Nowcasting day-ahead marginal emissions using multi-headed CNNs and deep generative models

TL;DR

This paper introduces a multi-headed CNN approach for real-time day-ahead marginal emissions forecasting, addressing the evolving energy system with flexible resources and complex dispatch scenarios.

Contribution

It develops a novel multi-headed CNN framework to accurately forecast marginal emissions in a dynamic, market-driven energy system, integrating deep generative models.

Findings

Effective day-ahead emissions forecasts achieved

Enhanced modeling of flexible energy resources

Improved accuracy over traditional methods

Abstract

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.