Estimating demand response potentials of domestic appliances: Insights from a Japanese survey

Intermittent renewable electricity generation increases flexibility needs in energy systems. Demand response (DR) programs can effectively address these needs. However, their success depends on consumer willingness to participate. This study quantifies load-shifting potentials from air conditioners, water heaters, electric vehicles, and washing machines based on survey data from 1,402 Japanese households. Logistic regression analyses link willingness to engage in DR programs with socio-demographic and dwelling characteristics, yielding specific participation rates by appliance and household type. These participation rates are integrated with appliance-specific ownership data and annual energy consumption calculations to estimate flexibility potentials for Japan’s Kanto region. The study employs three distinct quantification approaches: (1) a bottom-up activity-based model, (2) a top-down average-based estimate, and (3) a hybrid method using synthetic population data. The results indicate that water heaters consistently exhibit the highest load-shifting potential across all scenarios and time frames, both per household and regionally. By 2050, electric vehicles emerge as an equally important source of flexibility due to increased adoption rates and improvements in water heater efficiency. While the top-down estimation method effectively captures overall population-level flexibility potentials, the hybrid approach is critical for accurately representing household-level variations. These findings facilitate the development of targeted DR programs and contribute to more realistic flexibility modeling within energy systems analysis. Future research should further explore behavioral uncertainties and address additional barriers to the implementation of residential demand response. • Compares methods to estimate residential appliance-level load-shifting potentials. • Novel hybrid approach offers near bottom-up performance from limited, public data. • User interest cuts appliance-level demand response potential by more than half. • Building stock explains household heterogeneity in air conditioner potentials. • Household size shapes variation in washing machine and water heater potentials.