Version 1
: Received: 28 October 2024 / Approved: 29 October 2024 / Online: 30 October 2024 (03:39:09 CET)
How to cite:
An, Z.; Whitcomb, C. A. Optimizing Carbon Emissions in Electricity Markets: A System Engineering and Machine Learning Approach. Preprints2024, 2024102258. https://doi.org/10.20944/preprints202410.2258.v1
An, Z.; Whitcomb, C. A. Optimizing Carbon Emissions in Electricity Markets: A System Engineering and Machine Learning Approach. Preprints 2024, 2024102258. https://doi.org/10.20944/preprints202410.2258.v1
An, Z.; Whitcomb, C. A. Optimizing Carbon Emissions in Electricity Markets: A System Engineering and Machine Learning Approach. Preprints2024, 2024102258. https://doi.org/10.20944/preprints202410.2258.v1
APA Style
An, Z., & Whitcomb, C. A. (2024). Optimizing Carbon Emissions in Electricity Markets: A System Engineering and Machine Learning Approach. Preprints. https://doi.org/10.20944/preprints202410.2258.v1
Chicago/Turabian Style
An, Z. and Clifford Alan Whitcomb. 2024 "Optimizing Carbon Emissions in Electricity Markets: A System Engineering and Machine Learning Approach" Preprints. https://doi.org/10.20944/preprints202410.2258.v1
Abstract
This study addresses the urgent need to reduce carbon emissions in the power sector, a major contributor to global greenhouse gas emissions, by employing system engineering principles coupled with machine learning techniques. It focuses on analyzing the interplay between regional marginal prices (LMP) and carbon emissions within electricity markets. Leveraging a dataset that encompasses hourly LMP and carbon emissions data across various regions of New York State, the paper explores how market designs and operational strategies influence carbon output. The analysis utilizes neural networks to simulate and predict the effects of different market scenarios on carbon emissions, highlighting the role of LMP, loss costs, and congestion costs in environmental policy effectiveness. The results underscore the potential of system engineering to provide a holistic framework that integrates market dynamics, policy adjustments, and environmental impacts, thereby offering actionable insights into optimizing market designs for reduced carbon footprints. This approach not only enhances the understanding of the complex interactions within electricity markets but also supports the development of targeted strategies for achieving sustainable energy transitions.
Keywords
System Engineering; Machine Learning; Carbon Emissions; Regional Marginal Pricing
Subject
Engineering, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
