Multi-Objective Optimal Dispatch of Air Conditioning Loads Under Extreme Weather Conditions Based on Adaptive Hierarchical Aggregation

The increasing frequency of extreme weather events driven by climate change poses significant challenges to power system operation as air conditioning (AC) loads exhibit sharp surges during heat waves and cold spells, threatening both supply–demand balance and grid reliability. This study proposes a comprehensive framework integrating adaptive hierarchical aggregation of distributed AC loads, multi-objective Pareto optimization for demand response dispatch, and an intraday load curve optimization strategy tailored for extreme weather scenarios. The equivalent thermal parameter (ETP) model is employed to capture the thermodynamic dynamics of diverse AC populations across four industry types (residential, commercial, office, and industrial), while a four-layer aggregation architecture (industry–region–response–sensitivity) enables accurate estimation of adjustable capacity. A greedy-sampling-based multi-objective optimization method with random diversity injection was developed to construct Pareto frontiers that explicitly trade off compensation cost against user comfort loss. Subsequently, a scenario-adaptive load curve optimization strategy was designed for seven weather scenarios (ranging from normal summer to extreme heat waves and cold spells), employing differentiated peak-shaving and valley-filling policies based on natural load factor levels. Simulation results on a 2000-unit AC population over a 20 km × 20 km region demonstrate that the proposed method achieves peak load reductions of 3.6–12.2%, load factor improvements of 0.014–0.047, and total energy savings of 1.8–6.7% across all scenarios while maintaining user thermal comfort within acceptable bounds. The approach provides a quantitative tool for power system operators to manage AC loads as flexible demand-side resources under extreme climate conditions.