Optimum sizing of hybrid PV/Wind/battery installation using a fuzzy PSO

This paper proposes a sizing methodology to optimize the configuration of the hybrid energy system. For this we used an approach of automatic fuzzy rule base generation by means of Fuzzy-Adaptive Particle Swarm Optimization (PSO), which changes dynamically the acceleration coefficient rates ensuring the convergence. This algorithm allows us to obtain the optimal number of photovoltaic panels, wind turbines and storage units ensuring the minimal global high efficiency system total cost and guaranteeing the permanent availabilty of energy to cover the load energy requirements. The database of wind speed taken hourly, the solar irradiance and the load data are used to stochastically model the wind turbines, photovoltaic generation and load. The total cost is the objective function and the technical size is considered as a contraint.

[1]  Rodolfo Dufo-López,et al.  Design and control strategies of PV-Diesel systems using genetic algorithms , 2005 .

[2]  Lotfi A. Zadeh,et al.  Fuzzy Logic , 2009, Encyclopedia of Complexity and Systems Science.

[3]  Sarah Eichmann,et al.  Fuzzy Logic Intelligence Control And Information , 2016 .

[4]  A. A. Shaltout,et al.  Optimum sizing of hybrid WT/PV systems via open-space particle swarm optimization , 2012, 2012 Second Iranian Conference on Renewable Energy and Distributed Generation.

[5]  Ashutosh Tiwari,et al.  Applications of Soft Computing: From Theory to Praxis , 2009 .

[6]  Ziyad M. Salameh,et al.  Methodology for optimally sizing the combination of a battery bank and PV array in a wind/PV hybrid system , 1996 .

[7]  Oscar Castillo,et al.  Optimal design of fuzzy classification systems using PSO with dynamic parameter adaptation through fuzzy logic , 2013, Expert Syst. Appl..

[8]  Y. Baghzouz,et al.  Genetic-Algorithm-Based Optimization Approach for Energy Management , 2013, IEEE Transactions on Power Delivery.

[9]  Lotfi A. Zadeh,et al.  Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic , 1997, Fuzzy Sets Syst..

[10]  E. Mizutani,et al.  Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.

[11]  Andries Petrus Engelbrecht,et al.  Fundamentals of Computational Swarm Intelligence , 2005 .

[12]  Li Wang,et al.  A Study on Generator Capacity for Wind Turbines Under Various Tower Heights and Rated Wind Speeds Using Weibull Distribution , 2008, IEEE Transactions on Energy Conversion.

[13]  Faicel Hnaien,et al.  Estimation of Global Solar Radiation Using Three Simple Methods , 2013 .

[14]  K. E. Addoweesh,et al.  Optimum sizing of hybrid PV/wind/battery/diesel system considering wind turbine parameters using Genetic Algorithm , 2012, 2012 IEEE International Conference on Power and Energy (PECon).

[15]  Z Yuan Characteristic Research of Wind-PV Hybrid Power System , 2010 .

[16]  Jiangye Yuan,et al.  An improved WM method based on PSO for electric load forecasting , 2010, Expert Syst. Appl..