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Found 14 papers, 3 papers with code
Network-Constrained Unit Commitment with Flexible Temporal Resolution
Modern network-constrained unit commitment (NCUC) bears a heavy computational burden due to the ever-growing model scale.
Explainable Modeling for Wind Power Forecasting: A Glass-Box Approach with High Accuracy
Machine learning models (e. g., neural networks) achieve high accuracy in wind power forecasting, but they are usually regarded as black boxes that lack interpretability.
A Projection-Based Approach for Distributed Energy Resources Aggregation
Aggregating distributed energy resources (DERs) is of great significance to improve the overall operational efficiency of smart grid.
Life cycle economic viability analysis of battery storage in electricity market
Battery storage is essential to enhance the flexibility and reliability of electric power systems by providing auxiliary services and load shifting.
Look-Ahead AC Optimal Power Flow: A Model-Informed Reinforcement Learning Approach
With the increasing proportion of renewable energy in the generation side, it becomes more difficult to accurately predict the power generation and adapt to the large deviations between the optimal dispatch scheme and the day-ahead scheduling in the process of real-time dispatch.
Low-Carbon Economic Dispatch of Bulk Power Systems Using Nash Bargaining Game
Because the Nash bargaining solution satisfies Pareto effectiveness, we analyze the computational complexity of Pareto frontiers with parametric linear programming and interpret the inefficiency of the method.
Transmission Congestion Management with Generalized Generation Shift Distribution Factors
A major concern in modern power systems is that the popularity and fluctuating characteristics of renewable energy may cause more and more transmission congestion events.
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.
Evaluation of Look-ahead Economic Dispatch Using Reinforcement Learning
In this paper, we propose an evaluation approach to analyze the performance of RL agents in a look-ahead economic dispatch scheme.
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.
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.
Short-Term Electricity Price Forecasting based on Graph Convolution Network and Attention Mechanism
In electricity markets, locational marginal price (LMP) forecasting is particularly important for market participants in making reasonable bidding strategies, managing potential trading risks, and supporting efficient system planning and operation.
Review of Learning-Assisted Power System Optimization
With dramatic breakthroughs in recent years, machine learning is showing great potential to upgrade the toolbox for power system optimization.
Systems and Control Systems and Control Optimization and Control
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
