Optimization of biomass fuelled systems for distributed power generation using Particle Swarm Optimization

With sufficient territory and abundant biomass resources Spain appears to have suitable conditions to develop biomass utilization technologies. As an important decentralized power technology, biomass gasification and power generation has a potential market in making use of biomass wastes. This paper addresses biomass fuelled generation of electricity in the specific aspect of finding the best location and the supply area of the electric generation plant for three alternative technologies (gas motor, gas turbine and fuel cell-microturbine hybrid power cycle), taking into account the variables involved in the problem, such as the local distribution of biomass resources, transportation costs, distance to existing electric lines, etc. For each technology, not only optimal location and supply area of the biomass plant, but also net present value and generated electric power are determined by an own binary variant of Particle Swarm Optimization (PSO). According to the values derived from the optimization algorithm, the most profitable technology can be chosen. Computer simulations show the good performance of the proposed binary PSO algorithm to optimize biomass fuelled systems for distributed power generation.

[1]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[2]  Francisco Jurado,et al.  Combined molten carbonate fuel cell and gas turbine systems for efficient power and heat generation using biomass , 2003 .

[3]  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".

[4]  Richard L. Ottinger,et al.  Compendium of Sustainable Energy Laws: Directive 2001/77/EC of the European Parliament and of the Council of 27 September 2001 on the Promotion of Electricity Produced from Renewable Energy Sources in the Internal Electricity Market , 2005 .

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

[6]  F. Jurado,et al.  Adaptive control of a fuel cell-microturbine hybrid power plant , 2002, IEEE Power Engineering Society Summer Meeting,.

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

[8]  Douglas J. Nelson,et al.  Fuel cell systems: efficient, flexible energy conversion for the 21st century , 2001, Proc. IEEE.

[9]  P. Flynn,et al.  Biomass power cost and optimum plant size in western Canada , 2003 .

[10]  Walter G. Scott,et al.  Distributed Power Generation Planning and Evaluation , 2000 .

[11]  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.

[12]  Kenneth A. Kobe,et al.  Gas producers and blast furnaces , 1951 .

[13]  Francisco Jurado,et al.  Effect of the Use of a Gas Motor in a Biomass-Based Electric Power Plant , 2002 .

[14]  J. Teng,et al.  A Novel ACS-Based Optimum Switch Relocation Method , 2002, IEEE Power Engineering Review.

[15]  Atsushi Tsutsumi,et al.  Energy recuperation in solid oxide fuel cell (SOFC) and gas turbine (GT) combined system , 2003 .

[16]  James Kennedy,et al.  The particle swarm: social adaptation of knowledge , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[17]  Yuhui Shi,et al.  Particle swarm optimization: developments, applications and resources , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[18]  Mark C. Williams,et al.  U.S. distributed generation fuel cell program , 2004 .

[19]  Yoshikazu Fukuyama,et al.  A hybrid particle swarm optimization for distribution state estimation , 2003, 2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491).

[20]  Francisco Jurado,et al.  Optimal placement of biomass fuelled gas turbines for reduced losses , 2006 .