Optimal chiller loading considering the energy loss associated with the switching of chillers based on a novel hybrid fuzzy-metaheuristic method

Abstract

In existing research, the energy loss associated with the switching of chillers has not been taken into account when addressing the traditional optimal chiller loading (tra-OCL) problem. Chillers incur a certain amount of energy loss during the switching process in practical operations. Hence, it is crucial to investigate the optimal chiller loading problem considering the energy loss associated with the switching of chillers (loss-OCL). In this study, we propose a novel parameter adaptation imperialist competitive algorithm based on parallel fuzzy inference systems (PA-ICA-PF) to solve the loss-OCL problem. Three operators—velocity adaptation, velocity divergence, and mutation-crossover—are introduced to enhance assimilation, revolution, and imperialist competition stages. Two fuzzy inference systems are utilized to concurrently guide searches in the solution space, ensuring a balance between diversification and intensification. The proposed method was experimentally verified in an actual multi-chiller system. Experimental results indicate that the total energy consumption of the system, when addressing the loss-OCL, is significantly lower than the actual value but slightly higher than the tra-OCL. This suggests that the proposed method is capable of obtaining the optimal partial load ratio of chillers while accounting for energy losses. To further validate the effectiveness of the proposed algorithm, we compared it with existing algorithms. Experimental results demonstrate that the proposed PA-ICA-PF method exhibits the best performance.