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Performance evaluation of a photovoltaic park in Cyprus using irradiance sensors

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The power output of Photovoltaic (PV) modules is directly affected by the presence of clouds, due to changes in the irradiance caused by clouds, resulting in rapid fluctuations of the solar electricity generation of PV Parks. Thus, the computation of the performance of PV parks under cloudy conditions is essential for the optimal operation of the electricity grid. This paper presents solar irradiance measurements to be used to simulate the performance of a grid connected PV park, based on the incident global irradiance at the photovoltaic modules. The 150kWp PV Park is located on an inclined roof of an industrial building in Limassol Cyprus (latitude 34.7ºN, longitude 32.94ºE). The incident irradiance at the PV modules is computed as the sum of three components: the beam component from direct irradiation of the tilted surface, the diffuse component, and the reflected component. The measurements of the diffuse and global horizontal irradiance were recorded on site using a BF-5 Sunshine Sensor and the tilted irradiance using the Sunny Sensor Box. The measurements from the BF-5 Sunshine Sensor were previously validated against measurements from our meteorological station. For the computation of tilted irradiance, three isotropic and seven anisotropic models were used, using only measurements of global irradiance and calculations of solar position and irradiance incidence angle on the PV panels. The solar electricity generation of the park was correlated to the irradiance measurements from the Sunny Sensor Box, taking into consideration the interconnection of the Park and the measurements of the module temperature provided by a monitoring system located on-site. The power output of the PV modules to the inverters for different incident irradiance was estimated from the current-volt characteristic curves of the PV modules based on the assumption that the inverters’ maximum power point tracking mechanism adapts instantly to the fluctuations of solar irradiance, and thus, the PV modules always operate at maximum power output conditions. The influence of temperature on the power output of the PV modules was also introduced using the temperature coefficients of the PV modules. Results showed good agreement between measured and calculated power output.
Rocznik
Strony
296--305
Opis fizyczny
Bibliogr. 23 poz., tab., rys., wykr.
Twórcy
  • Cyprus University of Technology, Department of Environmental Science and Technology, Corner of Athinon and Anexartisias, 57, 3603, Lemesos, Cyprus
  • Cyprus University of Technology, Department of Environmental Science and Technology, Corner of Athinon and Anexartisias, 57, 3603, Lemesos, Cyprus
Bibliografia
  • [1] L. A. Fernandez-Jimenez, A. Munoz-Jimenez, A. Falces, M. Mendoza-Villena, E. Garcia-Garrido, P. M. Lara- Santillan, E. Zorzano-Alba, P. J. Zorzano-Santamaria, Short-term power forecasting system for photovoltaic plants, Renewable Energy 44 (2012) 311–317.
  • [2] P. Bacher, H. Madsen, H. A. Nielsen, Online short-term solar power forecasting, Solar Energy 83 (2009) 1772–1783.
  • [3] A. Bracale, P. Caramia, U. De Martinis, A. R. Di Fazio, An improved bayesian-based approach for short term photovoltaic power forecasting in smart grids, in: Proc. International Conference on Renewable Energies and Power Quality (ICREPQ’12), Santiago de Compostela, Spain, 2012.
  • [4] E. Lorenz, D. Heinemann, Comprehensive Renewable Energy, Elsevier LTD, 2012, Ch. Prediction of Solar Irradiance and Photovoltaic Power, pp. 239–292.
  • [5] A. Mellit, S. Saglam, S. A. Kalogirou, Artificial neural network-based model for estimating the produced power of a photovoltaic module, Renewable Energy 60 (2013) 71–78.
  • [6] K. H. Lam, W. C. Loc, W. M. Tob, The application of dynamic modelling techniques to the grid-connected pv (photovoltaic) systems, Energy 46 (2012) 264–274.
  • [7] R. Tapakis, A. G. Charalambides, Equipment and methodologies for cloud detection and classification: A review, Solar Energy 92 (2012) 392–430.
  • [8] H. Escrig, F. J. Batlles, J. Alonso, B. F. M., B. J. L., I. B. Salbidegoitia, B. J. I., Cloud detection, classification and motion estimation using geostationary satellite imagery for cloud cover forecast, Energy 55 (2013) 853–859.
  • [9] C. W. Chow, B. Urquharta, M. Lavea, A. Domingueza, J. Kleissl, J. Shields, B. Washom, Intra-hour forecasting with a total sky imager at the uc san diego solar energy testbed, Solar Energy 85 (2011) 2885–2893.
  • [10] M. A. Mosalam Shaltout, A. H. Hassen, Solar energy distribution over egypt using cloudiness from meteosat photos, Solar Energy 45 (1990) 345–351.
  • [11] Y. Dazhi, P. Jirutitijaroena, W. M. Walshb, Hourly solar irradiance time series forecasting using cloud cover index, Solar Energy 86 (2012) 3531–3543.
  • [12] V. Badescu, A new kind of cloudy sky model to compute instantaneous values of diffuse and global solar irradiance, Theoretical and Applied Climatology 72 (2002) 127–136.
  • [13] A. D. Erlykin, T. Sloan, A. W. Wolfendale, Clouds, solar irradiance and mean surface temperature over the last century, Journal of Atmospheric and Solar-Terrestrial Physics 72 (2010) 425–434.
  • [14] A. Dai, K. E. Trenberth, T. R. Karl, Effects of clouds, soil moisture, precipitation, and water vapor on diurnal temperature range, Journal of Climate 12 (1999) 2451–2473.
  • [15] B. Sun, P. Y. Groisman, R. S. Bradley, F. T. Keimig, Temporal changes in the observed relationship between cloud cover and surface air temperature, Journal of Climate 13 (2000) 4341–4357.
  • [16] Delta-T Devices. URL http://www.delta-t.co.uk/
  • [17] Geonica. URL http://www.geonica.com/
  • [18] C. Demain, M. Journee, C. Bertrand, Evaluation of different models to estimate the global solar radiation on inclined surfaces, Renewable Energy 50 (2013) 710–721.
  • [19] A. Padovan, D. Del Col, Measurement and modeling of solar irradiance components on horizontal and tilted planes, Solar Energy 84 (2010) 2068–2084.
  • [20] E. G. Evseev, A. I. Kudish, The assessment of different models to predict the global solar radiation on a surface tilted to the south, Solar Energy 83 (2009) 377–388.
  • [21] PVSyst. URL http://www.pvsyst.com/en/
  • [22] E. Skoplaki, A. G. Boudouvis, J. A. Palyvos, A simple correlation for the operating temperature of photovoltaic modules of arbitrary mounting, Solar Energy Materials and Solar Cells 92 (2008) 1393–1402.
  • [23] A. Tofighi, Performance evaluation of pv module by dynamic thermal model, Journal of Power Technologies 93 (2) (2013) 111–121.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-1d247457-6cc8-463a-b696-5ba2f0632d5c
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