基于HOA的建筑集中空调系统冷却塔出水温度优化控制研究
Research on optimization control of cooling tower outlet water temperature in a building centralized air conditioning system based on HOA
摘要：
为解决建筑集中空调系统冷却塔出水温度控制设定值的优化问题,基于建筑围护结构与集中空调系统的耦合计算方法,开展了建筑集中空调系统的运行模拟。针对系统运行中冷却塔出水温度控制设定值对冷水机组及冷却塔能耗产生不同的影响趋势,提出了采用一种基于种群的新型算法——河马优化算法(HOA)来实现能耗最小目标下的冷却塔出水温度控制设定值优化。以某办公建筑为例,通过动态模拟及优化,比较了3种控制方案不同冷却塔出水温度控制设定值对冷水机组和冷却塔运行能耗的影响。结果显示:采用HOA的方案可获得优化的冷却塔出水温度控制设定值,设定值下可实现冷水机组和冷却塔运行能耗最低;相较于其他算法,HOA寻优过程性能表现更为优秀,优化后的适应度值最小,迭代次数为5次时函数即可收敛,在MATLAB运行时仅需2.10 s即可完成1个时刻的优化;相较于常见的非控制、非优化工况,其典型日内最大节能率为3.49%,日均节能率为3.05%。
Abstract：
To solve the optimization problem of the control setting values of the cooling tower outlet water temperature in a building centralized air conditioning system, the operation simulation of the building centralized air conditioning system is carried out based on the coupling calculation method of the building envelope and centralized air conditioning system. Given the different influencing trends of the control setting values of the cooling tower outlet water temperature during system operation on the chiller and cooling tower energy consumption, a new population-based algorithm, the hippopotamus optimization algorithm (HOA) is proposed to optimize the control setting values of the cooling tower outlet water temperature under the goal of minimum energy consumption. Taking an office building as an example, the effects from the different control setting values of the cooling tower outlet water temperature in three control schemes on the operation energy consumption of the chillers and cooling towers are compared through dynamic simulation and optimization. The results show that the optimized control setting values of the cooling tower outlet water temperature can be obtained by using the HOA scheme, and the lowest operation energy consumption of the chiller and cooling tower can be achieved under the setting value. Compared with other algorithms, the performance of the HOA optimization process is better, the optimized fitness value is the smallest, the function converges when the number of iterations is 5 times, and the optimization of 1 moment can be completed in only 2.10 s when MATLAB is running. Compared with the common non-controlled and non-optimized working conditions, the typical daily maximum energy-saving rate is 3.49%, and the average daily energy-saving rate is 3.05%.
Keywords:hippopotamus optimization algorithm (HOA); centralized air conditioning system; cooling tower; outlet water temperature; optimization control; dynamic simulation; energy-saving rate