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Using time series simulation tools for assessing the effects of variable renewable energy generation on power and energy systems

Author

Listed:
  • Matti Koivisto
  • Kaushik Das
  • Feng Guo
  • Poul Sørensen
  • Edgar Nuño
  • Nicolaos Cutululis
  • Petr Maule

Abstract

The increasing share of variable renewable energy (VRE) generation poses challenges to power systems. Possible challenges include adequacy of reserves, planning and operation of power systems, and interconnection expansion studies in future power systems with very different generation patterns compared to today. To meet these challenges, there is a need to develop models and tools to analyze the variability and uncertainty in VRE generation. To address the varied needs, the tools should be versatile and applicable to different geographical and temporal scales. Time series simulation tools can be used to model both today and future scenarios with varying VRE installations. Correlations in Renewable Energy Sources (CorRES) is a simulation tool developed at Technical University of Denmark, Department of Wind Energy capable of simulating both wind and solar generation. It uses a unique combination of meteorological time series and stochastic simulations to provide consistent VRE generation and forecast error time series with temporal resolution in the minute scale. Such simulated VRE time series can be used in addressing the challenges posed by the increasing share of VRE generation. These capabilities will be demonstrated through three case studies: one about the use of large‐scale VRE generation simulations in energy system analysis, and two about the use of the simulations in power system operation, planning, and analysis. This article is categorized under: Wind Power > Systems and Infrastructure Energy Infrastructure > Systems and Infrastructure Energy Systems Economics > Systems and Infrastructure

Suggested Citation

  • 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.
    • Handle: RePEc:bla:wireae:v:8:y:2019:i:3:n:e329
    DOI: 10.1002/wene.329
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    References listed on IDEAS

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    3. Campion, Nicolas & Nami, Hossein & Swisher, Philip R. & Vang Hendriksen, Peter & Münster, Marie, 2023. "Techno-economic assessment of green ammonia production with different wind and solar potentials," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    4. Feng Guo & David Schlipf, 2021. "A Spectral Model of Grid Frequency for Assessing the Impact of Inertia Response on Wind Turbine Dynamics," Energies, MDPI, vol. 14(9), pages 1-19, April.
    5. Wang, Qiang & Li, Shuyu & Pisarenko, Zhanna, 2020. "Heterogeneous effects of energy efficiency, oil price, environmental pressure, R&D investment, and policy on renewable energy -- evidence from the G20 countries," Energy, Elsevier, vol. 209(C).
    6. Swisher, Philip & Murcia Leon, Juan Pablo & Gea-Bermúdez, Juan & Koivisto, Matti & Madsen, Helge Aagaard & Münster, Marie, 2022. "Competitiveness of a low specific power, low cut-out wind speed wind turbine in North and Central Europe towards 2050," Applied Energy, Elsevier, vol. 306(PB).
    7. Julio Barzola-Monteses & Mónica Mite-León & Mayken Espinoza-Andaluz & Juan Gómez-Romero & Waldo Fajardo, 2019. "Time Series Analysis for Predicting Hydroelectric Power Production: The Ecuador Case," Sustainability, MDPI, vol. 11(23), pages 1-19, November.
    8. Juan Gea-Bermúdez & Kaushik Das & Hardi Koduvere & Matti Juhani Koivisto, 2020. "Day-Ahead Market Modelling of Large-Scale Highly-Renewable Multi-Energy Systems: Analysis of the North Sea Region towards 2050," Energies, MDPI, vol. 14(1), pages 1-17, December.
    9. Sneum, Daniel Møller & González, Mario Garzón & Gea-Bermúdez, Juan, 2021. "Increased heat-electricity sector coupling by constraining biomass use?," Energy, Elsevier, vol. 222(C).
    10. Bakhtiari, Hamed & Zhong, Jin & Alvarez, Manuel, 2021. "Predicting the stochastic behavior of uncertainty sources in planning a stand-alone renewable energy-based microgrid using Metropolis–coupled Markov chain Monte Carlo simulation," Applied Energy, Elsevier, vol. 290(C).
    11. Pinciroli, Luca & Baraldi, Piero & Compare, Michele & Zio, Enrico, 2023. "Optimal operation and maintenance of energy storage systems in grid-connected microgrids by deep reinforcement learning," Applied Energy, Elsevier, vol. 352(C).
    12. Bakhtiari, Hamed & Zhong, Jin & Alvarez, Manuel, 2022. "Uncertainty modeling methods for risk-averse planning and operation of stand-alone renewable energy-based microgrids," Renewable Energy, Elsevier, vol. 199(C), pages 866-880.
    13. 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.
    14. Gea-Bermúdez, Juan & Bramstoft, Rasmus & Koivisto, Matti & Kitzing, Lena & Ramos, Andrés, 2023. "Going offshore or not: Where to generate hydrogen in future integrated energy systems?," Energy Policy, Elsevier, vol. 174(C).
    15. Olsen, Karen Pardos & Zong, Yi & You, Shi & Bindner, Henrik & Koivisto, Matti & Gea-Bermúdez, Juan, 2020. "Multi-timescale data-driven method identifying flexibility requirements for scenarios with high penetration of renewables," Applied Energy, Elsevier, vol. 264(C).

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