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

• URL: http://arxiv.org/abs/2310.01524v1
• Date: Mon, 2 Oct 2023 18:14:55 GMT
• Title: Nowcasting day-ahead marginal emissions using multi-headed CNNs and deep generative models
• Authors: Dhruv Suri, Anela Arifi, Ines Azevedo
• Abstract summary: forecasting day-ahead emissions in the current energy system has been widely studied. As we shift to an energy system characterized by flexible power markets, we need a near real-time workflow with two layers. We propose using multi-headed convolutional neural networks to generate day-ahead forecasts of marginal and average emissions for a given independent system operator.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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