| Autor: |
Leila Kamyabi, Tek Tjing Lie, Samaneh Madanian |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
TESEA, Transactions on Energy Systems and Engineering Applications, Vol 3, Iss 2 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2745-0120 |
| DOI: |
10.32397/tesea.vol3.n2.484 |
| Popis: |
Data science-based digital twin models of renewable energy system technologies developed in a real-time data-rich environment help develop better decisions and predictions than those in the present environment. Based on this real-time analysis of countrywide data, digital twin contributes to effective and reduced cost-based power system control at the localised level. Developing digital twin models from the collection of relevant data is an innovative technology. The challenge is how to leverage all the operational data and analyse the use of data from across transmission and distribution networks to help achieve the objectives. This paper presents an overview of the existing applications of digital twins in power systems. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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