DMPC Cooling Strategy with Demand Response for Multi-Zone Air Conditioning Systems Based on Improved Dung Beetle Algorithm
To address the issue of power grid shortage caused by the peak load of building air conditioning systems during the peak period of the summer,a demand response-based distributed model predictive control(DMPC)cooling strategy for multi-zone systems was proposed.A physical model and a mathematical model of energy consumption for the air conditioning systems were established in five zones of an office building in Xi'an,and the accuracy of the models was verified.A simulation model for muti-zone air conditioning system was constructed,the optimization objective was to minimize the operational energy consumption of air conditioning systems and minimize the error between the room temperature and the set value.The dung beetle algorithm was selected for optimization,overco-ming the shortages of its slow global search,premature convergence,and susceptibility to local optima,the chaotic mapping strategy was used to optimize the population initialization,and generate a more uniform population to improve the individual quality of the pop-ulation.helical search strategies for foraging and breeding behaviors were updated to further expand the global search of the algo-rithm,and the random perturbations with adaptive factors were introduced to improve the exploratory behavior and susceptibility to local optima.The dung beetle algorithm improved the rolling optimization of the DMPC,and compared with the PID control method with temperature feedback,the control performance of the DMPC was validated.Experimental results show that the response speeds of the DMPC are increased by 8.91,8.65,12.04,5.79,and 1.79％than that of the PID method in the respective zones,respective-ly.Additionally,combined with the demand response strategies,electricity prices are regulated time of use,which proposes the tem-perature and start-stop optimization strategies to shift peak loads of the air conditioning systems,the results indicate that the peak load transfer rates of the two pre cooling start-stop optimization strategies are increased by 27.29％and 29.16％,respectively,effec-tively redistributing peak cooling loads to off-peak periods and alleviating pressure in the power grid.
demand responsepeak shaving or load shiftingdistributed model predictive controlimproved dung beetle optimi-zation algorithmenergy saving optimization
万方数据
维普
