In order to improve the accuracy of large-scale air conditioning load aggregation and enhance the adjustable potential of air conditioning clusters

an optimization strategy for large-scale air conditioning load aggregation clusters under incentive conditions is proposed.Firstly

a large-scale air conditioning load aggregation architecture is established. Secondly

a second-order equivalent thermal parameter model for individual air conditioners is established

and the air conditioning loads in different regions are secondary aggregated based on the Monte Carlo method. Meanwhile

the relationship between user satisfaction and incentive levels is established. On this basis

optimization objective is to minimize the standard deviation between actual air conditioning aggregated power and the gap load

and to minimize the compensation cost for the air conditioning load aggregator. Particle swarm optimization algorithm is used to solve the problem. Finally

effectiveness of the proposed strategy is demonstrated through numerical examples.