Performance evaluation of a photovoltaic park in Cyprus using irradiance sensors
Abstract
The power output of Photovoltaic (PV) modules is directly affected by the presence of clouds, due to changes in the irradiance caused by the 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 for the simulation of 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 SensorBox. The measurements from the BF-5 Sunshine Sensor were previously validated against the 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 SensorBox, 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 are operating always at maximum power output conditions. The influence of the temperature to the power output of the PV modules was also introduced using the temperature coefficients of the PV modules. Results showed a good agreement between measured and calculated power output.References
[1] Fernandez-Jimenez LA., Munoz-Jimenez A, Falces A, Mendoza-Villena M, Garcia-Garrido E, Lara-Santillan PM, Zorzano-Alba E, Zorzano-Santamaria PJ. Short-term power forecasting system for photovoltaic plants, Renew Energ 44 (2012) 311-317.
[2] Bacher P, Madsen H, Nielsen HA. Online short-term solar power forecasting. Sol Energy 83 (2009) 1772–1783.
[3] Bracale A, Caramia P, De Martinis U, Di Fazio AR. An Improved Bayesian-based Approach for Short Term Photovoltaic Power Forecasting in Smart Grids. Proc. International Conference on Renewable Energies and Power Quality (ICREPQ’12), Santiago de Compostela, Spain, 2012.
[4] Lorenz, E, Heinemann, D. Prediction of Solar Irradiance and Photovoltaic Power. In: Sayigh A, editors. Comprehensive Renewable Energy, Elsevier LTD (2012) p.239-292.
[5] Mellit A, Sağlam S, Kalogirou SA. Artificial neural network-based model for estimating the produced power of a photovoltaic module, Renew. Energ. 60 (2013) 71-78.
[6] Lam KH, Loc WC, Tob WM. The application of dynamic modelling techniques to the grid-connected PV (photovoltaic) systems, Energy 46 (2012) 264-274.
[7] Tapakis R, Charalambides AG. Equipment and Methodologies for Cloud Detection and Classification: A review, Sol. Energy 92 (2012) 392-430.
[8] Escrig H, Batlles FJ, Alonso J, Baena FM, Bosch JL, Salbidegoitia IB, Burgaleta JI. Cloud detection, classification and motion estimation using geostationary satellite imagery for cloud cover forecast, Energy 55 (2013) 853-859.
[9] Chow CW, Urquharta B, Lavea M, Domingueza A, Kleissl J, Shields J, Washom B. Intra-hour forecasting with a total sky imager at the UC San Diego solar energy testbed, Sol Energy 85 (2011) 2885-2893.
[10] Mosalam Shaltout M.A, Hassen A.H. Solar energy distribution over Egypt using cloudiness from Meteosat photos, Sol Energy 45 (1990) 345-351.
[11] Dazhi Y, Jirutitijaroena P, Walshb WM. Hourly solar irradiance time series forecasting using cloud cover index, Sol Energy 86 (2012) 3531–3543.
[12] Badescu V. A new kind of cloudy sky model to compute instantaneous values of diffuse and global solar irradiance. Theor Appl Climatol 72 (2002) 127-136.
[13] Erlykin AD, Sloan T, Wolfendale AW. Clouds, solar irradiance and mean surface temperature over the last century, J Atmos Sol-Terr Phy 72 (2010) 425–434.
[14] Dai A, Trenberth KE, Karl TR. Effects of Clouds, Soil Moisture, Precipitation, and Water Vapor on Diurnal Temperature Range, J. Climate 12 (1999) 2451-2473.
[15] Sun B, Groisman PY, Bradley RS, Keimig FT. Temporal Changes in the Observed Relationship between Cloud Cover and Surface Air Temperature, J. Climate 13 (2000) 4341-4357.
[16] Delta-T Devices
http://www.delta-t.co.uk/.
[17] Geonica
http://www.geonica.com/.
[18] PVSyst
URL: http://www.pvsyst.com/en/.
[19] Skoplaki E, Boudouvis AG, Palyvos JA. A simple correlation for the operating temperature of photovoltaic modules of arbitrary mounting. Sol Energ Mat Sol C 92 (2008) 1393– 1402.
[20] Tofighi A. Performance Evaluation of PV Module by Dynamic Thermal Model. Journal of Power Technologies 93 (2013) 111–121.
[21] Demain C, Journée M, Bertrand C. Evaluation of different models to estimate the global solar radiation on inclined surfaces. Renew Energ 50 (2013) 710-721.
[22] Padovan A, Del Col D. Measurement and modeling of solar irradiance components on horizontal and tilted planes. Sol Energy 84 (2010) 2068–2084.
[23] Evseev EG, Kudish AI. The assessment of different models to predict the global solar radiation on a surface tilted to the south. Sol Energy 83 (2009) 377–388.
[2] Bacher P, Madsen H, Nielsen HA. Online short-term solar power forecasting. Sol Energy 83 (2009) 1772–1783.
[3] Bracale A, Caramia P, De Martinis U, Di Fazio AR. An Improved Bayesian-based Approach for Short Term Photovoltaic Power Forecasting in Smart Grids. Proc. International Conference on Renewable Energies and Power Quality (ICREPQ’12), Santiago de Compostela, Spain, 2012.
[4] Lorenz, E, Heinemann, D. Prediction of Solar Irradiance and Photovoltaic Power. In: Sayigh A, editors. Comprehensive Renewable Energy, Elsevier LTD (2012) p.239-292.
[5] Mellit A, Sağlam S, Kalogirou SA. Artificial neural network-based model for estimating the produced power of a photovoltaic module, Renew. Energ. 60 (2013) 71-78.
[6] Lam KH, Loc WC, Tob WM. The application of dynamic modelling techniques to the grid-connected PV (photovoltaic) systems, Energy 46 (2012) 264-274.
[7] Tapakis R, Charalambides AG. Equipment and Methodologies for Cloud Detection and Classification: A review, Sol. Energy 92 (2012) 392-430.
[8] Escrig H, Batlles FJ, Alonso J, Baena FM, Bosch JL, Salbidegoitia IB, Burgaleta JI. Cloud detection, classification and motion estimation using geostationary satellite imagery for cloud cover forecast, Energy 55 (2013) 853-859.
[9] Chow CW, Urquharta B, Lavea M, Domingueza A, Kleissl J, Shields J, Washom B. Intra-hour forecasting with a total sky imager at the UC San Diego solar energy testbed, Sol Energy 85 (2011) 2885-2893.
[10] Mosalam Shaltout M.A, Hassen A.H. Solar energy distribution over Egypt using cloudiness from Meteosat photos, Sol Energy 45 (1990) 345-351.
[11] Dazhi Y, Jirutitijaroena P, Walshb WM. Hourly solar irradiance time series forecasting using cloud cover index, Sol Energy 86 (2012) 3531–3543.
[12] Badescu V. A new kind of cloudy sky model to compute instantaneous values of diffuse and global solar irradiance. Theor Appl Climatol 72 (2002) 127-136.
[13] Erlykin AD, Sloan T, Wolfendale AW. Clouds, solar irradiance and mean surface temperature over the last century, J Atmos Sol-Terr Phy 72 (2010) 425–434.
[14] Dai A, Trenberth KE, Karl TR. Effects of Clouds, Soil Moisture, Precipitation, and Water Vapor on Diurnal Temperature Range, J. Climate 12 (1999) 2451-2473.
[15] Sun B, Groisman PY, Bradley RS, Keimig FT. Temporal Changes in the Observed Relationship between Cloud Cover and Surface Air Temperature, J. Climate 13 (2000) 4341-4357.
[16] Delta-T Devices
http://www.delta-t.co.uk/.
[17] Geonica
http://www.geonica.com/.
[18] PVSyst
URL: http://www.pvsyst.com/en/.
[19] Skoplaki E, Boudouvis AG, Palyvos JA. A simple correlation for the operating temperature of photovoltaic modules of arbitrary mounting. Sol Energ Mat Sol C 92 (2008) 1393– 1402.
[20] Tofighi A. Performance Evaluation of PV Module by Dynamic Thermal Model. Journal of Power Technologies 93 (2013) 111–121.
[21] Demain C, Journée M, Bertrand C. Evaluation of different models to estimate the global solar radiation on inclined surfaces. Renew Energ 50 (2013) 710-721.
[22] Padovan A, Del Col D. Measurement and modeling of solar irradiance components on horizontal and tilted planes. Sol Energy 84 (2010) 2068–2084.
[23] Evseev EG, Kudish AI. The assessment of different models to predict the global solar radiation on a surface tilted to the south. Sol Energy 83 (2009) 377–388.
Published
2014-10-13
How to Cite
TAPAKIS, Rogiros Dimitrs; CHARALAMBIDES, Alexandros George.
Performance evaluation of a photovoltaic park in Cyprus using irradiance sensors.
Journal of Power Technologies, [S.l.], v. 94, n. 4, p. 296--305, oct. 2014.
ISSN 2083-4195.
Available at: <https://papers.itc.pw.edu.pl/index.php/JPT/article/view/521>. Date accessed: 21 nov. 2024.
Issue
Section
Solar Power Technologies
Keywords
Solar Electricity Generation; Global Tilted Irradiance; Photovoltaics; Simulation;
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