publication . Article . 2017

RE-Europe, a large-scale dataset for modeling a highly renewable European electricity system.

Jensen, Tue V.; Pinson, Pierre;
Open Access English
  • Published: 28 Nov 2017 Journal: Scientific Data, volume 4 (issn: 2052-4463, Copyright policy)
  • Publisher: Springer Science and Business Media LLC
  • Country: Denmark
Abstract
Future highly renewable energy systems will couple to complex weather and climate dynamics. This coupling is generally not captured in detail by the open models developed in the power and energy system communities, where such open models exist. To enable modeling such a future energy system, we describe a dedicated large-scale dataset for a renewable electric power system. The dataset combines a transmission network model, as well as information for generation and demand. Generation includes conventional generators with their technical and economic characteristics, as well as weather-driven forecasts and corresponding realizations for renewable energy generation...
Subjects
free text keywords: Statistics, Probability and Uncertainty, Statistics and Probability, Education, Library and Information Sciences, Information Systems, Computer Science Applications, Data Descriptor, Renewable energy, Energy supply and demand
Related Organizations
59 references, page 1 of 4

1. Chu, S. & Majumdar, A. Opportunities and challenges for a sustainable energy future. Nature 488, 294-303 (2012).

2. Jacobson, M. Z. Energy modelling: Clean grids with current technology. Nature Climate Change 6, 441-442 (2016).

3. 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. Journal of Power Sources 225, 60-74 (2013). [OpenAIRE]

4. Morales, J. M., Conejo, A., Madsen, H., Pinson, P. & Zugno, M. Integrating Renewables in Electricity Markets (Springer, 2014).

5. Oggioni, G., Murphy, F. H. & Smeers, Y. Evaluating the impacts of priority dispatch in the European electricity market. Energy Economics 42, 183-200 (2014).

6. Aravena, I. & Papavasiliou, A. Renewable Energy Integration in Zonal Markets. Trans. Power Syst 32, 1334-1394 (2017). [OpenAIRE]

7. Lyon, J. D., Hedman, K. W. & Zhang, M. Reserve Requirements to Efficiently Manage Intra-Zonal Congestion. Trans. Power Syst 29, 251-258 (2014).

8. Van den Bergh, K. et al. Benefits of coordinating sizing, allocation and activation of reserves among market zones. Electric Power Systems Research 143, 140-148 (2017).

9. Giebel, G., Brownsword, R., Kariniotakis, G., Denhard, M. & Draxl, C. US Energy Information AdministrationThe State-Of-TheArt in Short-Term Prediction of Wind Power: A Literature Overview, 2nd edition (ANEMOS.plus, 2011). [OpenAIRE]

10. Hong, T., Pinson, P., Zareipour, H., Troccoli, A. & Hyndman, R. robabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond. Journal of Forecasting 32, 896-913 (2016).

11. Hutcheon, N. & Bialek, J. W. Updated and validated power flow model of the main continental European transmission network. PowerWorld Knowledge Base https://www.powerworld.com/knowledge-base/updated-and-validated-power-flow-model-of-themain-continental-european-transmission-network (2013). [OpenAIRE]

12. Bukhsh, W. A. & McKinnon, K. Network data of real transmission networks. http://www.maths.ed.ac.uk/OptEnergy/NetworkData/index.html (2013).

13. Matke, C., Medjroubi, W. & Kleinhans, D. SciGRID-An Open Source Reference Model for the European Transmission Network. www.scigrid.de (2016). [OpenAIRE]

14. Scharf, M. & Nebel, A. osmTGmod github repository. www.github.com/wupperinst/osmTGmod (2016).

15. Gupta, R. Global Energy Observatory. http://www.globalenergyobservatory.com (2015).

59 references, page 1 of 4
Related research
Abstract
Future highly renewable energy systems will couple to complex weather and climate dynamics. This coupling is generally not captured in detail by the open models developed in the power and energy system communities, where such open models exist. To enable modeling such a future energy system, we describe a dedicated large-scale dataset for a renewable electric power system. The dataset combines a transmission network model, as well as information for generation and demand. Generation includes conventional generators with their technical and economic characteristics, as well as weather-driven forecasts and corresponding realizations for renewable energy generation...
Subjects
free text keywords: Statistics, Probability and Uncertainty, Statistics and Probability, Education, Library and Information Sciences, Information Systems, Computer Science Applications, Data Descriptor, Renewable energy, Energy supply and demand
Related Organizations
59 references, page 1 of 4

1. Chu, S. & Majumdar, A. Opportunities and challenges for a sustainable energy future. Nature 488, 294-303 (2012).

2. Jacobson, M. Z. Energy modelling: Clean grids with current technology. Nature Climate Change 6, 441-442 (2016).

3. 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. Journal of Power Sources 225, 60-74 (2013). [OpenAIRE]

4. Morales, J. M., Conejo, A., Madsen, H., Pinson, P. & Zugno, M. Integrating Renewables in Electricity Markets (Springer, 2014).

5. Oggioni, G., Murphy, F. H. & Smeers, Y. Evaluating the impacts of priority dispatch in the European electricity market. Energy Economics 42, 183-200 (2014).

6. Aravena, I. & Papavasiliou, A. Renewable Energy Integration in Zonal Markets. Trans. Power Syst 32, 1334-1394 (2017). [OpenAIRE]

7. Lyon, J. D., Hedman, K. W. & Zhang, M. Reserve Requirements to Efficiently Manage Intra-Zonal Congestion. Trans. Power Syst 29, 251-258 (2014).

8. Van den Bergh, K. et al. Benefits of coordinating sizing, allocation and activation of reserves among market zones. Electric Power Systems Research 143, 140-148 (2017).

9. Giebel, G., Brownsword, R., Kariniotakis, G., Denhard, M. & Draxl, C. US Energy Information AdministrationThe State-Of-TheArt in Short-Term Prediction of Wind Power: A Literature Overview, 2nd edition (ANEMOS.plus, 2011). [OpenAIRE]

10. Hong, T., Pinson, P., Zareipour, H., Troccoli, A. & Hyndman, R. robabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond. Journal of Forecasting 32, 896-913 (2016).

11. Hutcheon, N. & Bialek, J. W. Updated and validated power flow model of the main continental European transmission network. PowerWorld Knowledge Base https://www.powerworld.com/knowledge-base/updated-and-validated-power-flow-model-of-themain-continental-european-transmission-network (2013). [OpenAIRE]

12. Bukhsh, W. A. & McKinnon, K. Network data of real transmission networks. http://www.maths.ed.ac.uk/OptEnergy/NetworkData/index.html (2013).

13. Matke, C., Medjroubi, W. & Kleinhans, D. SciGRID-An Open Source Reference Model for the European Transmission Network. www.scigrid.de (2016). [OpenAIRE]

14. Scharf, M. & Nebel, A. osmTGmod github repository. www.github.com/wupperinst/osmTGmod (2016).

15. Gupta, R. Global Energy Observatory. http://www.globalenergyobservatory.com (2015).

59 references, page 1 of 4
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