Distributed Diffusion-Oriented Cooperation for Power Sharing in Self-Expanding Microgrids

Jingang Lai, Xiaoqing Lu, Fei Wang
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Abstract

This paper proposes a multiagent-based distributed self-organizing control scheme that will enable the power sharing of massive renewable distributed generators (DG) in a low-voltage microgird. The proposed fully distributed self-organizing control strategy can achieve effective power sharing challenge among massive DGs by adopts a peer-to-peer diffusion protocol, in which DGs from the same neighborhood are allowed to communicate with each other at every iteration. Furthermore, learning adaptability via automatic agent discovery is proposed to alter topologies as a result of incoming or outgoing DGs in the microgird network. The resulting algorithm is distributed, cooperative and able to respond in real time to changes in the environment. Eventually, the proposed algorithm is superior over consensus algorithms in terms of convergence speed and utilizes reduced communication infrastructure compared to centralized controllers. The proposed approach can handle networks of different size and topology using the information about the number of nodes which is also evaluated in a distributed fashion. The effectiveness of the proposed control strategy is verified under various scenarios by a modified IEEE 34-bus test network.
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面向分布式扩散的自扩展微电网电力共享合作
提出了一种基于多智能体的分布式自组织控制方案,实现了低压微电网中大型可再生分布式发电机组的电力共享。所提出的全分布式自组织控制策略采用点对点扩散协议,允许来自同一邻域的dg在每次迭代时相互通信,从而实现大规模dg之间有效的权力共享挑战。此外,提出了通过自动智能体发现的学习适应性来改变微网网络中传入或传出dg的拓扑结构。所得到的算法是分布式的、协作的,并且能够实时响应环境的变化。最终,与集中式控制器相比,所提出的算法在收敛速度方面优于共识算法,并且利用了更少的通信基础设施。所提出的方法可以处理不同大小和拓扑的网络,使用节点数量的信息也以分布式的方式进行评估。通过改进的IEEE 34总线测试网络,在各种场景下验证了所提控制策略的有效性。
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