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A power reduction-based demand response regulation strategy for air conditioning temperature staging is proposed to address the problems of wasteful energy use caused by users' arbitrary adjustment of air conditioning temperature setpoints and imprecise response to grid control commands on the demand side during the summer peak electricity consumption. Taking an office building VRV air conditioning as the research object, a physical simulation model of office building VRV and a mathematical model of power consumption are established and validated. A optimization model is proposed for VRV air conditioning indoor units temperature staging control based on different comfort levels and incentive tariffs, with the optimization objectives is to minimize the average deviation between the actual power and the target value of the air conditioner during the regulation period and to minimize the incentive compensation cost of the load aggregator to the user. The artificial hummingbird algorithm is selected as the optimization algorithm. To address the shortcomings of the algorithm, such as slow search speed, low accuracy of the search and easy premature convergence, the Hammersley sequence is used in the population initialization stage to generate a more uniform initial population to improve the convergence speed and accuracy of the algorithm, and the Gaussian variational operator is used in the search The Gaussian variation operator is used to perturb the hummingbird positions in the search phase to further enhance the exploration capability of the algorithm. The improved artificial hummingbird algorithm was used to solve the model and compared with the results of the artificial hummingbird algorithm, the particle swarm algorithm, the grey wolf optimization algorithm and the whale optimization algorithm, to demonstrate the effectiveness of the proposed strategy; The model is solved using Improved Artificial Hummingbird Algorithm and compared with the optimization results of four optimization algorithms, namely artificial hummingbird algorithm, particle swarm optimization algorithm, grey wolf optimization algorithm and whale optimization algorithm, to demonstrate the effectiveness of the proposed strategy. The experimental results show that the improved artificial hummingbird algorithm can improve the power regulation accuracy by up to 83.1% and reduce the incentive cost by 8.36% while ensuring user comfort.