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Combination of meteorological reanalysis data and stochastic simulation for modelling wind generation variability

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  • Koivisto, Matti
  • Jónsdóttir, Guðrún Margrét
  • Sørensen, Poul
  • Plakas, Konstantinos
  • Cutululis, Nicolaos

Abstract

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.

Suggested Citation

  • Koivisto, Matti & Jónsdóttir, Guðrún Margrét & Sørensen, Poul & Plakas, Konstantinos & Cutululis, Nicolaos, 2020. "Combination of meteorological reanalysis data and stochastic simulation for modelling wind generation variability," Renewable Energy, Elsevier, vol. 159(C), pages 991-999.
    • Handle: RePEc:eee:renene:v:159:y:2020:i:c:p:991-999
    DOI: 10.1016/j.renene.2020.06.033
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    References listed on IDEAS

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    1. Matti Koivisto & Kaushik Das & Feng Guo & Poul Sørensen & Edgar Nuño & Nicolaos Cutululis & Petr Maule, 2019. "Using time series simulation tools for assessing the effects of variable renewable energy generation on power and energy systems," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 8(3), May.
    2. Andresen, Gorm B. & Søndergaard, Anders A. & Greiner, Martin, 2015. "Validation of Danish wind time series from a new global renewable energy atlas for energy system analysis," Energy, Elsevier, vol. 93(P1), pages 1074-1088.
    3. Brown, T. & Schlachtberger, D. & Kies, A. & Schramm, S. & Greiner, M., 2018. "Synergies of sector coupling and transmission reinforcement in a cost-optimised, highly renewable European energy system," Energy, Elsevier, vol. 160(C), pages 720-739.
    4. Staffell, Iain & Pfenninger, Stefan, 2016. "Using bias-corrected reanalysis to simulate current and future wind power output," Energy, Elsevier, vol. 114(C), pages 1224-1239.
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    Cited by:

    1. Ariyarathne, Sakitha & Gangammanavar, Harsha & Sundararajan, Raanju R., 2022. "Change point detection-based simulation of nonstationary sub-hourly wind time series," Applied Energy, Elsevier, vol. 310(C).
    2. Murcia, Juan Pablo & Koivisto, Matti Juhani & Luzia, Graziela & Olsen, Bjarke T. & Hahmann, Andrea N. & Sørensen, Poul Ejnar & Als, Magnus, 2022. "Validation of European-scale simulated wind speed and wind generation time series," Applied Energy, Elsevier, vol. 305(C).
    3. Rujie Zhu & Kaushik Das & Poul Ejnar Sørensen & Anca Daniela Hansen, 2023. "Optimal Participation of Co-Located Wind–Battery Plants in Sequential Electricity Markets," Energies, MDPI, vol. 16(15), pages 1-17, July.

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