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A Stochastic Multi-Commodity Logistic Model for Disaster Preparation in Distribution Systems

Journal Article · · IEEE Transactions on Smart Grid
 [1];  [1];  [2];  [2]
  1. Iowa State Univ., Ames, IA (United States)
  2. Argonne National Lab. (ANL), Lemont, IL (United States)
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In this paper, we propose a stochastic optimization approach for disaster preparation in distribution systems. For an upcoming storm, utilities should have a preparation plan that includes warehousing restoration supplies, securing staging sites (depots), and prepositioning crews and equipment. Pre-storm planning enables faster and more efficient post-disaster deploy ment of crews and equipment resources to damage locations. To assist utilities in making this important preparation, this paper develops a two-stage stochastic mixed integer linear program. The first stage determines the depots, number of crews in each site, and the amount of equipment. The second stage is the recourse action that deals with acquiring new equipment and assigning crews to repair damages in realized scenarios. The objective of the developed model is to minimize the costs of depots, crews, equipment, and penalty costs associated with delays in obtain ing equipment and restoration. We consider the uncertainties of damaged lines, number and type of equipment required, and expected repair times. The model is validated on modified IEEE 123-bus distribution test system.
Research Organization:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Solar Energy Technologies Office (EE-4S); National Science Foundation (NSF)
Grant/Contract Number:
AC02-06CH11357
OSTI ID:
1619244
Journal Information:
IEEE Transactions on Smart Grid, Journal Name: IEEE Transactions on Smart Grid Journal Issue: 1 Vol. 11; ISSN 1949-3053
Publisher:
IEEECopyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

24 POWER TRANSMISSION AND DISTRIBUTION
Allocation
disaster preparation
distribution system
extreme weather
stochastic programming