Artikel
Renewable energy based self-healing scheme in smart grid
The renewable energy based smart grid present a stable power supply system with low carbon emissions. The adaptability of work in smart grid-related approaches allows microgrids to load reliably. This research proposes a self-healing method with a large smart grid in different purpose. The proposed technologies include re-dispatch generation, reconfiguration organizations, and load restrictions. Smart network self-healing problems are defined as integer quadratic problems. This paper presents a flexible model solution to promote a horizontal generation. Innovation settings reliability of electrical reliability smart grid modification is used to instruct reproduction in various circumstances. The results of recreation show the ability of the proposed self-healing scheme because it reduces the cost of load outages and generation with ideal exchange activities. The proposed system demonstrates self-healing capabilities to ensure smart network security by constantly preventing fault and adaptation planning generations. Simulation results from the (Western Systems Coordinating Council) WSCC 3 generator system adjusted with the microgrid module confirm the power system of the proposed structure.
- Language
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                Englisch
 
- Bibliographic citation
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                Journal: Energy Reports ; ISSN: 2352-4847 ; Volume: 6 ; Year: 2020 ; Issue: 2 ; Pages: 166-172 ; Amsterdam: Elsevier
 
- Classification
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                Wirtschaft
 
- Subject
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                Microgrid
Reliability
Self-healing
Smart grid
 
- Event
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                Geistige Schöpfung
 
- (who)
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                Bagdadee, Amam Hossain
Zhang, Li
 
- Event
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                Veröffentlichung
 
- (who)
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                Elsevier
 
- (where)
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                Amsterdam
 
- (when)
 - 
                2020
 
- DOI
 - 
                
                    
                        doi:10.1016/j.egyr.2019.11.058
 
- Handle
 
- Last update
 - 
                
                    
                        10.03.2025, 11:41 AM CET
 
Data provider
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Object type
- Artikel
 
Associated
- Bagdadee, Amam Hossain
 - Zhang, Li
 - Elsevier
 
Time of origin
- 2020