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Embedding power system's reliability within a long-term Energy System Optimization Model: Linking high renewable energy integration and future grid stability for France by 2050

Author

Listed:
  • Gondia Sokhna Seck

    (IFPEN - IFP Energies nouvelles)

  • Vincent Krakowski

    (CMA - Centre de Mathématiques Appliquées - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris sciences et lettres)

  • Edi Assoumou

    (CMA - Centre de Mathématiques Appliquées - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris sciences et lettres)

  • Nadia Maïzi

    (CMA - Centre de Mathématiques Appliquées - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris sciences et lettres)

  • Vincent Mazauric

    (CMA - Centre de Mathématiques Appliquées - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris sciences et lettres, SE - Schneider Electric)

Abstract

The aim of this article is to take into account short-term power grid operation conditions in long-term prospective analysis in the case of France. It is the first time that the integration of system adequacy and transient stability has been achieved in prospective studies for an electro intensive country, following studies conducted on Reunion Island. The methodology relies on a quantitative assessment of the French power sector's reliability through an endogenous definition of a reliability indicator related to kinetic reserves into an Energy System Optimization Model (ESOM), TIMES-FR model. The result gives an overview of how the stability of the grid is maintained with an increasing share of renewables using additional backup and flexible options. We observe that it is technically possible to achieve around 65% of Variable Renewable Energy sources (VREs) in the installed capacity without impairing the reliability of the system. In more detail, the maximum VRE in total hourly power production that complies with the reliability constraint was assessed as around 84% in the 100 EnR scenario. However, in order to guarantee this system reliability, the cumulated new installed capacity, in a scenario with 100% renewable energy sources (RES) in the power mix, would represent the double of the Business-As-Usual (BAU) scenario over the period 2013-2050. Therefore, major upstream planning would be needed, and that more flexible options i.e. demand-response, storage technologies and interconnections or substitute or additional plants should be considered to satisfy the reliability constraint at any time by providing extra inertia to the system. This modelling exercise shows the importance of power exchanges with neighbours with higher share of RE in the power production.

Suggested Citation

  • Gondia Sokhna Seck & Vincent Krakowski & Edi Assoumou & Nadia Maïzi & Vincent Mazauric, 2020. "Embedding power system's reliability within a long-term Energy System Optimization Model: Linking high renewable energy integration and future grid stability for France by 2050," Post-Print hal-02418375, HAL.
    • Handle: RePEc:hal:journl:hal-02418375
    DOI: 10.1016/j.apenergy.2019.114037
    Note: View the original document on HAL open archive server: https://ifp.hal.science/hal-02418375
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    Citations

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    Cited by:

    1. Christos Agathokleous & Jimmy Ehnberg, 2020. "A Quantitative Study on the Requirement for Additional Inertia in the European Power System until 2050 and the Potential Role of Wind Power," Energies, MDPI, vol. 13(9), pages 1-14, May.
    2. Machado, Renato Haddad Simões & Rego, Erik Eduardo & Udaeta, Miguel Edgar Morales & Nascimento, Viviane Tavares, 2022. "Estimating the adequacy revenue considering long-term reliability in a renewable power system," Energy, Elsevier, vol. 243(C).
    3. He, Xinran & Ding, Tao & Zhang, Xiaosheng & Huang, Yuhan & Li, Li & Zhang, Qinglei & Li, Fangxing, 2023. "A robust reliability evaluation model with sequential acceleration method for power systems considering renewable energy temporal-spatial correlation," Applied Energy, Elsevier, vol. 340(C).
    4. Loisel, Rodica & Lemiale, Lionel & Mima, Silvana & Bidaud, Adrien, 2022. "Strategies for short-term intermittency in long-term prospective scenarios in the French power system," Energy Policy, Elsevier, vol. 169(C).
    5. Pampa Sinha & Kaushik Paul & Sanchari Deb & Sulabh Sachan, 2023. "Comprehensive Review Based on the Impact of Integrating Electric Vehicle and Renewable Energy Sources to the Grid," Energies, MDPI, vol. 16(6), pages 1-39, March.
    6. Wang, Tianjing & Tang, Yong, 2022. "Transfer-Reinforcement-Learning-Based rescheduling of differential power grids considering security constraints," Applied Energy, Elsevier, vol. 306(PB).
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    8. Fan, Jing-Li & Huang, Xi & Shi, Jie & Li, Kai & Cai, Jingwen & Zhang, Xian, 2023. "Complementary potential of wind-solar-hydro power in Chinese provinces: Based on a high temporal resolution multi-objective optimization model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    9. Nikita Belyak & Steven A. Gabriel & Nikolay Khabarov & Fabricio Oliveira, 2023. "Renewable Energy Expansion under Taxes and Subsidies: A Transmission Operator's Perspective," Papers 2302.10562, arXiv.org, revised Apr 2024.
    10. Heras, Jorge & Martín, Mariano, 2020. "Social issues in the energy transition: Effect on the design of the new power system," Applied Energy, Elsevier, vol. 278(C).
    11. Wilson Pavon & Manuel Jaramillo & Juan C. Vasquez, 2023. "A Review of Modern Computational Techniques and Their Role in Power System Stability and Control," Energies, MDPI, vol. 17(1), pages 1-17, December.
    12. Groissböck, Markus & Gusmão, Alexandre, 2020. "Impact of renewable resource quality on security of supply with high shares of renewable energies," Applied Energy, Elsevier, vol. 277(C).
    13. Connor Scott & Mominul Ahsan & Alhussein Albarbar, 2021. "Machine Learning Based Vehicle to Grid Strategy for Improving the Energy Performance of Public Buildings," Sustainability, MDPI, vol. 13(7), pages 1-22, April.
    14. Gonzalez-Moreno, A. & Marcos, J. & de la Parra, I. & Marroyo, L., 2022. "A PV ramp-rate control strategy to extend battery lifespan using forecasting," Applied Energy, Elsevier, vol. 323(C).
    15. Oskouei, Morteza Zare & Mohammadi-Ivatloo, Behnam & Abapour, Mehdi & Shafiee, Mahmood & Anvari-Moghaddam, Amjad, 2021. "Privacy-preserving mechanism for collaborative operation of high-renewable power systems and industrial energy hubs," Applied Energy, Elsevier, vol. 283(C).
    16. Thimet, P.J. & Mavromatidis, G., 2022. "Review of model-based electricity system transition scenarios: An analysis for Switzerland, Germany, France, and Italy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    17. Liu, Jia & Chen, Xi & Yang, Hongxing & Shan, Kui, 2021. "Hybrid renewable energy applications in zero-energy buildings and communities integrating battery and hydrogen vehicle storage," Applied Energy, Elsevier, vol. 290(C).
    18. Serrano-Arévalo, Tania Itzel & López-Flores, Francisco Javier & Raya-Tapia, Alma Yunuen & Ramírez-Márquez, César & Ponce-Ortega, José María, 2023. "Optimal expansion for a clean power sector transition in Mexico based on predicted electricity demand using deep learning scheme," Applied Energy, Elsevier, vol. 348(C).
    19. Gondia Sokhna Seck & Emmanuel Hache & Clement Bonnet & Marine Simoën & Samuel Carcanague, 2020. "Copper at the crossroads : Assessment of the interactions between low-carbon energy transition and supply limitations," Post-Print hal-03118509, HAL.
    20. Han, Shuo & He, Mengjiao & Zhao, Ziwen & Chen, Diyi & Xu, Beibei & Jurasz, Jakub & Liu, Fusheng & Zheng, Hongxi, 2023. "Overcoming the uncertainty and volatility of wind power: Day-ahead scheduling of hydro-wind hybrid power generation system by coordinating power regulation and frequency response flexibility," Applied Energy, Elsevier, vol. 333(C).
    21. Zhan, Xianwen & Han, Song & Rong, Na & Cao, Yun, 2023. "A hybrid transfer learning method for transient stability prediction considering sample imbalance," Applied Energy, Elsevier, vol. 333(C).
    22. Pietzcker, Robert C. & Osorio, Sebastian & Rodrigues, Renato, 2021. "Tightening EU ETS targets in line with the European Green Deal: Impacts on the decarbonization of the EU power sector," Applied Energy, Elsevier, vol. 293(C).
    23. Brumana, Giovanni & Franchini, Giuseppe & Ghirardi, Elisa & Perdichizzi, Antonio, 2022. "Techno-economic optimization of hybrid power generation systems: A renewables community case study," Energy, Elsevier, vol. 246(C).
    24. Potrč, Sanja & Nemet, Andreja & Čuček, Lidija & Varbanov, Petar Sabev & Kravanja, Zdravko, 2022. "Synthesis of a regenerative energy system – beyond carbon emissions neutrality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 169(C).
    25. Laha, Priyanka & Chakraborty, Basab, 2021. "Cost optimal combinations of storage technologies for maximizing renewable integration in Indian power system by 2040: Multi-region approach," Renewable Energy, Elsevier, vol. 179(C), pages 233-247.

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