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Medium-Term Hydrothermal Scheduling of the Infiernillo Reservoir Using Stochastic Dual Dynamic Programming (SDDP): A Case Study in Mexico

Author

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  • Ignacio Marín Cruz

    (Escuela Superior de Ingeniería Mecánica y Eléctrica, Instituto Politécnico Nacional, Av. Luis Enrique Erro s/n, Ciudad de México 07738, Mexico)

  • Mohamed Badaoui

    (Escuela Superior de Ingeniería Mecánica y Eléctrica, Instituto Politécnico Nacional, Av. Luis Enrique Erro s/n, Ciudad de México 07738, Mexico)

  • Ricardo Mota Palomino

    (Escuela Superior de Ingeniería Mecánica y Eléctrica, Instituto Politécnico Nacional, Av. Luis Enrique Erro s/n, Ciudad de México 07738, Mexico)

Abstract

This article aims to obtain and evaluate medium-term operating policies for the hydrothermal scheduling problem by using the stochastic dual dynamic programming (SDDP) approach. To this end, to feed the mathematical model and build the probability distribution functions that best fit each month of the actual inflow volume, monthly inflow data recorded from 1938 to 2018 for the Infiernillo reservoir located in Mexico were employed. Moreover, we simulated inflow volume scenarios using the Monte Carlo method for each month of a one-year planning period. The SDDP approach to solving the optimization problem consisted of the simulation of one forward scenario per iteration and the stabilization of the total operating cost as a convergence criterion, which results in an operating policy. We then assessed its quality by estimating the one-sided optimality gap. It is worth mentioning that the best operation policy required scenario trees of up to 17,000 inflow realizations per stage. Additionally, to study the evolution of the expected value along the planning horizon of the main variables involved in the medium-term hydrothermal scheduling problem, we simulated the best operation policy over 10,000 inflow scenarios. Finally, to show the practical value of the proposed approach, we report its computational complexity.

Suggested Citation

  • Ignacio Marín Cruz & Mohamed Badaoui & Ricardo Mota Palomino, 2023. "Medium-Term Hydrothermal Scheduling of the Infiernillo Reservoir Using Stochastic Dual Dynamic Programming (SDDP): A Case Study in Mexico," Energies, MDPI, vol. 16(17), pages 1-26, August.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:17:p:6288-:d:1228289
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    References listed on IDEAS

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    1. L. A. Terry & M. V. F. Pereira & T. A. Araripe Neto & L. F. C. A. Silva & P. R. H. Sales, 1986. "Coordinating the Energy Generation of the Brazilian National Hydrothermal Electrical Generating System," Interfaces, INFORMS, vol. 16(1), pages 16-38, February.
    2. Andrieu, L. & Henrion, R. & Römisch, W., 2010. "A model for dynamic chance constraints in hydro power reservoir management," European Journal of Operational Research, Elsevier, vol. 207(2), pages 579-589, December.
    3. Suvrajeet Sen & Julia L. Higle, 1999. "An Introductory Tutorial on Stochastic Linear Programming Models," Interfaces, INFORMS, vol. 29(2), pages 33-61, April.
    4. José L. Ceciliano-Meza & Juan Álvarez López & Armando De la Torre Sánchez & Rolando Nieva Gómez & Isaías Guillén Moya & Roberto Navarro Pérez & Favio Perales Martínez & César Torres Ruiz & Anselmo Sán, 2016. "Power System Operator in Mexico Reveals Millions in Savings by Updating Its Short-Term Thermal Unit Commitment Model," Interfaces, INFORMS, vol. 46(6), pages 493-502, December.
    5. Andy Philpott & Vitor de Matos & Erlon Finardi, 2013. "On Solving Multistage Stochastic Programs with Coherent Risk Measures," Operations Research, INFORMS, vol. 61(4), pages 957-970, August.
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