EGU2020-7939
https://doi.org/10.5194/egusphere-egu2020-7939
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Virtual energy storage-gain due to spatiotemporal coordination of hydropower over Europe

Anders Wörman1, Louise Crochemore2, Ilias Pechlivanidis2, Marc Gions Lopez2, Luigia Brandimarte1, Joakim Riml1, Shuang Hao1, Cintia Bertacchi Uvo3, and Stefan Busse4
Anders Wörman et al.
  • 1The Royal Institute of Technology, Division of River Engineering, Stockholm, Sweden (worman@kth.se)
  • 2Swedish Meteorological and Hydrological Institute, Norrköping, Sweden
  • 3Lund University, Lund, Sweden
  • 4Uniper, Sundsvall, Sweden

The viability of a renewable electricity system depends on long-term climate variations, uneven spatiotemporal distribution of renewable energy, and technical constraints. A major problem is to achieve a sustainable balance of water usage and consumption, as well as adequate energy and water distribution and storage capacities. In particular, hydropower offers a large capacity for energy storage and production flexibility, but only stands for a minor part of the total energy potential. In this study we explored the spatial and temporal variance of hydropower availability for a 35-year period based on historical hydro-meteorological data from large parts of Europe. A spectral analysis of these historical time-series shows that spatiotemporal coordination of the hydropower system covered in the Global Reservoir and Dam Database (GRanD) can potentially contribute with a “virtual” energy storage capacity that is up to four times the actual energy storage capacity contained in the existing hydropower reservoirs. Such virtual energy storage capacity implies reduced water storage demand, hence, indirectly contributes to reduced constraints of the food-water-energy nexus also in a wider system perspective. We found that the most significant benefits from a spatiotemporal management arise at distances of 1,200 – 3,000 km, i.e. on the continental scale, which can have implications for a future renewable energy system at large. The analysis also covers what we denote “energy-domain-specific drought”, which implies a shortage of energy storage capacity to avoid a deficit of energy for a given time period, and which may be reduced by the spatiotemporal coordination of power production.

How to cite: Wörman, A., Crochemore, L., Pechlivanidis, I., Gions Lopez, M., Brandimarte, L., Riml, J., Hao, S., Bertacchi Uvo, C., and Busse, S.: Virtual energy storage-gain due to spatiotemporal coordination of hydropower over Europe, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7939, https://doi.org/10.5194/egusphere-egu2020-7939, 2020

How to cite: Wörman, A., Crochemore, L., Pechlivanidis, I., Gions Lopez, M., Brandimarte, L., Riml, J., Hao, S., Bertacchi Uvo, C., and Busse, S.: Virtual energy storage-gain due to spatiotemporal coordination of hydropower over Europe, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7939, https://doi.org/10.5194/egusphere-egu2020-7939, 2020

Comments on the presentation

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Presentation version 1 – uploaded on 25 Apr 2020
  • CC1: Comment on EGU2020-7939, David C. Finger, 27 Apr 2020

    Very interesting and valuable analysis. Thank you for your contribution. Some questions remain:

    - Is the "hydropower availability" based on simulated runoff? Why not use observed runoff?

    - Was the hydropower infrastructure taken into account? E.g. in mountainous areas pumps storage plants can provide peak power (15min), while low land power plants provide base power without much storage. Could this be included in the analysis?

    - Perhaps presenting the x-axis of the power spectrum in the logarithmic scale would reveal interesting differences. (Please include units)

    Looking forward to your presentations. Thank you!

    • AC1: Reply to CC1, Anders Wörman, 27 Apr 2020

      Thanks for your interest in the study!

      Simulation of runoff allows a denser representation of runoff on 35,408 sub-watersheds compared to number of discharge and precipitation stations. Such simulations uses measured precipitation that is generalized with altitude correction and the E-HYPE runoff model simulate the runoff processes, including evapotranspiration. This high-density estimation of runoff is needed in order to estimate the landscape hydropower potential. Precisely, at the GranD hydropower stations there is also some discharge data available, but full time-series was not included in this data base. The average discharge from the GranD data was compared with the simulated runoff and which showed consistency in the mean discharge values. However, it was decided to use the simulated throughout this study in the daily discharge time-series. 

      No technical constraints were taken into consideration, since the aim was restricted to an estimation of the virtual energy storage gain that potentially can arise due to spatiotemporal coordination of the production. Such gain assumes that transmission of energy is not a limitation and that there is no need for spillage at the hydropower plants due to limited storage.

      The diagrams should include units! We’ll change the presentation.