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.

Self-powered photovoltaic windows, which integrate photovoltaic with electrochromic devices, have attracted widespread attention of scholars since they can generate electricity in situ and reduce building energy consumption by modulating the transmitted solar radiation. However, previous studies mainly focused on the material development and performance characterization, lack of comfort assessment and energy saving potential of its application to buildings. To address this issue, an adjustable semi-transparent photovoltaic (ATPV) window which integrates CdTe-based photovoltaic and WO₃-based electrochromic, was taken as the research object, and a novel rule-based control strategy taking the beam solar radiation luminous efficacy (CtrlEff) as decision variable was proposed for the first time. The ATPV window model was established in WINDOW software based on the measured data, and then it was exported to integrated with a medium office building model in EnergyPlus for performance evaluation including the visual comfort, thermal comfort, net energy consumption, and net-zero energy ratio. The results of a case study in Changsha (E 112°, N 28°) indicated that the ATPV window under the CtrlEff strategy can effectively reduce the southward and westward intolerable glare by 86.9% and 94.9%, respectively, and increase the thermal comfort hours by 5% and 2%, compared to the Low-E window. Furthermore, the net-zero energy consumption can be decreased by 58.7%, 65.7%, 64.1%, and 53.8% for south, west, east, and north orientations, and the corresponding net-zero energy ratios are 65.1%, 54.6%, 62.7%, and 61.6%, respectively. The findings of this study provide new strategies for the control and optimization of the adjustable window.