Flexible resource optimization strategy for low-carbon parks based on behavioral cloning TD3 reinforcement learning
DOI：10.19783/j.cnki.pspc.240303
Key Words:park integrated energy system  multiple flexible resources  reinforcement learning  behavioral cloning  low-carbon economic dispatch
Author NameAffiliation
SHU Zhan1 1. State Grid Jiangxi Electric Power Research Institute, Nanchang 330096, China
2. School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, China 
SUN Min1 1. State Grid Jiangxi Electric Power Research Institute, Nanchang 330096, China
2. School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, China 
WU Yue1 1. State Grid Jiangxi Electric Power Research Institute, Nanchang 330096, China
2. School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, China 
WAN Zijing1 1. State Grid Jiangxi Electric Power Research Institute, Nanchang 330096, China
2. School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, China 
DUAN Weinan2 1. State Grid Jiangxi Electric Power Research Institute, Nanchang 330096, China
2. School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, China 
PENG Chunhua2 1. State Grid Jiangxi Electric Power Research Institute, Nanchang 330096, China
2. School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, China 
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Abstract:Industrial parks in China are significant contributors to the country’s carbon dioxide emissions. Prioritizing the achievement of carbon neutrality in parks is a crucial in helping China reach its ‘dual-carbon’ goal. This paper presents the construction of a low-carbon park integrated energy system. The system incorporates electrolyzers and hydrogen-blended gas turbines with carbon capture technology into the energy supply side, and considers various flexible resources on the storage, supply, and consumption sides. To efficiently optimize the low-carbon economic dispatch of various flexible resources in this integrated energy system, a TD3 reinforcement learning algorithm considering behavioral cloning is proposed for offline training and online optimization. Finally, the superiority of the proposed optimization strategy is verified through simulation examples.
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