Hua, Weiqi and You, Minglei and Sun, Hongjian (2019) 'Real-time price elasticity reinforcement learning for low carbon energy hub scheduling based on conditional random field.', in 2019 IEEE/CIC International Conference on Communications Workshops in China (ICCC Workshops) ; proceedings. , pp. 204-209.
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.
|Item Type:||Book chapter|
|Full text:||(AM) Accepted Manuscript|
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|Publisher Web site:||https://doi.org/10.1109/ICCChinaW.2019.8849941|
|Publisher statement:||© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.|
|Date accepted:||24 June 2019|
|Date deposited:||01 July 2019|
|Date of first online publication:||2019|
|Date first made open access:||18 September 2019|
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