基于共识admm的不平衡配电网电压无功优化

Adedoyin Inaolaji, Alper Savasci, S. Paudyal, S. Kamalasadan
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引用次数: 3

摘要

电压-无功优化(VVO)通常由一个中央协调器执行,该协调器为有功配电网中的有效电压调节提供网络中控制设备的最佳设定值。然而,这样的中央控制方案容易出现单点故障,并导致隐私问题。相反,分布式优化方法将整个网络分解为子系统,使得局部控制器计算局部优化问题,并且与相邻控制代理的通信有限,从而提高了数据的尊严。因此,本工作采用基于乘法器交替方向法(ADMM)的分布式VVO方法。线性化的Dist3Flow模型(LinDist3Flow)被用作凸网格模型,分布式VVO在受电压幅值和逆变器有功和无功能力限制的情况下,使不平衡有功配电系统中的电压偏差最小化。在IEEE-123节点系统和2522节点系统上的实例研究表明,基于ADMM算法的VVO收敛到与集中式VVO相同的解,但ADMM算法的性能对惩罚参数的选择比较敏感。此外,如果集中式公式是凸的,并且对于甚至大型馈线来说都是可处理的,那么解决分布式对应物可能不一定提供任何计算优势,但出于数据隐私和处理器故障的鲁棒性等原因,仍然是可取的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Consensus ADMM-Based Distributed Volt-VAr Optimization for Unbalanced Distribution Networks
Volt-VAr optimization (VVO) is usually performed by a central coordinator which provides the optimal setpoints of the control devices in the network for efficient voltage regulation in active distribution networks. However, such a central control scheme is prone to a single-point failure and results in privacy concerns. Conversely, distributed optimization methods decompose the entire network into subsystems such that local controllers compute a local optimization problem and have limited communication with their neighboring control agents, thereby enhancing data dignity. This work, therefore, adopts a distributed VVO approach which is based on the alternating direction method of multipliers (ADMM). The linearized Dist3Flow model (LinDist3Flow) is used as a convex grid model and the distributed VVO minimizes voltage deviation in an unbalanced active distribution system subject to limits on voltage magnitudes and inverter active and reactive power capabilities. Case studies implemented on the IEEE-123 node system and a 2522-node system demonstrate that ADMM-based VVO converges to the same solution as that obtained from the centralized VVO, but the performance of the ADMM algorithm is sensitive to the choice of the penalty parameter. Moreover, if the centralized formulation is convex and already tractable for even large-scale feeders, then solving the distributed counterpart might not necessarily provide any computational advantage but is still desirable for reasons such as data privacy and robustness to processor failure.
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