Optimal Design of a Hybrid Solar -Wind-Diesel Power System for Rural Electrification Using Imperialist Competitive Algorithm

In this paper, optimal design of a stand-alone hybrid solar- wind- diesel power generation system using Imperialist Competitive Algorithm, Particle swarm optimization and ant colony optimization is presented. The final goal of this paper is minimization of net present cost of hybrid system for lifetime of project ( here 20 years) considering by reliable supply of load and loss of power probability (LPSP) reliability index. In order to find out the least expenditure and best combination, the result of these algorithms compared together. Among these algorithms, the imperialist competitive algorithm is faster and more accurate than others and has more certain design in comparison to PSO and ACO algorithms. In this paper, first  the mathematical model of various parts of hybrid system is presented. Then the purposed algorithm is used. Finally, simulation results ( number of PV panels, number of wind turbines, number of battery storages, system total cost ,power diagram of hybrid power system components and reliability diagram) for solar-wind –diesel systems is presented.

[1]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[2]  Ali Naci Celik,et al.  Optimisation and techno-economic analysis of autonomous photovoltaic–wind hybrid energy systems in comparison to single photovoltaic and wind systems , 2002 .

[3]  Orhan Ekren,et al.  Size optimization of a PV/wind hybrid energy conversion system with battery storage using simulated annealing , 2010 .

[4]  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 .

[5]  A.H. Gastaj,et al.  Optimal sizing of hybrid power system using genetic algorithm , 2005, 2005 International Conference on Future Power Systems.

[6]  G. Barakat,et al.  Optimal sizing of stand-alone hybrid wind/PV system with battery storage , 2007, 2007 European Conference on Power Electronics and Applications.

[7]  B. Ould Bilal,et al.  Optimal design of a hybrid solar–wind-battery system using the minimization of the annualized cost system and the minimization of the loss of power supply probability (LPSP) , 2010 .

[8]  Ibrahim El-Amin,et al.  Techno-economic evaluation of off-grid hybrid photovoltaic-diesel-battery power systems for rural electrification in Saudi Arabia--A way forward for sustainable development , 2009 .

[9]  Ajai Gupta,et al.  Modelling of hybrid energy system - Part I: Problem formulation and model development. , 2011 .

[10]  Wei Zhou,et al.  OPTIMAL SIZING METHOD FOR STAND-ALONE HYBRID SOLAR–WIND SYSTEM WITH LPSP TECHNOLOGY BY USING GENETIC ALGORITHM , 2008 .

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

[12]  Ajai Gupta,et al.  Steady-state modelling of hybrid energy system for off grid electrification of cluster of villages , 2010 .

[13]  G. J. Rios-Moreno,et al.  Optimal sizing of renewable hybrids energy systems: A review of methodologies , 2012 .

[14]  Kostas Kalaitzakis,et al.  Methodology for optimal sizing of stand-alone photovoltaic/wind-generator systems using genetic algorithms , 2006 .

[15]  Caro Lucas,et al.  Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition , 2007, 2007 IEEE Congress on Evolutionary Computation.

[16]  Wei Zhou,et al.  A novel optimization sizing model for hybrid solar-wind power generation system , 2007 .

[17]  G. H. Riahy,et al.  Optimal design of a reliable hydrogen-based stand-alone wind/PV generating system, considering component outages , 2009 .

[18]  Lu Zhang,et al.  Optimal sizing study of hybrid wind/PV/diesel power generation unit , 2011 .

[19]  Sandip Deshmukh,et al.  Modeling of hybrid renewable energy systems , 2008 .

[20]  Zhang Jianhua,et al.  A new methodology for optimizing the size of hybrid PV/wind system , 2008, 2008 IEEE International Conference on Sustainable Energy Technologies.

[21]  Thomas Stützle,et al.  Ant Colony Optimization , 2009, EMO.