Data-driven risk analysis for probabilistic three-phase grid-supportive demand side management

Niels Blaauwbroek, Phuong Nguyen, Han Slootweg

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)
25 Downloads (Pure)

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.

Original languageEnglish
Article number2514
Number of pages18
JournalEnergies
Volume11
Issue number10
DOIs
Publication statusPublished - 1 Oct 2018

Fingerprint

Risk Analysis
Risk analysis
Data-driven
Grid
Horizon
Low Voltage
Electric potential
Flexibility
Voltage
Situational Awareness
Complex networks
Network Analysis
Electric network analysis
Learning systems
Mathematical operators
Recommendations
Resolve
Machine Learning
Sharing
Monitoring

Keywords

  • Demand side management
  • Network sensitivity
  • Neural networks
  • Operation limit violations
  • Probabilistic power flow

Cite this

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Data-driven risk analysis for probabilistic three-phase grid-supportive demand side management. / Blaauwbroek, Niels; Nguyen, Phuong; Slootweg, Han.

In: Energies, Vol. 11, No. 10, 2514, 01.10.2018.

Research output: Contribution to journalArticleAcademicpeer-review

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AU - Slootweg, Han

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AB - 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.

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