Multi-objective optimal dispatch of household flexible loads based on their real-life operating characteristics and energy-related occupant behavior

A model-based optimal dispatch framework was proposed to optimize operation of residential flexible loads considering their real-life operating characteristics, energy-related occupant behavior, and the benefits of different stakeholders. A pilot test was conducted for a typical household. According to the monitored appliance-level data, operating characteristics of flexible loads were identified and the models of these flexible loads were developed using multiple linear regression and K-means clustering methods. Moreover, a data-mining approach was developed to extract the occupant energy usage behavior of various flexible loads from the monitored data. Occupant behavior of appliance usage, such as daily turn-on times, turn-on moment, duration of each operation, preference of temperature setting, and flexibility window, were determined by the developed data-mining approach. Based on the established flexible load models and the identified occupant energy usage behavior, a many-objective nonlinear optimal dispatch model was developed aiming at minimizing daily electricity costs, occupants' dissatisfaction, CO2 emissions, and the average ramping index of household power profiles. The model was solved with the assistance of the NSGA-III and TOPSIS methods. Results indicate that the proposed framework can effectively optimize the operation of household flexible loads. Compared with the benchmark, the daily electricity costs, CO2 emissions, and average ramping index of household power profiles of the optimal plan were reduced by 7.3%, 6.5%, and 14.4%, respectively, under the TOU tariff, while those were decreased by 9.5%, 8.8%, and 23.8%, respectively, under the dynamic price tariff. The outputs of this work can offer guidance for the day-ahead optimal scheduling of household flexible loads in practice.

Automated summary

A model-based optimal dispatch framework was proposed to optimize the operation of residential flexible loads. The framework considered the operating characteristics of flexible loads and the energy-related occupant behavior. By developing models and using data mining methods, the framework effectively reduced daily electricity costs, CO2 emissions, and the average ramping index of household power profiles.