Power Flow Analysis Considering Automatic Generation Control for Multi-Area Interconnection Power Networks

• Published in: IEEE transactions on industry applications Vol. 53; no. 6; pp. 5200 - 5208
• Main Authors: Dong, Xiaoming, Sun, Hua, Wang, Chengfu, Yun, Zhihao, Wang, Yiming, Zhao, Penghui, Ding, Yuanyuan, Wang, Yong
• Format: Journal Article
• Language: English
• Published: IEEE 01.11.2017
• Subjects: Automatic generation control; Jacobian matrices; Load flow; Mathematical model; power flow; Power grids; Power markets; Power system analysis computing; power system control; power system interconnection; power transmission; Reactive power
• ISSN: 0093-9994; 1939-9367
• DOI: 10.1109/TIA.2017.2738618

In addition to maintaining the power balance, the major tasks of multi-area automatic generation control (AGC) involve regulating the power exchange among subareas. This study presents a new power flow model allowing for multi-area AGC that achieves cooperation among the participating generators, which are deployed separately in the interconnected subarea networks. The formulations of the power exchange among the subareas are derived from the active power flow equations of the tie-lines. The node parameters representing the level and allocation of the unbalanced power are introduced. Then, the proposed power flow model is established by integrating the formulations of the node power injection and power exchange. Meanwhile, the unknown variables in the integrated model can be synchronously adjusted by the Newton iteration method, which depends on the integrated Jacobian matrix. Furthermore, being improved based on the integrated formulations, some related algorithms, including but not limited to sensitivity analysis, are expected to produce more precise solutions. The case studies of the IEEE 5 bus test system, the IEEE RTS-1996 test system, and the IEEE 300 bus test system illustrate the convergence and efficiency of the proposed power flow method. Moreover, the case studies also demonstrate that the integrated formulations based sensitivity analysis can generate more reliable results.