费迎阳, 江岳文, 洪启腾. 交直流混合配电网改进区间潮流算法[J]. 电网技术, 2021, 45(7): 2683-2691. DOI: 10.13335/j.1000-3673.pst.2020.0519
引用本文: 费迎阳, 江岳文, 洪启腾. 交直流混合配电网改进区间潮流算法[J]. 电网技术, 2021, 45(7): 2683-2691. DOI: 10.13335/j.1000-3673.pst.2020.0519
FEI Yingyang, JIANG Yuewen, HONG Qiteng. Improved Interval Power Flow Algorithm for AC/DC Hybrid Distribution Network[J]. Power System Technology, 2021, 45(7): 2683-2691. DOI: 10.13335/j.1000-3673.pst.2020.0519
Citation: FEI Yingyang, JIANG Yuewen, HONG Qiteng. Improved Interval Power Flow Algorithm for AC/DC Hybrid Distribution Network[J]. Power System Technology, 2021, 45(7): 2683-2691. DOI: 10.13335/j.1000-3673.pst.2020.0519

交直流混合配电网改进区间潮流算法

Improved Interval Power Flow Algorithm for AC/DC Hybrid Distribution Network

  • 摘要: 将区间泰勒展开法应用到交直流混合配电网中,结合增广直角坐标潮流模型,提出了一种适用于求解交直流混合配电网中存在间歇性电源以及波动性负荷的区间潮流算法。该算法考虑了主从控制策略与对等控制策略下直流配电网中节点电压和功率的波动对交流配电网与电压源换流器(voltage source converter,VSC)中潮流和调制比的影响,采用区间泰勒展开法避免了区间迭代与非线性优化,基于增广直角坐标潮流模型的雅克比矩阵与海森矩阵稀疏度极高,提高了计算效率。最后,在IEEE33节点扩展的交直流混合配电网中验证所提算法,并通过将结果与蒙特卡罗算法运行后的结果相比较,验证了该算法的有效性。

     

    Abstract: In this paper, by applying the interval Taylor expansion method to the AC/DC hybrid distribution network and combining it with the augmented rectangular coordinate model, an interval power flow algorithm suitable for solving the intermittent power supply and fluctuating load in the AC/DC hybrid distribution network is proposed. The algorithm considers the influence of the fluctuation of voltage and power in the DC distribution network on the power flow and modulation ratio of the AC distribution network and the voltage source converter under the master-slave control strategy and the peer-to-peer control strategy, which successfully avoids the interval iteration and the nonlinear programming. Based on the augmented rectangular coordinate model, the Jacobian matrix and the Hessen matrix are extremely sparse, which improves the calculation efficiency. Finally, this paper verifies the algorithm in the IEEE33 extended AC/DC hybrid distribution network, and the effectiveness of the algorithm is also proved by comparing the results with those after the Monte Carlo algorithm is run.

     

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