakt: |
As installed wind generation capacities increase, there is a need to model variability in wind generation in detail to analyse its impacts on power systems. Utilization of meteorological reanalysis data and stochastic simulation are possible approaches for modelling this variability. In this paper, a combination of these two approaches is used to model wind generation variability. Parameters for the model are determined based on measured wind speed data. The model is used to simulate wind generation from the level of a single offshore wind power plant to the aggregate onshore wind generation of western Denmark. The simulations are compared to two years of generation measurements on 15 min resolution. The results indicate that the model, combining reanalysis data and stochastic simulation, can successfully model wind generation variability on different geographical aggregation levels on sub-hourly resolution. It is shown that the addition of stochastic simulation to reanalysis data is required when modelling offshore wind generation and when analysing onshore wind in small geographical regions. • A model combining reanalysis data and stochastic simulation is presented. • Wind generation is simulated on different geographical aggregation levels. • The results are validated using two years of measured generation data. • Focus is given to sub-hourly resolution and modelling of ramping. • The need of stochastic simulation is assessed in the different studied cases. [ABSTRACT FROM AUTHOR] |
| Databáze: |
GreenFILE |
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Full Text from ScienceDirect