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Advanced Spatial and Technological Aggregation Scheme for Energy System Models

Author

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  • Shruthi Patil

    (Institute for Energy and Climate Research—Techno-Economic Systems Analysis (IEK-3), Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, 52428 Jülich, Germany
    These authors contributed equally to this work.)

  • Leander Kotzur

    (Institute for Energy and Climate Research—Techno-Economic Systems Analysis (IEK-3), Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, 52428 Jülich, Germany
    These authors contributed equally to this work.)

  • Detlef Stolten

    (Institute for Energy and Climate Research—Techno-Economic Systems Analysis (IEK-3), Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, 52428 Jülich, Germany
    Chair for Fuel Cells, RWTH Aachen University, c/o Institute for Energy and Climate Research—Techno-Economic Systems Analysis (IEK-3), Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, 52428 Jülich, Germany)

Abstract

Energy system models that consider variable renewable energy sources (VRESs) are computationally complex. The greater spatial scope and level of detail entailed in the models exacerbates complexity. As a complexity-reduction approach, this paper considers the simultaneous spatial and technological aggregation of energy system models. To that end, a novel two-step aggregation scheme is introduced. First, model regions are spatially aggregated to obtain a reduced region set. The aggregation is based on model parameters such as VRES time series, capacities, etc. In addition, spatial contiguity of regions is considered. Next, technological aggregation is performed on each VRES, in each region, based on their time series. The aggregations’ impact on accuracy and complexity of a cost-optimal, European energy system model is analyzed. The model is aggregated to obtain different combinations of numbers of regions and VRES types. Results are benchmarked against an initial resolution of 96 regions, with 68 VRES types in each. System cost deviates significantly when lower numbers of regions and/or VRES types are considered. As spatial and technological resolutions increase, the cost fluctuates initially and stabilizes eventually, approaching the benchmark. Optimal combination is determined based on an acceptable cost deviation of <5% and the point of stabilization. A total of 33 regions with 38 VRES types in each is deemed optimal. Here, the cost is underestimated by 4.42 % , but the run time is reduced by 92.95 % .

Suggested Citation

  • Shruthi Patil & Leander Kotzur & Detlef Stolten, 2022. "Advanced Spatial and Technological Aggregation Scheme for Energy System Models," Energies, MDPI, vol. 15(24), pages 1-26, December.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:24:p:9517-:d:1004289
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    References listed on IDEAS

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