Renewable energy production throughout low-voltage grids has gradually increased in electrical distribution systems, therefore introducing small energy producers - prosumers. This paradigm challenges the traditional unidirectional energy distribution flow to include disperse power production from renewables. To understand how energy usage can be optimized in the dynamic electrical grid, it is important to understand the behavior of prosumers and their impact on the grid's operational procedures. The main focus of this study is to investigate how grid operators can obtain an automatic data-driven system for the low-voltage electrical grid management, by analyzing the available grid topology and time-series consumption data from a real-life test area. The aim is to argue for how different consumer profiles, clustering and prediction methods contribute to the grid-related operations. Ultimately, this work is intended for future research directions that can contribute to improving the trade-off between systematic and scalable data models and software computational challenges.
|Tidsskrift||Procedia Computer Science|
|Status||Udgivet - 2020|
|Begivenhed||Complex Adaptive Systems Conference: Leveraging AI and Machine Learning for Societal Challenges - Malvern, USA|
Varighed: 13 nov. 2019 → 15 nov. 2019
|Konference||Complex Adaptive Systems Conference|
|Periode||13/11/2019 → 15/11/2019|