Abstract
Electrochemical energy storage (ES) units (e.g. batteries) have been field-validated as an efficient back-up resource that enhances resilience of distribution systems. However, using these units for resilience is insufficient to justify their installation economically and, therefore, these units are often installed in locations where they yield the greatest economic value during normal operations. Motivated by the recent progress in mobile 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 the locations where extra back-up capacity is necessary during disasters. 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 form dynamic microgrids (MGs) and to avoid the expected load shedding caused by disasters. Since the proposed model cannot be solved efficiently with off-the-shelf solvers, even for relatively small instances, we apply the progressive hedging algorithm. The proposed model and algorithm are tested on a 15-bus radial distribution test system.
| Original language | English (US) |
|---|---|
| Journal | IEEE Transactions on Smart Grid |
| DOIs | |
| State | Accepted/In press - Jan 1 2018 |
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Keywords
- distribution system
- Energy storage
- Generators
- grid resilience
- Investment
- microgrid
- Microgrids
- Mobile energy storage
- Optimization
- Planning
- progressive hedging.
- Resilience
ASJC Scopus subject areas
- Computer Science(all)
Cite this
Enhancing Distribution System Resilience with Mobile Energy Storage and Microgrids. / Kim, Jip; Dvorkin, Yury.
In: IEEE Transactions on Smart Grid, 01.01.2018.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Enhancing Distribution System Resilience with Mobile Energy Storage and Microgrids
AU - Kim, Jip
AU - Dvorkin, Yury
PY - 2018/1/1
Y1 - 2018/1/1
N2 - Electrochemical energy storage (ES) units (e.g. batteries) have been field-validated as an efficient back-up resource that enhances resilience of distribution systems. However, using these units for resilience is insufficient to justify their installation economically and, therefore, these units are often installed in locations where they yield the greatest economic value during normal operations. Motivated by the recent progress in mobile 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 the locations where extra back-up capacity is necessary during disasters. 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 form dynamic microgrids (MGs) and to avoid the expected load shedding caused by disasters. Since the proposed model cannot be solved efficiently with off-the-shelf solvers, even for relatively small instances, we apply the progressive hedging algorithm. The proposed model and algorithm are tested 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 enhances resilience of distribution systems. However, using these units for resilience is insufficient to justify their installation economically and, therefore, these units are often installed in locations where they yield the greatest economic value during normal operations. Motivated by the recent progress in mobile 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 the locations where extra back-up capacity is necessary during disasters. 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 form dynamic microgrids (MGs) and to avoid the expected load shedding caused by disasters. Since the proposed model cannot be solved efficiently with off-the-shelf solvers, even for relatively small instances, we apply the progressive hedging algorithm. The proposed model and algorithm are tested on a 15-bus radial distribution test system.
KW - distribution system
KW - Energy storage
KW - Generators
KW - grid resilience
KW - Investment
KW - microgrid
KW - Microgrids
KW - Mobile energy storage
KW - Optimization
KW - Planning
KW - progressive hedging.
KW - Resilience
UR - http://www.scopus.com/inward/record.url?scp=85054375544&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85054375544&partnerID=8YFLogxK
U2 - 10.1109/TSG.2018.2872521
DO - 10.1109/TSG.2018.2872521
M3 - Article
JO - IEEE Transactions on Smart Grid
JF - IEEE Transactions on Smart Grid
SN - 1949-3053
ER -