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Security-Constrained Multi-Stage Robust Dynamic Economic Dispatch with Bulk Storage

Author

Listed:
  • Li Dai

    (Changjiang Institute of Survey, Planning, Design and Research, Wuhan 430010, China)

  • Renshi Ye

    (Changjiang Institute of Survey, Planning, Design and Research, Wuhan 430010, China)

  • Dahai You

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430000, China)

  • Xianggen Yin

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430000, China)

Abstract

As wind penetration rates continue to increase, the main challenge faced by operators is how to schedule flexible resources, such as traditional generation and storage, in the future to ensure the safe and stable operation of power grids under multiple uncertainties. In this paper, a security-constrained multi-stage robust dynamic economic dispatch model with storage (SMRDEDS) is proposed to address multiple uncertainties of wind power outputs and N-1 contingencies. Compared to the traditional two-stage robust dynamic economic dispatch model, the proposed multi-stage dispatch model yields sequential operation decisions with uncertainties revealed gradually over time. What is more, a combined two-stage Benders’ decomposition and relaxed approximation–robust dual dynamic programming (RA-RDDP) is proposed to handle the computational issue of multi-stage problems due to large-scale post-contingency constraints and the convergence issue of the stochastic dual dynamic programming (SDDP) algorithm. First, a two-stage Benders’ decomposition algorithm is applied to relax the SMRDEDS model into a master problem and sub-problem. The master problem determines the generator output and storage charge and discharge, and the sub-problem determines the total generation and storage reserve capacity to cover all the generator N-1 contingencies. Second, a relaxed approximation–RDDP algorithm is proposed to solve the multi-stage framework problem. Compared to the traditional SDDP algorithm and RDDP algorithm, the proposed RA-RDDP algorithm uses the inner relaxed approximation and outer approximation methods to approximate the upper and lower bounds of the future cost-to-go function, which overcomes the convergence issue of the traditional SDDP algorithm and solution efficiency of the RDDP algorithm. We tested the proposed algorithm on the IEEE-3 bus, IEEE-118 bus, and the German power system. The simulation results verify the effectiveness of the proposed model and proposed algorithm.

Suggested Citation

  • Li Dai & Renshi Ye & Dahai You & Xianggen Yin, 2025. "Security-Constrained Multi-Stage Robust Dynamic Economic Dispatch with Bulk Storage," Energies, MDPI, vol. 18(5), pages 1-21, February.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:5:p:1073-:d:1597389
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    References listed on IDEAS

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    1. Weng, Haoen & Hu, Yongli & Liang, Min & Xi, Jiayang & Yin, Baocai, 2025. "Optimizing bidding strategy in electricity market based on graph convolutional neural network and deep reinforcement learning," Applied Energy, Elsevier, vol. 380(C).
    2. Angelos Georghiou & Angelos Tsoukalas & Wolfram Wiesemann, 2019. "Robust Dual Dynamic Programming," Operations Research, INFORMS, vol. 67(3), pages 813-830, May.
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    1. Luis Carlos Pérez Guzmán & Gina Idárraga-Ospina & Sergio Raúl Rivera Rodríguez, 2025. "Enhancing Processing Time for Uncertainty Cost Quantification: Demonstration in a Scheduling Approach for Energy Management Systems," Sustainability, MDPI, vol. 17(15), pages 1-19, July.

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