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Mobile Emergency Generator Pre-Positioning and Real-Time Allocation for Resilient Response to Natural Disasters

  • Shunbo Lei
  • , Jianhui Wang
  • , Chen Chen
  • , Yunhe Hou
  • The University of Hong Kong
  • Argonne National Laboratory

Research output: Contribution to journalArticlepeer-review

457 Scopus citations

Abstract

Truck-mounted mobile emergency generators (MEGs) are critical flexibility resources of distribution systems (DSs) for resilient emergency response to natural disasters. However, they are currently under-utilized. For better utilization, this paper proposes dispatching MEGs as distributed generators in DSs to restore critical loads by forming multiple microgrids (MGs). As the travel time of MEGs on road networks (RNs) can greatly influence the outage duration of critical loads, a two-stage dispatch framework consisting of pre-positioning and real-time allocation is introduced, and the traffic issue is considered via the vehicle routing problem. Pre-positioning places MEGs in staging locations prior to a natural disaster, while real-time allocation sends MEGs from staging locations to restore critical loads by forming MGs in DSs after the natural disaster strikes. Specifically, with the objective of minimizing the expected outage duration of loads considering their priorities and demand sizes, pre-positioning is done via a scenario-based two-stage stochastic optimization problem, in which the first-stage pre-positioning decisions are evaluated by numbers of second-stage real-time allocation problems corresponding to considered scenarios of DS damage and RN damage/congestion. A scenario decomposition algorithm is applied to solve this problem. Illustrative cases demonstrate the effectiveness of the proposed dispatch scheme and algorithm.

Original languageEnglish
Pages (from-to)2030-2041
Number of pages12
JournalIEEE Transactions on Smart Grid
Volume9
Issue number3
DOIs
StatePublished - May 2018
Externally publishedYes

Keywords

  • Distribution system
  • microgrid
  • mobile emergency generator
  • resilience
  • stochastic optimization

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