Complementarity and Variability of Wind and Solar Energy in Complex Terrain
Keywords:
Wind, Solar Energy, Complementarity, Detrended Fluctuation Analysis, Cross-correlationSynopsis
This is a Chapter in:
Book:
Automated Systems, Data, and Sustainable Computing
Series:
Chronicle of Computing
Chapter Abstract:
Wind and solar energy are expected to play a major role, in the near future electricity generation mix. However, wind and solar energy-based generation are intermittent and non-dispatchable, not being suitable to supply base-load electric power. Their greater penetration and grid integration are critical issues due to their inherent intermittency and variability. Moreover, there are strong evidence that wind and solar energy are showing complementary over appropriate time and space scales. This work investigates such spatiotemporal complementarity and variability as a means by which electricity planners, developers, and grid operators might advance uses and grid integration of wind energy. Over 14 years of synchronous wind velocity and solar radiation measurements at several sites, located in complex terrain of Nevada are used in this study. To do so we used auto-correlations and cross-correlations in wind speed and solar radiation time series, by applying detrended fluctuation analysis and detrended cross-correlation analysis.
Cite this paper as:
Belu R., Karocin D. (2022) Complementarity and Variability of Wind and Solar Energy in Complex Terrain. In: Tiako P.F. (ed) Automated Systems, Data, and Sustainable Computing. Chronicle of Computing. OkIP. https://doi.org/10.55432/978-1-6692-0001-7_10
Author Contact:
Radian Belu
radian_belu@subr.edu
References
Alessio, E., Carbone, A., Castelli, G., & Frappietro, V., (2002). Second-order moving average and scaling of stochastic time series, Eur. Phys. J. B, 27, 197-200.
Belu, R., & Koracin, D. (2009). Wind Characteristics and Wind Energy Potential in Western Nevada, Renewable Energy, 34(10), 2246-2251.
Belu, R., & Koracin, D. (2013). Statistical and Spectral Analysis of the Wind Characteristics in the Western Nevada,J. of Wind Energy, 1, (12 pages), Article ID 739162, doi:10.1155/2013/739162.
Belu, R., & Koracin, D. (2015). Wind Energy Analysis and Assessment, Advances in Energy Research, 20(1), 1-55.
Belu, R., & Koracin, D. (2019). Regional Analysis of Wind Variability and Patterns in Complex Terrain,” Geofizika, 36(2), 1-27, DOI:10.15233/gfz.2019.36.6.
Calif R. & Schmitt F.G. (2014), Multiscaling and joint multiscaling description of the atmospheric wind speed and the aggregate power output from a wind farm. Nonlinear Processes in Geophysics, 21, 379-392.
Govindan, R. B., & Kantz, H. (2004). Long-term correlations and multifractality in surface wind speed. Europhysics Letters, 68, 184-190.
Hajian, S. M. & Sadegh, M., (2010). Multifractal Detrended Cross-Correlation Analysis of sunspot numbers and river flow fluctuations, Physica A, 389, 4942-4957.
Jereza, S., Trigo, R. M., Sarsa, A. Lorente-Plazas, R. Pozo-Vázquez, D., & Montávez, D. P., (2013). Spatio-temporal complementarity between solar and wind power in the Iberian Peninsula, Energy Procedia, 40, 48 – 57.
Kiraly, A., & Janosi, I. M. (2005). Detrended fluctuation analysis of daily temperature records: Geographic dependence over Australia, Meteorol Atmos Phys, 88, 119–128, DOI 10.1007/s00703-004-0078-7
Koscielny-Bunde E., Kantelhardt J. W., Braun P., Bunde A., & Havlin, S. (2006). Long-term persistence and multifractality of river runoff records:detrended fluctuation studies, J. Hydrol., 322, 120–137.
Malamud, B.D., & Turcotte, D. L. (2006). The applicability of power-law frequency statistics to floods. J Hydrololgy, 322, 168–180.
Marinho, E. B. S., Sousa, A. M., & Andrade, R.F. (2013). Using Detrended Cross-Correlation Analysis in Geophysical Data, Physica, A, 392, 2195-2201.
Monforti, F., Huld, T., Bódis, K., Vitali, L., D’Isidoro, M., & Lacal-Arántegui, R., (2014). Assessing complementarity of wind and solar resources for energy production in Italy. A Monte Carlo approach, Renewable Energy, 63, 576-586.
Oliveira Santos, M., Stosic, T. & Stosic, B., (2012). Long-term correlations in hourly wind speed records in Pernambuco, Brazil, Physica, A, 391, 1546-1552.
Peng, C. K. S. Havelin, S., Stanley, H., & Goldberger, A. (1995). Quantification of scaling exponents and crossover phenomena in nonstationary time series,Chaos, 5, 82–89.
Podobnik, B., & Stanley, H. E., (2008). Detrended Cross-Correlation Analysis: A New Method for Analyzing Two Nonstationary Time series, Phys. Rev. Lett., 100, 084102.
Podobnik, B., Horvatic, D., Petersen, M. A. & Stanley, H. E., (2009). Crosscorrelation between volume change and price change, Proc. Natl. Acad. Sci., 106, 22079-22084.
Sales dos Anjos, P., Alves da Silva, A. S. Stosic, D. & Stosic, T. (2015). Longterm correlations and cross-correlations in wind speed and solar radiation temporal series from Fernando de Noronha Island, Brazil, Physica A, 424, 90-96.
Santos-Alamillios, F., Pozo-Vasquez, D. Ruiz-Arais, J. Lara-Fanego, V. & Tovar-Pescador, J., (2012). Analysis of Spatiotemporal Balancing between Wind and Solar Energy Resources in the Southern Iberian Peninsula, J. Applied Meteorology and Climatology, 51, 2005-2024.
Sioshansi, R. & Denholm, P. (2013). Benefits of Co-locating Concentrating Solar Power and Wind, IEEE Trans Energy Sustainability, 4(4), 877-885
Suteanu, C., (2015). A methodology for the time-scale-sensitive evaluation of wind speed and direction variability, Energy Procedia, 76, 200-206.
Vassoler, V. T. & Zebende, G. F., (2013). DCCA cross-correlation coefficient apply in time series of air temperature and air relative humidity, Physica A, 392, 1756-1761.
Zebende, G. F. (2011). DCCA cross-correlation coefficient: Quantifying the level of cross-correlation, Physica A, 390, 614-618.
Zebendea, G. F., da Silva, M. F., & Machado Filho, A., (2012). DCCA crosscorrelation coefficient differentiation: Theoretical and practical approaches,” Physica A, 391, 2438-2443.
