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Uncertain Scheduling of the Power System Based on Wasserstein Distributionally Robust Optimization and Improved Differential Evolution Algorithm

Author

Listed:
  • Jie Hao

    (School of Electrical Engineering, Northwest Minzu University, Lanzhou 730030, China)

  • Xiuting Guo

    (School of Science, Lanzhou University of Technology, Lanzhou 730050, China)

  • Yan Li

    (School of Electrical Engineering, Northwest Minzu University, Lanzhou 730030, China)

  • Tao Wu

    (School of Electrical Engineering, Northwest Minzu University, Lanzhou 730030, China)

Abstract

The rapid development of renewable energy presents challenges to the security and stability of power systems. Aiming at addressing the power system scheduling problem with load demand and wind power uncertainty, this paper proposes the establishment of different error fuzzy sets based on the Wasserstein probability distance to describe the uncertainties of load and wind power separately. Based on these Wasserstein fuzzy sets, a distributed robust chance-constrained scheduling model was established. In addition, the scheduling model was transformed into a linear programming problem through affine transformation and CVaR approximation. The simplex method and an improved differential evolution algorithm were used to solve the model. Finally, the model and algorithm proposed in this paper were applied to model and solve the economic scheduling problem for the IEEE 6-node system with a wind farm. The results show that the proposed method has better optimization performance than the traditional method.

Suggested Citation

  • Jie Hao & Xiuting Guo & Yan Li & Tao Wu, 2024. "Uncertain Scheduling of the Power System Based on Wasserstein Distributionally Robust Optimization and Improved Differential Evolution Algorithm," Energies, MDPI, vol. 17(15), pages 1-21, August.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:15:p:3846-:d:1449875
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    References listed on IDEAS

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