A physics-informed multi-agent reinforcement learning controller for wind farms with battery storage improves simulated profit by 11% and reduces grid power fluctuation by 19% versus a conventional MPC baseline.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
eess.SY 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Coordinated Power Smoothing Control for Wind Storage Integrated System with Physics-informed Deep Reinforcement Learning
A physics-informed multi-agent reinforcement learning controller for wind farms with battery storage improves simulated profit by 11% and reduces grid power fluctuation by 19% versus a conventional MPC baseline.