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Non-Iterative Semi-Implicit Integration Method for Active Distribution Networks With a High Penetration of Distributed Generations

机译:具有分布式代代高渗透的主动分配网络的非迭代半隐式集成方法

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With the increasing penetration of distributed generations (DGs), the equations governing active distribution networks (ADNs) exhibit stronger nonlinearity and greater stiffness. Additionally, the uncertainties associated with DGs mean that ADNs face more frequent and diversified disturbances. The novel properties of ADNs exacerbate the instabilities and computational burdens on iterations of time-domain simulation when using traditional explicit and implicit integration algorithms. This article proposes a novel semi-implicit integration method incorporating an adaptive Jacobian matrix to solve the differential equations (DEs) governing ADNs, resulting in a non-iterative technique with good numerical stability. The proposed approach simultaneously combines the advantages of both explicit and implicit methods. Moreover, a parameter optimization strategy that comprehensively considers stability, efficiency, and accuracy conditions and an adaptive Jacobian matrix update strategy are developed to further improve the numerical performance of the proposed method. Finally, the proposed method is validated using a modified 33-node system and a practical 436-node distribution system. The simulation results demonstrate the prominent advantages of the proposed method in terms of stability and efficiency compared with the modified Euler and trapezoidal methods.
机译:随着分布式代代(DGS)的渗透性增加,管理有源分配网络(ADN)的方程表现出更强的非线性和更大的刚度。另外,与DGS相关的不确定性意味着ADN面向更频繁和多样化的扰动。 ADN的新颖性质加剧了在使用传统的显式和隐式集成算法时对时域仿真迭代的不稳定和计算负担。本文提出了一种新颖的半隐式积分方法,其包含自适应雅加诺矩阵来解决控制ADN的微分方程(DES),导致具有良好数值稳定性的非迭代技术。所提出的方法同时结合了明确和隐式方法的优势。此外,开发了一种全面考虑稳定性,效率和准确性条件的参数优化策略以及自适应雅略矩阵更新策略,以进一步提高所提出的方法的数值性能。最后,使用修改的33节点系统和实用的436节点分配系统验证了所提出的方法。仿真结果表明,与改进的欧拉和梯形方法相比,在稳定性和效率方面表明了该方法的突出优点。

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