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.
Original language | English |
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Title of host publication | 2019 IEEE/CIC International Conference on Communications Workshops in China (ICCC Workshops) |
Publisher | IEEE |
Pages | 204-209 |
Number of pages | 6 |
ISBN (Print) | 978-1-7281-0739-4 |
DOIs | |
Publication status | Published - 13 Aug 2019 |
Event | 2019 IEEE/CIC International Conference on Communications Workshops in China (ICCC Workshops) - Changchun, China Duration: 11 Aug 2019 → 13 Aug 2019 |
Conference
Conference | 2019 IEEE/CIC International Conference on Communications Workshops in China (ICCC Workshops) |
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Period | 11/08/19 → 13/08/19 |
Keywords
- Cogeneration
- Carbon dioxide
- Boilers
- Elasticity
- Optimal scheduling
- Natural gas
- Resistance heating