In order to mitigate climate change, governments all over the world have started a sustainable transformation of their energy generation systems. The associated introduction of renewable energy sources (RESs) into the energy system has led to a significant transformation of the energy sector in numerous countries worldwide. While most energy systems can accommodate moderate shares of variable renewables, severe challenges, such as grid imbalances or massive curtailment of electricity generation, occur at higher shares. Germany, as one of the forerunners in transforming the energy system (“Energiewende”), has already experienced such challenges in the past. A major reason for these challenges is that variable renewables, such as solar and wind energy, are located in regions with favorable weather conditions, which, in Germany, have a quite low energy demand. This leads to an imbalance of electricity supply and demand. Further stress factors for the energy system originate from the rising share of intermittent electricity production and the direct infeed of the produced electricity into the distribution grid. These changes in the electricity generation require an expansion and reinforcement of the electricity infrastructure. However, due to public resistance, this expansion and reinforcement is lagging behind. As a result, a local overstress of the electricity infrastructure can occur in times of high renewable electricity production. In order to still balance electricity supply and demand, system operators often need to reduce the production output of renewable and conventional power plants. In 2017, the system operators reducted 5,518 gigawatt-hour (GWh) of renewable energy output—the so-called RES curtailment. This accounts for approximately 2.9% of the total electricity produced by renewables. The associated costs for RES curtailment totaled e610 million in 2017. In this context, our study aims at identifying the main drivers for curtailing renewables and at explaining the regional variability of RES curtailment costs. More specifically, we analyze the RES curtailment costs of four distribution system operators (DSOs) in Germany in the period 2015–2017 by means of an econometric model. To further refine the analysis, the DSO regions are partitioned into 1,111 subregions based on substations on the high-to-medium voltage level. To this end, we apply a Voronoi tesselation, which allocates all renewables and conventional power plants to the closest substation. In order to investigate RES curtailment costs, we apply a two-step Heckit sample selection model, which accounts for non-randomly selected variables. The selection equation is a binary choice model that estimates the probability of occurrence of curtailment in a subregion associated with different types of renewables, conventional power plants, and the prevalent load. This analysis considers all subregions of the respective DSOs. In contrast, the outcome equation considers only those subregions that experienced curtailment costs in each year of the period 2015–2017. The latter

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可再生能源馈入电网的位置（In）效率：空间回归分析

为了缓解气候变化，世界各国政府已经开始对其能源生产系统进行可持续转型。相关的可再生能源 (RES) 引入能源系统已导致全球许多国家的能源部门发生重大转变。虽然大多数能源系统可以容纳中等比例的可变可再生能源，但在较高份额时会出现严峻挑战，例如电网失衡或大规模削减发电量。德国作为能源系统转型的先行者之一（“Energiewende”），过去已经经历过这样的挑战。这些挑战的一个主要原因是可变可再生能源，如太阳能和风能，位于天气条件有利的地区，在德国，有相当低的能源需求。这导致电力供需失衡。能源系统的进一步压力因素源于间歇性电力生产的份额不断上升以及所产生的电力直接输入配电网。发电的这些变化需要扩大和加强电力基础设施。然而，由于公众的抵制，这种扩张和加固滞后。因此，在可再生电力产量高的时期，当地的电力基础设施可能会出现超压。为了仍然平衡电力供需，系统运营商通常需要减少可再生能源和传统发电厂的产量。2017年系统运营商减少5家，518 吉瓦时 (GWh) 的可再生能源输出——即所谓的 RES 限电。这约占可再生能源发电总量的 2.9%。2017 年 RES 弃电的相关成本总计为 6.1 亿欧元。在此背景下，我们的研究旨在确定弃用可再生能源的主要驱动因素，并解释 RES 弃电成本的区域差异。更具体地说，我们通过计量经济学模型分析了 2015-2017 年德国四家配电系统运营商 (DSO) 的可再生能源弃电成本。为了进一步细化分析，DSO 区域被划分为 1,111 个基于高到中电压等级的变电站的子区域。为此，我们应用了 Voronoi 细分，将所有可再生能源和传统发电厂分配到最近的变电站。为了研究 RES 弃电成本，我们应用了两步 Heckit 样本选择模型，该模型考虑了非随机选择的变量。选择方程是一个二元选择模型，用于估计与不同类型的可再生能源、常规发电厂和普遍负荷相关的子区域发生限电的概率。该分析考虑了各个 DSO 的所有子区域。相比之下，结果方程只考虑了在 2015-2017 年期间每年经历了限电成本的那些次区域。后者 选择方程是一个二元选择模型，用于估计与不同类型的可再生能源、常规发电厂和普遍负荷相关的子区域发生限电的概率。该分析考虑了各个 DSO 的所有子区域。相比之下，结果方程只考虑了在 2015-2017 年期间每年经历了限电成本的那些次区域。后者 选择方程是一个二元选择模型，用于估计与不同类型的可再生能源、常规发电厂和普遍负荷相关的子区域发生限电的概率。该分析考虑了各个 DSO 的所有子区域。相比之下，结果方程只考虑了在 2015-2017 年期间每年经历了限电成本的那些次区域。后者