Clustering Data-Driven Local Control Schemes in Active Distribution Grids
METADATA ONLY
Loading...
Author / Producer
Date
2021-03
Publication Type
Journal Article
ETH Bibliography
yes
Citations
Altmetric
METADATA ONLY
Data
Rights / License
Abstract
Controllable distributed energy resources (DERs) in active distribution grids (ADGs) provide operational flexibility to system operators, thereby, offering the means to address various challenges. Existing local controllers for these resources are communication-free, robust, and cheap, but with suboptimal performance compared to centralized approaches that heavily rely on monitoring and communication. Data-driven local controls can bridge the gap by providing customized local controllers designed from historical data, offline optimization, and machine learning methods. These local controllers emulate the optimal behavior under expected operating conditions, without the use of communication. However, they exhibit high implementation overhead with the need of individual programming of DER controllers, especially when there are many DERs or when new units are installed at a later stage. In this article, we propose a clustering method to decrease the implementation overhead by reducing the individual DER controls into a smaller set while still achieving high performance. We show the performance of the method on a three-phase, unbalanced, low-voltage, distribution network.
Permanent link
Publication status
published
External links
Editor
Book title
Journal / series
Volume
15 (1)
Pages / Article No.
1467 - 1476
Publisher
IEEE
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Active distribution networks; data-driven control design; machine learning; optimal power flow (OPF); optimal control; time-series clustering
Organisational unit
09481 - Hug, Gabriela / Hug, Gabriela