Zobrazeno 1 - 10
of 691
pro vyhledávání: '"Cremer, A. L."'
Autor:
Cremer, Jochen L., Kelly, Adrian, Bessa, Ricardo J., Subasic, Milos, Papadopoulos, Panagiotis N., Young, Samuel, Sagar, Amar, Marot, Antoine
Advanced control, operation, and planning tools of electrical networks with ML are not straightforward. 110 experts were surveyed to show where and how ML algorithms could advance. This paper assesses this survey and research environment. Then, it de
Externí odkaz:
http://arxiv.org/abs/2405.17184
Accelerated development of demand response service provision by the residential sector is crucial for reducing carbon-emissions in the power sector. Along with the infrastructure advancement, encouraging the end users to participate is crucial. End u
Externí odkaz:
http://arxiv.org/abs/2310.07389
With the increasing penetration of renewable power sources such as wind and solar, accurate short-term, nowcasting renewable power prediction is becoming increasingly important. This paper investigates the multi-modal (MM) learning and end-to-end (E2
Externí odkaz:
http://arxiv.org/abs/2304.07151
This paper presents a decentralized Multi-Agent Reinforcement Learning (MARL) approach to an incentive-based Demand Response (DR) program, which aims to maintain the capacity limits of the electricity grid and prevent grid congestion by financially i
Externí odkaz:
http://arxiv.org/abs/2304.04086
Due to the increasing system stability issues caused by the technological revolutions of power system equipment, the assessment of the dynamic security of the systems for changing operating conditions (OCs) is nowadays crucial. To address the computa
Externí odkaz:
http://arxiv.org/abs/2304.04046
Implementing accurate Distribution System State Estimation (DSSE) faces several challenges, among which the lack of observability and the high density of the distribution system. While data-driven alternatives based on Machine Learning models could b
Externí odkaz:
http://arxiv.org/abs/2301.01835
Publikováno v:
In Electric Power Systems Research October 2024 235
Publikováno v:
In Electric Power Systems Research October 2024 235
Publikováno v:
In Electric Power Systems Research October 2024 235
Autor:
Marot, Antoine, Donnot, Benjamin, Chaouache, Karim, Kelly, Adrian, Huang, Qiuhua, Hossain, Ramij-Raja, Cremer, Jochen L.
Artificial agents are promising for real-time power network operations, particularly, to compute remedial actions for congestion management. However, due to high reliability requirements, purely autonomous agents will not be deployed any time soon an
Externí odkaz:
http://arxiv.org/abs/2110.12908