scholarly journals Research on Optimal Control Strategy of Ice Storage Air Conditioning System

Author(s):  
Shi-ran LIU ◽  
Zi-xuan LIU ◽  
Chong-ming LIU
2013 ◽  
Vol 671-674 ◽  
pp. 2515-2519
Author(s):  
Xue Mei Wang ◽  
Zhen Hai Wang ◽  
Xing Long Wu

This project aims to study the optimal control model of the ice-storage system which is theoretically close to the optimal control and also applicable to actual engineering. Using Energy Plus, the energy consumption simulation software, and the simple solution method of optimal control, researchers can analyze and compare the annual operation costs of the ice-storage air-conditioning system of a project in Beijing under different control strategies. Researchers obtained the power rates of the air-conditioning system in the office building under the conditions of chiller-priority and optimal contro1 throughout the cooling season. Through analysis and comparison, they find that after the implementation of optimal control, the annually saved power bills mainly result from non-design conditions, especially in the transitional seasons.


2013 ◽  
Vol 655-657 ◽  
pp. 1492-1495
Author(s):  
Ting Wu ◽  
Gang Wu ◽  
Zhe Jing Bao ◽  
Wen Jun Yan

Ice storage air-conditioning system can bring benefits to power supplier and consumers for its advantage of shifting power consumption at peak hours during day to the off-peak hours at night. In this paper, we adopted an improved particle swarm optimization algorithm to develop an optimal control strategy for ice storage air-conditioning system with the aim of minimizing operation cost subject to various operational constrains. Comparing with the usual chiller-priority and ice-storage-priority control strategy, the proposed control scheme can not only meet the building cooling load but also achieve the minimum operation cost.


2019 ◽  
Vol 11 (18) ◽  
pp. 5122 ◽  
Author(s):  
Nam-Chul Seong ◽  
Jee-Heon Kim ◽  
Wonchang Choi

This study is aimed at developing a real-time optimal control strategy for variable air volume (VAV) air-conditioning in a heating, ventilation, and air-conditioning (HVAC) system using genetic algorithms and a simulated large-scale office building. The two selected control variables are the settings for the supply air temperature and the duct static pressure to provide optimal control for the VAV air-conditioning system. Genetic algorithms were employed to calculate the optimal control settings for each control variable. The proposed optimal control conditions were evaluated according to the total energy consumption of the HVAC system based on its component parts (fan, chiller, and cold-water pump). The results confirm that the supply air temperature and duct static pressure change according to the cooling load of the simulated building. Using the proposed optimal control variables, the total energy consumption of the building was reduced up to 5.72% compared to under ‘normal’ settings and conditions.


Author(s):  
Hongpu Liang

Abstract For areas with hot summer and cold winter, air conditioning is an essential tool to improve the living environment, but the traditional air conditioning needs to consume a lot of energy in cooling and heating, the fan operation noise is large and the sense of blowing will be uncomfortable. This paper briefly introduced the basic structure of a ground-source heat pump-floor radiant air conditioning system and the control strategy which was used for regulating the operation of the air conditioning system. Then, in order to ensure the appropriate comfort and reduce energy consumption, the control strategy was optimized. An experimental room in Xining, Qinghai province, was taken as an example for analysis. The results showed that the thermal comfort under the control strategy proposed in this study was more stable at the moderate degree and the temperature was slightly higher under the traditional control strategy, although the indoor temperature fluctuation under the optimal control strategy was large and the temperature was slightly lower than that under the traditional control strategy; under the control strategy proposed in this study, the air conditioning system had shorter operation time and less power consumption.


1998 ◽  
Vol 120 (4) ◽  
pp. 275-281 ◽  
Author(s):  
G. P. Henze ◽  
M. Krarti

Ice storage systems have the reputation of saving cost for operating building cooling plants by appropriately recognizing time-of-use incentives in the utility rate structure. However, many systems can consume more electrical energy than a conventional cooling plant without ice storage. This excess energy problem is illustrated in this paper by a simplified cooling plant model employed in a simulation environment that allows the assessment of the control performance of various conventional and optimal strategies. The optimal control strategy of minimizing operating cost only is introduced and subsequently is modified to allow the simultaneous consideration of operating cost and energy consumption. This proposed optimal control strategy could be valuable if ice storage systems are to stand on their own merits in a deregulated utility environment. Due to the lack of demand charges under real-time pricing, even small energy penalties and their associated excess energy cost may jeopardize the feasibility of the ice storage system.


2011 ◽  
Vol 20 (6) ◽  
pp. 626-637 ◽  
Author(s):  
Zhongwei Sun ◽  
Shengwei Wang ◽  
Na Zhu

This paper presents a model-based outdoor air flow rate optimal control strategy for multi-zone variable air volume air-conditioning systems with the primary air-handling units. An adaptive optimisation algorithm is adopted for optimising the set point of the outdoor air flow rate to minimise the energy cost, which could compromise the energy consumption of the primary fan and the cooling energy saving by the cold outdoor air. The primary fan energy consumption can be predicted using a simplified incremental fan model and the main parameters of this model are identified online. The cooling energy saving by the outdoor air is estimated online using the enthalpies of the air streams. The lower limit of the outdoor air flow rate is determined by a CO2-based adaptive demand-controlled ventilation strategy using the dynamic multi-space equation to maintain the satisfied indoor air quality (IAQ). Tests were conducted to evaluate the performance of the control strategy applied to a practical building system in simulation environment. The results show that the proposed optimal control strategy can reduce energy consumption significantly, while maintaining a satisfactory IAQ.


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