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Alternative Generation Sources Portfolio: Optimal Resources Allocation and Risk Analysis Supported by Genetics Algorithms

机译:替代能源组合:遗传算法支持的最优资源分配和风险分析

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摘要

The natural resources characteristics and current economic factors encourage investments in alternative sources of electric power generation in Brazil. Different technologies can compose a portfolio of generating plants with energetic synergism as a function of the seasonal diversity of their potential production. In such portfolios, it is sought to obtain financial gains by virtue of complementarity generation among candidates sources, under investor's pre-established risk control criteria. From this perspective, our study aims to present an optimization model - supported by genetic algorithms - to define the optimal financial resources allocation for composing renewable sources portfolio (wind, small hydro and biomass cogeneration), given a specified budget and risk-aversion criteria measured by means of the Conditional Value-at-Risk. Case studies involving the cited sources illustrate the application of the model and its potential for supporting analysis and decision making.
机译:自然资源的特征和当前的经济因素鼓励在巴西投资替代性发电资源。不同的技术可以构成具有能量协同作用的发电植物,这取决于其潜在生产的季节性多样性。在此类投资组合中,力求根据投资者预先建立的风险控制标准,通过候选源之间的互补性产生来获得财务收益。从这个角度出发,我们的研究旨在提出一种优化模型-在遗传算法的支持下-在给定的预算和规避风险的标准下,为构成可再生能源组合(风能,小水电和生物质热电联产)定义最佳财政资源分配通过条件风险值。涉及引用来源的案例研究说明了该模型的应用及其在支持分析和决策方面的潜力。

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