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On the solution variability reduction of Stochastic Dual Dynamic Programming applied to energy planning

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  • Soares, Murilo Pereira
  • Street, Alexandre
  • Valladão, Davi Michel

Abstract

In the hydrothermal energy operation planning of Brazil and other hydro-dependent countries, Stochastic Dual Dynamic Programming (SDDP) computes a risk-averse optimal policy that often considers river-inflow autoregressive models. In practical applications, these models induce an undesirable variability of primal (thermal generation) and dual (marginal cost and spot price) solutions that are highly sensitive to changes in current inflow conditions. This work proposes two differing approaches to stabilize SDDP solutions to the energy operation planning problem: the first approach regularizes primal variables by considering an additional penalty on thermal dispatch revisions over time, and the second approach indirectly reduces thermal generation and marginal cost variability by disregarding past inflow information in the cost-to-go function and compensates with an increase in risk aversion. For comparison purposes, we assess solution quality with a set of proposed indexes summarizing each important aspect of a hydrothermal operation planning policy. In conclusion, we show that it is possible to obtain high-quality solutions in comparison to current benchmarks with significantly reduced variability.

Suggested Citation

  • Soares, Murilo Pereira & Street, Alexandre & Valladão, Davi Michel, 2017. "On the solution variability reduction of Stochastic Dual Dynamic Programming applied to energy planning," European Journal of Operational Research, Elsevier, vol. 258(2), pages 743-760.
    • Handle: RePEc:eee:ejores:v:258:y:2017:i:2:p:743-760
    DOI: 10.1016/j.ejor.2016.08.068
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    References listed on IDEAS

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    Cited by:

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    5. Moreira, Alexandre & Pozo, David & Street, Alexandre & Sauma, Enzo & Strbac, Goran, 2021. "Climate‐aware generation and transmission expansion planning: A three‐stage robust optimization approach," European Journal of Operational Research, Elsevier, vol. 295(3), pages 1099-1118.
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    7. Mateus Waga & Davi Valladão & Alexandre Street & Thuener Silva, 2022. "Disentangling Shareholder Risk Aversion from Leverage-Dependent Borrowing Cost on Corporate Policies," Computational Economics, Springer;Society for Computational Economics, vol. 60(3), pages 1-24, October.
    8. Chen, X.P. & Hewitt, N. & Li, Z.T. & Wu, Q.M. & Yuan, Xufeng & Roskilly, Tony, 2017. "Dynamic programming for optimal operation of a biofuel micro CHP-HES system," Applied Energy, Elsevier, vol. 208(C), pages 132-141.
    9. Jing Liu & Yongping Li & Guohe Huang & Cai Suo & Shuo Yin, 2017. "An Interval Fuzzy-Stochastic Chance-Constrained Programming Based Energy-Water Nexus Model for Planning Electric Power Systems," Energies, MDPI, vol. 10(11), pages 1-23, November.
    10. Paula Medina Maçaira & Yasmin Monteiro Cyrillo & Fernando Luiz Cyrino Oliveira & Reinaldo Castro Souza, 2019. "Including Wind Power Generation in Brazil’s Long-Term Optimization Model for Energy Planning," Energies, MDPI, vol. 12(5), pages 1-20, March.

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