رویکرد بهینه سازی شبیه سازی برای بهره وری انرژی سیستم های آب سرد

This study combines an energy simulation program with a hybrid optimization algorithm to identify the optimal settings and minimize the energy consumption of chilled water system. This study employed EnergyPlus, a flexible and highly accurate energy simulation program, to construct the model. To determine the optimal temperature settings for chilled and cooled water for chilled water system, this model used a hybrid optimization algorithm that combines the particle swarm optimization algorithm and the Hooke–Jeeves algorithm. We selected four days in summer and four days in winter to conduct the optimization. The results indicated that the optimized settings reduced the total energy consumed by the chilled water system by 9.4% in summer and 11.1% in winter compared to conventional settings.

After an air conditioning system is selected, enabling the system to function at the optimum level is crucial. A highly-efficient system that cannot conserve energy, despite being designed to, is useless. Ensuring the system functions at the optimal level requires not only fault detection and diagnosis, but also system optimization for energy efficiency. Numerous studies have been conducted regarding the optimization of air conditioning systems. Chang et al. [1] and [2] proposed a new energy conservation method called optimal chiller loading (OCL) and used the Lagrangian method for optimization. However, their study showed that this optimization method could not converge at low loads; thus, they employed a genetic algorithm (GA) to resolve the issue. Lee et al. [3] and [4] showed that the particle swarm optimization (PSO) algorithm and differential evolution algorithm were more effective for OCL. Lu et al. [5] used a modified GA to identify the optimal air and water loop pressure settings for heating, ventilation, and air-conditioning systems. Fong et al. [6] and [7] used a simulation program to construct a model for optimizing the supply air temperature set-point and supply water temperature set-point of an air-conditioning system. Evolutionary programming (EP) was then used to optimize the settings, after which a robust evolutionary algorithm was employed. The results showed that using this method facilitated a superior optimal solution and greater efficiency compared to that of EP and GA. Ma and Wang [8] used an optimal control strategy to optimize the chilled water supply temperature set-point and the critical loop differential pressure set-points for a chilled water system. Kusiak et al. [9] adopted a data mining method to establish an energy consumption model and used the PSO algorithm to optimize the supply air temperature and supply air static pressure control settings. This study collected information on the supervisory control and data acquisition (SCADA) system for an office building, used the energy simulation program EnergyPlus [10] to establish a model, and combined optimization algorithms to optimize the supply temperature of chilled and cooling water in the chilled water system of an office building. The PSO [11] and Hooke–Jeeves [12] algorithms were combined to identify the optimal and recommended settings for various cooling loads.

The proposed optimization method in this study to a chilled water system was verified that it could efficiently optimize the results. Because the chillers consume the greatest proportion of energy in the chilled water system, the optimized results indicated that optimal chilled and cooling water temperature set-points can significantly reduce the energy consumed by water chillers. However, because the optimal temperature for chilled water is higher than conventionally set, the pump must supply more water, consuming more energy. Additionally, because the optimal temperature for cooling water is lower than conventionally set, the cooling towers must supply more air, consuming more energy. The results of this study show that the simulated energy consumption of the actual operation model of the chilled water system used in this study is already superior to that of the conventional operational model. However, there is still room for energy conservation. If the temperature set-points obtained by optimization can be used during actual operations, even more energy can be conserved. Compared to constant temperature set-point optimization, this study conserved energy by optimizing the temperature set-point of chilled water hourly. Although the difference is not significant, it is still an improvement in energy conservation.