DRL-HEMS: Deep Reinforcement Learning Agent for Demand Response in Home Energy Management Systems Considering Customers and Operators Perspectives

With the smart grid and smart homes development, different data are made available, providing a source for training algorithms, such as deep reinforcement learning (DRL), in smart grid applications. These algorithms allowed the home energy management systems (HEMSs) to deal with the computational complexities and the uncertainties at the end-user side. This article proposes a multi-objective DRL-HEMS: a data-driven solution, which is a trained DRL agent in a HEMS to optimize the energy consumption of a household with different appliances, an energy storage system, a photovoltaic system, and an electric vehicle. The proposed solution reduces the electricity cost considering the resident's comfort level and the loading level of the distribution transformer. The distribution transformer load is optimized by optimizing its loss-of-life. The performance of DRL-HEMS is evaluated using real-world data, and results show that it can optimize multiple appliances operation, reduce electricity bill cost, dissatisfaction cost, and the transformer loading condition.

Automated summary

This article proposes a data-driven multi-objective DRL-HEMS solution that optimizes household energy consumption, reduces electricity cost, and considers resident comfort and transformer loading condition.