论文标题
在德国建模限制：空间分辨率如何影响线路拥塞
Modeling Curtailment in Germany: How Spatial Resolution Impacts Line Congestion
论文作者
论文摘要
本文研究了网络在空间汇总的网络时研究网络模型中网络限制对可再生能源产生和减少的影响。我们寻求使用传输系统Pypsa-Eur的开放模型来繁殖2013 - 2018年在德国的历史降低。我们的模拟包括空间和时间考虑因素，包括每行拥塞以及每个控制区和季度的缩减。结果表明，由于电力需求的不准确分配和对超载地点的可再生能力分配，高网络分辨率的缩减被大大高估了。但是，随着网络聚集到较小数量的节点，传输网络的高拥塞率降低，从而减少了缩减。定义了一种捕获电力需求和发电厂分配错误的措施，并暗示了可取的空间分辨率。因此，我们能够平衡准确的节点分配和网络拥塞的影响，揭示了减少的模型可以从最新的历史数据中降低。这表明可以减少网络以改善计算时间并捕获网络约束对可变可再生能源进料的最重要影响。
This paper investigates the effects of network constraints in energy system models at transmission level on renewable energy generation and curtailment as the network is being spatially aggregated. We seek to reproduce historically measured curtailment in Germany for the years 2013-2018 using an open model of the transmission system, PyPSA-Eur. Our simulations include spatial and temporal considerations, including congestion per line as well as curtailment per control zone and quarter. Results indicate that curtailment at high network resolution is significantly overestimated due to inaccurate allocation of electricity demand and renewable capacities to overloaded sites. However, high congestion rates of the transmission network decrease as the network is clustered to a smaller number of nodes, thus reducing curtailment. A measure to capture errors in the assignment of electricity demand and power plants is defined and hints towards a preferable spatial resolution. Thus, we are able to balance the effects of accurate node assignment and network congestion revealing that a reduced model can capture curtailment from recent historical data. This shows that it is possible to reduce the network to improve computation times and capture the most important effects of network constraints on variable renewable energy feed-in at the same time.
