.png){kind=icon}
返回
Learning Provably Stable Local Volt/Var Controllers for Efficient Network Operation
.png){kind=icon}
OA
AI
DOI：10.1109/TPWRS.2023.3268684.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper develops a data-driven framework to synthesize local Volt/Var control strategies for distributed energy resources (DERs) in power distribution grids (DGs). Aiming to improve DG operational efficiency, as quantified by a generic optimal reactive power flow (ORPF) problem, we propose a two-stage approach. The first stage involves learning the manifold of optimal operating points determined by an ORPF instance. To synthesize local Volt/Var controllers, the learning task is partitioned into learning local surrogates (one per DER) of the optimal manifold with voltage input and reactive power output. Since these surrogates characterize efficient DG operating points, in the second stage, we develop local control schemes that steer the DG to these operating points. We identify the conditions on the surrogates and control parameters to ensure that the locally acting controllers collectively converge, in a global asymptotic sense, to a DG operating point agreeing with the local surrogates. We use neural networks to model the surrogates and enforce the identified conditions in the training phase. AC power flow simulations on the IEEE 37-bus network empirically bolster the theoretical stability guarantees obtained under linearized power flow assumptions. The tests further highlight the optimality improvement compared to prevalent benchmark methods.
Keyword:
Distributed energy resources
global stability
local control
Volt/Var control
期刊
IF:
7.2
论文数:
1.1W
被引数:
5.0W
机构
引用论文
Options for Control of Reactive Power by Distributed Photovoltaic Generators.png)
PROCEEDINGS OF THE IEEE
OAAI
PROCEEDINGS OF THE IEEEIF25.9
OAAI分享收藏Input/output force analysis of Stewart platform type of manipulatorsProceedings of 1993 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '93)
Proceedings of 1993 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '93)IF0
PREAI
分享收藏
查看更多内容