A fast linearized AC power flow-constrained robust unit commitment approach with customized redundant constraint identification method

Frontiers in Energy Research

Energy Research

Kaikai Wang¹  Peng Wang¹  Yingying Hu¹  Hongrui Yin²  Chenjia Gu²  Zhiyuan Li² 

[1] Economic and Technological Research Institute State Grid Shanxi Electric Power Company, Taiyuan, China;Shaanxi Key Laboratory of Smart Grid, School of Electrical Engineering, Xi’an Jiaotong University, Xi’an, China;

关键词: power system operation;    uncertainty of renewable energy resources;    linearized AC power flow;    robust unit commitment;    redundant constraint identification method;   

DOI  :  10.3389/fenrg.2023.1218461

 received in 2023-05-07, accepted in 2023-07-03,  发布年份 2023

来源: Frontiers

【 摘 要 】

The large-scale integration of renewable energy resources in the power system challenges its economic and secure operation. Particularly, the increasing penetration of renewable energy will result in insufficient system voltage regulation and reactive power support capabilities, and may cause high risks of nodal voltage and branch flow violations. Therefore, to hedge the operational risks under the worst realization of uncertainties of renewable energy sources, a two-stage robust unit commitment (UC) model is developed. Meanwhile, the convexified AC power flow model is incorporated in the robust UC model to more accurately characterize the real-time operating status of power systems. On this basis, an AC power flow-constrained robust unit commitment (ACRUC) model is formulated. A circular linearization method is then adopted to handle the quadratic constraints in the original AC power flow model, transforming them into tractable linear constraints. Furthermore, to reduce the computational complexity caused by the large-scale newly-added constraints after the linearization process, a customized redundant constraint identification (RCI) method is developed, in which two different modes (i.e., cold and warm start modes) are designed considering the difference in base case system operating condition for linearizing branch losses. Then, the redundant network security constraints could be identified by solving a series of relatively simple optimization subproblems. Numerical results on the modified NERL-118 test system indicate that the proposed model could accurately depict actual operation and scheduling conditions, and also verify that the proposed customized RCI method could effectively reduce the problem scale and improve the solution efficiency.

【 授权许可】

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Copyright © 2023 Li, Yin, Wang, Gu, Wang and Hu.