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Analysing decarbonizing strategies in the European power system applying stochastic dominance constraints

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  • Domínguez, Ruth
  • Vitali, Sebastiano
  • Carrión, Miguel
  • Moriggia, Vittorio

Abstract

In this paper we develop an analysis of the efficiency of the expansion strategies to be followed to attain the emissions targets established by the European Commission in the Energy Roadmap 2050. A multi-stage investment model in generating and storage capacity from the point of view of a central planner is presented, considering long-term uncertainties in the decision-making process, such as the demand growth and the investment and fuel costs, and short-term variability. To evaluate the wellness of the expansion strategies according to the CO2 emissions generated and the total cost, second-order stochastic dominance constraints are introduced in the model. This approach allows to obtain better expansion strategies enforcing acceptable distributions of CO2 emissions. The numerical study is carried out considering the case of the European power system. The predictions and suggestions made by the European Commission towards 2050 are the basis to define the benchmark solutions, whose outcomes are analysed. The results obtained from this study highlight that a renewable capacity of at least 2900 GW is needed to attain a net zero CO2 emission European power system. The strategy based on carbon capture and storage does not reduce effectively CO2 emissions while it represents an expensive alternative. Including stochastic dominance in the optimization model allows to obtain less expensive alternative expansion strategies with comparatively lower CO2 emissions in the worst scenarios.

Suggested Citation

  • Domínguez, Ruth & Vitali, Sebastiano & Carrión, Miguel & Moriggia, Vittorio, 2021. "Analysing decarbonizing strategies in the European power system applying stochastic dominance constraints," Energy Economics, Elsevier, vol. 101(C).
  • Handle: RePEc:eee:eneeco:v:101:y:2021:i:c:s0140988321003297
    DOI: 10.1016/j.eneco.2021.105438
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    References listed on IDEAS

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    Cited by:

    1. Kang, Jidong & Wu, Zhuochun & Ng, Tsan Sheng & Su, Bin, 2023. "A stochastic-robust optimization model for inter-regional power system planning," European Journal of Operational Research, Elsevier, vol. 310(3), pages 1234-1248.
    2. Slimane Smouh & Fatima Zohra Gargab & Badr Ouhammou & Abdel Ali Mana & Rachid Saadani & Abdelmajid Jamil, 2022. "A New Approach to Energy Transition in Morocco for Low Carbon and Sustainable Industry (Case of Textile Sector)," Energies, MDPI, vol. 15(10), pages 1-26, May.
    3. 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.
    4. Madurai Elavarasan, Rajvikram & Pugazhendhi, Rishi & Irfan, Muhammad & Mihet-Popa, Lucian & Khan, Irfan Ahmad & Campana, Pietro Elia, 2022. "State-of-the-art sustainable approaches for deeper decarbonization in Europe – An endowment to climate neutral vision," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).

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    More about this item

    Keywords

    CO2 emissions; Renewable energy; European decarbonization roadmap; Capacity expansion; Stochastic dominance; Multi-stage stochastic optimization;
    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
    • 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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