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.

考虑到住宅灵活负荷的实际运行特性、能源相关的居住者行为以及不同利益相关者的利益，提出了基于模型的优化调度框架来优化住宅灵活负荷的运行。对一个典型家庭进行了试点测试。根据监测到的电器级数据，识别柔性负载的运行特性，并使用多元线性回归和K均值聚类方法开发这些柔性负载的模型。此外，还开发了一种数据挖掘方法，可以从监测数据中提取各种灵活负载的占用者能源使用行为。使用者使用电器的行为，例如每天的开启次数、开启时刻、每次操作的持续时间、温度设置偏好和灵活性窗口，由开发的数据挖掘方法确定。基于建立的灵活负荷模型和识别的用户能源使用行为，开发了多目标非线性优化调度模型，旨在最小化日常电费、用户不满、二氧化碳排放₂排放量，以及家庭电力概况的平均爬升指数。该模型借助 NSGA-III 和 TOPSIS 方法进行求解。结果表明，所提出的框架可以有效优化家庭灵活负载的运行。与基准相比，分时电价下，优化方案的日用电成本、CO 2 排放量和家庭用电曲线平均爬坡指数分别降低了7.3%、6.5%和14.4 % _。动态电价下分别提高9.5%、8.8%和23.8%。本工作的成果可为实践中家庭灵活负荷日前优化调度提供指导。