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Highly resolved optimal renewable allocation planning in power systems under consideration of dynamic grid topology

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  • Slednev, Viktor
  • Bertsch, Valentin
  • Ruppert, Manuel
  • Fichtner, Wolf

Abstract

The system integration of an increasing amount of electricity generation from decentralised renewable energy sources (RES-E) is a major challenge for the transition of the European power system. The feed-in profiles and the potential of RES-E vary along the geographical and temporal dimension and are also subject to technological choices and changes. To support power system planning in the context of RES-E expansion and allocation planning required for meeting RES-E targets, analyses are needed assessing where and which RES-E capacities are likely to be expanded. This requires models that are able to consider the power grid capacity and topology including their changes over time. We therefore developed a model that meets these requirements and considers the assignment of RES-E potentials to grid nodes as variable. This is a major advancement in comparison to existing approaches based on a fixed and pre-defined assignment of RES-E potentials to a node. While our model is generic and includes data for all of Europe, we demonstrate the model in the context of a case study in the Republic of Ireland. We find wind onshore to be the dominating RES-E technology from a cost-efficient perspective. Since spatial wind onshore potentials are highest in the West and North of the country, this leads to a high capacity concentration in these areas. Should policy makers wish to diversify the RES-E portfolio, we find that a diversification mainly based on bioenergy and wind offshore is achievable at a moderate cost increase. Including solar photovoltaics into the portfolio, particularly rooftop installations, however, leads to a significant cost increase but also to a more scattered capacity installation over the country.

Suggested Citation

  • Slednev, Viktor & Bertsch, Valentin & Ruppert, Manuel & Fichtner, Wolf, 2017. "Highly resolved optimal renewable allocation planning in power systems under consideration of dynamic grid topology," MPRA Paper 79706, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:79706
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    References listed on IDEAS

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    Cited by:

    1. Fitiwi, Desta & Lynch, Muireann Á. & Bertsch, Valentin, 2019. "Optimal development of electricity generation mix considering fossil fuel phase-out and strategic multi-area interconnection," Papers WP616, Economic and Social Research Institute (ESRI).
    2. Lynch, Muireann Á & Devine, Mel & Bertsch, Valentin, 2018. "The role of power-to-gas in the future energy system: how much is needed and who wants to invest?," Papers WP590, Economic and Social Research Institute (ESRI).
    3. Valentin Bertsch & Valeria Di Cosmo, 2018. "Are Renewables Profitable in 2030? A Comparison between Wind and Solar across Europe," Working Papers 2018.28, Fondazione Eni Enrico Mattei.
    4. McKenna, R.C. & Bchini, Q. & Weinand, J.M. & Michaelis, J. & König, S. & Köppel, W. & Fichtner, W., 2018. "The future role of Power-to-Gas in the energy transition: Regional and local techno-economic analyses in Baden-Württemberg," Applied Energy, Elsevier, vol. 212(C), pages 386-400.
    5. Jason Harold, Valentin Bertsch, Thomas Lawrence, and Magie Hall, 2021. "Drivers of People's Preferences for Spatial Proximity to Energy Infrastructure Technologies: A Cross-country Analysis," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4).
    6. Plötz, Patrick & Gnann, Till & Jochem, Patrick & Yilmaz, Hasan Ümitcan & Kaschub, Thomas, 2019. "Impact of electric trucks powered by overhead lines on the European electricity system and CO2 emissions," Energy Policy, Elsevier, vol. 130(C), pages 32-40.
    7. Lynch, Muireann & Devine, Mel T. & Bertsch, Valentin, 2019. "The role of power-to-gas in the future energy system: Market and portfolio effects," Energy, Elsevier, vol. 185(C), pages 1197-1209.
    8. Bertsch, Valentin & Di Cosmo, Valeria, 2020. "Are renewables profitable in 2030 and do they reduce carbon emissions effectively? A comparison across Europe," MPRA Paper 101822, University Library of Munich, Germany.

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    More about this item

    Keywords

    optimal renewable allocation planning; dynamic grid topology; large-scale optimisation;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy

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