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Found 8 papers, 2 papers with code
Tracking and Analyzing the Short-Run Impact of COVID-19 on the U.S. Electricity Sector
The novel coronavirus disease (COVID-19) has rapidly spread around the globe in 2020, with the U. S. becoming the epicenter of COVID-19 cases and deaths in late March.
Computers and Society
Open-Access Data and Toolbox for Tracking COVID-19 Impact on Power Systems
Intervention policies against COVID-19 have caused large-scale disruptions globally, and led to a series of pattern changes in the power system operation.
Bounding Regression Errors in Data-driven Power Grid Steady-state Models
Results identify how the bounds decrease with additional power grid physical knowledge or more training data.
Sparse Oblique Decision Tree for Power System Security Rules Extraction and Embedding
Data-driven techniques provide a promising way to identify security rules that can be embedded in economic dispatch model to keep power system operating states secure.
Embedding Lithium-ion Battery Scrapping Criterion and Degradation Model in Optimal Operation of Peak-shaving Energy Storage
A new battery life model with scrapping parameters is then derived using this criterion.
Estimating Demand Flexibility Using Siamese LSTM Neural Networks
There is an opportunity in modern power systems to explore the demand flexibility by incentivizing consumers with dynamic prices.
Improving Sample Efficiency of Deep Learning Models in Electricity Market
The superior performance of deep learning relies heavily on a large collection of sample data, but the data insufficiency problem turns out to be relatively common in global electricity markets.
Real-time scheduling of renewable power systems through planning-based reinforcement learning
The well-trained scheduling agent significantly reduces renewable curtailment and load shedding, which are issues arising from traditional scheduling's reliance on inaccurate day-ahead forecasts.
