• Archer, C. L., , and M. Z. Jacobson, 2003: Spatial and temporal distribution of U.S. winds and wind power at 80 m derived from measurements. J. Geophys. Res., 108 .4289, doi:10.1029/2002JD002076.

    • Search Google Scholar
    • Export Citation
  • Archer, C. L., , and M. Z. Jacobson, 2007: Supplying baseload power and reducing transmission requirements by interconnecting wind farms. J. Appl. Meteor. Climatol., 46 , 17011717.

    • Search Google Scholar
    • Export Citation
  • ARIA Technologies, 2002: Evaluation of the wind potential of different regions of Corsica (in French). Tech. Rep., 69 pp.

  • Burlando, M., , F. Castino, , and C. F. Ratto, 2002: Validation of a method for wind power estimation: The case of Bonifacio. Proc. World Wind Energy Conf. and Exhibition, Berlin, Germany, World Wind Energy Association.

  • Burlando, M., , E. Georgieva, , and C. F. Ratto, 2007: Parameterisation of the planetary boundary layer for diagnostic wind models. Bound.-Layer Meteor., 125 , 389397. doi:10.1007/s10546-007-9220-7.

    • Search Google Scholar
    • Export Citation
  • Burlando, M., , M. Antonelli, , and C. F. Ratto, 2008: Mesoscale wind climate analysis: Identification of anemological regions and wind regimes. Int. J. Climatol., 28 , 629641.

    • Search Google Scholar
    • Export Citation
  • Buzzi, A., , and S. Tibaldi, 1978: Cyclogenesis in the lee of the Alps: A case study. Quart. J. Roy. Meteor. Soc., 104 , 271287.

  • Chinchilla, M., , S. Arnalte, , J. C. Burgos, , and J. L. Rodríguez, 2005: Power limits of grid-connected modern wind energy systems. Renewable Energy, 31 , 14551470.

    • Search Google Scholar
    • Export Citation
  • Egger, J., 1988: Alpine lee cyclogenesis—Verification of theories. J. Atmos. Sci., 45 , 21872203.

  • Ferziger, J. H., , and M. Perić, 2002: Computational Methods in Fluid Dynamics. 3rd ed. Springer-Verlag, 437 pp.

  • Finardi, S., , G. Tinarelli, , A. Nanni, , G. Brusasca, , and G. Carboni, 2001: Evaluation of a 3-D flow and pollutant dispersion modelling system to estimate climatological ground level concentrations in complex coastal sites. Int. J. Environ. Pollut., 16 , 472482.

    • Search Google Scholar
    • Export Citation
  • Giebel, G., , J. Badger, , I. Perez, , P. Louka, , G. Kallos, , A. M. Palomares, , C. Lac, , and G. Descombes, 2006: Short-term forecasting using advanced physical modelling—The results of the Anemos Project. Proc. EWEC 06, Athens, Greece, European Wind Energy Association, 29 pp.

  • Gipe, P., 1995: Wind Energy—Comes of Age. John Wiley and Sons, 560 pp.

  • Global Wind Energy Council, 2006: Global Wind 2006 Report, 56 pp. [Available online at http://www.gwec.net/fileadmin/documents/Publications/gwec-2006_final_01.pdf.].

  • Holttinen, H., , and R. Hirvonen, 2005: Power system requirements for wind power. Wind Power in Power Systems, T. Ackermann, Ed., John Wiley and Sons, 143–167.

    • Search Google Scholar
    • Export Citation
  • Kahn, E., 1979: The reliability of distributed wind generators. Electr. Power Syst. Res., 2 , 114.

  • Kariniotakis, G., and Coauthors, 2006: Next generation short-term forecasting of wind power—Overview of the ANEMOS project. Proc. EWEC 06, Athens, Greece, European Wind Energy Association, 10 pp.

  • Kaufmann, P., , and C. D. Whiteman, 1999: Cluster-analysis classification of wintertime wind patterns in the Grand Canyon region. J. Appl. Meteor., 38 , 11311147.

    • Search Google Scholar
    • Export Citation
  • Lissaman, P. B. S., , G. W. Gyatt, , and A. D. Zalay, 1982: Numerical modeling sensitivity analysis of the performance of wind turbine arrays. Pacific Northwest Laboratory Rep. PNL-4183, Richland, WA, 97 pp.

  • Madsen, H., , P. Pinson, , G. Kariniotakis, , H. Aa. Nielsen, , and T. S. Nielsen, 2005: Standardizing the performance evaluation of short term wind power prediction models. Wind Eng., 29 , 475489.

    • Search Google Scholar
    • Export Citation
  • Milligan, M. R., , and T. Factor, 2000: Optimizing the geographic distribution of wind plants in Iowa for maximum economic benefit and reliability. Wind Eng., 24 , 271290.

    • Search Google Scholar
    • Export Citation
  • Milligan, M. R., , and K. Porter, 2005: Determining the capacity value of wind: A survey of methods and implementation. National Renewable Energy Laboratory Rep. NREL/CP-500-38062, Golden, CO, 30 pp.

  • Mosetti, G., , C. Poloni, , and B. Diviacco, 1994: Optimization of wind turbine positioning in large wind farms by means of a genetic algorithm. J. Wind Eng. Ind. Aerodyn., 51 , 105116.

    • Search Google Scholar
    • Export Citation
  • OptiFlow, 2002a: Realisation of wind potential maps through numerical simulations over the study area “Plaine Orientale” (in French). Tech. Rep., 41 pp.

  • OptiFlow, 2002b: Realisation of wind potential maps through numerical simulations over the study area “Cap Corse” (in French). Tech. Rep., 40 pp.

  • Pantaleo, A., , A. Pellerano, , and M. Trovato, 2003: Technical issues for wind energy integration in power systems: Projections in Italy. Wind Eng., 27 , 473493.

    • Search Google Scholar
    • Export Citation
  • Persaud, S., , B. Fox, , and D. Flynn, 2003: Effects of large scale wind power on total system variability and operation: Case study of Northern Ireland. Wind Eng., 27 , 320.

    • Search Google Scholar
    • Export Citation
  • Ratto, C. F., , R. Festa, , O. Nicora, , R. Mosiello, , A. Ricci, , D. P. Lalas, , and O. A. Frumento, 1990: Wind field numerical simulation: A new user-friendly code. Proc. European Community Wind Energy Conf., Madrid, Spain, 130–134.

  • Ratto, C. F., , R. Festa, , C. Romeo, , O. A. Frumento, , and M. Galluzzi, 1994: Mass-consistent models for wind fields over complex terrain: The state of the art. Environ. Softw., 9 , 247268.

    • Search Google Scholar
    • Export Citation
  • Ratto, C. F., , M. Burlando, , F. Castini, , and L. Rusca, 2000: Evaluation and cartography of the wind potential of the Bonifacio municipality in southern Corsica (in French). Department of Physics of the University of Genoa Tech. Rep., 73 pp.

  • Sánchez, I., 2006: Short-term prediction of wind energy production. Int. J. Forecasting, 22 , 4356.

  • Simonsen, T. K., , and B. G. Stevens, 2004: Regional wind energy analysis for the Central United States. Proc. Global Wind Power 2004, Chicago, IL, American Wind Energy Association, 16 pp.

  • Steinbuch, M., , W. W. de Boer, , O. H. Bosgra, , S. A. W. M. Peters, , and J. Ploeg, 1988: Optimal control of wind power plants. J. Wind Eng. Ind. Aerodyn., 27 , 1–3. 237246.

    • Search Google Scholar
    • Export Citation
  • Trigo, I. F., , T. D. Davies, , and G. R. Bigg, 1999: Objective climatology of cyclones in the Mediterranean region. J. Climate, 12 , 16851696.

    • Search Google Scholar
    • Export Citation
  • Van der Hoven, I., 1957: Power spectrum of horizontal wind speed in the frequency range from 0.0007 to 900 cycles per hour. J. Meteor., 14 , 160164.

    • Search Google Scholar
    • Export Citation
  • Weibull, W., 1951: A statistical distribution function of wide applicability. J. Appl. Mech., 18 , 293297.

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Optimization of the Regional Spatial Distribution of Wind Power Plants to Minimize the Variability of Wind Energy Input into Power Supply Systems

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  • 1 Department of Physics, University of Genoa, Genoa, Italy, and National Consortium of Universities for Physics of Atmospheres and Hydrospheres (CINFAI), Toronto, Ontario, Canada
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Abstract

In contrast to conventional power generation, wind energy is not a controllable resource because of its stochastic nature, and the cumulative energy input of several wind power plants into the electric grid may cause undesired fluctuations in the power system. To mitigate this effect, the authors propose a procedure to calculate the optimal allocation of wind power plants over an extended territory to obtain a low temporal variability without penalizing too much the overall wind energy input into the power system. The procedure has been tested over Corsica (France), the fourth largest island in the Mediterranean Basin. The regional power supply system of Corsica could be sensitive to large fluctuations in power generation like wind power swings caused by the wind intermittency. The proposed methodology is based on the analysis of wind measurements from 10 anemometric stations located along the shoreline of the island, where most of the population resides, in a reasonably even distribution. First the territory of Corsica has been preliminarily subdivided into three anemological regions through a cluster analysis of the wind data, and the optimal spatial distribution of wind power plants among these regions has been calculated. Subsequently, the 10 areas around each station have been considered independent anemological regions, and the procedure to calculate the optimal distribution of wind power plants has been further refined to evaluate the improvements related to this more resolved spatial scale of analysis.

Corresponding author address: Federico Cassola, Department of Physics, University of Genoa, Via Dodecaneso 33, 16146 Genoa, Italy. Email: cassola@fisica.unige.it

Abstract

In contrast to conventional power generation, wind energy is not a controllable resource because of its stochastic nature, and the cumulative energy input of several wind power plants into the electric grid may cause undesired fluctuations in the power system. To mitigate this effect, the authors propose a procedure to calculate the optimal allocation of wind power plants over an extended territory to obtain a low temporal variability without penalizing too much the overall wind energy input into the power system. The procedure has been tested over Corsica (France), the fourth largest island in the Mediterranean Basin. The regional power supply system of Corsica could be sensitive to large fluctuations in power generation like wind power swings caused by the wind intermittency. The proposed methodology is based on the analysis of wind measurements from 10 anemometric stations located along the shoreline of the island, where most of the population resides, in a reasonably even distribution. First the territory of Corsica has been preliminarily subdivided into three anemological regions through a cluster analysis of the wind data, and the optimal spatial distribution of wind power plants among these regions has been calculated. Subsequently, the 10 areas around each station have been considered independent anemological regions, and the procedure to calculate the optimal distribution of wind power plants has been further refined to evaluate the improvements related to this more resolved spatial scale of analysis.

Corresponding author address: Federico Cassola, Department of Physics, University of Genoa, Via Dodecaneso 33, 16146 Genoa, Italy. Email: cassola@fisica.unige.it

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