2018 IEEE Power &Amp; Energy Society General Meeting (PESGM) 2018
DOI: 10.1109/pesgm.2018.8586564
|View full text |Cite
|
Sign up to set email alerts
|

Restoration using Distributed Energy Resources for Resilient Power Distribution Networks

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
2
0

Year Published

2022
2022
2022
2022

Publication Types

Select...
1

Relationship

0
1

Authors

Journals

citations
Cited by 1 publication
(2 citation statements)
references
References 6 publications
0
2
0
Order By: Relevance
“…Several tests were conducted using a 53-bus test system and a 2313-bus system, and the results showed that the islanded operation of the renewable energy sources provided more flexibility to restore more loads. A network reconfiguration model for distribution networks with a high level of renewable energy penetration was established in [47] to minimize the unserved demand in a power distribution network in which some islands are formed owing to multiple line outages. A case study on the IEEE 33-bus distribution network was presented to illustrate the results and validate the effectiveness of the proposed model.…”
Section: 4mentioning
confidence: 99%
See 1 more Smart Citation
“…Several tests were conducted using a 53-bus test system and a 2313-bus system, and the results showed that the islanded operation of the renewable energy sources provided more flexibility to restore more loads. A network reconfiguration model for distribution networks with a high level of renewable energy penetration was established in [47] to minimize the unserved demand in a power distribution network in which some islands are formed owing to multiple line outages. A case study on the IEEE 33-bus distribution network was presented to illustrate the results and validate the effectiveness of the proposed model.…”
Section: 4mentioning
confidence: 99%
“…Therefore, further research is required on the power system restoration solution algorithm that considers the privacy problem of multi-energy systems. Weight coefficient [38] 2013 Group decision-making [17] Decision supporting system [19] Probability and timing [71] 2014 Extended black-start [15] Node importance [37] Security-constrained unit commitment [56] 2015 Group decision support system [29,30] Interpretative structural modelling [68] 2016 Network partitioning [13] Extended black-start [16] Uncertain factors [20] Battery energy storage systems [26] Disaster economics [50] 2017 Extended black-start [14] Distributed generations [23] Grid resiliency [53] 2018 Microgrids [22] Wind power plants with energy storage systems [27] Island partitioning [44] Distributed energy resources [47] Distributed energy resources [48] Stochastic approach [55] Graph theory [62] Islanded operation of distributed generations [63] Distributed generation scheduling [64] Outage duration uncertainty [73] General stochastic Petri net approach [74] 2019 Network reliability [12] Decision making [18] Photovoltaic-battery energy storage systems [25] Multi-objective optimization [32] Restoration path [40] Distributed energy resources [43] Renewable energy [45] Unbalanced active distribution systems [51] Distributed generations [54] Decentralized scheme…”
Section: Restoration Technologies and Distributed Solution Algorithms...mentioning
confidence: 99%