Synchronized optimization of chilled water temperature in compression refrigeration system based on PII-AWOA

The widespread adoption of air conditioning systems has heightened concerns over energy consumption. Despite extensive research on optimizing compression refrigeration systems, achieving global optimization remains challenging. This paper addresses this issue by proposing a novel optimization strategy for vapor compression refrigeration systems. Central to this strategy is the optimization of chilled water temperature, a critical factor influencing overall system efficiency. This variable is synchronized with evaporating pressure (P_e) and condensing pressure (P_c) to achieve simultaneous optimal settings. Simulation results underscore the efficacy of this approach in significantly reducing energy consumption during steady-state operation. Moreover, an Adaptive Whale Optimization Algorithm based on Population Information Interactions (PII-AWOA) is developed to enhance optimization performance. Firstly, a population information utilization strategy is designed to improve the selection of optimal individuals in the original algorithm. In addition, adaptive measures are introduced to equalize the exploration and exploitation capabilities of the algorithm. Finally, individual neighborhoods are divided within the population, enabling ordinary individuals to utilize the information of others in their neighborhood, thereby realizing inter-individual information interaction. Experimental results indicate that the PII-AWOA algorithm demonstrates superior precision in convergence on multiple test functions and effectively mitigates the risk of local minima. Comparative analyses with the ZOA algorithm highlight the superior convergence speed and enhanced energy-saving capabilities of the proposed PII-AWOA algorithm.