11 citations · 35 across the 16 of their papers we have counts for
12 papers · 1 filter
Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging
Zachary J. Lee, George Lee, Ted Lee +6
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…
DeepOPF+: A Deep Neural Network Approach for DC Optimal Power Flow for Ensuring Feasibility
Tianyu Zhao, Xiang Pan, Minghua Chen +2
Deep Neural Networks (DNNs) approaches for the Optimal Power Flow (OPF) problem received considerable attention recently. A key challenge of these approaches lies in ensuring the f…
Real-time Flexibility Feedback for Closed-loop Aggregator and System Operator Coordination
Tongxin Li, Steven H. Low, Adam Wierman
Aggregators have emerged as crucial tools for the coordination of distributed, controllable loads. However, to be used effectively, aggregators must be able to communicate the avai…
Learning Optimal Power Flow: Worst-Case Guarantees for Neural Networks
Andreas Venzke, Guannan Qu, Steven Low +1
This paper introduces for the first time a framework to obtain provable worst-case guarantees for neural network performance, using learning for optimal power flow (OPF) problems a…
Combining Model-Based and Model-Free Methods for Nonlinear Control: A Provably Convergent Policy Gradient Approach
Guannan Qu, Chenkai Yu, Steven Low +1
Model-free learning-based control methods have seen great success recently. However, such methods typically suffer from poor sample complexity and limited convergence guarantees. T…
Approaching Prosumer Social Optimum via Energy Sharing with Proof of Convergence
Yue Chen, Changhong Zhao, Steven H. Low +1
With the advent of prosumers, the traditional centralized operation may become impracticable due to computational burden, privacy concerns, and conflicting interests. In this paper…