1. (Position paper) Characterizing the behavior of small producers in smart grids: A data sanity analysis
- Author
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Rasmus Løvenstein Olsen, Pere Barlet, Eduardo Prieto, Jose M. Gutierrez, Oriol Gomis, Maria Stefan, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. Departament d'Enginyeria Elèctrica, Universitat Politècnica de Catalunya. CBA - Sistemes de Comunicacions i Arquitectures de Banda Ampla, and Universitat Politècnica de Catalunya. CITCEA - Centre d'Innovació Tecnològica en Convertidors Estàtics i Accionaments
- Subjects
Computer science ,Distributed computing ,02 engineering and technology ,Prediction methods ,Renewable energy sources ,Clustering ,Data modeling ,Energy optimization ,Data sanity ,0202 electrical engineering, electronic engineering, information engineering ,Cluster analysis ,General Environmental Science ,Energies::Energia elèctrica::Automatització i control de l'energia elèctrica [Àrees temàtiques de la UPC] ,business.industry ,Electric power distribution ,020206 networking & telecommunications ,Grid ,Electrical grid ,Renewable energy ,Smart grid ,Scalability ,User modeling and applications ,General Earth and Planetary Sciences ,Energia elèctrica -- Distribució ,020201 artificial intelligence & image processing ,Energies renovables ,business - Abstract
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. This work is financially supported by the Danish project RemoteGRID, which is a ForskEL program under Energinet.dk with grant agreement no. 2016-1-12399.
- Published
- 2020