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A Machine Learning Framework to Deconstruct the Primary Drivers for Electricity Market Price Events
Power grids are moving towards 100% renewable energy source bulk power grids, and the overall dynamics of power system operations and electricity markets are changing.
Deep Reinforcement Learning to Maximize Arterial Usage during Extreme Congestion
Collisions, crashes, and other incidents on road networks, if left unmitigated, can potentially cause cascading failures that can affect large parts of the system.
Training Machine Learning Models to Characterize Temporal Evolution of Disadvantaged Communities
Disadvantaged communities (DAC), as defined by the Justice40 initiative of the Department of Energy (DOE), USA, identifies census tracts across the USA to determine where benefits of climate and energy investments are or are not currently accruing.
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.
