Recovery Strategy of Distribution Network Based on Power Supply Capability Assessment of Local Area Configured with Energy Gateway

The growing integration of DG enhances distribution network resilience, but existing centralized fault recovery strategies face critical limitations: excessive computational burdens on DCs and the insufficient utilization of local control capabilities, particularly with large-scale DG deployments. Current studies often fail to balance computational efficiency and dynamic recovery coordination between centralized and decentralized resources. To address the issue, a hierarchical control architecture is proposed that involves collaboration between the DC and LA energy gateways. By dynamically quantifying LA PSC through net power feasibility analysis, the framework optimizes network reconfiguration (DC level) and decentralized DG scheduling (gateway level). Validated on a modified IEEE 45-bus system, the strategy restored 7 MWh (4:00 fault) and 5 MWh (10:00 fault) of load, outperforming static methods by 26.9× in the mid-day case. While effective in urban grids, rural DG-sparse areas require future integration of mobile storage. The work balances centralized coordination and decentralized execution, offering a scalable resilience solution for modern networks.

Authors

Institutions

Publication Details

Journal
Energies
Published
2025-04-27
DOI
https://doi.org/10.3390/en18092215
Citations
1
Primary Topic
Power Systems and Technologies
Type
article
Field-Weighted Citation Impact
0.73
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Recovery Strategy of Distribution Network Based on Power Supply Capability Assessment of Local Area Configured with Energy Gateway

Weijia Guo, Xiang Jiang, Xiaoxia Guo, Dawei Wang et al.
1 citations
Energies
Power Systems and Technologies
0.73
article

Recovery Strategy of Distribution Network Based on Power Supply Capability Assessment of Local Area Configured with Energy Gateway

Weijia Guo, Xiang Jiang, Xiaoxia Guo, Dawei Wang, Junpeng Zhu
article en
1 citations

Abstract

The growing integration of DG enhances distribution network resilience, but existing centralized fault recovery strategies face critical limitations: excessive computational burdens on DCs and the insufficient utilization of local control capabilities, particularly with large-scale DG deployments. Current studies often fail to balance computational efficiency and dynamic recovery coordination between centralized and decentralized resources. To address the issue, a hierarchical control architecture is proposed that involves collaboration between the DC and LA energy gateways. By dynamically quantifying LA PSC through net power feasibility analysis, the framework optimizes network reconfiguration (DC level) and decentralized DG scheduling (gateway level). Validated on a modified IEEE 45-bus system, the strategy restored 7 MWh (4:00 fault) and 5 MWh (10:00 fault) of load, outperforming static methods by 26.9× in the mid-day case. While effective in urban grids, rural DG-sparse areas require future integration of mobile storage. The work balances centralized coordination and decentralized execution, offering a scalable resilience solution for modern networks.

EnergiesVol. 18(9)
Hohai University (CN), Shanghai Electric (China) (CN)
Openalex Percentile: Top 29%
Power Systems and Technologies
0.73
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Recovery Strategy of Distribution Network Based on Power Supply Capability Assessment of Local Area Configured with Energy Gateway — Weijia Guo, Xiang Jiang, et al. · Energies (2025) | TGRS Research Map | TGRS