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A Distributed Online Learning Approach for Energy Management With Communication Noises
IEEE Transactions on Sustainable Energy ( IF 8.8 ) Pub Date : 2021-10-14 , DOI: 10.1109/tste.2021.3119657
Xinyue Chang , Yinliang Xu , Hongbin Sun

An online distributed neurodynamic optimization method for energy management in smart grids is proposed to minimize the operational cost of smart grids, considering various constraints, such as output bounds, state of charge of battery storage systems, network congestion and voltage limits. The proposed distributed neurodynamic optimization method adopts a consensus protocol using the signs of the information differences between neighbors for information exchanging and a recurrent neutral network for optimization without dual multipliers. It has the following merits. First, the calculation and information exchange of dual multipliers are avoided to mitigate the computational complexity and reduce communication bandwidth. Second, the proposed optimization method is robust against communication noises from various components. Third, the uncertainties of renewable energy and loads are addressed by the online optimization approach, which takes advantage of the historical data. The convergence, optimality, consensus and robustness of the proposed distributed neurodynamic optimization method are rigorously proved in theory and verified in case studies on the modified IEEE 33-bus and IEEE 123-bus distribution systems.

中文翻译:

一种具有通信噪声的能源管理分布式在线学习方法

考虑到各种约束,如输出边界、电池存储系统的充电状态、网络拥塞和电压限制,提出了一种用于智能电网能量管理的在线分布式神经动力学优化方法,以最小化智能电网的运营成本。所提出的分布式神经动力学优化方法采用共识协议,使用邻居之间的信息差异符号进行信息交换,并采用循环中性网络进行优化,无需双重乘法器。它具有以下优点。首先,避免了双乘法器的计算和信息交换,以降低计算复杂度并减少通信带宽。其次,所提出的优化方法对来自各种组件的通信噪声具有鲁棒性。第三,可再生能源和负荷的不确定性通过利用历史数据的在线优化方法来解决。所提出的分布式神经动力学优化方法的收敛性、最优性、一致性和鲁棒性在理论上得到了严格的证明,并在修改后的 IEEE 33 总线和 IEEE 123 总线分配系统的案例研究中得到了验证。
更新日期:2021-12-21
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