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Found 15 papers, 5 papers with code
Reverse Kron reduction of Multi-phase Radial Network
We consider the problem of identifying the admittance matrix of a three-phase radial network from voltage and current measurements at a subset of nodes.
Stability Constrained Reinforcement Learning for Decentralized Real-Time Voltage Control
In this paper, we propose a stability-constrained reinforcement learning (RL) method for real-time voltage control, that guarantees system stability both during policy learning and deployment of the learned policy.
DeepOPF-AL: Augmented Learning for Solving AC-OPF Problems with Multiple Load-Solution Mappings
The existence of multiple load-solution mappings of non-convex AC-OPF problems poses a fundamental challenge to deep neural network (DNN) schemes.
Interface Networks for Failure Localization in Power Systems
Transmission power systems usually consist of interconnected sub-grids that are operated relatively independently.
Ensuring DNN Solution Feasibility for Optimization Problems with Convex Constraints and Its Application to DC Optimal Power Flow Problems
We systematically calibrate inequality constraints used in DNN training, thereby anticipating prediction errors and ensuring the resulting solutions remain feasible.
Robustness and Consistency in Linear Quadratic Control with Untrusted Predictions
Motivated by online learning methods, we design a self-tuning policy that adaptively learns the trust parameter $\lambda$ with a competitive ratio that depends on $\varepsilon$ and the variation of system perturbations and predictions.
DeepOPF-V: Solving AC-OPF Problems Efficiently
AC optimal power flow (AC-OPF) problems need to be solved more frequently in the future to maintain stable and economic power system operation.
Learning-Based Predictive Control via Real-Time Aggregate Flexibility
To be used effectively, an aggregator must be able to communicate the available flexibility of the loads they control, as known as the aggregate flexibility to a system operator.
Optimization and Control Systems and Control Systems and Control
Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging
We describe the architecture and algorithms of the Adaptive Charging Network (ACN), which was first deployed on the Caltech campus in early 2016 and is currently operating at over 100 other sites in the United States.
ACN-Sim: An Open-Source Simulator for Data-Driven Electric Vehicle Charging Research
ACN-Sim is a data-driven, open-source simulation environment designed to accelerate research in the field of smart electric vehicle (EV) charging.
DeepOPF: A Feasibility-Optimized Deep Neural Network Approach for AC Optimal Power Flow Problems
High percentage penetrations of renewable energy generations introduce significant uncertainty into power systems.
Adaptive Network Response to Line Failures in Power Systems
Transmission line failures in power systems propagate and cascade non-locally.
Line Failure Localization of Power Networks Part II: Cut Set Outages
Transmission line failure in power systems prop-agate non-locally, making the control of the resulting outages extremely difficult.
Line Failure Localization of Power Networks Part I: Non-cut Outages
Transmission line failures in power systems propagate non-locally, making the control of the resulting outages extremely difficult.
On Identification of Distribution Grids
Large-scale integration of distributed energy resources into residential distribution feeders necessitates careful control of their operation through power flow analysis.
