Individual household demand response potential evaluation and identification based on machine learning algorithms

Modern power systems are facing an increase in the penetration of renewables to achieve carbon neutrality targets in the future. Individual household demand response (DR) is a potential resource to address the fluctuation caused by renewable energy generation. However, the DR potential of residential customers is more challenging to evaluate and identify than the DR at the aggregated level due to the uncertainty in individual customers' electricity consumption behavior. In this study, to address this issue, a DR potential evaluation and identification framework based on machine learning algorithms is proposed. Firstly, the customer's DR capacity under dynamic time-of-use electricity tariffs is carefully calculated. Then several novel indicators are proposed to depict customer's electricity consumption characteristics. Next, some prevalent machine learning methods are leveraged to learn the mapping between the developed indicators and DR capacity. Besides, to further improve the classification accuracy and analyze the performance of the unlabeled samples, the self-training method is applied by using customers' data who are issued normal flat electricity prices. Finally, the proposed framework was applied to a publicly available dataset and the results indicated that the proposed approach reached prospective classification accuracy and proved the validity of the proposed indicators and identification framework.