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Real-time price elasticity reinforcement learning for low carbon energy hub scheduling based on conditional random field

Hua, Weiqi, You, Minglei and Sun, Hongjian 2019. 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, 11-13 August 2019. 2019 IEEE/CIC International Conference on Communications Workshops in China (ICCC Workshops). IEEE, pp. 204-209. 10.1109/ICCChinaW.2019.8849941

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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.

Item Type: Conference or Workshop Item (Paper)
Date Type: Published Online
Status: Published
Schools: Engineering
Publisher: IEEE
ISBN: 9781728107387
ISSN: 2474-9133
Date of Acceptance: 26 November 2019
Last Modified: 04 Jan 2021 13:30
URI: https://orca.cardiff.ac.uk/id/eprint/137118

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