Zobrazeno 1 - 10
of 32
pro vyhledávání: '"artificial intelligence and data analytics"'
Publikováno v:
IET Smart Grid, Vol 7, Iss 5, Pp 593-609 (2024)
Abstract The delivery of flexibility from distributed assets guarantees the stable operation of the power system as increasing volumes of renewable energy are deployed. Nevertheless, verifying the adequate provision is challenging when considering be
Externí odkaz:
https://doaj.org/article/247676920a104ee8a456879da8c6829c
Publikováno v:
IET Smart Grid, Vol 7, Iss 5, Pp 572-582 (2024)
Abstract Obtaining inverter controller information may be a premise for seeking its dynamic behaviour. But accurate knowledge of such information would be unrealistic for real functioning inverter‐interfaced generators (IIGs), which hinders the sta
Externí odkaz:
https://doaj.org/article/6d87fb713d51408785b588f0b7dad1be
Publikováno v:
IET Smart Grid, Vol 7, Iss 5, Pp 653-671 (2024)
Abstract Virtual power plants (VPPs) have been widely recognized as a key enabler for energy system neutrality. The communication traffic of a VPP fundamentally indicates its activeness in interacting with the power system, thus providing a new dimen
Externí odkaz:
https://doaj.org/article/6fbb17418a674e0a90c882edc5a7608c
Publikováno v:
IET Smart Grid, Vol 7, Iss 4, Pp 460-472 (2024)
Abstract Measures for balancing the electrical grid, such as peak shaving, require accurate peak forecasts for lower aggregation levels of electrical loads. Thus, the Big Data Energy Analytics Laboratory (BigDEAL) challenge—organised by the BigDEAL
Externí odkaz:
https://doaj.org/article/9e2850e66e7e44ed907abc76778aa4cf
Publikováno v:
IET Smart Grid, Vol 7, Iss 4, Pp 473-484 (2024)
Abstract As weather dependence of the electricity network grows, there is an increasing need to predict the time at which the network peak load will occur. Improving forecasts of peak hour can lead to more accurate scheduling of generation as well as
Externí odkaz:
https://doaj.org/article/9fc36f57f730489fbec6a937591ab68d
Publikováno v:
IET Smart Grid, Vol 7, Iss 3, Pp 221-240 (2024)
Abstract Energy management in a renewable energy‐based microgrid has a key role in improving energy utilisation and reducing the microgrid operation cost. The optimal energy management strategy can be significantly affected by the intermittency of
Externí odkaz:
https://doaj.org/article/bb1837f100124ba7b3674dbca46ca1bf
Publikováno v:
IET Smart Grid, Vol 7, Iss 2, Pp 157-171 (2024)
Abstract The energy transition drives the adoption of heat pumps (HPs). Their peak loads have a large impact on distribution networks. Therefore, the proposed methodology calculates the annual impact on medium to low voltage (MV/LV) transformers base
Externí odkaz:
https://doaj.org/article/3460f5c56b4d40c3b422bd053fd15628
Publikováno v:
IET Smart Grid, Vol 7, Iss 2, Pp 130-141 (2024)
Abstract With a high penetration of renewable energies, scenario generation for wind and solar power is essential for the operation of modern power systems. Beyond the typical scenarios, extreme scenarios like full‐capacity generation for consecuti
Externí odkaz:
https://doaj.org/article/6a6f1e10ca5f48ccaa5c5bdb375a4a75
Publikováno v:
IET Smart Grid, Vol 7, Iss 2, Pp 172-185 (2024)
Abstract In modern power systems, predicting the time when peak loads will occur is crucial for improving efficiency and minimising the possibility of network sections becoming overloaded. However, most works in the load forecasting field are not foc
Externí odkaz:
https://doaj.org/article/4289589d31284d05b761bff8df672363
Publikováno v:
IET Smart Grid, Vol 6, Iss 5, Pp 492-502 (2023)
Abstract The availability of large datasets is crucial for the development of new power system applications and tools; unfortunately, very few are publicly and freely available. The authors designed an end‐to‐end generative framework for the crea
Externí odkaz:
https://doaj.org/article/1557d5a1829c42009b52b5a9d3d7d432