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A method for predicting long-term average performance of photovoltaic systems

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  • Hove, Tawanda

Abstract

A method for predicting the long-term average conventional energy displaced by a photovoltaic system comprising of a photovoltaic array, a storage battery, some power conditioning equipment with maximum power tracking capability and an auxiliary power facility, is described. System simulation is done over the average day of the month. Average hourly energy flows are estimated from a knowledge of array test parameters, monthly average hourly ambient temperature and monthly average daily hemispherical radiation. The monthly average diffuse component of radiation can be predicted from the hemispherical radiation by the use of an appropriate empirical correlation relating the monthly average diffuse fraction to monthly average clearness index. Hourly average radiation values are estimated from daily values using a statistical model. The condition that there should be no net battery energy gain during the average day enables the correct setting of the battery energy level at the beginning of the day. For a given hourly load profile, for example a constant 24 h-per-day load, a chart relating annual solar fraction with array and storage battery size, for a given location and set of array test parameters, can be plotted as a basis for design and economic optimisation of the system.

Suggested Citation

  • Hove, Tawanda, 2000. "A method for predicting long-term average performance of photovoltaic systems," Renewable Energy, Elsevier, vol. 21(2), pages 207-229.
    • Handle: RePEc:eee:renene:v:21:y:2000:i:2:p:207-229
    DOI: 10.1016/S0960-1481(99)00131-7
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    References listed on IDEAS

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    1. Hove, T & Göttsche, J, 1999. "Mapping global, diffuse and beam solar radiation over Zimbabwe," Renewable Energy, Elsevier, vol. 18(4), pages 535-556.
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    2. Eltawil, Mohamed A. & Zhao, Zhengming, 2010. "Grid-connected photovoltaic power systems: Technical and potential problems--A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(1), pages 112-129, January.
    3. Rawat, Rahul & Kaushik, S.C. & Lamba, Ravita, 2016. "A review on modeling, design methodology and size optimization of photovoltaic based water pumping, standalone and grid connected system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1506-1519.
    4. Skoplaki, E. & Palyvos, J.A., 2009. "Operating temperature of photovoltaic modules: A survey of pertinent correlations," Renewable Energy, Elsevier, vol. 34(1), pages 23-29.
    5. Celik, A.N., 2007. "Effect of different load profiles on the loss-of-load probability of stand-alone photovoltaic systems," Renewable Energy, Elsevier, vol. 32(12), pages 2096-2115.
    6. Sabo, Mahmoud Lurwan & Mariun, Norman & Hizam, Hashim & Mohd Radzi, Mohd Amran & Zakaria, Azmi, 2017. "Spatial matching of large-scale grid-connected photovoltaic power generation with utility demand in Peninsular Malaysia," Applied Energy, Elsevier, vol. 191(C), pages 663-688.
    7. Li, Fuxiang & Wu, Wei, 2022. "Coupled electrical-thermal performance estimation of photovoltaic devices: A transient multiphysics framework with robust parameter extraction and 3-D thermal analysis," Applied Energy, Elsevier, vol. 319(C).
    8. Ramírez, Andres Felipe & Valencia, Carlos Felipe & Cabrales, Sergio & Ramírez, Carlos G., 2021. "Simulation of photo-voltaic power generation using copula autoregressive models for solar irradiance and air temperature time series," Renewable Energy, Elsevier, vol. 175(C), pages 44-67.
    9. Adefarati, T. & Bansal, R.C. & Naidoo, R. & Onaolapo, K.A. & Bettayeb, M. & Olulope, P.K. & Sobowale, A.A., 2024. "Design and techno-economic assessment of a standalone photovoltaic-diesel-battery hybrid energy system for electrification of rural areas: A step towards sustainable development," Renewable Energy, Elsevier, vol. 227(C).
    10. Keddouda, Abdelhak & Ihaddadene, Razika & Boukhari, Ali & Atia, Abdelmalek & Arıcı, Müslüm & Lebbihiat, Nacer & Ihaddadene, Nabila, 2024. "Photovoltaic module temperature prediction using various machine learning algorithms: Performance evaluation," Applied Energy, Elsevier, vol. 363(C).
    11. Su, Yan & Chan, Lai-Cheong & Shu, Lianjie & Tsui, Kwok-Leung, 2012. "Real-time prediction models for output power and efficiency of grid-connected solar photovoltaic systems," Applied Energy, Elsevier, vol. 93(C), pages 319-326.
    12. Ayompe, L.M. & Duffy, A. & McCormack, S.J. & Conlon, M., 2010. "Validated real-time energy models for small-scale grid-connected PV-systems," Energy, Elsevier, vol. 35(10), pages 4086-4091.
    13. Barbieri, Florian & Rajakaruna, Sumedha & Ghosh, Arindam, 2017. "Very short-term photovoltaic power forecasting with cloud modeling: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 242-263.
    14. Siecker, J. & Kusakana, K. & Numbi, B.P., 2017. "A review of solar photovoltaic systems cooling technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 192-203.
    15. Alagoz, B.B. & Kaygusuz, A. & Karabiber, A., 2012. "A user-mode distributed energy management architecture for smart grid applications," Energy, Elsevier, vol. 44(1), pages 167-177.
    16. Zhou, Wei & Yang, Hongxing & Fang, Zhaohong, 2007. "A novel model for photovoltaic array performance prediction," Applied Energy, Elsevier, vol. 84(12), pages 1187-1198, December.

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