Parameter Optimization for Var Planning of Systems with High Penetration of Wind Power: an Adaptive Equivalent Reduction Method

• Published in: IEEE transactions on sustainable energy Vol. 14; no. 4; pp. 1 - 13
• Main Authors: Xue, Lin, Niu, Tao, Fang, Sidun, Li, Zhengshuo
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.10.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Adaptively parameter order equivalent reduced optimization (APO-ERO); Computer applications; dynamic Var device planning; Electric potential; Equivalence; Error reduction; high wind power penetration (HWPP) system; Mathematical models; Optimization; Optimization models; Parameters; Planning; Power system dynamics; Reactive power; Reduced order models; Renewable energy; Security; Static VAr compensators; Voltage; Wind power; Wind power generation
• ISSN: 1949-3029; 1949-3037
• DOI: 10.1109/TSTE.2023.3269532

Voltage-related issues such as dramatic voltage fluctuation, lack of Var reserves, become more prominent in high wind power penetration (HWPP) system, leading to increasing of large-scale cascading trip risks within 100-200ms. During the cascading process, fast dynamic Var support, which is strongly related to internal parameters of Var devices, is quite crucial for HWPP system security. If device parameters optimization is also considered in the Var planning, it will be more promising to achieve less investment cost and guarantee transient security for HWPP system. However, the complex control elements and numerous internal parameters optimization cause a huge computational burden of planning issues. Therefore, this paper proposes an adaptively parameter order equivalent reduced optimization (APO-ERO) approach to efficiently and accurately solve the Var planning problem with parameter allocation. First, the original high-order Var device model is adaptively reduced to an optimal low-order model that minimizes the reduced order error. Next, an equivalent low-order optimization model in the upper layer and an inverse mapping parameter allocation model in the lower layer are established to relieve the computational burden. Finally, the two different test systems with HWPP verify that the proposed method can improve computational efficiency on the premise of guaranteeing accuracy.