.png){kind=icon}
.png){kind=icon}
Return
Distributed Control Enforcing Group Sparsity in Smart Grids
.png){kind=icon}
OA
AI
DOI：10.1016/j.ifacol.2020.12.156.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In modern smart grids, charging of local energy storage devices is coordinated within the distribution grid to compensate the volatile aggregated power demand on the time interval of interest. However, this results in a perpetual usage of all batteries which in return reduces their lifetime. In this paper, we enforce group sparsity by using an l(p, q)-regularization on the control to counteract this phenomenon. This leads to a non-smooth convex optimization problem, for which a tailored Alternating Direction Method of Multipliers algorithm is proposed. Furthermore, the algorithm is embedded in a Model Predictive Control framework. Numerical simulations show that the proposed scheme yields sparse control while achieving reasonable overall peak shaving. Copyright (C) 2020 The Authors.
Keywords:
Distributed control
Predictive control
Structural optimization
Smart power applications
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
I
IF:
0
Papers:
985
Citations:
0