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首页> 外文会议>Electrical Power Distribution Networks (EPDC), 2012 Proceedings of 17th Conference on >Optimization of a typical biomass fueled power plant using Genetic algorithm and binary particle swarm optimization
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Optimization of a typical biomass fueled power plant using Genetic algorithm and binary particle swarm optimization

机译：利用遗传算法和二进制粒子群算法对典型的生物质燃料发电厂进行优化

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Over thousands tons of animal manures are produced in Iran. The major animal manures producers are located in central regions. Animal manures collection is an autochthonous and important renewable energy sources that in most cases are released in nature by ranchers. In this paper, a typical animal manure producer region is considered and optimal location and size of a typical biomass fueled power plant is determined. Genetic algorithm (GA) is used as the major approach of determination and 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. Binary particle swarm optimization algorithm is also used as the second approach of optimization and eventually results obtained from both algorithm are compared. In this work we use profitability index (PI) as the fitness function of Genetic algorithm and the point with the maximum PI is selected.
机译：伊朗生产了数千吨动物粪便。主要的动物粪便生产商位于中部地区。动物粪便的收集是一种自然的，重要的可再生能源，大多数情况下是牧场主释放的。在本文中，考虑了典型的动物粪便生产地区，并确定了典型的以生物质为燃料的发电厂的最佳位置和规模。遗传算法（GA）被用作确定的主要方法，这种方法将有效地确定最佳位置，生物质供应面积和发电厂规模，从而为投资者提供最佳收益。二进制粒子群优化算法也被用作第二种优化方法，并且最终比较了两种算法的结果。在这项工作中，我们使用获利能力指数（PI）作为遗传算法的适应度函数，并选择具有最大PI的点。

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