A low-carbon scheduling method for multi-energy flow buildings based on deep reinforcement learning

Abstract

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.

Keywords

• ‘dual-carbon’ goals
• multi-energy flow
• low-carbon scheduling
• deep rl