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Daily energy planning of a household photovoltaic panel

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  • Ammar, Mohsen Ben
  • Chaabene, Maher
  • Elhajjaji, Ahmed

Abstract

This paper puts forward an energy planning approach which offers a daily optimum management of a household photovoltaic panel generation (PVG) without using storage equipment. The approach considers the PVG of the last 10Â days to estimate the one of the next day, using a Neuro-Fuzzy algorithm. The estimated PVG is planned according to the consumer's needs so as to use the maximum of the generated energy. The algorithm decides by means of fuzzy rules the connection times of appliances, having different powers, to the photovoltaic panel (PVP) output during the day. The decision is made on the basis of optimization criteria with respect to different user operation modes. The approach is validated on a 260Â Wp PVP and a set of four appliances of 30Â W, 40Â W, 60Â W and 75Â W. The system is installed at the National Engineering School, University of Sfax (ENIS) - Tunisia. The daily energetic assessment confirms that the PVG planning makes use of the estimated available energy in between 70% and 80%.

Suggested Citation

  • Ammar, Mohsen Ben & Chaabene, Maher & Elhajjaji, Ahmed, 2010. "Daily energy planning of a household photovoltaic panel," Applied Energy, Elsevier, vol. 87(7), pages 2340-2351, July.
    • Handle: RePEc:eee:appene:v:87:y:2010:i:7:p:2340-2351
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    2. Baños, R. & Manzano-Agugliaro, F. & Montoya, F.G. & Gil, C. & Alcayde, A. & Gómez, J., 2011. "Optimization methods applied to renewable and sustainable energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(4), pages 1753-1766, May.
    3. Lujano-Rojas, Juan M. & Monteiro, Cláudio & Dufo-López, Rodolfo & Bernal-Agustín, José L., 2012. "Optimum load management strategy for wind/diesel/battery hybrid power systems," Renewable Energy, Elsevier, vol. 44(C), pages 288-295.
    4. Gulin, Marko & Vašak, Mario & Perić, Nedjeljko, 2013. "Dynamical optimal positioning of a photovoltaic panel in all weather conditions," Applied Energy, Elsevier, vol. 108(C), pages 429-438.
    5. Comodi, Gabriele & Giantomassi, Andrea & Severini, Marco & Squartini, Stefano & Ferracuti, Francesco & Fonti, Alessandro & Nardi Cesarini, Davide & Morodo, Matteo & Polonara, Fabio, 2015. "Multi-apartment residential microgrid with electrical and thermal storage devices: Experimental analysis and simulation of energy management strategies," Applied Energy, Elsevier, vol. 137(C), pages 854-866.
    6. Suganthi, L. & Iniyan, S. & Samuel, Anand A., 2015. "Applications of fuzzy logic in renewable energy systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 585-607.
    7. Papaioannou, Ioulia T. & Purvins, Arturs, 2012. "Mathematical and graphical approach for maximum power point modelling," Applied Energy, Elsevier, vol. 91(1), pages 59-66.

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