no code implementations • 18 Jan 2024 • Osten Anderson, Wanshi Hong, Bin Wang, Nanpeng Yu
In particular, we examine the potential cost savings of electrical generation infrastructure by enabling flexible charging and bidirectional charging for these trucks.
no code implementations • 5 Jan 2024 • Osten Anderson, Nanpeng Yu, Konstantinos Oikonomou, Di wu
To this end, we propose a novel method for selecting representative periods of any length.
no code implementations • 26 Nov 2023 • Jingtao Qin, Nanpeng Yu
The recent advances in graph neural networks (GNN) enable it to enhance the B&B algorithm in modern MIP solvers by learning to dive and branch.
no code implementations • 13 Nov 2023 • Yuanbin Cheng, Koji Yamashita, Jim Follum, Nanpeng Yu
The global deployment of the phasor measurement units (PMUs) enables real-time monitoring of the power system, which has stimulated considerable research into machine learning-based models for event detection and classification.
no code implementations • 13 Sep 2023 • Osten Anderson, Nanpeng Yu, Mikhail Bragin
With California's ambitious goal to achieve decarbonization of the electrical grid by the year 2045, significant challenges arise in power system investment planning.
no code implementations • 1 Feb 2023 • Mikhail A. Bragin, Zuzhao Ye, Nanpeng Yu
The timely transportation of goods to customers is an essential component of economic activities.
no code implementations • 25 Oct 2022 • Brandon Foggo, Koji Yamashita, Nanpeng Yu
This paper introduces pmuGE (phasor measurement unit Generator of Events), one of the first data-driven generative model for power system event data.
no code implementations • 9 Jun 2022 • Jingtao Qin, Yuanqi Gao, Mikhail Bragin, Nanpeng Yu
Unit commitment (UC) is a fundamental problem in the day-ahead electricity market, and it is critical to solve UC problems efficiently.
1 code implementation • 20 Apr 2022 • Yuanqi Gao, Nanpeng Yu
To facilitate the development of reinforcement learning (RL) based power distribution system Volt-VAR control (VVC), this paper introduces a suite of open-source datasets for RL-based VVC algorithm research that is sample efficient, safe, and robust.
1 code implementation • 3 Apr 2022 • Brandon Foggo, Koji Yamashita, Nanpeng Yu
We have trained this model on thousands of actual events and created a dataset denoted pmuBAGE (the Benchmarking Assortment of Generated PMU Events).
no code implementations • 1 Nov 2021 • Zuzhao Ye, Yuanqi Gao, Nanpeng Yu
In this paper, we propose a novel centralized allocation and decentralized execution (CADE) reinforcement learning (RL) framework to maximize the charging station's profit.
no code implementations • 6 Apr 2021 • Yinglun Li, Nanpeng Yu, Wei Wang
We leverage the proposed algorithmic virtual bid trading strategy to evaluate both the profitability of the virtual bid portfolio and the efficiency of U. S. wholesale electricity markets.
no code implementations • 17 Feb 2021 • Wenyu Wang, Nanpeng Yu
In this paper, we develop a physics-informed graphical learning algorithm to estimate network parameters of three-phase power distribution systems.
no code implementations • 13 Nov 2020 • Jie Shi, Brandon Foggo, Nanpeng Yu
Online power system event identification and classification is crucial to enhancing the reliability of transmission systems.
no code implementations • 6 Jul 2020 • Yuanqi Gao, Wei Wang, Nanpeng Yu
Volt-VAR control (VVC) is a critical application in active distribution network management system to reduce network losses and improve voltage profile.
no code implementations • 10 Jun 2020 • Brandon Foggo, Nanpeng Yu
We derive the closed-form expression of the maximum mutual information - the maximum value of $I(X;Z)$ obtainable via training - for a broad family of neural network architectures.
no code implementations • 4 Nov 2019 • Brandon Foggo, Nanpeng Yu
This paper considers the problem of Phase Identification in power distribution systems.
no code implementations • 25 Feb 2019 • Brandon Foggo, Nanpeng Yu
We use this framework to prove that two methods, Facility Location Selection and Transductive Experimental Design, reduce these losses.
no code implementations • 15 Feb 2019 • Brandon Foggo, Nanpeng Yu, Jie Shi, Yuanqi Gao
It then bounds this expected total variation as a function of the size of randomly sampled datasets in a fairly general setting, and without bringing in any additional dependence on model complexity.