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In the smart grid, the demand-side management of power load plays an essential role to improve power load curve where time-of-use (TOU) price incentive is widely used to mobilize the multi-time-scale response ability of demand-side. Considering the seasonal and daily characteristics of user power load, in this paper, the dynamic multi-objective optimization of TOU price under multi-model structure is mainly studied. First, the preprocessing of raw power load data is systematically discussed, including abnormal data elimination and missing data reconstruction. Then, combining adaptive affinity propagation (adAP) clustering and fuzzy k-nearest neighbor (FKNN) clustering, generalized seasons are divided while peak-flat-valley time periods are redivided for each season. Subsequently, a frequency-separated demand response model structure involving steady-state and uncertainty terms is built to approach actual user load response with balanced model complexity and accuracy. For each seasonal model using this structure, the multi-objective optimization problem of TOU price is formed where non-dominated sorting genetic algorithm-II (NSGA-II) with probabilistic deviation sorting order strategy is proposed to select the balance optimal solutions. Finally, the effectiveness of the above methods is validated. The simulation results show that if the optimized seasonal TOU prices are adopted, the power load curve can be obviously improved increasing load rate, reducing electricity price expenditure, and ensuring user satisfaction degree.

Multi-Objective Optimization of Time-of-Use Price for Tertiary Industry Based on Generalized Seasonal Multi- Model Structure