Nowcasting day-ahead marginal emissions using multi-headed CNNs and deep
  generative models
Dhruv Suri Anela Arifi Ines Azevedo
Nowcasting day-ahead marginal emissions factors is increasingly important for
power systems with high flexibility and penetration of distributed energy
resources. With a significant share of firm generation from natural gas and
coal power plants, forecasting day-ahead emissions in the current energy system
has been widely studied. In contrast, as we shift to an energy system
characterized by flexible power markets, dispatchable sources, and competing
low-cost generation such as large-scale battery or hydrogen storage, system
operators will be able to choose from a mix of different generation as well as
emission pathways. To fully develop the emissions implications of a given
dispatch schedule, we need a near real-time workflow with two layers. The first
layer is a market model that continuously solves a security-constrained
economic dispatch model. The second layer determines the marginal emissions
based on the output of the market model, which is the subject of this paper. We
propose using multi-headed convolutional neural networks to generate day-ahead
forecasts of marginal and average emissions for a given independent system
operator.