Considering the popularity of electric vehicles and the flexibility of household appliances,it is feasible to dispatch energy in home energy systems under dynamic electricity prices to optimize electricity cost and co...Considering the popularity of electric vehicles and the flexibility of household appliances,it is feasible to dispatch energy in home energy systems under dynamic electricity prices to optimize electricity cost and comfort residents.In this paper,a novel home energy management(HEM)approach is proposed based on a data-driven deep reinforcement learning method.First,to reveal the multiple uncertain factors affecting the charging behavior of electric vehicles(EVs),an improved mathematical model integrating driver’s experience,unexpected events,and traffic conditions is introduced to describe the dynamic energy demand of EVs in home energy systems.Second,a decoupled advantage actor-critic(DA2C)algorithm is presented to enhance the energy optimization performance by alleviating the overfitting problem caused by the shared policy and value networks.Furthermore,separate networks for the policy and value functions ensure the generalization of the proposed method in unseen scenarios.Finally,comprehensive experiments are carried out to compare the proposed approach with existing methods,and the results show that the proposed method can optimize electricity cost and consider the residential comfort level in different scenarios.展开更多
基金Project supported by the National Natural Science Foundation of China(Nos.62293502,62293500,62293504,62073138,and 62173147)the Fundamental Research Funds for the Central Universities,China(No.222202317006)the Nanyang Technological University Startup Grant and MOE Tier 1(No.RG59/22)。
文摘Considering the popularity of electric vehicles and the flexibility of household appliances,it is feasible to dispatch energy in home energy systems under dynamic electricity prices to optimize electricity cost and comfort residents.In this paper,a novel home energy management(HEM)approach is proposed based on a data-driven deep reinforcement learning method.First,to reveal the multiple uncertain factors affecting the charging behavior of electric vehicles(EVs),an improved mathematical model integrating driver’s experience,unexpected events,and traffic conditions is introduced to describe the dynamic energy demand of EVs in home energy systems.Second,a decoupled advantage actor-critic(DA2C)algorithm is presented to enhance the energy optimization performance by alleviating the overfitting problem caused by the shared policy and value networks.Furthermore,separate networks for the policy and value functions ensure the generalization of the proposed method in unseen scenarios.Finally,comprehensive experiments are carried out to compare the proposed approach with existing methods,and the results show that the proposed method can optimize electricity cost and consider the residential comfort level in different scenarios.
