Research On Demand Response Regulation Strategy Of Multi-Connected Air Conditioners Based On Temperature Staging Optimization

In the summer peak period,the large-scale use of air-conditioning equipment and the user’s arbitrary adjustment of the air-conditioning temperature setpoint will aggravate the building energy consumption,and also increase the burden on the electric power system,which can threaten the safety of the power grid if seriously.Therefore,it is necessary for the air conditioning equipment in the building to be centrally regulated during the peak load period in summer.Demand response is an effective load regulation tool that plays a very important role in promoting the sound operation of the power system.In this paper,a Variable refrigerant volume(VRV)air conditioning system in an office building in Xi’an’s high-tech zone is used as the research object,and the following research points are made with regard to the regulation methods and strategies for air conditioning participation in demand response during the peak electricity consumption period in summer.(1)Based on the building HVAC blueprints and relevant parameter data obtained,an Energy Plus physical simulation model of the VRV air-conditioning system in the office building was established and the accuracy of the model was verified through the actual energy consumption data collected from the units.Based on the First-order equivalent thermal parameter model for air conditioners and the air conditioning energy efficiency model,the mathematical model of the VRV air conditioning power consumption was derived by least squares identification based on the simulated operating data from the simulation model.(2)Study and analyze the influence of indoor temperature setpoints on air conditioning energy consumption and human comfort through simulation.A multiobjective optimization model based on dynamic indoor temperature settings in multiple zones of the building is established with energy consumption and comfort as the objective functions and temperature setpoints in different zones of the building as the decision variables,and the model is optimally solved by using Energy Plus and j EPlus+EA combined simulation to derive the optimal indoor temperature settings for each room to meet the optimal energy consumption and comfort.(3)A multi-objective optimization(ITSC-MOO)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 of the air conditioner and the target value of the regulation during the regulation period and to minimize the incentive compensation cost of the load aggregator to the user.To address the problems of poor convergence performance and accuracy in solving practical engineering problems,a hybrid Hammersley sequence initialization and Gaussian variation perturbation of artificial hummingbird algorithm(HAGSAHA)is proposed.The optimization performance of the improved algorithm is tested using Benchmarking functions,and it is proved that it has good convergence performance and optimization accuracy.The ITSC-MOO model is solved using HAGSAHA and compared with the optimization results of four optimization algorithms,namely artificial hummingbird algorithm(AHA),particle swarm optimization algorithm(PSO),grey wolf optimization algorithm(GWO)and whale optimization algorithm(WOA),to demonstrate the effectiveness of the proposed strategy.The results of the study show that the use of dynamic regulation of air conditioning temperature set values can reduce the energy consumption of the building VRV air conditioning system by an average of 5.14% for all hours of operation throughout the day,while ensuring the comfort of indoor occupants.The proposed demand response regulation strategy for VRV air conditioners based on the improved artificial hummingbird algorithm improves the control accuracy by up to 83.1%,54.3% and 66.3%respectively compared to other algorithms under three power reduction commands,and reduces the average incentive cost by up to 8.36%,while ensuring that the comfort of users is within the reasonable range during the regulation period.