Distributed optimization with Markovian switching targets and stochastic observation noises with applications to DC microgrids
Distributed optimization with Markovian switching targets and stochastic observation noises with applications to DC microgrids
复制标题
DOI：
10.1007/s11432-022-3582-5
复制
发表时间：
2022-11
期刊：
影响因子：
--
通讯作者：
S. Xie;L. Wang;M. Nazari;G. Yin;Gun Li
中科院分区：
文献类型：
--
作者：
S. Xie;L. Wang;M. Nazari;G. Yin;Gun Li
研究了具有马尔可夫切换目标和随机观测噪声的分布式优化问题。为了解决微网最优功率平衡中的目标跟踪和更新跟踪问题，并同时抑制观测噪声，提出了分布式优化算法。观测噪声和马尔可夫切换目标之间的相互作用可能会在减少优化误差和选择步长方面引入基本的权衡。此外，在非频繁马尔可夫切换假设下，严格而全面地建立了均方优化误差界、切换常微分方程（ODE）极限以及优化误差的渐近分布.最后，以直流磁悬浮发电机为例进行了仿真研究。
A distributed optimization problem with Markovian switching targets and stochastic observation noises is considered in this paper. In order to solve target following and renewable following for microgrid (MG) optimal power balancing, and to attenuate observation noises simultaneously, distributed optimization algorithms are developed. The interaction between observation noises and Markovian switching targets may introduce a fundamental tradeoff in reducing the optimization errors and choosing the step size. Furthermore, under infrequent Markovian switching assumptions, the mean-square optimization error bounds, the switching ordinary differential equation (ODE) limit, and the asymptotic distributions of the optimization errors are established rigorously and comprehensively. A simulation example on a DC MG is presented to show the main results of the paper.