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Using Stochastic Dual Dynamic Programming to Solve the Multi-Stage Energy Management Problem in Microgrids

Author

Listed:
  • Alejandra Tabares

    (Departamento de Ingeniería Industrial, Facultad de Ingeniería, Universidad de los Andes, Cr 1 Este No. 19A-40, Bogotá 111711, Colombia)

  • Pablo Cortés

    (Departamento de Ingeniería Industrial, Facultad de Ingeniería, Universidad de los Andes, Cr 1 Este No. 19A-40, Bogotá 111711, Colombia)

Abstract

In recent years, the adoption of renewable energy sources has significantly increased due to their numerous advantages, which include environmental sustainability and economic viability. However, the management of electric microgrids presents complex challenges, particularly in the orchestration of energy production and consumption under the uncertainty of fluctuating meteorological conditions. This study aims to enhance decision-making processes within energy management systems specifically designed for microgrids that are interconnected with primary grids, addressing the stochastic and dynamic nature of energy generation and consumption patterns among microgrid users. The research incorporates stochastic models for energy pricing in transactions with the main grid and probabilistic representations of energy generation and demand. This comprehensive methodology allows for an accurate depiction of the volatile dynamics prevalent in the energy markets, which are critical in influencing microgrid operational performance. The application of the Stochastic Dual Dynamic Programming (SDDP) algorithm within a multi-stage adaptive framework for microgrids is evaluated for its effectiveness compared to deterministic approaches. The SDDP algorithm is utilized to develop robust strategies for managing the energy requirements of 1, 2, and 12 prosumers over a 24 h planning horizon. A comparative analysis against the precise solutions obtained from dynamic programming via Monte Carlo simulations indicates a strong congruence between the strategies proposed by the SDDP algorithm and the optimal solutions. The results provide significant insights into the optimization of energy management systems in microgrid settings, emphasizing improvements in operational performance and cost reduction.

Suggested Citation

  • Alejandra Tabares & Pablo Cortés, 2024. "Using Stochastic Dual Dynamic Programming to Solve the Multi-Stage Energy Management Problem in Microgrids," Energies, MDPI, vol. 17(11), pages 1-24, May.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:11:p:2628-:d:1404758
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    References listed on IDEAS

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    1. Anthony Papavasiliou & Yuting Mou & Léopold Cambier & Damien Scieur, 2018. "Application of stochastic dual dynamic programming to the real-time dispatch of storage under renewable supply uncertainty," LIDAM Reprints CORE 2943, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Shapiro, Alexander, 2011. "Analysis of stochastic dual dynamic programming method," European Journal of Operational Research, Elsevier, vol. 209(1), pages 63-72, February.
    3. Li, Mengyu & Zhang, Xiongwen & Li, Guojun & Jiang, Chaoyang, 2016. "A feasibility study of microgrids for reducing energy use and GHG emissions in an industrial application," Applied Energy, Elsevier, vol. 176(C), pages 138-148.
    4. Anthony Papavasiliou & Yuting Mou & Léopold Cambier & Damien Scieur, 2018. "Application of stochastic dual dynamic programming to the real-time dispatch of storage under renewable supply uncertainty," LIDAM Reprints CORE 3044, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    5. Cagnano, A. & De Tuglie, E. & Mancarella, P., 2020. "Microgrids: Overview and guidelines for practical implementations and operation," Applied Energy, Elsevier, vol. 258(C).
    6. Mudhafar Al-Saadi & Maher Al-Greer & Michael Short, 2023. "Reinforcement Learning-Based Intelligent Control Strategies for Optimal Power Management in Advanced Power Distribution Systems: A Survey," Energies, MDPI, vol. 16(4), pages 1-38, February.
    7. Onu, Uchenna Godswill & Zambroni de Souza, Antonio Carlos & Bonatto, Benedito Donizeti, 2023. "Drivers of microgrid projects in developed and developing economies," Utilities Policy, Elsevier, vol. 80(C).
    8. John R. Birge, 1985. "Decomposition and Partitioning Methods for Multistage Stochastic Linear Programs," Operations Research, INFORMS, vol. 33(5), pages 989-1007, October.
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