Ice Storage Air-conditioning System Research Articles

Case study of variable speed photovoltaic direct-driven ice-storage air conditioning system in Wuhu

In order to meet the contradiction between the growing demand for refrigeration and energy scarcity, this paper proposes a novel photovoltaic ice storage air conditioning system. The system is directly driven by a photovoltaic array. The performance of the system was studied through experiments, and a mathematical model was constructed for comparative verification. The experimental results show that the daytime cooling COP of the system is 1.18, the photovoltaic conversion efficiency is 6.29 %, and the nighttime cooling time is 9 h, which basically meets the cooling needs. Through simulation, the performance of the system was optimized, and the results showed that an increase in the initial water volume and temperature of the ice storage tank would increase the cooling COP; the spacing between evaporator pipes increased from 45 mm to 60 mm, resulting in a 7.11% increase in COP; the optimal spacing between evaporator fins is 9 mm, which improves performance by 9.11 %.

Load Forecasting and Operation Optimization of Ice-Storage Air Conditioners Based on Improved Deep-Belief Network

The prediction of cold load in ice-storage air conditioning systems plays a pivotal role in optimizing air conditioning operations, significantly contributing to the equilibrium of regional electricity supply and demand, mitigating power grid stress, and curtailing energy consumption in power grids. Addressing the issues of minimal correlation between input and output data and the suboptimal prediction accuracy inherent in traditional deep-belief neural-network models, this study introduces an enhanced deep-belief neural-network combination prediction model. This model is refined through an advanced genetic algorithm in conjunction with the “Statistical Products and Services Solution” version 25.0 software, aiming to augment the precision of ice-storage air conditioning load predictions. Initially, the input data undergo processing via the “Statistical Products and Services Solution” software, which facilitates the exclusion of samples exhibiting low coupling. Subsequently, the improved genetic algorithm implements adaptive adjustments to surmount the challenge of random weight parameter initialization prevalent in traditional deep-belief networks. Consequently, an optimized deep-belief neural-network load prediction model, predicated on the enhanced genetic algorithm, is established and subjected to training. Ultimately, the model undergoes simulation validation across three critical dimensions: operational performance, prediction evaluation indices, and operating costs of ice-storage air conditioners. The results indicate that, compared to existing methods for predicting the cooling load of ice-storage air conditioning, the proposed model achieves a prediction accuracy of 96.52%. It also shows an average improvement of 14.12% in computational performance and a 14.32% reduction in model energy consumption. The prediction outcomes align with the actual cooling-load variation patterns. Furthermore, the daily operational cost of ice-storage air conditioning, derived from the predicted cooling-load data, has an error margin of only 2.36%. This contributes to the optimization of ice-storage air conditioning operations.

Model predictive control for the ice-storage air-conditioning system coupled with multi-objective optimization

Optimal operation of ice-storage air conditioning (IAC) system is beneficial to balance the power grid pressure, enhance load flexibility and reduce system operating costs. Conventional control methods, like fixed scheduling and storage priority, are insufficient for dynamically regulating the IAC system in response to real-time variations in building load. This results in low system operation efficiency and the inability to fully utilize the ice storage device to achieve economic and energy-saving benefits. This study proposes a novel method coupling multi-objective optimization with model predictive control (MPC), which can effectively determine the optimal operation strategy and achieve precise control of IAC system. At the strategy level, a multi-objective global optimization model is established, considering both cost and energy efficiency. This model enables the determination of the optimal load distribution of the chiller and ice storage, which serves as the reference trajectory for MPC. At the control level, an MPC controller is established to achieve accurate tracking of the reference trajectory. This controller uses an artificial neural network as the prediction model and employs the particle swarm algorithm as the optimization solver. It also incorporates continuous feedback correction based on load changes. The results indicate that the optimal operation strategy obtained using multi-objective optimization can theoretically reduce energy consumption by 25% and operating cost by 20.9% compared with the storage priority control. The MPC can exactly track the reference trajectory of the ideal operation strategy, with an energy consumption error of 0.2% and an operating cost error of 0.4%, which precisely regulates the optimal operation strategy and generally improves the economic and energy saving benefits of the IAC system.

The Optimal Daily Dispatch of Ice-Storage Air-Conditioning Systems

In this paper, the Ant-based Radial Basis Function Network (ARBFN) is proposed to determine the optimal daily dispatch of ice-storage air-conditioning systems. ARBFN is a novel algorithm that is integrated into the Ant Colony Optimization and Radial Basis Function Network. ARBFN is used to construct the function of the cost and operation for each chiller and ice-storage tank and is used to simulate the polynomial function of the cooling load and the cost of power consumption. The best learning rate in the training process is adjusted in ARBFN to improve the accuracy of constructing models for chillers and ice-storage tanks. The electricity savings are thus 4.130% on a summer day and 7.381% on a non-summer day. The results have shown that ARBFN can more accurately calculate the actual power consumption and cooling capability of each chiller and ice-storage tank. Lastly, ACO is used to calculate the daily dispatch of the ice-storage air-conditioning system. The results demonstrated the optimization of energy savings and efficiency for the operation of the ice-storage air-conditioning system.

Study on the Dynamic Generation of Subcooled Water Using a Compact Heat Exchanger

The dynamic generation of ice slurry from subcooled water is one of the most promising ways to make ice; this process is utilized widely in ice storage air-conditioning systems. However, the random occurrence of ice blockage during the generation of subcooled water using conventional heat exchangers prevents the increase in subcooling, thereby reducing the efficiency of the release of the subcooled water and converting it into ice slurry. A more efficient approach to reducing the fluid passage time is to employ a compact heat exchanger with a highly efficient heat transfer performance, a heat transfer length of only 21.5 mm, and a hydraulic diameter of 0.32 mm. A compact heat exchanger was used to build a dynamic generation setup for subcooled water, and 40 wt% of non-freezing liquid and tap water was used as the working fluid for heat exchange to generate subcooled water. The results show that the compact heat exchanger can achieve a greater subcooling degree (3.8 K) and longer duration (108 min). This study further explored the potential for dynamic ice making from deep, subcooled water and improved the overall structure of the compact heat exchanger used. The experimental setup is recommended based on the analysis of the results.

Flow Characteristics and Heat-Transfer Enhancement of Air Agitation in Ice Storage Air Conditioning Systems

A large number of bubbles generated by the air agitation device in an external melting ice storage system can cause the disturbance of the ice–water mixture, which can enhance the heat transfer and contribute to the reduction in energy consumption. The structural design and optimization of the air agitation device in an external melting ice storage system is the key issue for energy savings. In this study, the influence of different orifice spacings and diameters on the distribution of the gas–liquid flow field, gas holdup, heat-transfer coefficient, and power consumption in the ice storage tank was investigated by numerical simulation. The simulated results showed that the heat-transfer coefficient of the ice–water mixture with air bubbles should be 3–5 times higher than the natural convection when the air superficial velocity is 0.03 m/s. The gas holdup was mainly affected by the orifice spacing, and the maximum varied from 5.0% to 8.2%. When the orifice spacing was less than 150 mm, the gas holdup changed a little in the horizontal direction, and the uniformity became worse when the orifice spacing was larger than 180 mm. An orifice diameter larger than 3 mm can improve the heat transfer and cause less air-compressing energy consumption, which decreased by approximately 1.62%.

Development of Fault Diagnosing System for Ice-Storage Air-Conditioning Systems

This paper proposes a fault diagnosing system for the Ice-Storage Air-Conditioning System (ISACS) to supervise the operation conditions of the brine chillers. Combining the Radial Basis Function Network (RBFN) and Robust Quality Design (RQD), an Enhanced RBFN (ERBFN) is proposed to pursue fast and accurate fault diagnosis. The RQD method is used to adjust the parameters in the RBFN training stage to improve the searching ability, and good performance with a close spike tracking capability can be seen. The efficiency of the brine chiller in the ISACS was considered as the quality characteristics, the values measured by all instruments were considered as control factors, and noise factors were abnormal variable control factors in the system. ERBFN can improve the efficiency of the ISACS and prevent the equipment from being damaged without warning. ERBFN is used for fault diagnosis to ensure the ISACS performance is normal. Experimental results are provided to show the effectiveness of the proposed method. The new artificial neural network algorithm proposed in this paper was successfully applied to the fault diagnosis of ISACS. It not only provides a reference for enterprises but can also be applied to studies on other topics in the future.

A multi-objective optimization operation strategy for ice-storage air-conditioning system based on improved firefly algorithm

Reasonable distribution of cooling load between chiller and ice tank is the key to realize the economical and energy-saving operation of ice-storage air-conditioning (ISAC) system. A multi-objective optimization model based on improved firefly algorithm (IFA) was established in this study to fully exploit the energy-saving potential and economic benefit of the ISAC system. The proposed model took the partial load rate of each chiller and the cooling ratio of the ice tank as optimization variables, and the lowest energy consumption loss rate and the lowest operating cost of the ISAC system were calculated. Chaotic logic self-mapping was used to initialize population to avoid falling into local optimum, and Cauchy mutation was used to increase the population’s diversity to improve the algorithm’s global search ability. The experimental results show that compared with the operation strategy based on constant proportion, particle swarm optimization (PSO) algorithm, and firefly algorithm (FA), the optimal operation strategy based on IFA can achieve more significant energy-saving and economic benefits. Meanwhile, the convergence accuracy and stability of the algorithm are significantly improved. Practical application: The optimized operation strategy of the ice-storage air-conditioning system can reduce energy loss and operating costs. The traditional operation strategies have the problems of low optimization precision and poor optimization effect. Therefore, this study presents an optimal operation strategy based on IFA. The convergence accuracy and stability of the algorithm are increased after the algorithm is improved. The operation strategy can get the maximum energy-saving effect and economic benefit of the ISAC system.

Predictive model of cooling load for ice storage air-conditioning system by using GBDT

Cooling load prediction can provide reliable data support for the development of low energy consumption building. It is difficult for traditional models to achieve stable prediction accuracy under different load mode when the amount of data is limited. To resolve this problem, gradient boosting decision tree (GBDT) model based on selection of input variables was proposed to predict the cooling load of commercial buildings. First, the input variables that affect prediction cooling load were determined through correlation analysis on the original data set. Then, the Pearson analysis was done on the original data to get the optimal combination of input variables. Next, the ensemble learning technology was used to build GBDT model. In order to improve the prediction accuracy of the model, the grid search method was applied to determine the hyperparameter combination of GBDT. Experimental results showed that in the high-load mode, the mean absolute error (MAE) of the GBDT model was reduced by about 24% compared with the SVM, and was reduced by about 37% compared with the DNN model. Under the general load mode, MAE was reduced by about 30% and 53% respectively. Compared with the other models, the prediction accuracy of GBDT model has been significantly improved under different load mode. The proposed cooling load prediction model can provide reliable data support for the predictive control of buildings.

Experimental and numerical investigation on a novel photovoltaic direct-driven ice storage air-conditioning system

In this paper, a photovoltaic direct-driven ice storage air-conditioning (PDISAC) system is proposed and performance of the system is experimentally and theoretically investigated. The proposed system is a battery or inverter less photovoltaic direct-driven system where the DC compressor is directly connected to the PV array. Through the test, it has been found that the proposed system was able to lower the test room temperature below 298.15 K, while the reference room temperature was 306.15 K. Moreover, the experimental refrigeration efficiency and solar-energy utilization efficiency were reported 1.028 and 7.1% respectively. Then, a mathematical model for the progress of ice making and cold storage is presented and verified by the experimental results. Besides, the factors such as the ambient temperature, the initial water temperature and volume in the ice storage tank, and the thickness of the ice layer surrounding the coil have been studied. The simulation results indicate that the initial heat transfer rate at the condensation side drops by 10.8% and refrigeration efficiency drops by 32.7% with the ambient temperature increasing from 298.15 K to 308.15 K. The increment of initial water temperature and initial water volume will lead to an increase in refrigerating capacity and refrigeration efficiency.

Model Predictive Control of Solar PV-Powered Ice-Storage Air-Conditioning System Considering Forecast Uncertainties

This paper proposes a dynamic programming (DP)-based stochastic model predictive control (SMPC) method for the economic operation of solar PV-powered ice-storage air-conditioning (PIAC) systems. The forecast data of PV generation and building cooling load are considered as stochastic variables in this paper. To deal with the uncertainties of the day-ahead forecast data, Latin hypercube sequential sampling, Cholesky decomposition and Simultaneous backward reduction are adopted to provide representative scenarios for SMPC. The value function matrix is employed to solve the receding-horizon optimization problem formulated by DP. With updated short-term forecast information, SMPC is able to reduce the impact of inaccurate forecasts on the operation of PIAC systems. A study of typical operation cases demonstrates the effectiveness of the proposed method, which ensures the satisfaction of cooling supply and yields solutions closer to the global optimality than the traditional MPC method.

Systematic Method for the Energy-Saving Potential Calculation of Air Conditioning Systems via Data Mining. Part II: A Detailed Case Study

Increased data monitoring enables the energy-efficient operation of air-conditioning systems via data-mining. The latter is projected to have lesser consumption but more comprehensive diagnosis than traditional methods. Following the companion paper that proposed a systematic method for energy-saving potential calculations via data-mining, this article presents a detailed case study in an ice-storage air-conditioning system by employing the proposed method. Raw data were preprocessed prior to recognizing the constant- and variable-speed devices in the system. Classification and regression tree algorithms were utilized to identify the operating modes of the system. The regression models between the energy-consumption and operating-state parameters of the nine pumps and two chillers were fitted. Furthermore, the constraints pertaining to system operation were summarized. From the results, the particle swarm optimization method was applied to elucidate the benchmark energy cost and the consequent cost savings potential. The cost savings potential for the chiller plant room during the investigation duration of 59 d reached as high as 24.03%. The case study demonstrates the feasibility, effectiveness, and stability of the systematic approach. Further studies can facilitate the development of corresponding control strategies based on the potential analysis results, to investigate better optimization algorithm, and visualize the analysis process.

A demand-side integrated flexible load regulation optimization strategy for clean energy consumption

In order to absorb large-scale clean energy for power grid, the use of energy storage technology is paid more and more attention. However, most of the current researches are focused on a single kind of energy storage forms, and few are involved in the joint use of multiple energy storage devices. This paper aims at the insufficient application of a single energy storage device and the conflict between multiple energy storage technologies. A demand-side integrated flexible load regulation optimization method is proposed. For the heat storage electric boiler system and the ice storage air conditioning system, the combined algorithm is used to optimize their operation. It is clear that the combined algorithm based on genetic algorithm combined with cold, hot and electrical system has a more significant effect of absorbing clean energy than the single energy storage system. And, the example result proves the correctness and feasibility of the proposed comprehensive demand side response strategy.

A multi-objective operation strategy optimization for ice storage systems based on decentralized control structure

Improving the operational efficiency of chillers and science-based planning the cooling load distribution between the chillers and ice tank are core issues to achieve low-cost and energy-saving operations of ice storage air-conditioning systems. In view of the problems existing in centralized control architecture applied in heating, ventilation, and air conditioning, a distributed multi-objective particle swarm optimization improved by differential evolution algorithm based on a decentralized control structure was proposed. The energy consumption, operating cost, and energy loss were taken as the objectives to solve the chiller’s hourly partial load ratio and the cooling ratio of ice tank. A large-scale shopping mall in Xi’an was used as a case study. The results show that the proposed algorithm was efficient and provided significantly higher energy-savings than the traditional control strategy and particle swarm optimization algorithm, which has the advantages of good convergence, high stability, strong robustness, and high accuracy. Practical application: The end equipment of the electromechanical system is the basic component through the building operation. Based on this characteristic, taken electromechanical equipment as the computing unit, this paper proposes a distributed multi-objective optimization control strategy. In order to fully explore the economic and energy-saving effect of ice storage system, the optimization algorithm solves the chillers operation status and the load distribution. The improved optimization algorithm ensures the diversity of particles, gains fast optimization speed and higher accuracy, and also provides a better economic and energy-saving operation strategy for ice storage air-conditioning projects.

Analysis on the Operation of Ice Storage Air Conditioning System in Practical Engineering

Cold storage technology is one of the effective means to alleviate the imbalance of power load during peak and valley period. Based on a practical project, the operation of the ice storage air conditioning system was monitored for 26 days. Temperature, pressure, ice storage and other parameters were recorded. Through the analysis of the operation data and air temperature, it is found that the operation of the ice storage system is stable and consistent with the design condition. But there are still some shortcomings in the operating strategy. This paper provides some suggestions for the design and operation of air conditioning system in future projects.

Self-Learning Optimal Control for Ice-Storage Air Conditioning Systems via Data-Based Adaptive Dynamic Programming

In this article, the optimal control scheme for ice-storage air conditioning (IAC) system is solved via a data-based adaptive dynamic programming (ADP) method. It is the first time that ADP is employed to design a self-learning scheme, which obtains the optimal control policy of IAC system. First, based on the data of the temperature, irradiance, and cooling load in an actual project, a prediction model of cooling load is built by a three-layer neural network with the performance verification. Second, the operation of the IAC system is analyzed. Third, a data-based ADP method is designed to realize a self-learning optimal control for the IAC system. Then, numerical results show that using the data-based optimal control method can reduce the operation costs. Finally, the comparison results show that the developed ADP method improves the system efficiency, minimizing the overall cost. Thus, the superiority of the developed algorithm is verified.

An economic evaluation of refrigeration and air-conditioning system retrofit in large-scale public buildings: A case study of Beijing Capital International Airport Terminals

Given users’ increasing demand for thermal comfort in large-scale public buildings, energy consumption of the refrigeration and air-conditioning system continually increases. There is a strong need for implementing Refrigeration and Air-Conditioning System Retrofit (RACSR) in order to achieve better energy performance and carbon emission reductions. Despite the importance of adopting RACSR, it remains uncertain whether various RACSR schemes demonstrate cost effectiveness to attract investors. This paper establishes a methodological framework to carry out an economic evaluation of RACSR projects. By conducting a case study of the Beijing Capital International Airport Terminals retrofit project, this research finds that: (1) Both the Conventional Electrical Air-Conditioning System (CEACS) and the Ice Storage Air-Conditioning System (ISACS) retrofit schemes exhibit favorable environmental performance and cost effectiveness; (2) Although the CEACS scheme achieves higher energy savings, its economic performance is inferior than that of the ISACS scheme; (3) Upfront capital investment is the most sensitive factor affecting the economic viability; (4) The energy saving effects of retrofit schemes seem not to be obviously affected by climate demarcation, but the economic performance of the ISACS increases from cold to warm climate zones; (5) The current economic subsidy policies are too favorable, thus once the government adjusts the subsidy level according to the theoretical environmental benefit, it will largely affect the interests of investors; (6) If the ISACS scheme is adopted, the peak electricity demand will be reduced and electricity usage in off-peak period will increase, which would benefit the stability and reliability of wider electricity networks. This paper offers a useful template to conduct an economic evaluation of RACSR schemes through comprehensive consideration of life-cycle costs and benefits. It offers important recommendations for policy makers and building owners to develop incentive policies and management interventions to encourage the implementation of RACSR.

The heat transfer enhancement of low temperature airflow foam effect in the ice storage charging process

Phase change cold storage, as an energy-saving application, was widely used in central air-conditioning system of commercial building to fit the intermittent and the changing cooling load. Ice storage with different container structures was developed, but the low charging efficiency and non-linear energy storage rate were difficult to match the dynamic change of cooling load and the energy-saving strategy of the chiller system, and increased the energy consumption and the complexity of the ice storage air-conditioning system control. Low temperature airflow (LTA) was introduced into water in the low temperature environment. Gas, liquid and solid coexisted in the phase transformation process, and the process of heat and mass transfer underwent complex changes. The effects of these changes on phase transformation were studied by experiment. It was found that porous frozen body was generated with a large number of microchannels inside. The porous structure dispersed the airflow into isolated bubbles, and foam generated. Foam enlarged the heat transfer area and prolonged the contact time between LTA and phase change material (PCM), sharply improving the heat transfer efficiency, and strengthening the heat transfer between PCM and its geometry boundary. When the air flow rate was 25 L/min, the freezing rate increased by 4 times than that of freezing without LTA. This study concluded that it was simple, practical and efficient to introduce LTA into PCM considering the heat transfer enhancement of the foam effect. It will have a prosperous application in ice storage system.

Research on Optimal Control Strategy of Ice Storage Air Conditioning System

Ice storage air conditioning technology is a very valuable power demand side management technology. This paper establishes the goal of minimizing the operating costs of ice storage air conditioning system for the whole day. The cooling capacity of air-conditioning refrigeration and ice melting refrigeration in ice storage air conditioning systems and the total cooling capacity requirement of ice storage air conditioning systems are limited to the optimization of constraints in model. The paper applies trust region algorithm to solve the established optimization model, and uses MatLab to program. The simulation results of the example verify the correctness of the proposed model and the programming procedure.

Experimental Evaluation of Fundamental and Phase Change Characteristics for Carbon-Based Suspensions

In this study, carbon-based materials prepared using an isoperibol oxygen bomb calorimeter of varying oxygen pressure were added to water to form carbon-based suspensions (CBSs). The thermal conductivity, viscosity, density, and contact angle of CBSs were measured by appropriate instruments to understand the fundamental characteristics of CBSs. A differential scanning calorimeter (DSC) was used to measure and analyze the phase change characteristics of CBSs to evaluate the feasibility of employing CBSs as phase change materials (PCMs) in ice-storage air-conditioning systems. The experimental results revealed that the thermal conductivity, viscosity, and density of CBSs did not significantly change but a significant reduction in the contact angle of sample’s droplets occurred compared with that of water. The CBS of S5 had the lowest melting temperature and subcooling degree (SD) and highest freezing temperature in the DSC experiments; thus, S5 was determined to be the most suitable CBS for use as a PCM in this study.