Optimal Energy Management for Residential House Aggregators with Uncertain User Behaviors Using Deep Reinforcement Learning

This paper addresses the home energy management (HEM) problem for a large number of residential houses, which can be regarded as a high-dimensional optimization problem. To cope with the high-dimensional issue, the concept of the aggregator is utilized to reduce the state and action space. And a two-stage deep reinforcement learning (DRL) based approach is proposed for the aggregators to track the schedule from the superior grid and guarantee the operation constraints. In the first stage, a DRL control agent is set to learn the optimal scheduling strategy interacting with the environment based on the soft-actor-critic (SAC) framework and generate the aggregate control actions. In the second stage, the aggregate control actions are disaggregated to individual appliances considering the users' behaviors. The uncertainty of the EV charging demand is quantitatively described by the driver's experience. An aggregate anxiety concept is introduced to characterize both the driver's anxiety on the EV's range and uncertain events. Finally, simulation studies verify the effectiveness of the proposed approach under dynamic user behaviors, and the comparisons also show the superiority of the proposed approach over the method mentioned in benchmarks.