A Honey Bee Foraging approach for optimal location of a biomass power plant

Over eight million hectares of olive trees are cultivated worldwide, especially in Mediterranean countries, where more than 97% of the world's olive oil is produced. The three major olive oil producers worldwide are Spain, Italy, and Greece. Olive tree pruning residues are an autochthonous and important renewable source that, in most of cases, farmers burn through an uncontrolled manner. Besides, industrial uses have not yet been developed. The aim of this paper consists of a new calculation tool based on particles swarm (Binary Honey Bee Foraging, BHBF). Effectively, this approach will make possible to determine the optimal location, biomass supply area and power plant size that offer the best profitability for investor. Moreover, it prevents the accurate method (not feasible from computational viewpoint). In this work, Profitability Index (PI) is set as the fitness function for the BHBF approach. Results are compared with other evolutionary optimization algorithms such as Binary Particle Swarm Optimization (BPSO), and Genetic Algorithms (GA). All the experiments have shown that the optimal plant size is 2 MW, PI = 3.3122, the best location corresponds to coordinate: X = 49, Y = 97 and biomass supply area is 161.33 km2. The simulation times have been reduced to the ninth of time than the greedy (accurate) solution. Matlab® is used to run all simulations.

[1]  Francisco Jurado,et al.  Biomass Gasification, Gas Turbine, and Diesel Engine , 2001 .

[2]  M. J. Negro,et al.  Production of fuel ethanol from steam-explosion pretreated olive tree pruning , 2008 .

[3]  L. Jiménez,et al.  Influence of process variables in the ethanol pulping of olive tree trimmings. , 2001, Bioresource technology.

[4]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[5]  Francisco Jurado,et al.  Optimization of biomass fuelled systems for distributed power generation using Particle Swarm Optimization , 2008 .

[6]  Michela Robba,et al.  Optimizing forest biomass exploitation for energy supply at a regional level , 2004 .

[7]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[8]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[9]  Consolación Gil,et al.  Optimization methods applied to renewable and sustainable energy: A review , 2011 .

[10]  Li-Pei Wong,et al.  A Bee Colony Optimization Algorithm for Traveling Salesman Problem , 2008, 2008 Second Asia International Conference on Modelling & Simulation (AMS).

[11]  Mohd Wazir Mustafa,et al.  Optimal Allocation of FACTS Devices for ATC Enhancement Using Bees Algorithm , 2009 .

[12]  Yoshiki Yamagata,et al.  A spatial evaluation of forest biomass usage using GIS , 2009 .

[13]  Craig A. Tovey,et al.  On Honey Bees and Dynamic Server Allocation in Internet Hosting Centers , 2004, Adapt. Behav..

[14]  Jürgen Branke,et al.  Multiswarms, exclusion, and anti-convergence in dynamic environments , 2006, IEEE Transactions on Evolutionary Computation.

[15]  A. Rahimi-Kian,et al.  A Novel Binary Particle Swarm Optimization Method Using Artificial Immune System , 2005, EUROCON 2005 - The International Conference on "Computer as a Tool".

[16]  Duc Truong Pham,et al.  The Bees Algorithm: Modelling foraging behaviour to solve continuous optimization problems , 2009 .

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

[18]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[19]  Mete Kalyoncu,et al.  Optimisation of a fuzzy logic controller for a flexible single-link robot arm using the Bees Algorithm , 2009, 2009 7th IEEE International Conference on Industrial Informatics.

[20]  N. Russell,et al.  Bioremediation and biovalorisation of olive-mill wastes , 2009, Applied Microbiology and Biotechnology.

[21]  Duc Truong Pham,et al.  Optimal design of mechanical components using the Bees Algorithm , 2009 .

[22]  Johannes Schmidt,et al.  Cost-effective CO2 emission reduction through heat, power and biofuel production from woody biomass: A spatially explicit comparison of conversion technologies , 2010 .

[23]  Yoshikazu Fukuyama,et al.  A Hybrid Particle Swarm Optimization for Distribution State Estimation , 2002, IEEE Power Engineering Review.

[24]  Belén Melián-Batista,et al.  A Nature Inspired Approach for the Uncapacitated Plant Cycle Location Problem , 2008, NICSO.

[25]  Kinattingal Sundareswaran,et al.  Design and Development of Feed-back Controller for a Boost Converter Using a Colony of Foraging Bees , 2009 .

[26]  Nicolás Ruiz-Reyes,et al.  Comparison of metaheuristic techniques to determine optimal placement of biomass power plants , 2009 .

[27]  Gary Boone,et al.  Optimal capacitor placement in distribution systems by genetic algorithm , 1993 .

[28]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .