A multi-strategy improved sparrow search algorithm of large-scale refrigeration system: Optimal loading distribution of chillers

Abstract

At present, the energy consumption of the parallel chillers system accounts for about 60% of the whole energy consumption of central air conditioning system and 25– 40% of the total energy consumption. Unreasonable load distribution brings huge energy consumption and carbon emissions during building operation. In order to reduce energy consumption and carbon emissions of the parallel chillers system in large-scale refrigeration system, we proposed an improved sparrow search algorithm (TMSSA). This algorithm initializes the population using Tent chaotic map to increase population diversity, which can enhance the possibility of TMSSA to obtain the optimal solution before the optimization iteration, and speed up the convergence process. Besides, a Levy flight mechanism is applied to improve the position update of the producer, enhancing the randomness and local search ability of this algorithm. Moreover, Gaussian mutation method is utilized to perturb the position of scroungers, strengthening the ability of the algorithm to escape local optima to improve robustness of TMSSA. To evaluate the proposed algorithm, 12 benchmark functions were chosen, and the results showed that it overcomes the limitations of traditional SSA regarding local optima trapping. The algorithm is also used to solve the optimal chiller loading (OCL) problem, which has 3 typical cases, and comparisons were made with other algorithms. The results further demonstrate that TMSSA is highly accurate, fast-converging, and robust with excellent energy-saving performance.