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Impact of different levels of geographical disaggregation of wind and PV electricity generation in large energy system models: A case study for Austria

机译:风能和光伏发电的不同地理分解水平对大型能源系统模型的影响:以奥地利为例

摘要

This paper assesses how different levels of geographical disaggregation of wind and photovoltaic energy resources could affect the outcomes of an energy system model by 2020 and 2050. Energy system models used for policy making typically have high technology detail but little spatial detail. However, the generation potential and integration costs of variable renewable energy sources and their time profile of production depend on geographic characteristics and infrastructure in place. For a case study for Austria we generate spatially highly resolved synthetic time series for potential production locations of wind power and PV. There are regional differences in the costs for wind turbines but not for PV. However, they are smaller than the cost reductions induced by technological learning from one modelled decade to the other. The wind availability shows significant regional differences where mainly the differences for summer days and winter nights are important. The solar availability for PV installations is more homogenous. We introduce these wind and PV data into the energy system model JRC-EU-TIMES with different levels of regional disaggregation. Results show that up to the point that the maximum potential is reached disaggregating wind regions significantly affects results causing lower electricity generation from wind and PV.
机译:本文评估了到2020年和2050年,不同水平的风能和光伏能源的地理分解将如何影响能源系统模型的结果。用于决策的能源系统模型通常具有较高的技术细节,但很少具有空间细节。但是,可变的可再生能源的发电潜力和整合成本及其生产时间分布取决于地理特征和现有的基础设施。对于奥地利的案例研究,我们为风力发电和光伏发电的潜在生产位置生成了空间高度分解的合成时间序列。风力涡轮机的成本存在地区差异,但光伏的成本差异不大。但是,它们比从一个模型十年到另一个模型十年的技术学习所导致的成本降低要小。可用风量显示出明显的区域差异,其中主要是夏季和冬季夜晚的差异很重要。光伏装置的太阳能可用性更加均匀。我们将这些风能和光伏数据引入具有不同区域分解水平的能源系统模型JRC-EU-TIMES。结果表明,直到达到最大电位的程度,风区域的分解都会显着影响结果,导致风和光伏发电量降低。

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