Ice-storage Air-conditioning Research Articles

Optimized the Microgrid Scheduling with Ice-Storage Air-Conditioning for New Energy Consumption

In the face of the stochastic, fluctuating, and intermittent nature of the new energy output, which brings significant challenges to the safe and stable operation of the power system, it is proposed to use the ice-storage air-conditioning to participate in the microgrid optimal scheduling to improve wind and light dissipation. This paper constructs an optimal scheduling model for the ice-storage air-conditioning to participate in the microgrid, analyzes the regulation advantages of the ice-storage air-conditioning’s cold storage and cold release process and its participation in the scheduling process; secondly, based on the scenario method, the scenario modeling of the microgrid distributed wind and light output uncertainty and cold load uncertainty is carried out; lastly, an optimal scheduling model is constructed to minimize the operation and maintenance cost of each unit and the storage cost of the ice-storage air-conditioning, the highest rate of clean energy generation and the lowest cost of electricity consumption of the air-conditioning users as the objective function. The example simulations show that the proposed optimal scheduling model can promote the new energy consumption rate of the microgrid, proving that the ice-storage air-conditioning is more economical compared with ordinary air-conditioning and that the operating cost within the optimized microgrid is reduced by about 10.5%. The cost of the air-conditioning users’ electricity consumption has been reduced by about 11.7% after responding to the regulation.

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.

Experimental and numerical study of ice storage and melting process of external melting ice coil

Ice storage air conditioning technology could achieve “peak cut” by storing ice during the valley period, melting ice during the peak period to achieve the role of peak load regulation. At the same time, it can promote consumption of renewable energy. This study deals with the experimental and numerical analysis of the solidification and melting processes of coiled ice storage. From the experimental observation, it was observed that the outer surface of the lower side of the coil was the first to form an ice layer. Then the outer surface of the upper side begins to form an ice layer, and the growth rate of the ice layer is faster than that of the lower side. The average growth rates of the ice layer on the upper and lower side are 0.112 mm/min and 0.079 mm/min, respectively. The density variation of water and natural convection were considered in this analysis. The numerical simulation results well match the phenomena and results of experimental research. The influence of different directions of cooling water inlet on the melting rate was also compared in this paper. Under the same initial conditions and boundary conditions, the melting process in the Mode 1 (along the coil) cools down faster; The ice melting process in Mode 2 (perpendicular to the coil) can provide more stable cooling, and the cooling duration is 130 s longer than that in Mode 1.

Experimental and Numerical Study of the Ice Storage Process and Material Properties of Ice Storage Coils

The coiled ice-storage-based air conditioning system plays a significant role in enhancing grid peak regulation and improving cooling economy. This paper presents theoretical and experimental studies conducted on the ice storage process of coiled ice storage air conditioning technology. The cooling of water is divided into two stages:10.0 °C to 4.0 °C and 4.0 °C to below 0.0 °C. Initially, the ice storage process forms an ice layer with a thickness of 2.50 mm on the lower surface of the coil, but eventually, the ice layer on the upper surface becomes 3.85 mm thicker than the lower surface as a result of the natural convection of water and density reversal at 4.0 °C. Furthermore, the impact of three coils with different thermal conductivity on the ice storage process was evaluated. It was observed that the thermal conductivity of R-HDPE (reinforced high-density polyethylene) was only 2.6 W/(m·K) higher than HDPE (high-density polyethylene), yet it reduced the freezing time by 34.85%, while the thermal conductivity of steel was 37.4 W/(m·K) higher than R-HDPE, but only decreased the freezing time by 9.40%. The results demonstrated that the rate of ice accumulation increased with thermal conductivity. However, when the coil material’s thermal conductivity surpassed that of ice, the further increase of thermal conductivity gradually weakened its impact on the ice storage rate.

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%.

Day-ahead interval optimization of combined cooling and power microgrid based on interval measurement

The combined cooling and power (CCP) microgrid plays an important role in achieving the goal of dual carbon, while the uncertainty of renewable energy (RE) output and the equipment operation mode bring challenges to the operation optimization. To address this problem, a day-ahead interval optimization model based on interval measurement of CCP microgrid is proposed. First, a large number of scenarios of RE output are generated using Latin hypercube sampling. And the scenarios are reduced by the fast backward reduction method (FBRM). The reduced scenarios are combined with the extreme value theorem to obtain the RE output interval range. Then, the demand response (DR) models of electrical load and cooling load are established to achieve peak-cutting and valley-filling. And the optimization model of ice storage air conditioning (ISAC) with different connection modes is established. Furthermore, the day-ahead interval optimization model is constructed of the CCP microgrid. Finally, the results of the day-ahead optimization model for ISAC under different operation modes are analyzed. The simulation results show the accuracy and effectiveness of the proposed model. The interval radius of the proposed method is reduced by 29.408% and 57.409% compared to the traditional interval method and the original scenario interval method, respectively. And the upper limit of the interval is reduced by 519.9923¥, 1987.6423¥, respectively. In the optimization with the participation of DR and ISAC, the operation cost can be reduced. The economic and stable operation of the CCP microgrid can be realized.

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.

Short-term hybrid forecasting model of ice storage air-conditioning based on improved SVR

The energy consumption of air conditioning system is more than half of building energy consumption. Accurate and efficient forecasting of building cooling load is one of the important ways to realize building energy saving and energy system management. We proposes a short-term hybrid forecasting model based on the Mean Impact Value-Improved Gray Wolf Optimizer-Support Vector Regression (MIV-IGWO-SVR) and applies this model to the ice storage air conditioning cooling load forecasting. First, the impact of historical meteorological data on cooling load was analyzed, and the Mean Impact Value (MIV) is used to filter out the main variables affecting the cooling load. Second, two new types of improvement strategies are proposed to optimize the Gray Wolf Optimizer (GWO), which is used in SVR parameter optimization. Finally, the model is verified using the measured data of a large public building in northern China. And the results showed that the MAPE and the RMSE predicted by the MIV-IGWO-SVR model are 0.4319% and 4.8511, respectively. Through comparative experiments, it can be concluded that the MIV-IGOWO-SVR model has higher prediction accuracy, shorter running time, and stronger robustness than neural networks under small sample data. Therefore, the forecasting model proposed in this paper provides effective method support for the building energy consumption prediction and can meet actual engineering needs.

Performance of Ice Generation System Using Supercooled Water with a Directed Evaporating Method

Ice slurry is widely used in the field of ice storage air conditioning, district cooling, seafood preservation, and milk processing. Ice generation using supercooled water is efficient, and the system structure is compact. However, a secondary refrigerant cycle is usually used in order to control the wall temperature and to prevent the “ice blocking” problem. Therefore, an ice generation system using supercooled water with a directed evaporating method is proposed and fabricated in order to improve the system performance, which is tested in the experiment. Then, two calculation methods are used to study the performance of entire ice generation system. We concluded that: (1) The system could run steady without “ice blocking” in the condition where the supercooled water temperature was higher than 271.7 K and the velocity was more than 2.1 m/s. The entire system COP could reach 1.6 when the condenser temperature was about 319 K. (2) The system COP could be improved by about 20% if the compressor output power was based on the theoretical refrigerant cycle. The system COP could reach about 2.5 if the proportion of extra power was 3% and the condenser temperature was 308 K. (3) The system COP with a directed evaporating method was about 14% higher than that with an indirected evaporating method. (4) An orthogonal test was built to quantify the influence of different critical parameters. The influence of factors on the system COP were as follows: condenser temperature > water flow > adiabatic compressibility > refrigerant. This work provided a good look at the performance of an ice generation system using supercooled water with a directed evaporating method. It can play an important role in guiding the design of a system of ice generation using supercooled water.

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.

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.

Coordination Control and Suitability Evaluation of Multiple Ice-Storage Tanks in an Air-conditioning System Under Unreliable Communication

Electric energy is an important part of building energy consumption, and air-conditioning accounted for most of the consumption of electric energy. Ice storage air conditioning is widely concerned because of its strong adjustability. Due to the limited space, it is often necessary to use multiple ice storage devices simultaneously to meet the application requirements. In this paper, a distributed coordination method for multiple ice storage devices is presented, which can allocate storage tasks according to the capacity of the device. Each device only needs to communicate with its neighbouring cold storage device, and finally achieve the control goal through distributed iteration. The convergence of the proposed algorithm is analysed by rigorous mathematics. If the communication topology between devices is connected, the algorithm can converge to the expected point. Finally, the validity and suitability of the algorithm and the correctness of theoretical analysis are verified and evaluation by digital simulation.

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.

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.

Research on Scheduling Model and Control Strategy of Ice Storage Air Conditioning in Urban Power Grid

In this paper, a two-layer scheduling model for ice storage central air conditioning is established: the system level optimization control of the upper layer and the energy management system of the lower massive decentralized air conditioning system. The system level consists of thermal power, hydropower, wind turbines and ice storage air conditioning polymerization models, with the goal of minimizing the abandonment of wind and water, generating the scheduling results of generator set and ice storage air conditioning, and delivering them to the lower energy management system; After receiving the scheduling result issued by the upper layer, the large-scale ice storage air-conditioning is dispatched by the load aggregator, and the minimum scheduling deviation is targeted, and the direct load control is performed under the condition that the user comfort is satisfied to complete the upper-level scheduling plan. Through simulation examples, it is proved that the ice storage air-conditioning realizes the effectiveness of the disposal of abandoned water and abandoned wind under the premise of ensuring user comfort.

Air conditioning dispatching and economic analysis of ice storage in urban power grid

Considering the characteristics of hydropower in Chongqing and the on-grid electricity price of power generation, water and wind power, and the time-of-use electricity price of users purchasing electricity, the model of Chongqing large-scale ice storage air-conditioning is established and is optimized based on the minimum power purchase cost of the power grid and the minimum operating cost of ice storage air-conditioning. The goal is to dispatch generators and ice storage air conditioners to maximize the benefits of the grid and users, while reducing the abandonment of water and wind power. The feasibility and effectiveness of the proposed control strategy are verified by simulating the control of ice storage air conditioning under different cooling load requirements.

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.