DER Allocation and Line Repair Scheduling for Storm-induced Failures in Distribution Networks
Name
1810.02315.pdf
Description
Submitted version
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735.55 KB
Format
Adobe PDF
Checksum (MD5)
c01645f7ce88e7da8890a9e79bc4994e
Author(s)
Chang, Derek
Shelar, Devendra Anil
Amin, Saurabh
chang, Derek
Date Issued
October 2018
Journal
Proceedings of the 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids
Publisher
IEEE
Citation
Chang, Derek, Devendra Shelar, and Saurabh Amin. "DER Allocation and Line Repair Scheduling for Storm-induced Failures in Distribution Networks." IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, October 2018, Aalbord, Denmark, IEEE, 2018.
Version
Original manuscript
Abstract
Electricity distribution networks (DNs) in many regions are increasingly subjected to disruptions caused by tropical storms. Distributed Energy Resources (DERs) can act as temporary supply sources to sustain 'microgrids' resulting from disruptions. In this paper, we investigate the problem of suitable DER allocation to facilitate more efficient repair operations and faster recovery. First, we estimate the failure probabilities of DN components (lines) using a stochastic model of line failures which parametrically depends on the location-specific storm wind field. Next, we formulate a two-stage stochastic mixed integer program, which models the distribution utility's decision to allocate DERs in the DN (pre-storm stage); and accounts for multi-period decisions on optimal dispatch and line repair scheduling (post-storm stage). A key feature of this formulation is that it jointly optimizes electricity dispatch within the individual microgrids and the line repair schedules to minimize the sum of the cost of DER allocation and cost due to lost load. To illustrate our approach, we use the sample average approximation method to solve our problem for a small-size DN under different storm intensities and DER/crew constraints.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/smartgridcomm.2018.8587563
