no code implementations • 20 May 2023 • Wenqi Cui, Guanya Shi, Yuanyuan Shi, Baosen Zhang
Ensuring the frequency stability of electric grids with increasing renewable resources is a key problem in power system operations.
no code implementations • 15 May 2023 • Yan Jiang, Wenqi Cui, Baosen Zhang, Jorge Cortés
Existing settings of decentralized learning either require players to have full information or the system to have certain special structure that may be hard to check and hinder their applicability to practical systems.
no code implementations • 3 Apr 2023 • Ling Zhang, Daniel Tabas, Baosen Zhang
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.
no code implementations • 17 Mar 2023 • Jochen Stiasny, Spyros Chatzivasileiadis, Baosen Zhang
The involved Jacobian matrix is calculated with Automatic Differentiation and its size depends only on the network size but not on the component dynamics.
1 code implementation • 30 Nov 2022 • Wenqi Cui, Linbin Huang, Weiwei Yang, Baosen Zhang
Off-policy and Offline RL methods have been proposed to reduce the number of interactions with the physical environment by learning control policies from historical data.
1 code implementation • 29 Nov 2022 • Daniel Tabas, Ahmed S. Zamzam, Baosen Zhang
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.
no code implementations • 28 Oct 2022 • Atinuke Ademola-Idowu, Baosen Zhang
An increase in the integration of Inverter Based Resources (IBRs) to the electric grid, will lead to a corresponding decrease in the amount of connected synchronous generators, resulting in a decline in the available rotational inertia system-wide.
no code implementations • 8 Jul 2022 • Bosong Li, Baosen Zhang, Daniel S. Kirschen
This paper discusses how a cyber attack could take advantage of torsional resonances in the shaft of turbo-generators to inflict severe physical damage to a power system.
1 code implementation • 1 Jun 2022 • Wenqi Cui, Yan Jiang, Baosen Zhang, Yuanyuan Shi
We explicitly characterize the stability conditions and engineer neural networks that satisfy them by design.
no code implementations • 1 May 2022 • Yan Jiang, Wenqi Cui, Baosen Zhang, Jorge Cortés
Specifically, we use RL to learn a neural network-based control policy mapping from the integral variables of DAI to the controllable power injections which provides optimal transient frequency control, while DAI inherently ensures the frequency restoration and optimal economic dispatch.
no code implementations • 23 Mar 2022 • Daniel Tabas, Baosen Zhang
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.
no code implementations • 9 Mar 2022 • Wenqi Cui, Baosen Zhang
Because of the intermittent nature of these resources, the stability of distribution systems under large disturbances and time-varying conditions is becoming a key issue in practical operations.
no code implementations • 28 Jan 2022 • Sina Taheri, Vassilis Kekatos, Harsha Veeramachaneni, Baosen Zhang
The feasible set of the aggregator is then approximated by an ellipsoid upon training a convex quadratic classifier using the labeled dataset.
1 code implementation • 30 Nov 2021 • Yize Chen, Baosen Zhang
Recent proliferation in electric vehicles (EVs) are posing profound impacts over the operation of electrical grids.
1 code implementation • 1 Nov 2021 • Wenqi Cui, Weiwei Yang, Baosen Zhang
System topology and fault information are encoded by taking a multi-dimensional Fourier transform, allowing us to leverage the fact that the trajectories are sparse both in time and spatial frequencies.
no code implementations • 20 Oct 2021 • Daniel Tabas, Baosen Zhang
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.
no code implementations • 4 Oct 2021 • Ling Zhang, Baosen Zhang
Using standard and modified IEEE 22-bus, 39-bus, and 118-bus networks, we show that our approach is able to obtain the globally optimal cost even when the training data is mostly comprised of suboptimal solutions.
no code implementations • 3 Oct 2021 • Wenqi Cui, Jiayi Li, Baosen Zhang
We explicitly engineer the structure of neural network controllers such that they satisfy the Lipschitz constraints by design.
no code implementations • 13 Sep 2021 • Ling Zhang, Baosen Zhang
In this paper, we propose a simple iterative approach to improve the quality of solutions to ACOPF problems.
1 code implementation • 5 Mar 2021 • Wenqi Cui, Baosen Zhang
The learned neural Lyapunov function is then utilized as a regularization to train the neural network controller by penalizing actions that violate the Lyapunov conditions.
no code implementations • 14 Sep 2020 • Yuanyuan Shi, Baosen Zhang
This is the first result (to the best of our knowledge) on the convergence property of learning algorithms with continuous action spaces that do not fall in the no-regret class.
1 code implementation • 11 Sep 2020 • Wenqi Cui, Yan Jiang, Baosen Zhang
As more inverter-connected renewable resources are integrated into the grid, frequency stability may degrade because of the reduction in mechanical inertia and damping.
1 code implementation • 15 Jun 2020 • Yize Chen, Weiwei Yang, Baosen Zhang
In this work, we focus on the problem of load forecasting.
1 code implementation • 20 Mar 2020 • Liyuan Zheng, Yuanyuan Shi, Lillian J. Ratliff, Baosen Zhang
This paper focuses on finding reinforcement learning policies for control systems with hard state and action constraints.
1 code implementation • 4 Feb 2020 • Chase P. Dowling, Baosen Zhang
Faults in HVAC systems degrade thermal comfort and energy efficiency in buildings and have received significant attention from the research community, with data driven methods gaining in popularity.
no code implementations • 14 Jun 2019 • Yan Yang, Zhifang Yang, Juan Yu, Baosen Zhang
A deep neural network (DNN) is used to approximate the power flow calculation and is trained according to the physical power flow equations to improve its learning ability.
no code implementations • 9 Mar 2019 • Pan Li, Baihong Jin, Ruoxuan Xiong, Dai Wang, Alberto Sangiovanni-Vincentelli, Baosen Zhang
We present a machine learning approach to the solution of chance constrained optimizations in the context of voltage regulation problems in power system operation.
no code implementations • 3 Apr 2018 • Aaron Jaech, Baosen Zhang, Mari Ostendorf, Daniel S. Kirschen
This paper addresses the problem of predicting duration of unplanned power outages, using historical outage records to train a series of neural network predictors.
1 code implementation • 2 Feb 2018 • Yize Chen, Pan Li, Baosen Zhang
We present a method to generate renewable scenarios using Bayesian probabilities by implementing the Bayesian generative adversarial network~(Bayesian GAN), which is a variant of generative adversarial networks based on two interconnected deep neural networks.
no code implementations • 8 Nov 2017 • Hao Wang, Baosen Zhang
In this paper, we derive a temporal arbitrage policy for storage via reinforcement learning.
1 code implementation • 7 Nov 2017 • Yize Chen, Xiyu Wang, Baosen Zhang
Simulation results indicate our method is able to generate scenarios that capture spatial and temporal correlations.
Optimization and Control
2 code implementations • 30 Jul 2017 • Yize Chen, Yishen Wang, Daniel Kirschen, Baosen Zhang
We demonstrate that the proposed method is able to generate realistic wind and photovoltaic power profiles with full diversity of behaviors.
no code implementations • 28 Apr 2017 • Pan Li, Baihong Jin, Dai Wang, Baosen Zhang
We also show that this optimization problem is convex for a wide variety of probabilistic distributions.
no code implementations • 13 Mar 2017 • Hossein Hosseini, Yize Chen, Sreeram Kannan, Baosen Zhang, Radha Poovendran
Advances in Machine Learning (ML) have led to its adoption as an integral component in many applications, including banking, medical diagnosis, and driverless cars.
no code implementations • 27 Feb 2017 • Hossein Hosseini, Sreeram Kannan, Baosen Zhang, Radha Poovendran
In this paper, we propose an attack on the Perspective toxic detection system based on the adversarial examples.
no code implementations • 2 Oct 2016 • Pan Li, Baosen Zhang
The accurate estimation of impact of demand response signals to customers' consumption is central to any successful program.
no code implementations • 27 Aug 2016 • Hossein Hosseini, Sreeram Kannan, Baosen Zhang, Radha Poovendran
We consider the setting where a collection of time series, modeled as random processes, evolve in a causal manner, and one is interested in learning the graph governing the relationships of these processes.
no code implementations • 9 Feb 2016 • Carlos Riquelme, Ramesh Johari, Baosen Zhang
We consider the problem of online active learning to collect data for regression modeling.
no code implementations • 5 Nov 2015 • Pan Li, Baosen Zhang, Yang Weng, Ram Rajagopal
Accurate prediction of user consumption is a key part not only in understanding consumer flexibility and behavior patterns, but in the design of robust and efficient energy saving programs as well.