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Found 4 papers, 1 papers with code
An Efficient Learning-Based Solver for Two-Stage DC Optimal Power Flow with Feasibility Guarantees
The challenge of finding good policies to approximate the second-stage decisions is that these solutions need to be feasible, which has been difficult to achieve with existing policies.
Interpreting Primal-Dual Algorithms for Constrained Multiagent Reinforcement Learning
Constrained multiagent reinforcement learning (C-MARL) is gaining importance as MARL algorithms find new applications in real-world systems ranging from energy systems to drone swarms.
Safe and Efficient Model Predictive Control Using Neural Networks: An Interior Point Approach
Model predictive control (MPC) provides a useful means for controlling systems with constraints, but suffers from the computational burden of repeatedly solving an optimization problem in real time.
Computationally Efficient Safe Reinforcement Learning for Power Systems
We propose a computationally efficient approach to safe reinforcement learning (RL) for frequency regulation in power systems with high levels of variable renewable energy resources.
