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A two-stage stochastic optimization planning framework to decarbonize deeply electric power systems

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  • Boffino, Luigi
  • Conejo, Antonio J.
  • Sioshansi, Ramteen
  • Oggioni, Giorgia

Abstract

In 2015, 195 countries signed the Paris Agreement under the United Nations Framework Convention on Climate Change. To achieve the ambitious greenhouse gas-reduction targets therein, the electric power sector must be transformed fundamentally. To this end, we develop a two-stage stochastic optimization model. The proposed model determines the optimal mix of generation and transmission capacity to build to serve future demands at least cost, while respecting technical constraints and climate-related considerations. The model uses a mix of AC and high-voltage DC transmission lines, conventional and renewable generation, and different types of energy-storage units to meet these objectives. Short- and long-term uncertainties are modeled using operating conditions and scenarios, respectively.

Suggested Citation

  • Boffino, Luigi & Conejo, Antonio J. & Sioshansi, Ramteen & Oggioni, Giorgia, 2019. "A two-stage stochastic optimization planning framework to decarbonize deeply electric power systems," Energy Economics, Elsevier, vol. 84(C).
    • Handle: RePEc:eee:eneeco:v:84:y:2019:i:c:s0140988319302385
    DOI: 10.1016/j.eneco.2019.07.017
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    Cited by:

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    6. Iacopo Savelli & Thomas Morstyn, 2020. "Electricity prices and tariffs to keep everyone happy: a framework for fixed and nodal prices coexistence in distribution grids with optimal tariffs for investment cost recovery," Papers 2001.04283, arXiv.org, revised Jun 2021.
    7. Côté, Elizabeth & Salm, Sarah, 2022. "Risk-adjusted preferences of utility companies and institutional investors for battery storage and green hydrogen investment," Energy Policy, Elsevier, vol. 163(C).
    8. Wojciech Drożdż & Grzegorz Kinelski & Marzena Czarnecka & Magdalena Wójcik-Jurkiewicz & Anna Maroušková & Grzegorz Zych, 2021. "Determinants of Decarbonization—How to Realize Sustainable and Low Carbon Cities?," Energies, MDPI, vol. 14(9), pages 1-19, May.
    9. Hannan, M.A. & Faisal, M. & Jern Ker, Pin & Begum, R.A. & Dong, Z.Y. & Zhang, C., 2020. "Review of optimal methods and algorithms for sizing energy storage systems to achieve decarbonization in microgrid applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    10. Kang, Jidong & Ng, Tsan Sheng & Su, Bin & Milovanoff, Alexandre, 2021. "Electrifying light-duty passenger transport for CO2 emissions reduction: A stochastic-robust input–output linear programming model," Energy Economics, Elsevier, vol. 104(C).
    11. Kang, Jidong & Ng, Tsan Sheng & Su, Bin, 2020. "Optimizing electricity mix for CO2 emissions reduction: A robust input-output linear programming model," European Journal of Operational Research, Elsevier, vol. 287(1), pages 280-292.
    12. Michael C. Ferris & Andy Philpott, 2023. "Renewable electricity capacity planning with uncertainty at multiple scales," Computational Management Science, Springer, vol. 20(1), pages 1-40, December.
    13. García-Cerezo, Álvaro & Baringo, Luis & García-Bertrand, Raquel, 2021. "Robust transmission network expansion planning considering non-convex operational constraints," Energy Economics, Elsevier, vol. 98(C).
    14. Felder, F.A. & Kumar, P., 2021. "A review of existing deep decarbonization models and their potential in policymaking," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    15. Russo, Marianna & Kraft, Emil & Bertsch, Valentin & Keles, Dogan, 2022. "Short-term risk management of electricity retailers under rising shares of decentralized solar generation," Energy Economics, Elsevier, vol. 109(C).
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    17. Carvallo, Claudio & Jalil-Vega, Francisca & Moreno, Rodrigo, 2023. "A multi-energy multi-microgrid system planning model for decarbonisation and decontamination of isolated systems," Applied Energy, Elsevier, vol. 343(C).

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    More about this item

    Keywords

    Generation-expansion planning; Transmission-expansion planning; Stochastic optimization; Climate policy; Energy storage;
    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
    • Q5 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics

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