An aggregator‐based resource allocation in the smart grid using an artificial neural network and sliding time window optimization
Abstract The success of an efficient and effective aggregator‐based residential demand response system in the smart grid relies on the day‐ahead customer incentive pricing (CIP) and the load shifting protocols. An artificial neural network model is designed to generate the day‐ahead CIP for the aggr...
Guardado en:
Autores principales: | Yingying Zheng, Berk Celik, Siddharth Suryanarayanan, Anthony A. Maciejewski, Howard Jay Siegel, Timothy M. Hansen |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Wiley
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/8656e677de0846dca5e718bb49d7085f |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
DIGITAL SUBSTATION COMPONENT SYSTEM "SMART GRID"
por: V.I. Vasilchenko, et al.
Publicado: (2014) -
A Sliding Window Data Compression Method for Spatial-Time DOA Estimation
por: Pin-Jiao Zhao, et al.
Publicado: (2021) -
Adversarial attacks on deep learning models in smart grids
por: Jingbo Hao, et al.
Publicado: (2022) -
A survey on blockchain‐enabled smart grids: Advances, applications and challenges
por: Chao Liu, et al.
Publicado: (2021) -
A multi-agent system for distribution network restoration in future smart grids
por: Amer Al-Hinai, et al.
Publicado: (2021)