Weiqi Hua
Real-Time Price Elasticity Reinforcement Learning for Low Carbon Energy Hub Scheduling Based on Conditional Random Field
Hua, Weiqi; You, Minglei; Sun, Hongjian
Abstract
Energy hub scheduling plays a vital role in optimally integrating multiple energy vectors, e.g., electricity and gas, to meet both heat and electricity demand. A scalable scheduling model is needed to adapt to various energy sources and operating conditions. This paper proposes a conditional random field (CRF) method to analyse the intrinsic characteristics of energy hub scheduling problems. Building on these characteristics, a reinforcement learning (RL) model is designed to strategically schedule power and natural gas exchanges as well as the energy dispatch of energy hub. Case studies are performed by using real-time digital simulator that enables dynamic interactions between scheduling decisions and operating conditions. Simulation results show that the CRF-based RL method can approach the theoretical optimal scheduling solution after 50 days training. Scheduling decisions are particularly more dependent on received price information during peak-demand period. The proposed method can reduce 9.76% of operating cost and 1.388 ton of carbon emissions per day, respectively.
Citation
Hua, W., You, M., & Sun, H. (2019, August). Real-Time Price Elasticity Reinforcement Learning for Low Carbon Energy Hub Scheduling Based on Conditional Random Field. Presented at 2019 IEEE/CIC International Conference on Communications Workshops in China (ICCC Workshops), Changchun, China
Presentation Conference Type | Edited Proceedings |
---|---|
Conference Name | 2019 IEEE/CIC International Conference on Communications Workshops in China (ICCC Workshops) |
Start Date | Aug 11, 2019 |
End Date | Aug 13, 2019 |
Acceptance Date | Jun 29, 2019 |
Online Publication Date | Sep 26, 2019 |
Publication Date | 2019-08 |
Deposit Date | Oct 28, 2021 |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 204-209 |
Book Title | 2019 IEEE/CIC International Conference on Communications Workshops in China (ICCC Workshops) |
ISBN | 9781728107394 |
DOI | https://doi.org/10.1109/iccchinaw.2019.8849941 |
Public URL | https://nottingham-repository.worktribe.com/output/6537525 |
Publisher URL | https://ieeexplore.ieee.org/document/8849941 |
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