A low-carbon scheduling method for multi-energy flow buildings based on deep reinforcement learning
- Resource Type
- article
- Authors
- XU Dong; LI Yichao; LI Yun; XU Gang; DU Jiawei
- Source
- Zhejiang dianli, Vol 43, Iss 2, Pp 126-136 (2024)
- Subject
- ‘dual-carbon’ goals
multi-energy flow
low-carbon scheduling
deep rl
Electrical engineering. Electronics. Nuclear engineering
TK1-9971
- Language
- Chinese
- ISSN
- 1007-1881
20240201
Building emissions reduction has become a crucial pathway for China to achieve its ‘dual-carbon’ goals. As an integrated energy entity coupled with multi-energy flow networks, smart buildings face challenges such as high carbon emissions, a high degree of coupling in multi-energy flow networks, and distinct dynamic characteristics in load energy consumption behavior. In response to these challenges, a low-carbon scheduling method for multi-energy flow buildings based on deep reinforcement learning (deep RL) is proposed. Firstly, a reward and punishment ladder-type carbon emissions trading mechanism is established based on the actual carbon emissions of smart buildings. Secondly, targeting the carbon market and multi-energy flow coupling networks, a low-carbon scheduling model for multi-energy flow buildings is developed, aiming to minimize operating costs as the objective function, and the scheduling is transformed into a Markov decision process (MDP). Subsequently, the Rainbow algorithm is employed to solve the optimal scheduling. Finally, the feasibility and effectiveness of the optimal scheduling model are verified through simulation analysis.