Data-Driven Risk Analysis for Probabilistic Three-Phase Grid-Supportive Demand Side Management

Niels Blaauwbroek, Phuong Nguyen and Han Slootweg
Additional contact information
Niels Blaauwbroek: Electrical Energy Systems Group, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
Phuong Nguyen: Electrical Energy Systems Group, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
Han Slootweg: Electrical Energy Systems Group, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands

Energies, 2018, vol. 11, issue 10, 1-18

Abstract: Along with the emerging development of demand side management applications, it is still a challenge to exploit flexibility realistically to resolve or prevent specific geographical network issues due to limited situational awareness of the (unbalanced low-voltage) network as well as complex time dependent constraints. To overcome these problems, this paper presents a time-horizon three-phase grid-supportive demand side management methodology for low voltage networks by using a universal interface that is established between the demand side management application and the monitoring and network analysis tools of the network operator. Using time-horizon predictions of the system states that the probability of operational limit violations is identified. Since this analysis is computationally intensive, a data driven approach is adopted by using machine learning. Time-horizon flexibility is procured, which effectively prevents operation limit violation from occurring independent of the objective that the demand side management application has. A practical example featuring fair power sharing demonstrates the effectiveness of the presented method for resolving over-voltages and under-voltages. This is followed by conclusions and recommendations for future work.

Keywords: demand side management; operation limit violations; probabilistic power flow; network sensitivity; neural networks (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1996-1073/11/10/2514/pdf (application/pdf)
https://www.mdpi.com/1996-1073/11/10/2514/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:11:y:2018:i:10:p:2514-:d:171263

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().