The analysis of future energy systems with increasing shares of renewable energy production poses various challenges to the models used in the field of energy system analysis. Aggregation is one solution to reduce the computation time of large optimisation problems, especially for optimisation models with endogenous capacity expansion. Since the economic viability of renewable investments is not directly driven by physical and technical characteristics but rather by the corresponding revenue and cost streams, we propose a novel aggregation method. Our framework covers both the spatial and the technological dimensions of wind investment choices—that is, siting and turbine choice. We define four value components related to, for example, the total yield or the site-specific infeed profile of investment choices. A clustering approach is applied to these value components to identify groups of investment choices that can be aggregated without excessive loss of accuracy. The scheme is applied in a case study of the German electricity system, and the influence of the different value components on the combined technological and spatial aggregation is analysed.

随着可再生能源生产份额的增加，对未来能源系统的分析对能源系统分析领域使用的模型提出了各种挑战。聚合是减少大型优化问题计算时间的一种解决方案，特别是对于具有内生容量扩展的优化模型。由于可再生能源投资的经济可行性不是由物理和技术特征直接驱动，而是由相应的收入和成本流驱动，我们提出了一种新的聚合方法。我们的框架涵盖了风能投资选择的空间和技术维度——即选址和涡轮机选择。我们定义了四个价值组成部分，例如，与投资选择的总收益或特定地点的进料概况有关。将聚类方法应用于这些价值组成部分，以识别可以在不过度损失准确性的情况下进行汇总的投资选择组。该方案应用于德国电力系统的案例研究，分析了不同价值组成部分对技术和空间聚合的影响。