关键词: 电驱车间, 空调系统, 相互约束, 改进黑翅鸢与粒子群算法, 动态节能优化方法

Abstract:

Aiming at the problems of high operational energy consumption and low efficiency of the air-conditioning system in the electric drive workshop, a dynamic energy-saving optimization method considering the mutual constraints of each device of the air-conditioning system is proposed based on the IBK-IPS algorithm. Firstly, the influence mechanism between each device of the air-conditioning system is analyzed, the mathematical model of energy consumption and constraints of each device is established, and the optimization objective function of system operation energy consumption is constructed. Then, a method based on the improved black-winged kite and particle swarm (IBK-IPS) algorithm is proposed to optimise the operating parameters such as water temperature, flow rate and air volume of each device of the air-conditioning system, in order to improve the precision and effect of the control of the operating parameters of the air-conditioning system. Secondly, the Simulink platform is used to establish the energy consumption simulation model of the cooling water system and chilled water system of the air-conditioning system, and simulation experiments are carried out to verify the effect and accuracy of the optimization of the operating parameters. Finally, the method is practically applied in an electric drive room to verify the practical effect and feasibility of the proposed method. The simulation and application verification results show that: 1) the operational energy consumption of the system is effectively reduced, and the energy saving rate reaches 11.23%~34.68%; 2) the operational energy efficiency of the system is effectively optimised, and the energy efficiency is improved by 11.53%~40.78%; 3) the energy saving of the IBK-IPS algorithm is better than that of the PS, BK, and BK-PS algorithms, and the algorithm's performance indexes improve 27.27%~40.78%, respectively, compared to the remaining three algorithms by 27.27%, 61.90%, and 69.23%; 4) In real application tests, the energy saving rate of the optimised system under five different loads is 22.62%, 17.24%, 16.94%, 14.97%, and 12.64%, respectively. In summary, the energy-saving optimization method proposed in the paper can effectively solve the problems of high energy consumption and low efficiency of the operation of the air-conditioning system of the electric drive workshop air-conditioning system, which has good energy-saving effect and practicability, and can provide new ideas for the research of energy-saving optimization of air-conditioning system.

Key words: electric drive workshop, air-conditioning system, mutual constraints, improved black-winged kite and particle swarm algorithm, dynamic energy saving optimization method