Enhancing Distribution Resilience with Mobile Energy Storage: A Progressive Hedging Approach

Jip Kim, Yury Dvorkin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Electrochemical energy storage (ES) units (e.g. batteries) have been field-validated as an efficient back-up resource that enhance resilience of the distribution system in case of natural disasters. However, using these units for resilience is not sufficient to economically justify their installation and, therefore, these units are often installed in locations where they incur the greatest economic value during normal operations. Motivated by the recent progress in transportable ES technologies, i.e. ES units can be moved using public transportation routes, this paper proposes to use this spatial flexibility to bridge the gap between the economically optimal locations during normal operations and disaster-specific locations where extra back-up capacity is necessary. We propose a two-stage optimization model that optimizes investments in mobile ES units in the first stage and can re-route the installed mobile ES units in the second stage to avoid the expected load shedding caused by disaster forecasts. Since the proposed model cannot be solved efficiently with off-the-shelf solvers, even for relatively small instances, we apply a progressive hedging algorithm. The proposed model and progressive hedging algorithm are tested through two illustrative examples on a 15-bus radial distribution test system.

Original languageEnglish (US)
Title of host publication2018 IEEE Power and Energy Society General Meeting, PESGM 2018
PublisherIEEE Computer Society
Volume2018-August
ISBN (Electronic)9781538677032
DOIs
StatePublished - Dec 21 2018
Event2018 IEEE Power and Energy Society General Meeting, PESGM 2018 - Portland, United States
Duration: Aug 5 2018Aug 10 2018

Other

Other2018 IEEE Power and Energy Society General Meeting, PESGM 2018
CountryUnited States
CityPortland
Period8/5/188/10/18

Fingerprint

Energy storage
Disasters
Transportation routes
Economics

Keywords

  • Energy storage
  • Progressive hedging
  • Resilience

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Nuclear Energy and Engineering
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering

Cite this

Kim, J., & Dvorkin, Y. (2018). Enhancing Distribution Resilience with Mobile Energy Storage: A Progressive Hedging Approach. In 2018 IEEE Power and Energy Society General Meeting, PESGM 2018 (Vol. 2018-August). [8585791] IEEE Computer Society. https://doi.org/10.1109/PESGM.2018.8585791

Enhancing Distribution Resilience with Mobile Energy Storage : A Progressive Hedging Approach. / Kim, Jip; Dvorkin, Yury.

2018 IEEE Power and Energy Society General Meeting, PESGM 2018. Vol. 2018-August IEEE Computer Society, 2018. 8585791.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Kim, J & Dvorkin, Y 2018, Enhancing Distribution Resilience with Mobile Energy Storage: A Progressive Hedging Approach. in 2018 IEEE Power and Energy Society General Meeting, PESGM 2018. vol. 2018-August, 8585791, IEEE Computer Society, 2018 IEEE Power and Energy Society General Meeting, PESGM 2018, Portland, United States, 8/5/18. https://doi.org/10.1109/PESGM.2018.8585791
Kim J, Dvorkin Y. Enhancing Distribution Resilience with Mobile Energy Storage: A Progressive Hedging Approach. In 2018 IEEE Power and Energy Society General Meeting, PESGM 2018. Vol. 2018-August. IEEE Computer Society. 2018. 8585791 https://doi.org/10.1109/PESGM.2018.8585791
Kim, Jip ; Dvorkin, Yury. / Enhancing Distribution Resilience with Mobile Energy Storage : A Progressive Hedging Approach. 2018 IEEE Power and Energy Society General Meeting, PESGM 2018. Vol. 2018-August IEEE Computer Society, 2018.
@inproceedings{9af4162cb67940f9bb0a7584ca4f68ad,
title = "Enhancing Distribution Resilience with Mobile Energy Storage: A Progressive Hedging Approach",
abstract = "Electrochemical energy storage (ES) units (e.g. batteries) have been field-validated as an efficient back-up resource that enhance resilience of the distribution system in case of natural disasters. However, using these units for resilience is not sufficient to economically justify their installation and, therefore, these units are often installed in locations where they incur the greatest economic value during normal operations. Motivated by the recent progress in transportable ES technologies, i.e. ES units can be moved using public transportation routes, this paper proposes to use this spatial flexibility to bridge the gap between the economically optimal locations during normal operations and disaster-specific locations where extra back-up capacity is necessary. We propose a two-stage optimization model that optimizes investments in mobile ES units in the first stage and can re-route the installed mobile ES units in the second stage to avoid the expected load shedding caused by disaster forecasts. Since the proposed model cannot be solved efficiently with off-the-shelf solvers, even for relatively small instances, we apply a progressive hedging algorithm. The proposed model and progressive hedging algorithm are tested through two illustrative examples on a 15-bus radial distribution test system.",
keywords = "Energy storage, Progressive hedging, Resilience",
author = "Jip Kim and Yury Dvorkin",
year = "2018",
month = "12",
day = "21",
doi = "10.1109/PESGM.2018.8585791",
language = "English (US)",
volume = "2018-August",
booktitle = "2018 IEEE Power and Energy Society General Meeting, PESGM 2018",
publisher = "IEEE Computer Society",

}

TY - GEN

T1 - Enhancing Distribution Resilience with Mobile Energy Storage

T2 - A Progressive Hedging Approach

AU - Kim, Jip

AU - Dvorkin, Yury

PY - 2018/12/21

Y1 - 2018/12/21

N2 - Electrochemical energy storage (ES) units (e.g. batteries) have been field-validated as an efficient back-up resource that enhance resilience of the distribution system in case of natural disasters. However, using these units for resilience is not sufficient to economically justify their installation and, therefore, these units are often installed in locations where they incur the greatest economic value during normal operations. Motivated by the recent progress in transportable ES technologies, i.e. ES units can be moved using public transportation routes, this paper proposes to use this spatial flexibility to bridge the gap between the economically optimal locations during normal operations and disaster-specific locations where extra back-up capacity is necessary. We propose a two-stage optimization model that optimizes investments in mobile ES units in the first stage and can re-route the installed mobile ES units in the second stage to avoid the expected load shedding caused by disaster forecasts. Since the proposed model cannot be solved efficiently with off-the-shelf solvers, even for relatively small instances, we apply a progressive hedging algorithm. The proposed model and progressive hedging algorithm are tested through two illustrative examples on a 15-bus radial distribution test system.

AB - Electrochemical energy storage (ES) units (e.g. batteries) have been field-validated as an efficient back-up resource that enhance resilience of the distribution system in case of natural disasters. However, using these units for resilience is not sufficient to economically justify their installation and, therefore, these units are often installed in locations where they incur the greatest economic value during normal operations. Motivated by the recent progress in transportable ES technologies, i.e. ES units can be moved using public transportation routes, this paper proposes to use this spatial flexibility to bridge the gap between the economically optimal locations during normal operations and disaster-specific locations where extra back-up capacity is necessary. We propose a two-stage optimization model that optimizes investments in mobile ES units in the first stage and can re-route the installed mobile ES units in the second stage to avoid the expected load shedding caused by disaster forecasts. Since the proposed model cannot be solved efficiently with off-the-shelf solvers, even for relatively small instances, we apply a progressive hedging algorithm. The proposed model and progressive hedging algorithm are tested through two illustrative examples on a 15-bus radial distribution test system.

KW - Energy storage

KW - Progressive hedging

KW - Resilience

UR - http://www.scopus.com/inward/record.url?scp=85060789222&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85060789222&partnerID=8YFLogxK

U2 - 10.1109/PESGM.2018.8585791

DO - 10.1109/PESGM.2018.8585791

M3 - Conference contribution

AN - SCOPUS:85060789222

VL - 2018-August

BT - 2018 IEEE Power and Energy Society General Meeting, PESGM 2018

PB - IEEE Computer Society

ER -