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, December). 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: Conference Paper (published)
• Conference Name: 2019 IEEE/CIC International Conference on Communications Workshops in China (ICCC Workshops).
• End Date: Aug 1, 2019
• Acceptance Date: Jun 24, 2019
• Publication Date: 2019
• Deposit Date: Jun 29, 2019
• Publicly Available Date: Sep 18, 2019
• Pages: 204-209
• Series ISSN: 2474-9133
• Book Title: 2019 IEEE/CIC International Conference on Communications Workshops in China (ICCC Workshops) ; proceedings.
• DOI: https://doi.org/10.1109/iccchinaw.2019.8849941
• Public URL: https://durham-repository.worktribe.com/output/1142574