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Net load variability in Nordic countries with a highly or fully renewable power system


Increasing the share of intermittent renewable energy (IRE) resources such as solar, wind, wave and tidal energy in a power system poses a challenge in terms of increased net load variability. Fully renewable power systems have previously been analysed, but more systematic analyses are needed that explore the effect of different IRE mixes on system-wide variability across different timescales and the optimal combinations of IRE for reducing variability on a given timescale. Here we investigate these questions for the Nordic power system. We show that the optimal mix of IRE is dependent on the frequency band considered. Long-term (>4 months) and short-term (<2 days) fluctuations can be similar to today’s, even for a fully renewable system. However, fluctuations with periods in between will inevitably increase significantly. This study indicates that, from a variability point of view, a fossil- and nuclear-free Nordic power system is feasible if properly balanced by hydropower.

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Figure 1: Scenarios of intermittent renewables.
Figure 2: Standard deviations for the frequency components in the 3 TWh scenarios.
Figure 3: Mixes of intermittent renewables.
Figure 4: Net load standard deviations.
Figure 5: Reduction of net load variability from overproduction and curtailment.
Figure 6: Hourly contributions for 2014 from nuclear, thermal and IRE.
Figure 7: Illustration of the overproduction and curtailment strategy.


  1. Renewables 2016 Global Status Report (REN21 Secretariat, 2016).

  2. The Power of Transformation: Wind, Sun and the Economics of Flexible Power Systems (International Energy Agency, 2014).

  3. Hirth, L. & Ziegenhagen, I. Balancing power and variable renewables: three links. Renew. Sustain. Energy Rev. 50, 1035–1051 (2015).

    Article  Google Scholar 

  4. Kondziella, H. & Bruckner, T. Flexibility requirements of renewable energy based electricity systems—a review of research results and methodologies. Renew. Sustain. Energy Rev. 53, 10–22 (2016).

    Article  Google Scholar 

  5. Jacobson, M. Z., Delucchi, M. A., Cameron, M. A. & Frew, B. A. Low-cost solution to the grid reliability problem with 100% penetration of intermittent wind, water, and solar for all purposes. Proc. Natl Acad. Sci. USA 112, 15060–15065 (2015).

    Article  Google Scholar 

  6. Pfenninger, S. et al. Potential for concentrating solar power to provide baseload and dispatchable power. Nat. Clim. Change 4, 689–692 (2014).

    Article  Google Scholar 

  7. Lund, P. D., Lindgren, J., Mikkola, J. & Salpakari, J. Review of energy system flexibility measures to enable high levels of variable renewable electricity. Renew. Sustain. Energy Rev. 45, 785–807 (2015).

    Article  Google Scholar 

  8. Bollen, M. H. J. Integration of Distributed Generation in the Power System (Wiley, 2011).

    Book  Google Scholar 

  9. Widén, J. Correlations between large-scale solar and wind power in a future scenario for Sweden. IEEE Trans. Sustain. Energy 2, 177–184 (2011).

    Article  Google Scholar 

  10. Santos-Alamillos, F. J., Pozo-Vázquez, D., Ruiz-Arias, J. A., Lara-Fanego, V. & Tovar-Pescador, J. Analysis of spatiotemporal balancing between wind and solar energy resources in the southern Iberian peninsula. J. Appl. Meteorol. Climatol. 51, 2005–2024 (2012).

    Article  Google Scholar 

  11. Becker, S. et al. Features of a fully renewable US electricity system: optimized mixes of wind and solar PV and transmission grid extensions. Energy 72, 443–458 (2014).

    Article  Google Scholar 

  12. Budischak, C. et al. Cost-minimized combinations of wind power, solar power and electrochemical storage, powering the grid up to 99.9% of the time. J. Power Sources 225, 60–74 (2013).

    Article  Google Scholar 

  13. Rasmussen, M. G., Andresen, G. B. & Greiner, M. Storage and balancing synergies in a fully or highly renewable pan-European power system. Energy Policy 51, 642–651 (2012).

    Article  Google Scholar 

  14. Heide, D., Greiner, M., von Bremen, L. & Hoffmann, C. Reduced storage and balancing needs in a fully renewable European power system with excess wind and solar power generation. Renew. Energy 36, 2515–2523 (2011).

    Article  Google Scholar 

  15. Lund, H. Large-scale integration of optimal combinations of PV, wind and wave power into the electricity supply. Renew. Energy 31, 503–515 (2006).

    Article  Google Scholar 

  16. Lund, H. & Mathiesen, B. V. Energy system analysis of 100% renewable energy systems—the case of Denmark in years 2030 and 2050. Energy 34, 524–531 (2009).

    Article  Google Scholar 

  17. Jacobson, M. Z. et al. 100% clean and renewable wind, water, and sunlight (WWS) all-sector energy roadmaps for the 50 United States. Energy Environ. Sci. 8, 2093–2117 (2015).

    Article  Google Scholar 

  18. Widén, J. et al. Variability assessment and forecasting of renewables: a review for solar, wind, wave and tidal resources. Renew. Sustain. Energy Rev. 44, 356–375 (2015).

    Article  Google Scholar 

  19. Andresen, G. B. et al. Fundamental properties of and transition to a fully renewable pan-European power system. EPJ Web Conf. 33, 04001 (2012).

    Article  Google Scholar 

  20. Huber, M., Dimkova, D. & Hamacher, T. Integration of wind and solar power in Europe: assessment of flexibility requirements. Energy 69, 236–246 (2014).

    Article  Google Scholar 

  21. Schlachtberger, D. P., Becker, S., Schramm, S. & Greiner, M. Backup flexibility classes in emerging large-scale renewable electricity systems. Energy Convers. Manage 125, 336–346 (2016).

    Article  Google Scholar 

  22. Stoutenburg, E. D., Jenkins, N. & Jacobson, M. Z. Variability and uncertainty of wind power in the California electric power system. Wind Energy 17, 1411–1424 (2014).

    Google Scholar 

  23. Lingfors, D. & Widén, J. Development and validation of a wide-area model of hourly aggregate solar power generation. Energy 102, 559–566 (2016).

    Article  Google Scholar 

  24. Olauson, J. & Bergkvist, M. Modelling the Swedish wind power production using MERRA reanalysis data. Renew. Energy 76, 717–725 (2015).

    Article  Google Scholar 

  25. Olauson, J., Bergström, H. & Bergkvist, M. Restoring the missing high-frequency fluctuations in a wind power model based on reanalysis data. Renew. Energy 96, 784–791 (2016).

    Article  Google Scholar 

  26. Jamaly, M., Bosch, J. L. & Kleissl, J. Aggregate ramp rates of distributed photovoltaic systems in San Diego County. IEEE Trans. Sustain. Energy 4, 519–526 (2013).

    Article  Google Scholar 

  27. Marinelli, M. et al. Wind and photovoltaic large-scale regional models for hourly production evaluation. IEEE Trans. Sustain. Energy 6, 916–923 (2015).

    Article  Google Scholar 

  28. Evans, D. L. Simplified method for predicting photovoltaic array output. Sol. Energy 27, 555–560 (1981).

    Article  Google Scholar 

  29. King, D. L. et al. Performance Model for Grid-Connected Photovoltaic Inverters (Sandia National Laboratories, 2007).

    Google Scholar 

  30. Rienecker, M. M. et al. MERRA: NASA’s modern-era retrospective analysis for research and applications. J. Clim. 24, 3624–3648 (2011).

    Article  Google Scholar 

  31. Ayob, M. N., Castellucci, V. & Waters, R. Wave energy potential and 1–50 TWh scenarios for the Nordic synchronous grid. Renew. Energy C (in the press).

  32. Dee, D. P. et al. The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc. 137, 553–597 (2011).

    Article  Google Scholar 

  33. Shanas, P. R. & Kumar, V. S. Trends in surface wind speed and significant wave height as revealed by ERA-Interim wind wave hindcast in the Central Bay of Bengal. Int. J. Climatol. 35, 2654–2663 (2015).

    Article  Google Scholar 

  34. Jadidoleslam, N., Özger, M. & Ağiralioğlu, N. Wave power potential assessment of Aegean Sea with an integrated 15-year data. Renew. Energy 86, 1045–1059 (2016).

    Article  Google Scholar 

  35. Portilla, J., Sosa, J. & Cavaleri, L. Wave energy resources: wave climate and exploitation. Renew. Energy 57, 594–605 (2013).

    Article  Google Scholar 

  36. Salter, S. H. Wave power. Nature 249, 720–724 (1974).

    Article  Google Scholar 

  37. Hicks, S. D. Understanding Tides (US Dept. of Commerce, National Oceanic and Atmospheric Administration, National Ocean Service, Center for Operational Oceanographic Products and Services, 2006).

    Google Scholar 

  38. Grabbe, M., Lalander, E., Lundin, S. & Leijon, M. A review of the tidal current energy resource in Norway. Renew. Sustain. Energy Rev. 13, 1898–1909 (2009).

    Article  Google Scholar 

  39. Legrand, C. Assessment of Tidal Energy Resource, Marine Renewable Energy Guides (The European Marine Energy Centre Ltd, 2009).

    Google Scholar 

  40. Bryden, I. G. & Couch, S. J. How much energy can be extracted from moving water with a free surface: a question of importance in the field of tidal current energy? Renew. Energy 32, 1961–1966 (2007).

    Article  Google Scholar 

  41. Rombauts, Y., Delarue, E. & D’haeseleer, W. Optimal portfolio-theory-based allocation of wind power: taking into account cross-border transmission-capacity constraints. Renew. Energy 36, 2374–2387 (2011).

    Article  Google Scholar 

  42. Statistics (Svenska Kraftnät, accessed 29 May 2015);

  43. The Wholesale Market (, accessed 1 June 2015);

  44. Last ned grunndata (Statnett, accessed 1 June 2015);

  45. Hourly Electricity Data (Finnish Energy, accessed 1 June 2015);

  46. STRÅNG—A Mesoscale Model for Solar Radiation (accessed 26 August 2015);

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This work was conducted within the StandUP for Energy strategic research framework. M. Hedlund and T. Kamf are acknowledged for help with choosing appropriate filters.

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Authors and Affiliations



M.N.A. and V.C. performed the modelling, developed the scenarios and wrote the text on wave energy. D.L. did the same for solar energy, N.C. for tidal energy and J.O. for wind energy. J.W. wrote the introduction. J.O. did the analysis of the combinations of sources and wrote most of the paper. All authors participated in meetings, discussed the methods and the scope of the paper, and commented on the manuscript.

Corresponding author

Correspondence to Jon Olauson.

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Competing interests

The authors declare no competing financial interests.

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Supplementary Information

Supplementary Figures 1–11, Supplementary Notes 1–4, Supplementary Tables 1–4 and Supplementary References. (PDF 1297 kb)

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Olauson, J., Ayob, M., Bergkvist, M. et al. Net load variability in Nordic countries with a highly or fully renewable power system. Nat Energy 1, 16175 (2016).

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