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Medium-term stochastic hydrothermal scheduling with short-term operational effects for large-scale power and water networks

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  • Navarro, Andrés
  • Favereau, Marcel
  • Lorca, Álvaro
  • Olivares, Daniel
  • Negrete-Pincetic, Matías

Abstract

The high integration of variable renewable sources in electric power systems entails a series of challenges inherent to their intrinsic variability. A critical challenge is to correctly value the water available in reservoirs in hydrothermal systems, considering the flexibility that it provides. In this context, this paper proposes a medium-term multistage stochastic optimization model for the hydrothermal scheduling problem solved with the stochastic dual dynamic programming algorithm. The proposed model includes operational constraints and simplified mathematical expressions of relevant operational effects that allow more informed assessment of the water value by considering, among others, the flexibility necessary for the operation of the system. In addition, the hydrological uncertainty in the model is represented by a vector autoregressive process, which allows capturing spatio-temporal correlations between the different hydro inflows. A calibration method for the simplified mathematical expressions of operational effects is also proposed, which allows a detailed short-term operational model to be correctly linked to the proposed medium-term linear model. Through extensive experiments for the Chilean power system, the results show that the difference between the expected operating costs of the proposed medium-term model, and the costs obtained through a detailed short-term operational model was only 0.1%, in contrast to the 9.3% difference obtained when a simpler base model is employed. This shows the effectiveness of the proposed approach. Further, this difference is also reflected in the estimation of the water value, which is critical in water shortage situations.

Suggested Citation

  • Navarro, Andrés & Favereau, Marcel & Lorca, Álvaro & Olivares, Daniel & Negrete-Pincetic, Matías, 2024. "Medium-term stochastic hydrothermal scheduling with short-term operational effects for large-scale power and water networks," Applied Energy, Elsevier, vol. 358(C).
    • Handle: RePEc:eee:appene:v:358:y:2024:i:c:s0306261923019189
    DOI: 10.1016/j.apenergy.2023.122554
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    References listed on IDEAS

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    1. de Queiroz, Anderson Rodrigo, 2016. "Stochastic hydro-thermal scheduling optimization: An overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 382-395.
    2. Huber, Matthias & Dimkova, Desislava & Hamacher, Thomas, 2014. "Integration of wind and solar power in Europe: Assessment of flexibility requirements," Energy, Elsevier, vol. 69(C), pages 236-246.
    3. Li, Jianglong & Ho, Mun Sing, 2022. "Indirect cost of renewable energy: Insights from dispatching," Energy Economics, Elsevier, vol. 105(C).
    4. Álvaro García-Cerezo & Luis Baringo & Raquel García-Bertrand, 2020. "Representative Days for Expansion Decisions in Power Systems," Energies, MDPI, vol. 13(2), pages 1-18, January.
    5. Nahmmacher, Paul & Schmid, Eva & Hirth, Lion & Knopf, Brigitte, 2016. "Carpe diem: A novel approach to select representative days for long-term power system modeling," Energy, Elsevier, vol. 112(C), pages 430-442.
    6. Street, Alexandre & Valladão, Davi & Lawson, André & Velloso, Alexandre, 2020. "Assessing the cost of the Hazard-Decision simplification in multistage stochastic hydrothermal scheduling," Applied Energy, Elsevier, vol. 280(C).
    7. Lohmann, Timo & Hering, Amanda S. & Rebennack, Steffen, 2016. "Spatio-temporal hydro forecasting of multireservoir inflows for hydro-thermal scheduling," European Journal of Operational Research, Elsevier, vol. 255(1), pages 243-258.
    8. Oscar Dowson & Lea Kapelevich, 2021. "SDDP.jl : A Julia Package for Stochastic Dual Dynamic Programming," INFORMS Journal on Computing, INFORMS, vol. 33(1), pages 27-33, January.
    9. Ueckerdt, Falko & Brecha, Robert & Luderer, Gunnar & Sullivan, Patrick & Schmid, Eva & Bauer, Nico & Böttger, Diana & Pietzcker, Robert, 2015. "Representing power sector variability and the integration of variable renewables in long-term energy-economy models using residual load duration curves," Energy, Elsevier, vol. 90(P2), pages 1799-1814.
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