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The increasing impact of weather on electricity supply and demand

Staffell, Iain, Pfenninger, Stefan·2018·Energy
DOI 10.1016/j.energy.2017.12.051
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  1. IPCC. Climate change 2014: mitigation of climate change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press; 2014.
  2. Williams JH, DeBenedictis A, Ghanadan R, Mahone A, Moore J, Morrow WR, et al. The technology path to deep greenhouse gas emissions cuts by 2050: the pivotal role of electricity. Science 2012;335:53e9. https://doi.org/10.1126/ science.1208365. · DOI
  3. Pfenninger S, Keirstead J. Comparing concentrating solar and nuclear power as baseload providers using the example of South Africa. Energy 2015;87: 303e14. https://doi.org/10.1016/j.energy.2015.04.077. · DOI
  4. von Hirschhausen C, Herold J, Oei P-Y. How a "Low Carbon" Innovation Can FaileTales from a "Lost Decade" for carbon capture, transport, and seques- tration (CCTS). Econ Energy Environ Pol 2012;1. https://doi.org/10.5547/2160- 5890.1.2.8. · DOI
  5. Grams CM, Beerli R, Pfenninger S, Staffell I, Wernli H. Balancing Europe's wind-power output through spatial deployment informed by weather re- gimes. Nat Clim Change 2017. https://doi.org/10.1038/nclimate3338. advance online publication. · DOI
  6. Sinden G. Characteristics of the UK wind resource: long-term patterns and relationship to electricity demand. Energy Pol 2007;35:112e27. https:// doi.org/10.1016/j.enpol.2005.10.003. · DOI
  7. Wild M, Folini D, Henschel F, Fischer N, Müller B. Projections of long-term changes in solar radiation based on CMIP5 climate models and their influ- ence on energy yields of photovoltaic systems. Sol Energy 2015;116:12e24. https://doi.org/10.1016/j.solener.2015.03.039. · DOI
  8. Staffell I. Measuring the progress and impacts of decarbonising British elec- tricity. Energy Pol 2017;102:463e75. https://doi.org/10.1016/ j.enpol.2016.12.037. · DOI
  9. Foxon TJ. Transition pathways for a UK low carbon electricity future. Energy Pol 2013;52:10e24. https://doi.org/10.1016/j.enpol.2012.04.001. · DOI
  10. Olauson J, Bergkvist M. Correlation between wind power generation in the European countries. Energy 2016;114:663e70. https://doi.org/10.1016/ j.energy.2016.08.036. · DOI
  11. Bruun Andresen G, Søndergaard AA, Rodriguez RA, Becker S, Greiner M. Weather-driven modeling of future renewable power systems. In: Pro- ceedings of the 8th sdewes conference; 2013.
  12. Staffell I, Pfenninger S. Using bias-corrected reanalysis to simulate current and future wind power output. Energy 2016;114:1224e39. https://doi.org/ 10.1016/j.energy.2016.08.068. · DOI
  13. Pfenninger S, Staffell I. Long-term patterns of European PV output using 30 years of validated hourly reanalysis and satellite data. Energy 2016;114: 1251e65. https://doi.org/10.1016/j.energy.2016.08.060. · DOI
  14. Brayshaw DJ, Troccoli A, Fordham R, Methven J. The impact of large scale atmospheric circulation patterns on wind power generation and its potential predictability: a case study over the UK. Renew Energy 2011;36:2087e96. https://doi.org/10.1016/j.renene.2011.01.025. · DOI
  15. Staffell I, Green R. How does wind farm performance decline with age? Renew Energy 2014;66:775e86. https://doi.org/10.1016/j.renene.2013.10.041. · DOI
  16. Cannon DJ, Brayshaw DJ, Methven J, Coker PJ, Lenaghan D. Using reanalysis data to quantify extreme wind power generation statistics: a 33 year case study in Great Britain. Renew Energy 2015;75:767e78. https://doi.org/ 10.1016/j.renene.2014.10.024. · DOI · Paper page
  17. Nahmmacher P, Schmid E, Hirth L, Knopf B. Carpe diem: a novel approach to select representative days for long-term power system modeling. Energy 2016;112:430e42. https://doi.org/10.1016/j.energy.2016.06.081. · DOI
  18. Poncelet K, Delarue E, Six D, Duerinck J, D'haeseleer W. Impact of the level of temporal and operational detail in energy-system planning models. Appl Energy 2016;162:631e43. https://doi.org/10.1016/j.apenergy.2015.10.100. · DOI
  19. Pfenninger S. Dealing with multiple decades of hourly wind and PV time series in energy models: a comparison of methods to reduce time resolution and the planning implications of inter-annual variability. Appl Energy 2017;197:1e13. https://doi.org/10.1016/j.apenergy.2017.03.051. · DOI
  20. Pfenninger S, Keirstead J. Renewables, nuclear, or fossil fuels? Scenarios for Great Britain's power system considering costs, emissions and import de- pendency. Appl Energy 2015;152:83e93. https://doi.org/10.1016/ j.apenergy.2015.04.102. · DOI
  21. Schroeder A, Oei P-Y, Sander A, Hankel L, Laurisch LC. The integration of renewable energies into the German transmission gridda scenario compar- ison. Energy Pol 2013;61:140e50. https://doi.org/10.1016/ j.enpol.2013.06.006. · DOI
  22. Grünewald P, Cockerill T, Contestabile M, Pearson P. The role of large scale storage in a GB low carbon energy future: issues and policy challenges. Energy Pol 2011;39:4807e15. https://doi.org/10.1016/j.enpol.2011.06.040. · DOI
  23. Widen J. Correlations between large-scale solar and wind power in a future scenario for Sweden. IEEE Trans Software Eng 2011;2:177e84. https://doi.org/ 10.1109/TSTE.2010.2101620. · DOI
  24. Budischak C, Sewell D, Thomson H, Mach L, Veron DE, Kempton W. Cost- minimized combinations of wind power, solar power and electrochemical storage, powering the grid up to 99.9% of the time. J Power Sources 2013;225: 60e74. https://doi.org/10.1016/j.jpowsour.2012.09.054. · DOI
  25. Rodriguez RA, Becker S, Greiner M. Cost-optimal design of a simplified, highly renewable pan-European electricity system. Energy 2015;83:658e68. https:// doi.org/10.1016/j.energy.2015.02.066. · DOI
  26. Bloomfield HC, Brayshaw DJ, Shaffrey LC, Coker PJ, Thornton HE. Quantifying the increasing sensitivity of power systems to climate variability. Environ Res Lett 2016;11:124025. https://doi.org/10.1088/1748-9326/11/12/124025. · DOI
  27. Thornton HE, Scaife AA, Hoskins BJ, Brayshaw DJ. The relationship between wind power, electricity demand and winter weather patterns in Great Britain. Environ Res Lett 2017;12:064017. https://doi.org/10.1088/1748-9326/aa69c6. · DOI
  28. Boßmann T, Staffell I. The shape of future electricity demand: exploring load curves in 2050s Germany and Britain. Energy 2015;90(Part 2):1317e33. https://doi.org/10.1016/j.energy.2015.06.082. · DOI
  29. Muratori M, Roberts MC, Sioshansi R, Marano V, Rizzoni G. A highly resolved modeling technique to simulate residential power demand. Appl Energy 2013;107:465e73. https://doi.org/10.1016/j.apenergy.2013.02.057. · DOI
  30. Richardson I, Thomson M, Infield D, Clifford C. Domestic electricity use: a high-resolution energy demand model. Energy Build 2010;42:1878e87. https://doi.org/10.1016/j.enbuild.2010.05.023. · DOI
  31. Swan LG, Ugursal VI. Modeling of end-use energy consumption in the resi- dential sector: a review of modeling techniques. Renew Sustain Energy Rev 2009;13:1819e35. https://doi.org/10.1016/j.rser.2008.09.033. · DOI
  32. Poulin A, Dostie M, Fournier M, Sansregret S. Load duration curve: a tool for technico-economic analysis of energy solutions. Energy Build 2008;40:29e35. https://doi.org/10.1016/j.enbuild.2007.01.020. · DOI
  33. Hyndman RJ, Fan S. Density forecasting for long-term peak electricity de- mand. IEEE Trans Power Syst 2010;25:1142e53. https://doi.org/10.1109/ TPWRS.2009.2036017. · DOI
  34. Kaytez F, Taplamacioglu MC, Cam E, Hardalac F. Forecasting electricity con- sumption: a comparison of regression analysis, neural networks and least squares support vector machines. Int J Electr Power Energy Syst 2015;67: 431e8. https://doi.org/10.1016/j.ijepes.2014.12.036. · DOI
  35. He Y, Jiao J, Chen Q, Ge S, Chang Y, Xu Y. Urban long term electricity demand forecast method based on system dynamics of the new economic normal: the case of Tianjin. Energy 2017;133:9e22. https://doi.org/10.1016/ j.energy.2017.05.107. · DOI
  36. Cooper SJG, Hammond GP, McManus MC, Pudjianto D. Detailed simulation of electrical demands due to nationwide adoption of heat pumps, taking account of renewable generation and mitigation. IET Renew Power Gener 2016;10: 380e7. https://doi.org/10.1049/iet-rpg.2015.0127. · DOI
  37. Wid en J, W€ ackelgård E. A high-resolution stochastic model of domestic ac- tivity patterns and electricity demand. Appl Energy 2010;87:1880e92. https://doi.org/10.1016/j.apenergy.2009.11.006. · DOI
  38. Heinen S, Turner W, Cradden L, McDermott F, O'Malley M. Electrification of residential space heating considering coincidental weather events and building thermal inertia: a system-wide planning analysis. Energy 2017;127: 136e54. https://doi.org/10.1016/j.energy.2017.03.102. · DOI
  39. Isaac M, van Vuuren DP. Modeling global residential sector energy demand for heating and air conditioning in the context of climate change. Energy Pol 2009;37:507e21. https://doi.org/10.1016/j.enpol.2008.09.051. · DOI
  40. Psiloglou BE, Giannakopoulos C, Majithia S, Petrakis M. Factors affecting electricity demand in Athens, Greece and London, UK: a comparative assessment. Energy 2009;34:1855e63. https://doi.org/10.1016/ j.energy.2009.07.033. · DOI
  41. Thornton HE, Hoskins BJ, Scaife AA. The role of temperature in the variability and extremes of electricity and gas demand in Great Britain. Environ Res Lett 2016;11:114015. https://doi.org/10.1088/1748-9326/11/11/114015. · DOI
  42. Pfenninger S. Energy scientists must show their workings. Nature 2017;542: 393. https://doi.org/10.1038/542393a. · DOI
  43. Pfenninger S, DeCarolis J, Hirth L, Quoilin S, Staffell I. The importance of open data and software: is energy research lagging behind? Energy Pol 2017;101: 211e5. https://doi.org/10.1016/j.enpol.2016.11.046. · DOI
  44. Pryor SC, Barthelmie RJ. Climate change impacts on wind energy: a review. Renew Sustain Energy Rev 2010;14:430e7. https://doi.org/10.1016/ j.rser.2009.07.028. · DOI · Paper page
  45. Breslow PB, Sailor DJ. Vulnerability of wind power resources to climate change in the continental United States. Renew Energy 2002;27:585e98. https://doi.org/10.1016/S0960-1481(01)00110-0. · DOI
  46. Hdidouan D, Staffell I. The impact of climate change on the levelised cost of wind energy. Renew Energy 2017;101:575e92. https://doi.org/10.1016/ j.renene.2016.09.003. · DOI · Paper page
  47. Crook JA, Jones LA, Forster PM, Crook R. Climate change impacts on future photovoltaic and concentrated solar power energy output. Energy Environ Sci 2011;4:3101e9. https://doi.org/10.1039/C1EE01495A. · DOI
  48. Green R. Are the British electricity trading and transmission arrangements future-proof? Util Pol 2010;18:186e94. https://doi.org/10.1016/ j.jup.2010.06.002. · DOI
  49. Molod A, Takacs L, Suarez M, Bacmeister J. Development of the GEOS-5 at- mospheric general circulation model: evolution from MERRA to MERRA2. Geosci Model Dev 2015;8:1339e56. https://doi.org/10.5194/gmd-8-1339- 2015. · DOI
  50. CIESIN. Gridded population of the world, version 4 (GPWv4): population count adjusted to match 2015 revision of UN WPP country totals. 2016. https://doi.org/10.7927/H4SF2T42. [Accessed 7 November 2016]. · DOI
  51. Parker DE, Legg TP, Folland CK. A new daily central England temperature series, 1772e1991. Int J Climatol 1992;12:317e42. https://doi.org/10.1002/ joc.3370120402. · DOI
  52. Schmidt O, Hawkes A, Gambhir A, Staffell I. The future cost of electrical energy storage based on experience rates. Nat Energy 2017. https://doi.org/10.1038/ nenergy.2017.110. · DOI
  53. Drew DR, Cannon DJ, Brayshaw DJ, Barlow JF, Coker PJ. The impact of future offshore wind farms on wind power generation in Great Britain. Resources 2015;4:155e71. https://doi.org/10.3390/resources4010155. · DOI
  54. Staffell I, Green R. Is there still merit in the merit order Stack? The impact of dynamic constraints on optimal plant mix. IEEE Trans Power Syst 2016;31: 43e53. https://doi.org/10.1109/TPWRS.2015.2407613. · DOI
  55. Denholm P, Hand M. Grid flexibility and storage required to achieve very high penetration of variable renewable electricity. Energy Pol 2011;39:1817e30. https://doi.org/10.1016/j.enpol.2011.01.019. · DOI
  56. Huber M, Dimkova D, Hamacher T. Integration of wind and solar power in Europe: assessment of flexibility requirements. Energy 2014;69:236e46. https://doi.org/10.1016/j.energy.2014.02.109. · DOI
  57. Fattori F, Anglani N, Staffell I, Pfenninger S. High solar photovoltaic penetra- tion in the absence of substantial wind capacity: storage requirements and effects on capacity adequacy. Energy 2017. https://doi.org/10.1016/ j.energy.2017.07.007. · DOI
  58. Lund PD, Lindgren J, Mikkola J, Salpakari J. Review of energy system flexibility measures to enable high levels of variable renewable electricity. Renew Sustain Energy Rev 2015;45:785e807. https://doi.org/10.1016/ j.rser.2015.01.057. · DOI
  59. Paulus M, Borggrefe F. The potential of demand-side management in energy- intensive industries for electricity markets in Germany. Appl Energy 2011;88: 432e41. https://doi.org/10.1016/j.apenergy.2010.03.017. · DOI · Paper page
  60. Hong J, Kelly NJ, Richardson I, Thomson M. Assessing heat pumps as flexible load. Proc IME J Power Energy 2013;227:30e42. https://doi.org/10.1177/ 0957650912454830. · DOI
  61. Staffell I, Brett D, Brandon N, Hawkes A. A review of domestic heat pumps. Energy Environ Sci 2012;5:9291e306. https://doi.org/10.1039/C2EE22653G. · DOI

Cited by

Sorted by citations

Authors
Staffell, I., & Pfenninger, S.
Venue
Energy
Publisher
Elsevier BV
Published
2018-02
Volume
145
Pages
65-78
ISSN
0360-5442
DOI
10.1016/j.energy.2017.12.051
Cited by
366
References
1
Language
en
Licence
Funders
  • Engineering and Physical Sciences Research Council (EP/M001369/1)
  • European Research Council (StG 2012-313553)

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