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Soft-linking of improved spatiotemporal capacity expansion model with a power flow analysis for increased integration of renewable energy sources into interconnected archipelago

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  • Mimica, Marko
  • Dominković, Dominik F.
  • Kirinčić, Vedran
  • Krajačić, Goran

Abstract

This present study offers a novel approach for the improvement of energy planning. This has become increasingly important as higher penetration of variable energy resources and increased interconnection between the different energy sectors require more detailed planning in terms of spatiotemporal modeling in comparison to the presently available approaches. In this study, we present a method that soft-linked the energy planning and power flow models, which enabled fast and reliable solving of optimization problems. A linear continuous optimization model was used for the energy system optimization and the non-linear problem for the power system analysis. The method is used to compare different energy planning scenarios; further, this also offers the possibility for implementation assessment of the proposed scenarios. The method was applied to interconnected islands for five different scenarios. It was determined that the detailed spatial approach resulted in 26.7% higher total system costs, 3.3 times lower battery capacity, and 14.9 MW higher renewable energy generation capacities installed than in the coarser spatial representation. Moreover, the results of the power flow model indicated that the highest voltage deviation was 16% higher than the nominal voltage level. This indicates the need for inclusion of implementation possibility assessments of energy planning scenarios.

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  • Mimica, Marko & Dominković, Dominik F. & Kirinčić, Vedran & Krajačić, Goran, 2022. "Soft-linking of improved spatiotemporal capacity expansion model with a power flow analysis for increased integration of renewable energy sources into interconnected archipelago," Applied Energy, Elsevier, vol. 305(C).
    • Handle: RePEc:eee:appene:v:305:y:2022:i:c:s030626192101179x
    DOI: 10.1016/j.apenergy.2021.117855
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    Cited by:

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    2. Zhou, Yuekuan, 2022. "Incentivising multi-stakeholders’ proactivity and market vitality for spatiotemporal microgrids in Guangzhou-Shenzhen-Hong Kong Bay Area," Applied Energy, Elsevier, vol. 328(C).
    3. Cailian Gu & Yibo Wang & Weisheng Wang & Yang Gao, 2023. "Research on Load State Sensing and Early Warning Method of Distribution Network under High Penetration Distributed Generation Access," Energies, MDPI, vol. 16(7), pages 1-15, March.
    4. Schledorn, Amos & Junker, Rune Grønborg & Guericke, Daniela & Madsen, Henrik & Dominković, Dominik Franjo, 2022. "Frigg: Soft-linking energy system and demand response models," Applied Energy, Elsevier, vol. 317(C).
    5. Reinert, Christiane & Nilges, Benedikt & Baumgärtner, Nils & Bardow, André, 2024. "This is SpArta: Rigorous Optimization of Regionally Resolved Energy Systems by Spatial Aggregation and Decomposition," Applied Energy, Elsevier, vol. 367(C).
    6. Falkoni, A. & Krajačić, G. & M Mimica, marko, 2024. "Model of a 100 % renewable energy system of self-sufficient wider urban area based on a short-term scale and the integration of the transport and thermal sector," Energy, Elsevier, vol. 305(C).
    7. Schledorn, Amos & Charousset-Brignol, Sandrine & Junker, Rune Grønborg & Guericke, Daniela & Madsen, Henrik & Dominković, Dominik Franjo, 2024. "Frigg 2.0: Integrating price-based demand response into large-scale energy system analysis," Applied Energy, Elsevier, vol. 364(C).

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