Comprehensive Approaches to a Low-Carbon Transition of the Power Grid
As the largest source of carbon emissions, the power grid has garnered significant attention for its decarbonization from academia, government, and industry. This transition is complex due to the involvement of various stakeholders, including power generation, transmission networks, and active customers. The purpose of this paper is to present a framework aimed at facilitating the low-carbon transition, focusing on emission reduction strategies across four components of the power system. On the generation side, retiring coal-fired generators and deploying renewable energy sources coupled with energy storage systems can significantly lower overall system emissions. Correspondingly, on the consumer side, the electrification of transportation offers a viable alternative to internal combustion engine vehicles, thereby reducing societal emissions. Furthermore, in terms of system control, planning methodologies such as gas-electric coupling optimization can expedite emission reduction efforts. For market design, effective policies like carbon taxes and carbon emission allowance trading incentivize power companies to actively lower emissions. Besides the above, advanced AI-based prediction methods, such as generative adversarial networks (GANs) and large language models (LLMs), enhance the accuracy of forecasting uncertainty including electricity prices and loads, improving the efficiency of transition. These evolving policies and grid technologies will enable us to decarbonize the grid and better mitigate the impacts of climate change driven by electricity consumption.
Authors
- Jing Qiu (ORCID: https://orcid.org/0000-0001-8507-0558)
- Lixin Yang (ORCID: https://orcid.org/0000-0003-3377-1998)
- Yi Yang
- Jiaqi Ruan (ORCID: https://orcid.org/0000-0003-2584-0738)
- Gaoqi Liang (ORCID: https://orcid.org/0000-0001-9060-1675)
- Chenxi Zhang (ORCID: https://orcid.org/0000-0001-9331-3652)
Institutions
- The University of Sydney (AU)
- Hong Kong Polytechnic University (HK)
- Harbin Institute of Technology (CN)
- Fujian Electric Power Survey & Design Institute (CN)
Publication Details
- Published
- 2024-11-29
- DOI
- https://doi.org/10.1109/ei264398.2024.10990385
- Primary Topic
- Integrated Energy Systems Optimization
- Type
- article
- Field-Weighted Citation Impact
- 0.00