2022•2022 China International Conference on Electricity Distribution (CICED)Requires access

Reactive Power Chance-constrained Optimization Method for an Active Distribution Network Considering DG Actively Regulation

Xiaojun Li, Anyi Li, Jifeng Liang, Tengkai Yu, Tiecheng Li, Tongfei Cui

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Abstract

The high proportion of Distributed generation (DG) connecting to the grid changes the operation characteristics of distribution network, and the traditional deterministic reactive power optimization method is difficult to adapt to the challenge brought by the uncertainty of DG operation. Therefore, a reactive power opportunity constraint optimization method for active distribution network taking into account the active regulation cost of DG is proposed. Constructed by power flow equations are linearized, and DG reactive active regulation of reactive power optimization model of mixed integer second order cone. Based on the reactive power of the traditional on-load voltage regulating and parallel compensation, the reactive power regulation means is expanded, considering DG adjustment cost and the number of traditional reactive power regulation constraints. Scene cut method is used to simplify the solving process, The optimal network loss and the minimum adjustment cost are realized. An example shows that compared with the traditional deterministic reactive power optimization method, the proposed method has higher robustness in dealing with the reactive power regulation problem caused by the uncertainty of DG operation.

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What this paper is about

The high proportion of Distributed generation (DG) connecting to the grid changes the operation characteristics of distribution network, and the traditional deterministic reactive power optimization method is difficult to adapt to the challenge brought by the uncertainty of DG operation. Therefore, a reactive power opportunity constraint optimization method for active distribution network taking into account the active regulation cost of DG is proposed. Constructed by power flow equations are linearized, and DG reactive active regulation of reactive power optimization model of mixed integer second order cone. Based on the reactive power of the traditional on-load voltage regulating and parallel compensation, the reactive power regulation means is expanded, considering DG adjustment cost and the number of traditional reactive power regulation constraints. Scene cut method is used to simplify the solving process, The optimal network loss and the minimum adjustment cost are realized. An example shows that compared with the traditional deterministic reactive power optimization method, the proposed method has higher robustness in dealing with the reactive power regulation problem caused by the uncertainty of DG operation.

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Available abstract

The high proportion of Distributed generation (DG) connecting to the grid changes the operation characteristics of distribution network, and the traditional deterministic reactive power optimization method is difficult to adapt to the challenge brought by the uncertainty of DG operation. Therefore, a reactive power opportunity constraint optimization method for active distribution network taking into account the active regulation cost of DG is proposed. Constructed by power flow equations are linearized, and DG reactive active regulation of reactive power optimization model of mixed integer second order cone. Based on the reactive power of the traditional on-load voltage regulating and parallel compensation, the reactive power regulation means is expanded, considering DG adjustment cost and the number of traditional reactive power regulation constraints. Scene cut method is used to simplify the solving process, The optimal network loss and the minimum adjustment cost are realized. An example shows that compared with the traditional deterministic reactive power optimization method, the proposed method has higher robustness in dealing with the reactive power regulation problem caused by the uncertainty of DG operation.

Key concepts: AC power, Robustness (evolution), Mathematical optimization, Computer science, Control theory (sociology), Distributed generation, Voltage optimisation, Optimization problem

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