Particle swarm optimization for biomass-fuelled systems with technical constraints

This paper introduces a binary particle swarm optimization-based method to accomplish optimal location of biomass-fuelled systems for distributed power generation. The approach also provides the supply area for the biomass plant and takes technical constraints into account. This issue can be formulated as a nonlinear optimization problem. In rural or radial distribution networks the main technical constraint is the impact on the voltage profile. Biomass is one of the most promising renewable energy sources in Europe, but more research is required to prove that power generation from biomass is both technically and economically viable. Forest residues are here considered as biomass source, and the fitness function to be optimized is the profitability index. A fair comparison between the proposed algorithm and genetic algorithms (GAs) is performed. For such goal, convergence curves of the average profitability index versus number of iterations are computed. The proposed algorithm reaches a better solution than GAs when considering similar computational cost (similar number of evaluations).

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