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Found 14 papers, 0 papers with code
Clustering Interval Load with Weather to Create Scenarios of Behind-the-Meter Solar Penetration
Forecasting load at the feeder level has become increasingly challenging with the penetration of behind-the-meter solar, as this self-generation (also called total generation) is only visible to the utility as aggregated net-load.
Forte: An Interactive Visual Analytic Tool for Trust-Augmented Net Load Forecasting
Accurate net load forecasting is vital for energy planning, aiding decisions on trade and load distribution.
Analyzing Distribution Transformer Degradation with Increased Power Electronic Loads
To have a good understanding of current standing challenges, a knowledge of the generation and load mix as well as the current harmonic estimations are essential for designing transformers and evaluating their performance.
Optimization-Based Resiliency Verification in Microgrids via Maximal Adversarial Set Characterization
An inner-approximation of the set of adversarial events which can be mitigated by the available flexibility is constructed using an optimization based approach.
KPF-AE-LSTM: A Deep Probabilistic Model for Net-Load Forecasting in High Solar Scenarios
With the expected rise in behind-the-meter solar penetration within the distribution networks, there is a need to develop time-series forecasting methods that can reliably predict the net-load, accurately quantifying its uncertainty and variability.
Graph Neural Network and Koopman Models for Learning Networked Dynamics: A Comparative Study on Power Grid Transients Prediction
In particular, accurate and timely prediction of the (electro-mechanical) transient dynamic trajectories of the power grid is necessary for early detection of any instability and prevention of catastrophic failures.
Developing and Validating Semi-Markov Occupancy Generative Models: A Technical Report
This report documents recent technical work on developing and validating stochastic occupancy models in commercial buildings, performed by the Pacific Northwest National Laboratory (PNNL) as part of the Sensor Impact Evaluation and Verification project under the U. S. Department of Energy (DOE) Building Technologies Office (BTO).
Harmonic Modeling, Data Generation, and Analysis of Power Electronics-Interfaced Residential Loads
Integration of electronics-based residential appliances and distributed energy resources in homes is expected to rise with grid decarbonization.
Sparse Control Synthesis for Uncertain Responsive Loads with Stochastic Stability Guarantees
Detailed numerical studies are carried out on IEEE 39-bus system to demonstrate the closed-loop stochastic stabilizing performance of the sparse controllers in enhancing frequency response under load uncertainties; as well as illustrate the fundamental trade-off between the allowable uncertainties and optimal control efforts.
Occupancy-Driven Stochastic Decision Framework for Ranking Commercial Building Loads
For effective integration of building operations into the evolving demand response programs of the power grid, real-time decisions concerning the use of building appliances for grid services must excel on multiple criteria, ranging from the added value to occupants' comfort to the quality of the grid services.
Stochastic Virtual Battery Modeling of Uncertain Electrical Loads Using Variational Autoencoder
Effective utilization of flexible loads for grid services, while satisfying end-user preferences and constraints, requires an accurate estimation of the aggregated predictive flexibility offered by the electrical loads.
Systems and Control Systems and Control
Virtual Battery Parameter Identification using Transfer Learning based Stacked Autoencoder
Recent studies have shown that the aggregated dynamic flexibility of an ensemble of thermostatic loads can be modeled in the form of a virtual battery.
Decomposition of Nonlinear Dynamical Systems Using Koopman Gramians
In this paper we propose a new Koopman operator approach to the decomposition of nonlinear dynamical systems using Koopman Gramians.
Learning Deep Neural Network Representations for Koopman Operators of Nonlinear Dynamical Systems
The Koopman operator has recently garnered much attention for its value in dynamical systems analysis and data-driven model discovery.
