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Identifying key elements for adequate simplifications of investment choices – The case of wind energy expansion

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  • Pöstges, Arne
  • Weber, Christoph

Abstract

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.

Suggested Citation

  • Pöstges, Arne & Weber, Christoph, 2023. "Identifying key elements for adequate simplifications of investment choices – The case of wind energy expansion," Energy Economics, Elsevier, vol. 120(C).
    • Handle: RePEc:eee:eneeco:v:120:y:2023:i:c:s0140988323000324
    DOI: 10.1016/j.eneco.2023.106534
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    More about this item

    Keywords

    Aggregation; Clustering; Value components; Wind energy expansion;
    All these keywords.

    JEL classification:

    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • O21 - Economic Development, Innovation, Technological Change, and Growth - - Development Planning and Policy - - - Planning Models; Planning Policy

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