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Proactive Load-Shaping Strategies with Privacy-Cost Trade-offs in Residential Households based on Deep Reinforcement Learning
Smart meters play a crucial role in enhancing energy management and efficiency, but they raise significant privacy concerns by potentially revealing detailed user behaviors through energy consumption patterns.
Deep Reinforcement Learning for Voltage Control and Renewable Accommodation Using Spatial-Temporal Graph Information
Renewable energy resources (RERs) have been increasingly integrated into distribution networks (DNs) for decarbonization.
Controllability of networked multiagent systems based on linearized Turing's model
Under mild conditions on the parameters of the linearized Turing's model, we prove the equivalence between controllability of the linearized Turing's model and controllability of a Laplace dynamic system with agents of first order dynamics.
Deep Reinforcement Learning for Optimal Power Flow with Renewables Using Graph Information
Considering uncertainties and voltage fluctuation issues introduced by RERs, in this paper, we propose a deep reinforcement learning (DRL)-based strategy leveraging spatial-temporal (ST) graphical information of power systems, to dynamically search for the optimal operation, i. e., optimal power flow (OPF), of power systems with a high uptake of RERs.
