A comprehensive study of the key parameters of the Simulated Annealing method for maximum power point tracking in photovoltaic systems

Maximum power point tracking (MPPT) is a key feature of photovoltaic systems as it enables these systems to be utilized with a higher efficiency. Many MPPT methods including conventional techniques for uniform conditions and advanced techniques for non-uniform conditions have been proposed in the literature. A Simulated Annealing (SA) based MPPT method is a good candidate for tracking the global peak under non-uniform environmental conditions. However, the SA based method contains many parameters which could affect the tracking performance. A method for exploring the impact of each parameter on the tracking speed and accuracy of the method, in addition to key results obtained using this methodology, is presented in this paper.

[1]  Chun Che Fung,et al.  Simulated annealing based economic dispatch algorithm , 1993 .

[2]  V. Agarwal,et al.  MATLAB-Based Modeling to Study the Effects of Partial Shading on PV Array Characteristics , 2008, IEEE Transactions on Energy Conversion.

[3]  Fred Glover,et al.  Scatter Search and Path Relinking: Advances and Applications , 2003, Handbook of Metaheuristics.

[4]  Bruce Hajek,et al.  A tutorial survey of theory and applications of simulated annealing , 1985, 1985 24th IEEE Conference on Decision and Control.

[5]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[6]  Yaow-Ming Chen,et al.  Calculation of the optimum installation angle for fixed solar-cell panels based on the genetic algorithm and the Simulated-annealing method , 2005 .

[7]  Yaghout Nourani,et al.  A comparison of simulated annealing cooling strategies , 1998 .

[8]  G. Farivar,et al.  A New Approach for Solar Module Temperature Estimation Using the Simple Diode Model , 2011, IEEE Transactions on Energy Conversion.

[9]  M.A. El-Shibini,et al.  Maximum power point tracking technique , 1989, Proceedings. Electrotechnical Conference Integrating Research, Industry and Education in Energy and Communication Engineering',.

[10]  Md Enamul Haque,et al.  A Simulated Annealing Global Maximum Power Point Tracking Approach for PV Modules Under Partial Shading Conditions , 2016, IEEE Transactions on Power Electronics.

[11]  B. Zahawi,et al.  Assessment of Perturb and Observe MPPT Algorithm Implementation Techniques for PV Pumping Applications , 2012, IEEE Transactions on Sustainable Energy.

[12]  Md Ali Azam,et al.  Microcontroller based maximum power tracking of PV using stimulated annealing algorithm , 2012, 2012 International Conference on Informatics, Electronics & Vision (ICIEV).

[13]  Sio-Iong Ao,et al.  Electrical Engineering and Intelligent Systems , 2014 .

[14]  H. Cohn,et al.  Simulated Annealing: Searching for an Optimal Temperature Schedule , 1999, SIAM J. Optim..

[15]  Roberto Carapellucci,et al.  Modeling and optimization of an energy generation island based on renewable technologies and hydrogen storage systems , 2012 .

[16]  B. C. McCallum.  Blind deconvolution by simulated annealing , 1990 .

[17]  Md. Enamul Haque,et al.  Maximum Power Point Tracking techniques for photovoltaic systems: A comprehensive review and comparative analysis , 2015 .

[18]  V. Cerný.  Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm , 1985 .

[19]  Sl Lynden.  A Simulated annealing global maximum power point tracking method for photovoltaic systems experiencing non-uniform environmental conditions , 2015 .

[20]  W. Warta,et al.  Solar cell efficiency tables (Version 45) , 2015 .

[21]  M. E. Haque,et al.  Comparison of the Perturb and Observe and simulated annealing approaches for maximum power point tracking in a photovoltaic system under partial shading conditions , 2014, 2014 IEEE Energy Conversion Congress and Exposition (ECCE).

[22]  D. Xiao,et al.  Application of a Simulated Annealing technique for global maximum power point tracking of PV modules experiencing partial shading , 2014, IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society.

[23]  Michael Bögl,et al.  Metaheuristic Search Concepts: A Tutorial with Applications to Production and Logistics , 2010 .

[24]  A. Keyhani.  Modeling of photovoltaic microgrids for bulk power grid studies , 2011, 2011 IEEE Power and Energy Society General Meeting.

[25]  M. E. Haque,et al.  A hybrid simulated annealing and perturb and observe method for maximum power point tracking in PV systems under partial shading conditions , 2015, 2015 Australasian Universities Power Engineering Conference (AUPEC).

[26]  D. Mitra,et al.  Convergence and finite-time behavior of simulated annealing , 1985, 1985 24th IEEE Conference on Decision and Control.

[27]  Junji Ohtsubo,et al.  Image recovery by simulated annealing with known Fourier modulus , 1991 .

[28]  P.L. Chapman,et al.  Comparison of Photovoltaic Array Maximum Power Point Tracking Techniques , 2007, IEEE Transactions on Energy Conversion.