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Resilience-Oriented Framework for Microgrid Planning in Distribution Systems
by
Bae, In-Su
, Son, Eun-Tae
, Kim, Dong-Min
, Kim, Sung-Yul
in
cost–benefit analysis
/ Disasters
/ distribution system resilience
/ empirical distribution function
/ expected value of resilience enhancement
/ Expected values
/ Failure
/ Long term planning
/ microgrid
/ Monte Carlo simulation
/ Probability distribution
/ Random variables
2022
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Resilience-Oriented Framework for Microgrid Planning in Distribution Systems
by
Bae, In-Su
, Son, Eun-Tae
, Kim, Dong-Min
, Kim, Sung-Yul
in
cost–benefit analysis
/ Disasters
/ distribution system resilience
/ empirical distribution function
/ expected value of resilience enhancement
/ Expected values
/ Failure
/ Long term planning
/ microgrid
/ Monte Carlo simulation
/ Probability distribution
/ Random variables
2022
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Do you wish to request the book?
Resilience-Oriented Framework for Microgrid Planning in Distribution Systems
by
Bae, In-Su
, Son, Eun-Tae
, Kim, Dong-Min
, Kim, Sung-Yul
in
cost–benefit analysis
/ Disasters
/ distribution system resilience
/ empirical distribution function
/ expected value of resilience enhancement
/ Expected values
/ Failure
/ Long term planning
/ microgrid
/ Monte Carlo simulation
/ Probability distribution
/ Random variables
2022
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Resilience-Oriented Framework for Microgrid Planning in Distribution Systems
Journal Article
Resilience-Oriented Framework for Microgrid Planning in Distribution Systems
2022
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Overview
Recently, it has been suggested that microgrids (MGs) can improve the resilience of distribution systems. However, predictions about future faults are uncertain. This makes calculating the exact value of the benefits of system resilience enhancement close to impossible at the time of MG planning. Therefore, this paper proposes a framework for MG planning, which focuses on resilience estimation. To consider the uncertainties of future failure events, the proposed method for estimating the resilience utilized the Monte Carlo simulation. In addition, an optimal scenario was estimated using a cost–benefit analysis and constraints on the expected value of resilience enhancement. In the case study, an actual MG installation at D-university was evaluated to obtain the optimal MG planning scenario. The results show that the capacity and installation locations of the distributed generators (DGs) impact the resilience enhancement. The proposed method can effectively derive the optimal MG planning scenario by evaluating the possibility of future operations based on the segmentation of both the system configuration and type of DG to improve the resilience of distribution systems.
Publisher
MDPI AG
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