Comparison of solar radiation and PV generation variability: system dispersion in the UK
- Author(s): Diane Palmer 1 ; Elena Koubli 1 ; Ian Cole 1 ; Thomas Betts 1 ; Ralph Gottschalg 1
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- Source:
Volume 11, Issue 5,
12 April 2017,
p.
550 – 557
DOI: 10.1049/iet-rpg.2016.0768 , Print ISSN 1752-1416, Online ISSN 1752-1424
This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Received
01/09/2016,
Accepted
16/02/2017,
Revised
13/01/2017,
Published
03/03/2017
This study investigates how the number and geographical distribution of solar installations may reduce aggregate irradiance variability and therefore lessen the overall impact of photovoltaic (PV) on grid distribution. The current distribution of UK solar farms is analysed. It is found that variability is linked to site clustering. Other factors may include distance and direction between sites, proximity to coast, local topography and weather patterns (i.e. wind, cloud etc.). These factors do not operate in isolation but form a complex and unpredictable system. The UK solar farm fleet currently comprises a range of system sizes which, when viewed en masse, reduces temporal variation in PV generation. The predominant southwest–northeast direction of solar farm groups is also beneficial in reducing output variability within grid supply point areas.
Inspec keywords: photovoltaic power systems; current distribution; solar power stations
Other keywords: solar radiation; PV generation variability; system dispersion; UK; grid distribution; current distribution; UK solar farms; geographical distribution; site clustering; aggregate irradiance variability reduction
Subjects: Solar power stations and photovoltaic power systems
References
-
-
1)
-
17. van Lieshout, M.N.M., Stein, A.: ‘Earthquake modelling at the country level using aggregated spatio-temporal point processes’, Math. Geosci., 2012, 44, (3), pp. 309–326, doi: 10.1007/s11004–011–9380–3.
-
-
2)
-
5. Remund, J., Calhau, C., Perret, L., et al: ‘Characterization of the spatio-temporal variations and ramp rates of solar radiation and PV’. Report, IEA-PVPS T14-05:2015I, 2015.
-
-
3)
-
6. Frearson, L., Rodden, P., Blackwell, J., et al: ‘Investigating the impact of solar radiation on grid stability with dispersed PV generation’. 31st EUPVSEC, 2015, pp. 2981–2987.
-
-
4)
-
15. Li, L., Bian, L., Rogerson, P., et al: ‘Point pattern analysis for clusters influenced by linear features: an application for mosquito larval sites’, Trans. GIS, 2015, 19, (6), pp. 835–847.
-
-
5)
-
9. Hoff, T., Perez, R.: ‘Modeling PV fleet output variability’, Sol. Energy, 2012, 86, pp. 2177–2189.
-
-
6)
-
27. Qi, L., Zheng, Z.: ‘Trajectory prediction of vessels based on data mining and machine learning’, J. Digit. Inf. Manage., 2016, 14, (1), pp. 33–40.
-
-
7)
-
1. https://www.gov.uk/government/statistics/solar-photovoltaics-deployment, accessed on 31 August 16.
-
-
8)
-
11. Perez, R., Hoff, T.E.: ‘Mitigating short-term PV output intermittency’. 28th EUPVSEC, 2013, pp. 3719–3726.
-
-
9)
-
29. Getis, A., Ord, J.K.: ‘The analysis of spatial association by use of distance statistics’, Geogr. Anal., 1992, 24, (3), pp. 189–206.
-
-
10)
-
10. Lave, M., Kleissl, J.: ‘Cloud speed impact on solar variability scaling – application to the wavelet variability model’, Sol. Energy, 2013, 91, pp. 11–21.
-
-
11)
-
8. Perez, R., Hoff, T.: ‘Solar resource variability’, in Kleissl, J. (ED.): ‘Solar energy forecasting and resource assessment’ (Academic Press, Oxford, 2013, 1st edn.), pp. 133–150, ch. 6.
-
-
12)
-
20. https://www.ordnancesurvey.co.uk/opendatadownload/products.html, accessed on 26 June 16.
-
-
13)
-
24. Hagenauer, J., Helbich, M.: ‘SPAWNN: a toolkit for spatial analysis with self-organizing neural networks’, Transactions in GIS, 2016, 20, (5), pp. 755–775, doi: 10.1111/tgis.12180.
-
-
14)
-
13. Mills, A., Wiser, R.: ‘Implications of wide-area geographic diversity for short-term variability of solar power’. LBNL Report, 3884E, 2010.
-
-
15)
-
14. Marcos, J., Morroyo, L., Lorenzo, E., et al: ‘Smoothing of PV power fluctuations by geographical dispersion’, Prog. Photovolt., Res. Appl., 2012, 20, pp. 226–237.
-
-
16)
-
31. Renard, F.: ‘Local influence of south–east France topography and land cover on the distribution and characteristics of intense rainfall cells’, Theor. Appl. Climatol., 2016, pp. 1–1, doi: 10.1007/s00704-015-1698-1.
-
-
17)
-
32. Raab, A., Schneider, A., Bonhage, A., et al: ‘Spatial analysis of charcoal kiln remains in the former royal forest district Tauer (Lower Lusatia, North German Lowlands)’, Geophysical Research Abstracts, 2016, 18, EGU2016-5610, EGU General Assembly 2016.
-
-
18)
-
3. Farrel, S.: ‘UK electricity grid holds back renewable energy, solar trade body warns’, Guardian, 2015. Available at https://www.theguardian.com/business/2015/may/10/uk-electricity-grid-renewable-energy-solar-trade-association.
-
-
19)
-
19. Met Office (2012): Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations Data (1853-current). NCAS British Atmospheric Data Centre, 2016. Available at http://www.catalogue.ceda.ac.uk/uuid/220a65615218d5c9cc9e4785a3234bd0.
-
-
20)
-
25. Datta, B., Durand, F., Laforge, S., et al: ‘Preliminary hydrogeologic modeling and optimal monitoring network design for a contaminated abandoned mine site area: application of developed monitoring network design software’, J. Water Resour. Prot., 2016, 8, pp. 46–64.
-
-
21)
-
7. Lave, M., Kleissl, J.: ‘Solar variability of four sites across the state of Colorado’, Renew. Energy, 2010, 35, (12), pp. 2867–2873.
-
-
22)
-
22. Flood Map for Planning (Rivers and Sea) Flood Zone 2, April2016. Available at http://www.environment.data.gov.uk/ds/catalogue/#/86ec354f-d465-11e4-b09e-f0def148f590, accessed on 26 June 16.
-
-
23)
-
16. Loo, B.P.Y., Yao, S., Wu, J.: ‘Spatial point analysis of road crashes in Shanghai: a GIS-based network kernel density method’. The 19th Int. Conf. on GeoInformatics, (Geoinformatics 2011), Shanghai, China, 24–26 June 2011. In Conf. Proc., 2011, pp. 1–6. Available at http://www.hdl.handle.net/10722/141610.
-
-
24)
-
12. Remund, J., Calhau, C., Marcel, D., et al: ‘Spatio-temporal variability of PV production’. 31st EUPVSEC, 2015, pp. 2088–2092.
-
-
25)
-
18. Rowley, P., Leicester, P., Palmer, D., et al: ‘Multi-domain analysis of photovoltaic impacts via integrated spatial and probabilistic modelling’, IET Renew. Power Gener., 2015, 9, (5), pp. 424–431.
-
-
26)
-
30. Ord, J.K., Getis, A.: ‘Local spatial autocorrelation statistics: distributional issues and an application’, Geogr. Anal., 1995, 27, (4), pp. 286–306.
-
-
27)
-
2. Bennet, P.: ‘National grid: more than 10 GW of solar will overload UK grid’, Sol. Power Portal, 2012. Available at http://www.solarpowerportal.co.uk/news/report_uk_to_install_8gw_of_solar_by_2016_2356.
-
-
28)
-
4. Torpey, J.: ‘Utility experiences in large scale and distributed solar PV in the U.S.’ APVA Grid Integration Workshop CSIRO Melbourne, Sunpower Corporation, 2011.
-
-
29)
-
26. Basse, R.M., Charif, O., Bodis, K.: ‘Spatial and temporal dimensions of land use change in cross border region of Luxembourg. Development of a hybrid approach integrating GIS, cellular automata and decision learning tree models’, Appl. Geogr., 2016, 67, pp. 94–108.
-
-
30)
-
23. Hagenauer, J.: ‘Weighted merge context for clustering and quantizing spatial data with self-organizing neural networks’, J. Geogr. Syst., 2016, 18, (1), pp. 1–15, doi: 10.1007/s10109-015-0220-8.
-
-
31)
-
21. http://www.magic.defra.gov.uk/Dataset_Download_Summary.htm, accessed on 26 June 16.
-
-
32)
-
28. Anselin, L.: ‘Local indicators of spatial association – LISA’, Geogr. Anal., 1995, 27, (2), pp. 11–93.
-
-
1)
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