Evaluation of residential demand response potential under multiple confidence scenarios based on Gaussian mixture model

Abstract

Given the practical issues of the traditional power system's insufficient operational flexibility in the face of changing residential power load and source-side output, demand response can effectively improve the flexibility, safety and economic benefits of system operation. The value of demand response is especially noticeable meanwhile the refined assessment of demand response potential is an important basic support. A method is proposed about evaluating aggregate demand response potential in the absence of historical demand response data based on Gaussian mixture model. Firstly, the typical data are selected through two-stage clustering of households and similar days to improve data representativeness. Then the Gaussian mixture model is introduced to accurately explore the probability distribution of household electricity consumption behavior and calculate individual households' demand response potential. Finally, the bottom-up weighted aggregation is implemented to evaluate the aggregate demand response potential under multiple confidence scenarios. According to empirical analysis, this method can mine hourly information of residential demand response potential from historical electricity consumption data, which can reflect the distribution of power load and demand response potential. Comparative analysis is used to validate the validity of typical data selection by two-stage clustering.

Keywords

• demand response potential
• clustering analysis
• gaussian mixture model
• multiple confidence scenarios
• laplacian scoring method
• affinity propagation algorithm