Research on Automatic Power Flow Convergence Adjustment Method Based on Modified DC Power Flow Algorithm

被引：0

作者：

Zhang S. ^{[1
]}

Zhang D. ^{[1
]}

Huang Y. ^{[1
]}

Li W. ^{[1
]}

Chen X. ^{[1
]}

Tang Y. ^{[1
]}

机构：

[1] State Key Laboratory of Power Grid Safety and Energy Conservation, China Electric Power Research Institute, Haidian District, Beijing

来源：

Dianwang Jishu/Power System Technology | 2021年 / 45卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Intelligent optimization algorithms; Modified DC power flow; Power flow adjustment; Power flow convergence; Power system; Reinforcement learning;

D O I：

10.13335/j.1000-3673.pst.2019.1969

中图分类号：

学科分类号：

摘要：

To get convergence solution for power flow calculation, the traditional method is to manually changing the presuppositions again and again. With the increase of scale and complication of power system, it is more difficult for dispatchers and planners to get convergent power flow calculation results, which makes it necessary to implement an automatic power flow convergence adjustment method. In this paper, an automatic power flow convergence adjustment method is proposed based on modified DC power flow algorithm. Due to the weak coupling characteristics in power flow calculation of high-voltage grid, the active and the reactive power were adjusted separately. For adjusting the active power, an index of fractional active power balance was proposed based on modified DC power flow algorithm. For the adjustment of the reactive power, a virtual reactive power grid and an index of fractional reactive power balance were proposed. Both the active and reactive power were adjusted using intelligent optimization algorithm or reinforcement learning for the power flow convergence restoration. Simulations in the IEEE 118-bus system and the real power system have proved the feasibility and effectiveness of the proposed method. © 2021, Power System Technology Press. All right reserved.

引用

页码：86 / 97

页数：11

共 29 条

[21] He Tianyu, Wei Zhinong, Sun Guoqiang, Et al., Modified direct current optimal power flow algorithm based on net loss equivalent load model, Automation of Electric Power Systems, 40, 6, pp. 58-64, (2016)
[22] Mnih V, Kavukcuoglu K, Silver D, Et al., Human-level control through deep reinforcement learning[J], Nature, 518, 7540, (2015)
[23] Silver D, Schrittwieser J, Simonyan K, Et al., Mastering the game of Go without human knowledge[J], Nature, 550, pp. 354-359, (2017)
[24] Vinyals O, Babuschkin I, Czarnecki W M, Et al., Grandmaster level in StarCraft II using multi-agent reinforcement learning[J], Nature, 575, pp. 350-354, (2019)
[25] Zhang D, Han X, Deng C., Review on the research and practice of deep learning and reinforcement learning in smart grids[J], CSEE Journal of Power and Energy Systems, 4, 3, pp. 362-370, (2018)
[26] Xu Huating, Yu Zhihong, Zheng Qingping, Et al., Deep reinforcement learning-based tie-line power adjustment method for power system operation state calculation[J], IEEE Access, 7, pp. 156160-156174, (2019)
[27] Wang Tianjing, Tang Yong, Guo Qiang, Et al., Automatic adjustment method of power flow calculation convergence for large-scale power grid based on knowledge experience and deep reinforcement learning, Proceedings of the CSEE, 40, 8, pp. 2396-2406, (2020)
[28] (2011)
[29] Zhang Yantao, Qin Xiaohui, Zhou Qinyong, Et al., Study on the acquisition method of the feasible solution for power flow calculation of large-scale power grids based on AC/DC decoupling, Proceedings of the CSEE, 35, 24, pp. 6275-6282, (2015)

← 1 2 3 →