Deep Reinforcement Learning Based Optimal Distribution Networks Operation
H. Shengren (TU Delft - Intelligent Electrical Power Grids)
P. Palensky – Promotor (TU Delft - Electrical Sustainable Energy)
P.P. Vergara Barrios – Copromotor (TU Delft - Intelligent Electrical Power Grids)
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Abstract
The integration of distributed energy resources (DERs) and the increasing penetration of renewable energy generation have significantly increased the complexity and uncertainty of modern distribution networks. These developments necessitate advanced dis
patch algorithms capable of handling the variability and operational constraints inherent in such systems. This thesis focuses on developing model-free deep reinforcement learning (DRL) algorithms to ensure reliable, safe, cost-effective operation in distribution networks (DNs). The research questions addressed in this thesis explore various challenges associated with the enforcement of operational constraints, learning efficiency, and computational cost reduction in DRL-based optimal operation of DNs.....