Kant KANYARUSOKE, Jasson GRYZAGORIDIS, Graeme OLIVER

Validation of TRNSYS modelling for a fixed slope photovoltaic panel

TRNSYS stands for transient system simulation software. This paper describes a procedure that was used to validate a TRNSYS model for estimating electricity yields from a fixed slope photovoltaic (PV) panel. The objective was to find how close to reality predicted energy yield for a specified panel can be, at a location near one of the weather stations listed in the software s database. The software was used to predict daily total incident radiation on a horizontal plane and electrical energy yields from a 90 Wp panel when sloped at 34 ◦ facing north at a test site in Cape Town, South Africa. The panel and other system components were then installed and tested to give actual electrical energy yields. The site was 5 km from a TRNSYS listed weather station. A local weather station logging 10-min data of actual total incident radiation on a horizontal plane enabled comparison with the model s estimate. Analysis of electrical energy yield gave statistical kappa values of 0.722 and 0.944 at actual to model acceptance ratio levels of 90% and 80%, respectively. Regression analysis of measured and model incident horizontal plane energy gave a coefficient of 0.782 across the year. It was thus concluded that within limits of meteorological phenomena behaviour, TRNSYS modelling reliably predicted energy yields from the PV panel installed in the neighbourhood of one of the software s listed stations.

PDF

___

[1] OECD/AfDB/UNDP. African economic outlook 2014: Global value chains and Africas industrialisation. Paris, France: OECD Publishing, 2014.
[2] International Energy Agency. 2014 Key world energy statistics. Paris, France: OECD/IEA, 2014.
[3] Bleeker AEM. Diffusion of solar PV from a TIS perspective and its transnational factors: A case study of Tanzania. 468017 ERM Research Project, Institute of Environmental studies, VU University Amsterdam, 2013.
[4] Oyedope SO. Energy and sustainable development in Nigeria: the way forward. Energy, Sustainability and Society 2012; 2: 15.
[5] Sambo AS, Zarma IH, Uguoke PE, Dioha IJ, Ganda YM. Implementation of standard solar PV projects in Nigeria. J Energy Tech & Policy 2014; 4: 22-28.
[6] Kanyarusoke KE, Gryzagoridis J, Oliver G. Predicting photovoltaic panel yields in sub-Sahara Africa. In ICEAS 2012 International Conference on Engineering and Applied Science; 2427 July 2012; Beijing, China: ICEAS. pp. 223-255.
[7] Aste N, Pero CD, Leonforte F, Manfren M. A simplified model for the estimation of energy production of PV systems. Energy 2013; 59: 503-512.
[8] Lalwani M, Kothari DP, Singh M. Investigation of solar photovoltaic simulation softwares. Int. J Appl Eng Res 2010; 1: 585-601.
[9] Ames DP, Pinthong K, Scott M, Khattar R, Solan D, Lee R. Open source map based software for photovoltaic system layout design. In iEMSs 2014 Conference 1519 June 2014; San Diego, CA, USA.
[10] Siddique MN, Ahmad A, Nawaz MK, Bukhari SBA. Optimal integration of hybrid (wind-solar) system with diesel power plant using HOMER. Turk J Elec Eng & Comp Sci 2014; 23: 1547-1557.
[11] Mermoud A, Wittmer B. PVSYST Users Manual PVSyst 6. Satigny Switzerland, PVSYST SA, 2014.
[12] Lee GR, Frearson L, Rodden P. An assessment of photovoltaic modelling software using real world performance data. In: 26th European Photovoltaic Solar Energy Conference and Exhibition, Hamburg, Germany, 2011.
[13] Mottillo M, Beausoleil-Morrison I, Couture L, Poissant Y. A comparison of two photovoltaic models. In: Canadian Solar Building Conference, Montreal, Canada, 2006.
[14] Mao C, Baltazar JC, Haberl J. Comparisons between TRNSYS software simulation and PV-CHART program on Photovoltaic system. Texas Engineering Experiment System, Texas A&M University, 2012.
[15] Al Riza DF, Gilani SI, Aris MS. Measurement and simulation of a standalone photovoltaic system for residential lighting in Malaysia. J Hydrocarb Mines Environ Res 2011; 2.
[16] Klein SA, Duffie JA, Mitchell JC, Kummer JP, Thornton JW, Bradley DE, Arias DA, Beckman WA, Duffie NA, Braun JE et al. TRNSYS 17: A transient system simulation program. Madison, WI, USA: Solar Energy Laboratory, University of Wisconsin, 2012.
[17] King DL, Boyson WE, Kratochvil JA. Photovoltaic Array Performance Model. Albuquerque, New Mexico, USA: Sandia National Laboratories, 2004.
[18] Mani M, Pillai R. Impact of dust on solar photovoltaic (PV) performance: research status, challenges and recommendations. Renew Sust Energ Rev 2010; 14: 3124-3131.
[19] Duffie JA, Beckman WA. Solar Engineering of Thermal Processes. 4th ed. Solar Energy Laboratory, University of Wisconsin-Madison, WI, USA: Wiley, 2013.
[20] Marnoto T, Soplan K, Daud WRW, Algoul M, Zaharim A. Mathematical model for determining the performance characteristics of Multi crystalline photovoltaic modules. In: Proc. 9th WSEAS Int. Conf. on Mathematical and Computational Methods in Science and Engineering, 57 Nov 2007, Trinidad and Tobago: WSEAS 2007. pp. 79-84.
[21] Pandiarajan N, Muthu R. Mathematical modelling of photovoltaic module with Simulink. In: ICEES 2011 International Conference on Electrical Energy Systems; 35 Jan 2011, Newport Beach, CA, USA: ICEES 2011. pp. 314-319.
[22] Pallant J. A Step by Step Guide to Data Analysis using IBM SPSS: SPSS Survival Manual. 5th ed. Maidenhead, Berkshire, UK: McGraw-Hill, 2013.
[23] Peat J. Health Science Research: A Handbook of Quantitative Methods. Sydney, Australia: Allen & Unwin, 2001.
[24] Cohen JW. Statistical Power Analysis for the Behavioural Sciences. 2nd ed. Hillsdale, NJ, USA: Lawrence Erlbaum Associates, 1988.
[25] Mason RL, Gunst RF, Hess JL. Statistical Design and Analysis of Experiments With Applications to Engineering and Science. 2nd ed. Hoboken, NJ, USA: Wiley, 2003.
[26] Taylor R. Interpretation of the correlation coefficient: a basic review. JDMS 1990; 1: 35-39.