A Human-machine Reinforcement Learning Method for Cooperative Energy Management

The increasing penetration of distributed energy resources and a large volume of unprecedented data from smart metering infrastructure can help consumers transit to an active role in the smart grid. In this paper, we propose a human-machine reinforcement learning (RL) framework in the smart grid context to formulate an energy management strategy for electric vehicles (EVs) and thermostatically controlled loads (TCLs) aggregators. The proposed model-free method accelerates the decision-making speed by substituting the conventional optimization process and is more capable of coping with the diverse system environment via on-line learning. The human intervention is coordinated with the machine learning to: 1) prevent the huge loss during the learning process; 2) realize emergency control; 3) find preferable control policy. The performance of the proposed human-machine reinforcement learning framework is verified in case studies.