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Found 34 papers, 17 papers with code
Contributions of Individual Generators to Nodal Carbon Emissions
Each generator can have distinct carbon emission rates.
Deriving Loss Function for Value-oriented Renewable Energy Forecasting
By integrating it into the upper-level objective for minimizing expected operation cost, we convert the bilevel problem to a single-level one and derive the loss function for training the model.
Value-oriented Renewable Energy Forecasting for Coordinated Energy Dispatch Problems at Two Stages
To this end, we formulate the forecast model parameter estimation as a bilevel program at the training phase, where the lower level solves the day-ahead and real-time energy dispatch problems, with the forecasts as parameters; the optimal solutions of the lower level are then returned to the upper level, which optimizes the model parameters given the contextual information and minimizes the expected operation cost of the two stages.
Online learning for robust voltage control under uncertain grid topology
In this work, we combine a nested convex body chasing algorithm with a robust predictive controller to achieve provably finite-time convergence to safe voltage limits in the online setting where there is uncertainty in both the network topology as well as load and generation variations.
Predicting Strategic Energy Storage Behaviors
Energy storage are strategic participants in electricity markets to arbitrage price differences.
K2: A Foundation Language Model for Geoscience Knowledge Understanding and Utilization
Large language models (LLMs) have achieved great success in general domains of natural language processing.
Structured Neural-PI Control with End-to-End Stability and Output Tracking Guarantees
We study the optimal control of multiple-input and multiple-output dynamical systems via the design of neural network-based controllers with stability and output tracking guarantees.
Leveraging Predictions in Power System Frequency Control: an Adaptive Approach
Ensuring the frequency stability of electric grids with increasing renewable resources is a key problem in power system operations.
Optimal Vehicle Charging in Bilevel Power-Traffic Networks via Charging Demand Function
Specifically, the power network determines the charging price at various locations, while EVs on the traffic network optimize the charging power given the price, acting as price-takers.
Bridging Transient and Steady-State Performance in Voltage Control: A Reinforcement Learning Approach with Safe Gradient Flow
Deep reinforcement learning approaches are becoming appealing for the design of nonlinear controllers for voltage control problems, but the lack of stability guarantees hinders their deployment in real-world scenarios.
Neural Operators of Backstepping Controller and Observer Gain Functions for Reaction-Diffusion PDEs
The designs of gains for controllers and observers for PDEs, such as PDE backstepping, are mappings of system model functions into gain functions.
Neural Operators for Bypassing Gain and Control Computations in PDE Backstepping
While, in the existing PDE backstepping, finding the gain kernel requires (one offline) solution to an integral equation, the neural operator (NO) approach we propose learns the mapping from the functional coefficients of the plant PDE to the kernel function by employing a sufficiently high number of offline numerical solutions to the kernel integral equation, for a large enough number of the PDE model's different functional coefficients.
Machine Learning Accelerated PDE Backstepping Observers
State estimation is important for a variety of tasks, from forecasting to substituting for unmeasured states in feedback controllers.
BEAR: Physics-Principled Building Environment for Control and Reinforcement Learning
Recent advancements in reinforcement learning algorithms have opened doors for researchers to operate and optimize building energy management systems autonomously.
Energy Storage Price Arbitrage via Opportunity Value Function Prediction
This paper proposes a novel energy storage price arbitrage algorithm combining supervised learning with dynamic programming.
FI-ODE: Certifiably Robust Forward Invariance in Neural ODEs
Forward invariance is a long-studied property in control theory that is used to certify that a dynamical system stays within some pre-specified set of states for all time, and also admits robustness guarantees (e. g., the certificate holds under perturbations).
Carbon-Aware EV Charging
This paper examines the problem of optimizing the charging pattern of electric vehicles (EV) by taking real-time electricity grid carbon intensity into consideration.
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.
Robust Online Voltage Control with an Unknown Grid Topology
Voltage control generally requires accurate information about the grid's topology in order to guarantee network stability.
KCRL: Krasovskii-Constrained Reinforcement Learning with Guaranteed Stability in Nonlinear Dynamical Systems
However, current reinforcement learning (RL) methods lack stabilization guarantees, which limits their applicability for the control of safety-critical systems.
Structured Neural-PI Control for Networked Systems: Stability and Steady-State Optimality Guarantees
We explicitly characterize the stability conditions and engineer neural networks that satisfy them by design.
CEM-GD: Cross-Entropy Method with Gradient Descent Planner for Model-Based Reinforcement Learning
It then uses the top trajectories as initialization for gradient descent and applies gradient updates to each of these trajectories to find the optimal action sequence.
SAVER: Safe Learning-Based Controller for Real-Time Voltage Regulation
Fast and safe voltage regulation algorithms can serve as fundamental schemes for achieving a high level of renewable penetration in the modern distribution power grids.
Polymatrix Competitive Gradient Descent
In this work we propose polymatrix competitive gradient descent (PCGD) as a method for solving general sum competitive optimization involving arbitrary numbers of agents.
Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds
Certified robustness is a desirable property for deep neural networks in safety-critical applications, and popular training algorithms can certify robustness of a neural network by computing a global bound on its Lipschitz constant.
Improving Robustness of Reinforcement Learning for Power System Control with Adversarial Training
Due to the proliferation of renewable energy and its intrinsic intermittency and stochasticity, current power systems face severe operational challenges.
Understanding the Safety Requirements for Learning-based Power Systems Operations
Case studies performed on both voltage regulation and topology control tasks demonstrated the potential vulnerabilities of the standard reinforcement learning algorithms, and possible measures of machine learning robustness and security are discussed for power systems operation tasks.
Stability Constrained Reinforcement Learning for Real-Time Voltage Control
Deep reinforcement learning (RL) has been recognized as a promising tool to address the challenges in real-time control of power systems.
End-to-End Demand Response Model Identification and Baseline Estimation with Deep Learning
This paper proposes a novel end-to-end deep learning framework that simultaneously identifies demand baselines and the incentive-based agent demand response model, from the net demand measurements and incentive signals.
Stable Online Control of Linear Time-Varying Systems
In this work, we propose an efficient online control algorithm, COvariance Constrained Online Linear Quadratic (COCO-LQ) control, that guarantees input-to-state stability for a large class of LTV systems while also minimizing the control cost.
Multi-Agent Reinforcement Learning in Cournot Games
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.
Safe Reinforcement Learning of Control-Affine Systems with Vertex Networks
This paper focuses on finding reinforcement learning policies for control systems with hard state and action constraints.
Robust Reinforcement Learning for Continuous Control with Model Misspecification
We provide a framework for incorporating robustness -- to perturbations in the transition dynamics which we refer to as model misspecification -- into continuous control Reinforcement Learning (RL) algorithms.
Product Review Summarization by Exploiting Phrase Properties
The main idea of our method is to leverage phrase properties to choose a subset of optimal phrases for generating the final summary.
