Load scheduling optimization for user-centric residential demand response leveraging time use surveys

This paper presents a novel optimization algorithm that integrates user behavior into day-ahead load scheduling for residential demand response (DR) by utilizing data from the Harmonized European Time Use Survey (HETUS). The proposed algorithm schedules the operation of household appliances, such as laundry, dishwashing, cooking, and cleaning, based on dynamic pricing and user preferences, aiming to minimize electricity costs while mitigating the impact on user convenience. A sensitivity analysis is conducted to optimize the weighting factor between cost and behavior, ensuring a balanced trade-off. The algorithm introduces buffer zones and alternative scheduling to provide additional flexibility for users. The methodology is tested using data from Austria, showing significant cost savings of up to 50 % with minimal disruption to user behavior. The results provide the basis for a more personalized energy management solution, potentially increasing user participation and improving the effectiveness of residential DR programs. • Empirical time use survey data are integrated into residential demand response scheduling optimization. • A novel algorithm balances electricity costs and user convenience using dynamic weighting. • Dynamic buffer zones and alternate schedules enhance practical user flexibility and adherence. • The method is validated using Austrian data, confirming the method’s effectiveness under real pricing and usage patterns. • The approach is scalable and adaptable for diverse markets using standardized survey data.