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Energy Management Algorithms in Smart Grids : State of the Art and Emerging Trends
- Source :
- International Journal of Artificial Intelligence & Applications, International Journal of Artificial Intelligence & Applications, 2016, 7 (4), pp.25-45. ⟨10.5121/ijaia.2016.7403⟩
- Publication Year :
- 2016
- Publisher :
- HAL CCSD, 2016.
-
Abstract
- The electric grid is radically evolving into the smart grid, which is characterized by improved energy efficiency of available resources. The smart grid permits interactions among its computational and physical elements thanks to the integration of Information and Communication Technologies (ICTs). ICTs provide energy management algorithms and allow renewable energy integration and energy price minimization. Given the importance of renewable energy, many researchers developed energy management (EM) algorithms to minimize renewable energy intermittency. EM plays an important role in the control of users' energy consumption and enables increased consumer participation in the market. These algorithms provide consumers with information about their energy consumption patterns and help them adopt energy-efficient behaviour. In this paper, we present a review of the state of the energy management algorithms. We define a set of requirements for EM algorithms and evaluate them qualitatively. We also discuss emerging tools and trends in this area.
- Subjects :
- business.industry
Energy management
Computer science
020209 energy
02 engineering and technology
Energy consumption
Grid
7. Clean energy
Energy engineering
Renewable energy
[INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI]
Smart grid
0202 electrical engineering, electronic engineering, information engineering
business
Algorithm
Energy (signal processing)
ComputingMilieux_MISCELLANEOUS
Efficient energy use
Subjects
Details
- Language :
- English
- ISSN :
- 09762191
- Database :
- OpenAIRE
- Journal :
- International Journal of Artificial Intelligence & Applications, International Journal of Artificial Intelligence & Applications, 2016, 7 (4), pp.25-45. ⟨10.5121/ijaia.2016.7403⟩
- Accession number :
- edsair.doi.dedup.....f8459d0abc3324d63c28072d5ba6cd00
- Full Text :
- https://doi.org/10.5121/ijaia.2016.7403⟩