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STABILITY STUDY OF NEW POWER SYSTEM BASED ON MULTI-INTELLIGENT BODY COLLABORATION.

Authors :
XIANYOU WU
ZHIQIAN YANG
XIN DU
LIANGNIAN LV
AISIKAER
YANCHEN YANG
Source :
Scalable Computing: Practice & Experience; Mar2024, Vol. 25 Issue 2, p661-667, 7p
Publication Year :
2024

Abstract

Developing, implementing, and maintaining a multi-intelligent body collaboration system necessitates significant investments in finances, time, and expertise. While multi-intelligent body collaboration has the potential to enhance power system stability significantly, it also comes with challenges related to interoperability, security, system complexity, and resource allocation. Resource allocation and training costs can be substantial. Addressing these challenges is crucial to harnessing the full benefits of this approach and ensuring the reliable and efficient operation of power systems. Effective communication and coordination strategies among intelligent agents are integral to maintaining power system stability. Timely information exchange, load balancing, disturbance management, and the integration of AI contribute to a more resilient and adaptive energy grid. As technology advances, refining these strategies will be essential to meet the growing demands of an ever-evolving power landscape. As technology marches forward, it becomes increasingly evident that the refinement of these strategies is paramount. The dynamism of the power landscape, driven by technological advancements and evolving needs, necessitates an agile and adaptable power system. The fusion of multiintelligent bodies and modern technology stands as a testament to our collective pursuit of a more reliable, efficient, and sustainable energy future. In this ever-evolving landscape, the innovation and enhancement of these strategies are our compass, guiding us toward a brighter and more efficient future. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
18951767
Volume :
25
Issue :
2
Database :
Complementary Index
Journal :
Scalable Computing: Practice & Experience
Publication Type :
Academic Journal
Accession number :
175734878
Full Text :
https://doi.org/10.12694/scpe.v25i2.2533