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Scalable Assessment and Optimization of Power Distribution Automation Networks

In: Principles of Performance and Reliability Modeling and Evaluation

Author

Listed:
  • Alberto Avritzer

    (Corporate Technology)

  • Lucia Happe

    (Karlsruhe Institute of Technology (KIT))

  • Anne Koziolek

    (Karlsruhe Institute of Technology (KIT))

  • Daniel Sadoc Menasche

    (Federal University of Rio de Janeiro)

  • Sindhu Suresh

    (Corporate Technology)

  • Jose Yallouz

    (Israel Institute of Technology (Technion))

Abstract

In this chapter, we present a novel state space exploration method for distribution automation power grids built on top of an analytical survivability model. Our survivability model-based approach enables efficient state space exploration in a principled way using random-greedy heuristic strategies. The proposed heuristic strategies aim to maximize survivability under budget constraints, accounting for cable undergrounding and tree trimming costs, with load constraints per feeder line. The heuristics are inspired by the analytical results of optimal strategies for simpler versions of the allocation problem. Finally, we parameterize our models using historical data of recent large storms. We have looked into the named storms that occurred during the 2012 Atlantic hurricane season as provided by the U.S. Government National Hurricane Center and numerically evaluated the proposed heuristics with data derived from our abstraction of the Con Edison overhead distribution power grid in Westchester county.

Suggested Citation

  • Alberto Avritzer & Lucia Happe & Anne Koziolek & Daniel Sadoc Menasche & Sindhu Suresh & Jose Yallouz, 2016. "Scalable Assessment and Optimization of Power Distribution Automation Networks," Springer Series in Reliability Engineering, in: Lance Fiondella & Antonio Puliafito (ed.), Principles of Performance and Reliability Modeling and Evaluation, pages 321-340, Springer.
    • Handle: RePEc:spr:ssrchp:978-3-319-30599-8_12
    DOI: 10.1007/978-3-319-30599-8_12
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