基于深度强化学习的建筑综合能源系统主从博弈优化调度OA

As the global society is increasingly concerned about the transition to sustainable energy practices,building integrated energy systems optimization is significant in improving low-carbon and economic energy consumption.Therefore,research is conducted on the scheduling and pricing strategies of building energy operators.Firstly,the information interaction characteristics of both the supply side and the demand side are considered.A two-side optimization model of the building integrated energy system based on the Stackelberg game framework is established with the supply side as the leader and the demand side as the follower.Secondly,a deep deterministic strategy gradient algorithm is proposed based on the adaptive action exploration mechanism to solve the constructed model efficiently given the multiple information interactions between the two sides of the Stackelberg game framework.The adaptive action exploration mechanism constructs the action selection strategy of the adaptive exploration coefficient improvement algorithm based on the variance of the cumulative rewards and the average loss value of the critic network,ensuring the algorithm's accuracy and stability.Finally,the effectiveness of the proposed algorithm is verified by examples.The experimental results show that compared with other deep reinforcement learning algorithms,the proposed algorithm can improve the convergence accuracy and stability of the algorithm,as well as the total revenue of the energy operator,thus assisting the energy supply side in making better decisions.