基于深度强化学习的多能流楼宇低碳调度方法OACSTPCD

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 characteris-tics 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 punish-ment 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.