Optimum Efficiency of PV Panel Using Genetic Algorithms to Touch Proximate Zero Energy House (NZEH)

Bdoor Majed Ahmed, Nibal Fadel Farman ALhialy

Abstract


By optimizing the efficiency of a modular simulation model of the PV module structure by genetic algorithm, under several weather conditions, as a portion of recognizing the ideal plan of a Near Zero Energy Household (NZEH), an ideal life cycle cost can be performed. The optimum design from combinations of NZEH-variable designs, are construction positioning, window-to-wall proportion, and glazing categories, which will help maximize the energy created by photovoltaic panels. Comprehensive simulation technique and modeling are utilized in the solar module I-V and for P-V output power. Both of them are constructed on the famous five-parameter model.  In addition, the efficiency of the PV panel is established by the genetic algorithm under the standard test conditions (STC) and a comparison between the theoretical and experimental results is done to achieve maximum performance ranging from 0.15 to 0.16, particularly with an error of about - 0.333 for an experimental power of 30 Watts compared with the theoretical power of 30.1 Watts.  The results obtained by the genetic algorithm give the best value for efficiency at the range of 16% to 17% of solar radiation, from 500–600 W/m2. These values are almost identical to the efficiency obtained from the results of the operation, where the best value for efficiency in the experimental results was seen to be 15.7%.


Keywords


Genetic Algorithm; Optimum Efficiency; Photovoltaic Panels; Model of Single-Diode.

References


Nibal Fadel Farman, Zeina Ali Abdul Redha and Shaymaa A. Mahdi. “Optimization the Efficiency of Continuous Solar Adsorption Refrigeration System with Genetic Algorithm." 2nd International Conference on the Applications of Information Technology in Developing Renewable Energy Processes & Systems (IT-DREPS), (December. 2017):6-7. doi: 10.1109/IT-DREPS.2017.8277821.

Latief, Y., M A Berawi, A B Koesalamwardi, L, S R Supriadi. “Near Zero Energy House (NZEH) Design Optimization to Improve Life Cycle Cost Performance Using Genetic Algorithm. IOP Conf. Series: Earth and Environmental Science 124 (March 2018): 012006. doi:10.1088/1755-1315/124/1/012006.

Fatima Harkouss, Farouk Fardoun, Pascal Henry Biwole. “Multi-Objective Optimization Methodology for Net Zero Energy Buildings.” Journal of Building Engineering 16 (March 2018): 57-71. doi:10.1016/j.jobe.2017.12.003.

Alima, D. “Sensitivity of Solar Photovoltaic (PV) Efficiency to Climate Change and Dust: Comparative Study between Niamey and Abidjan."WASCAL. (November 2015). Master Thesis in Climate Change and Energy Delivered by: Universitat Abdou Moumouni – Niamey, Niger.

Zaihidee, F, M., Mekhilef, S., Seyedmahmoudian, M. and Horan, B. “Dust as an unalterable deteriorative factor affecting PV panel's efficiency: Why and how.” Renewable and Sustainable Energy Reviews 65 (November 2016): 1267–1278. doi: 10.1016/j.rser.2016.06.068.

Ismail, M.S., Moghavvemi, M. and Mahlia, T.M.I. “Characterization of PV Panel and Global Optimization of its Model parameters using Genetic Algorithm,” Energy Conversion and Management. 73 (September 2013):10–25. doi:10.1016/j.enconman.2013.03.033.

Jervase, J.A., Bourdoucen, H. and Al-Lawati, A. “" Solar Cell Parameter Extraction Using Genetic Algorithms.” Measurement Science and Technology 12 (November 2001): 1922–1925. doi:10.1088/0957-0233/12/11/322.

Ramoji, S, K., Rath, B, B. and Kumar, D, V. “Optimization of Hybrid PV/Wind Energy System Using Genetic Algorithm (GA).” International Journal of Engineering Research and Applications 4 (January 2014): 29-37. ISSN: 2248-9622.

Satish Kumar Ramoji1 and B. Jagadish Kumar. “ Optimal Economical sizing of a PV-Wind Hybrid Energy System using Genetic Algorithm and Teaching Learning Based Optimization. ” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering 3 (February 2014): 7352-7367: ISSN: 2278 – 7798.

Rodriguez, J, D., Petrone, G., Paja, C, A. and Spagnuolo, G. “A genetic Algorithm for Identifying the Single Diode Model Parameters of a Photovoltaic Panel." Mathematics and Computers in Simulation.131 (October 2017): 38–54. doi:10.1016/j.matcom. 2015.10.008.

Zagrouba, M., Sellami, A., Bouaı¨cha, M. and Ksouri, M. “Identification of PV Solar Cells and Modules Parameters Using the Genetic Algorithms: Application to Maximum Power Extraction." Solar Energy 84 (May 2010): 860–866. doi:10.1016/j.solener.2010.02.012.

Einar Eimhjellen," Optimal design of photovoltaic power plants. ” Master thesis in Applied and Computational Mathematics. Department of Mathematics, University of Bergen Spring 2018.

Ayman Y., Mohammed El-Telbany, and Abdelhalim Z. “The Role of Artificial Intelligence in Photo-Voltaic Systems Design and Control: A review." Renewable and Sustainable Energy Reviews 78 (October 2017): 72–79. doi:10.1016/j.rser.2017.04.046.

Rahmani, Javad and Sadeghian, Ehsan and Dolatiary, Soheil. “Comparison between Ideal and Estimated PV Parameters Using Evolutionary Algorithms to Save the Economic Costs.” International Research Journal of Engineering and Technology (IRJET) 05, (October 2018): 208-216.

Ricardo Pereira, Laura Aelenei. “Optimization assessment of the energy performance of a BIPV/T-PCM system using Genetic Algorithms.” Renewable Energy 137 (July 2019): 157-166. doi:10.1016/j.renene.2018.06.118.

Legacy, City of Irvine “Solar Photovoltaic System for Single Family Dwelling Community Development.” Available online: https://legacy.cityofirvine.org/civica/filebank/blobdload.asp?BlobID=13485 (Accessed on 1 May 2019).

El Tayyan, A. A. “ An Approach To Extract The Parameters Of Solar Cells From Their Illuminated IV Curves Using The Lambert W Function." Turkish Journal of Physics 39 (January 2015): 1-15.‏ doi:10.3906/fiz-1309-7.

Emad Talib Hashim and Zainab Riyadh Talib. “Modelling and Simulation of Solar Module performance using Five Parameters Model by using Matlab in Baghdad City." Journal of Engineering, (October 2018): 10-15. doi:10.31026/j.eng. 2018.10.02

Vergura, S. “A Complete and Simplified Datasheet-Based Model of PV Cells in Variable Environmental Conditions for Circuit Simulation." Energies 9 (April 2016): 329. doi: 10.3390/en9050326.

Cem Emeksiz, Akif Akbulut, Zafer Dogan, Mehmet Akar. “Optimization of PV Module with Single-diode Model for Tokat Region." International Journal of Research, Science & Management 4 (June, 2017): 78-85. doi:10.5281/zenodo.802328.

Hashim, E. T., and Hussien, S. A. M. “Synchronous Buck Converter with Perturb and Observe Maximum Power Point Tracking Implemented on a Low –Cost Arduino-microcontroller.” Journal of Engineering 24 (February 2018): 16-33. ISSN: 17264073 25203339.

Emad Talib Hashim and Akram Abdulameer Abbood. “Temperature Effect on Power Drop of Different Photovoltaic Modules.” Journal of Engineering, (May 2016): 129-143.

Lamini, C., Benhlima, S. and Elbekri, A. “Genetic Algorithm Based Approach for Autonomous Mobile Robot Path Planning, Procedia Computer Science 127 (2018): 180-189. doi:10.1016/j.procs. 2018.01.113.

Khan, M, W., and Alam, M. “A Survey of Application: Genomics and Genetic Programming, a New Frontier.” Genomics 100 (August 2012): 65–71. doi:10.1016/j.ygeno.2012.05.014.

Marandi, R, G., and Smith, B, K." Fluid Genetic Algorithm (FGA). ” Journal of Computational Design and Engineering 4 (April 2017): 158–167. doi:10.1016/j.jcde.2017.03.001.

Joanna Ferdyn-Grygierek, and Krzysztof Grygierek. “Multi-Variable Optimization of Building Thermal Design Using Genetic Algorithms.” Energies 10 (October 2017): 1570. doi:10.3390/en10101570.

Tiejun Li, Guifang Shao, Wangda Zuo and Sen Huang. “Genetic Algorithm for Building Optimization - State-of-the-Art Survey.” Proceedings of the 9th International Conference on Machine Learning and Computing (ICMLC 2017), (February 2017). doi:10.1145/3055635.3056591.


Full Text: PDF

DOI: 10.28991/cej-2019-03091375

Refbacks

  • There are currently no refbacks.




Copyright (c) 2019 Nibal Fadel Farman ALhialy

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
x
Message