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

Author

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  • 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)

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.

Suggested Citation

  • Niels Blaauwbroek & Phuong Nguyen & Han Slootweg, 2018. "Data-Driven Risk Analysis for Probabilistic Three-Phase Grid-Supportive Demand Side Management," Energies, MDPI, vol. 11(10), pages 1-18, September.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:10:p:2514-:d:171263
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    References listed on IDEAS

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    2. Anastasios Dounis, 2019. "Special Issue “Intelligent Control in Energy Systems”," Energies, MDPI, vol. 12(15), pages 1-9, August.

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