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Analysis of Weather and Time Features in Machine Learning-aided ERCOT Load Forecasting
Overall, case studies demonstrated the effectiveness of ML models trained with different weather and time input features for ERCOT load forecasting.
Convolutional Neural Network-based RoCoF-Constrained Unit Commitment
This paper presents a convolutional neural network (CNN) based RoCoF-constrained unit commitment (CNN-RCUC) model to guarantee RoCoF stability following the worst generator outage event while ensuring operational efficiency.
Active Linearized Sparse Neural Network-based Frequency-Constrained Unit Commitment
An active data sampling method is proposed to maintain the bindingness of the frequency related constraints.
Graph Neural Network-based Power Flow Model
A comprehensive performance analysis is conducted, comparing the proposed GNN-based power flow model with the traditional DC power flow model, as well as deep neural network (DNN) and convolutional neural network (CNN).
Machine Learning Assisted Inertia Estimation using Ambient Measurements
The proposed LRCN and GCN based inertia estimation models achieve an accuracy of 97. 34% and 98. 15% respectively.
Selectively Linearized Neural Network based RoCoF-Constrained Unit Commitment in Low-Inertia Power Systems
Conventional synchronous generators are gradually being replaced by inverter-based resources, such transition introduces more complicated operation conditions.
Deep Learning based Security-Constrained Unit Commitment Considering Locational Frequency Stability in Low-Inertia Power Systems
RoCoF predictor is trained to predict the highest locational RoCoF based on a high-fidelity simulation dataset.
Wholesale Electricity Price Forecasting using Integrated Long-term Recurrent Convolutional Network Model
Electricity price is a key factor affecting the decision-making for all market participants.
Long-Term Recurrent Convolutional Network-based Inertia Estimation using Ambient Measurements
The increasing integration of renewable energy resources imports different dynamics into traditional power systems; therefore, the estimation of system inertia using mathematical model becomes more difficult.
Security-Constrained Unit Commitment Con-sidering Locational Frequency Stability in Low-Inertia Power Grids
With increasing installation of wind and solar generation, conventional synchronous generators in power systems are gradually displaced resulting in a significant reduction in system inertia.
Optimal Allocation of Virtual Inertia Devices for Enhancing Frequency Stability in Low-Inertia PowerSystems
In this study, a state-space model for the power system network is developed with VI as a frequency regulation method.
Dynamic Estimation of Power System Inertia Distribution Using Synchrophasor Measurements
Integration of intermittent renewable energy sources in modern power systems is increasing very fast.
