Quantifying the relative importance of the spatial and temporal resolution in energy systems optimisation model

ArXiv ID: 2310.10518 “View on arXiv”

Authors: Unknown

Abstract

An increasing number of studies using energy system optimisation models are conducted with higher spatial and temporal resolution. This comes with a computational cost which places a limit on the size, complexity, and detail of the model. In this paper, we explore the relative importance of structural aspects of energy system models, spatial and temporal resolution, compared to uncertainties in input parameters such as final energy demand, discount rate and capital costs. We use global sensitivity analysis to uncover these interactions for two developing countries, Kenya, and Benin, which still lack universal access to electricity. We find that temporal resolution has a high influence on all assessed results parameters, and spatial resolution has a significant influence on the expansion of distribution lines to the unelectrified population. The larger overall influence of temporal resolution indicates that this should be prioritised compared to spatial resolution.

Keywords: energy system optimization, global sensitivity analysis, spatial resolution, temporal resolution, electricity access

Complexity vs Empirical Score

• Math Complexity: 6.0/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced global sensitivity analysis (e.g., Sobol indices) with non-linear model interactions, and demonstrates empirical rigor through case studies in Kenya and Benin with detailed parameter exploration and error bars.

  flowchart TD
    A["Research Goal<br>Determine relative importance of<br>spatial vs. temporal resolution<br>in energy system optimization models"] --> B
    
    subgraph B ["Methodology & Data"]
        direction TB
        B1["Global Sensitivity Analysis GSA"]
        B2["Input Data<br>Energy demand, Discount rate,<br>Capital costs, Grid constraints"]
        B1 & B2 --> B3["Model Application<br>Kenya & Benin case studies"]
    end

    B3 --> C["Computational Process<br>Iterative simulation varying<br>spatial & temporal resolution<br>with parameter uncertainties"]

    C --> D["Key Findings Outcomes"]
    
    subgraph D
        direction LR
        D1["Temporal Resolution<br>High influence on all results"]
        D2["Spatial Resolution<br>Significant influence on<br>distribution line expansion"]
    end

    D --> E["Conclusion<br>Prioritize temporal resolution<br>over spatial resolution"]