Research Progress on the Performance Enhancement Technology of Ice-on-Coil Energy Storage
Abstract
:1. Introduction
2. Research on Ice-on-Coil Type Ice Melting Methods
2.1. Ice-on-Coil Type Ice Melting Method
2.1.1. Internal Ice Melting Method
2.1.2. External Ice Melting Method
2.1.3. Combined Internal and External Ice Melting Methods
3. Research on Enhanced Heat Transfer of Ice-on-Coil
4. Research on Operation Strategy of the Ice Storage System
5. Discussion and Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Type | Technical Characteristics | Energy Storage Density (kJ/kg) | Evaporating Temperature (°C) | System Efficiency (%) | Cooling Temperature (°C) | Cold Storage Medium | Heat Transfer Performance | Operation and Maintenance Costs |
---|---|---|---|---|---|---|---|---|
Water storage | Water storage | 20.9 | 0.0 | 90.0 | 5.0–13.0 | Water | relatively good | Low |
Eutectic salt | Eutectic salt | 96.0 | 0.0 | 90.0 | 9.0–10.0 | Eutectic salt | general | High |
Ice storage | Coil | 384.0 | −10.0 | 70.0 | 1.0–3.0 | ice | good | High |
ice ball | 350.0 | −10.0 | 70.0 | 1.0–3.0 | ice | general | High | |
slushy | 208.0 | −3.0 | 85.0 | 1.0–3.0 | slushy | good | High | |
Gas hydrate | Gas hydrate | 302.0–464.0 | 0.0–3.0 | 90.0–95.0 | 6.0–3.0 | hydrate | good | High |
Ice Storage Cases | RT | Type | Refrigeration Machine | Cold Storage Technology |
---|---|---|---|---|
Marina Bay Cooling Project, Singapore | 26,000 | Office building | 16 refrigeration units | Ice-on-coil |
Qatar Pearl Tower | 30,000 | Shopping centers | 52 refrigeration units | Ice-on-coil |
Cooling for Business Bay Administration Building, UAE | 32,500 | High-rise office Building | 16 refrigeration units | Ice-on-coil |
Texas Medical Center Cooling System | 120,000 | Buildings | Absorption and electric chillers | Ice-on-coil |
Type | Num. | Parameters and Dimensions | Main Contents | Main Conclusions | Figures | Ref. |
---|---|---|---|---|---|---|
Fins | 1 | (a) Four fins of 4 × 7 mm; (b) Four fins of 2 × 8 mm; (c) Eight fins of 2 × 3.5 mm; (d) Eight fins of 1 × 4 mm. | Comparison of the enhancement effect of four different parameters of fins on the ice storage capacity of coils. | As the number of fins increases and their height decreases, the solidification rate of the fins diminishes. | [42] | |
2 | Pitch: 20, 30, 40, 50, 60, 70 mm; height: 30 mm ring fins. | Study of optimal spacing between ring fins. | The 50 mm spacing is optimized and the ring fins have a high ice storage rate of 21%. | [47] | ||
3 | The number of fins is 6, 8, and 10; the height of the fins is 20, 30, and 40 mm; and the thickness of the fins is 1, 3, and 5 mm. | The effects of fin height, fin thickness, and number of fins on the ice storage process are analyzed. New evaluation criteria are proposed to optimize the fin parameters. | The optimal fin thickness and fin number are recommended to be 3 mm and 8 mm to achieve the largest performance enhancement with the least mass penalty. | [49] | ||
4 | Six fins; fins and tube thicknesses are the same; Ro = 4 Ri; Hf = 2.5 Ri; | Study the effect of fins of different lengths and PCM materials on heat transfer time. | Increasing fin length reduces melting and solidification time by over 14%, while composite PCM reduces it by over 20%. | [50] | ||
5 | Four fins; the height is 45 mm. | Study of optimum height and angle of variable thickness fins. | Fastest heat transfer at half-fin angle (β = 8°). | [51] | ||
6 | (a) Pinned tube: 16 pins, spacing 3~9.5 mm, length 20~40 mm; (b) Ring finned tube: five fins, spacing 52~92 mm, thickness 0.3~1.0 mm. | Analyze and compare the effects of different parameters of pinned and finned tubes on the heat transfer rate. | Finned tubes have a larger heat transfer area than pinned tubes and store ice faster. | [52] | ||
7 | Y-fins; L1 = 0~11 mm; L2 = 0~28.5 mm; L3 = 4.5 mm; L4 = 28.5 mm; β = 13.4°~35.4°. | Single and double bifurcated Y-fins are proposed and geometrically optimized. | Double bifurcation increases discharge efficiency by about 24%; a smaller beta angle results in shorter run times. | [53] | ||
8 | Five fins; Height is 0.2, 0.4, 0.6, 0.8 times pipe diameter; 0°, 45°, 60°, 72°. | Study of the effect of angle between fins on heat transfer performance. | Using fins concentrated at the bottom improves efficiency and reduces heat transfer time by up to 50%. | [54] | ||
9 | New symmetrical fins. | Fills a gap in previous studies when buoyancy effects are present in the solidification process. | Efficiency increased by 14.3% with the addition of nano thermal conductive particles combined with fins. | [55] | ||
10 | Eight V-shaped fins; The angle of the fin is 45°. | Study of the effect of V-fin and nanoparticles on heat transfer time in triplex-tube heat transfer. | The use of fins has a greater impact on heat transfer than the addition of nanoparticles. | [56] | ||
11 | New V-fins. | The ice storage performance of V-shaped fins was compared with that of straight fins; the effects of V-shaped fin angle, length, and sub-width on ice storage were investigated. | The smaller length or width ratio of the V-fin has a shorter ice storage time. Compared to straight fins, V-shaped fins reduce the total ice storage time by 64.2%. | [57] | ||
12 | Bionic fins. | Proposed new annular and axial fins, design and optimization of tree branch bionic fins. | The radial fin design in Figure (a) has a tube spacing of 100 mm. The axial fin design with a tube spacing of 160 mm, as shown in Figure (b), was designed for longer charging and discharging times. The new axial fin design with a tube spacing of 70 mm, as shown in Figure (c). The Axial-70 fin design has a high power rate during the heat discharging of the storage unit. | [58] | ||
Thin rings | 1 | Thin ring thickness is 0.25, 0.5, 1, 2, and 3 mm. | Study of the effect of thin rings of different thicknesses on the rate of ice storage. | Thin ring tubes had a 34% higher ice storage rate than smooth tubes. | [47] | |
2 | Single parallel; Double parallel; Staggered; The thickness is 0.25, 1, 2, 3 mm. | Study of the effect of the parameters of thin rings on the heat transfer time. | Staggering the thin rings and setting the thickness to 1 mm optimizes the overall heat transfer performance. | [48] |
Main Factors | Numbers | Technical Approach | Main Contents | Main Conclusions | Ref. |
---|---|---|---|---|---|
Cold storage capacity | 1 | Cooling model optimization. | Taking four typical buildings as the research object, the influence of different control strategies and electricity price structures on the optimal ice storage rate was studied. | When the ice storage rate is less than 0.267, the cooler priority control strategy will be more appropriate to the optimized control strategy; when the ice storage rate is >0.35, the ice storage priority control strategy is more appropriate to the optimized control strategy. | [62] |
2 | Particle swarm optimization. | Outlines a study of load-shifting control strategies using different hot and cold energy storage facilities. | Load-shifting controls using building thermal mass can achieve more than a 30% reduction in daily peak loads and significant total cost savings from 8.5% to 29%. | [63] | |
3 | Model optimization. | A model-based optimal design method using genetic algorithms is developed to actively optimize the storage capacity. | Significant net annual cost savings of up to over USD 80,000 can be achieved by utilizing relatively small-scale active thermal and cooling energy storage systems, which is equivalent to 6.7% of a typical daily cooling load. | [64] | |
A simulation-based approach to optimize the design of small-scale active energy storage systems in buildings to limit their power demand is proposed. | Demand-limiting controls using small storage tanks can save about 7% of a building’s total annual power consumption costs each year. The optimal storage capacity is less than 5% of the daily cooling load. | [65] | |||
running cost | 1 | Forward dynamic programming algorithm. | An efficient model prediction controller for the charging and discharging of the ice storage part was developed (energy costs, equipment costs). | The model-predictive controller (MPC) receives tariff updates and re-optimizes the cooling system strategy according to the new prices. The open-loop optimal control does not have this information and operates the chiller at a very high price, resulting in operating costs that are approximately 40% higher. | [66] |
2 | Dynamic programming algorithm. | Discussing the optimization of the ice storage air conditioning system under the condition of considering the minimum life cycle cost and efficiency of ice storage tank. | In the ice priority mode, life cycle costs are lower than for a conventional air conditioning system from the fourth year of operation, and under the 10-year life cycle, nine ice-storage units with an ice charging rate of 36.2% incur minimum cost. | [67] | |
The cost optimization analysis is carried out on the selection of cold storage equipment and chiller, and the influence of charge and discharge strategy and electricity price strategy on system operation is determined. | The optimal operating protocol for storage charging and discharging is determined by a dynamic programming method that minimizes the operating cost over an entire year. | [68] | |||
3 | Nonlinear programming methods. | A strategy based on mixed integer nonlinear programming is proposed to optimize the running schedule of building energy systems. | This strategy can significantly reduce operating energy costs (about 25%), reducing or even increasing to about 47% when using thermal energy storage systems. | [69] | |
4 | Model optimization. | Model-based real-time predictive optimal control of active and passive building thermal storage inventories in a test facility using time-of-use differentiated tariffs without demand charges is demonstrated. | When the optimal controller is given an imperfect weather forecast, the utility cost of the energy resource station can save 17% over the base case and 27% over the reference case. | [70] | |
5 | Mathematical modeling. | Optimized for life cycle economics. | Under the full load storage scenario and the base tariff structure, the daily savings would be USD 549.4/day, with the energy storage capital costs being paid off over 10 years, afterwards the daily saving in operational cost will be USD 4011.76/day. | [71,72] | |
6 | Neural network model. | Combination of neural network-based model prediction and genetic algorithm. | When time-of-use pricing or real-time pricing are adopted, the operating costs of district cooling networks are reduced by approximately 16% and 13%, respectively. | [73,74,75] | |
7 | Software modeling. | A model-based predictive control algorithm for cooling systems of small commercial buildings is proposed. | The proposed MPC algorithm can save about 5–20% per year, and the chiller priority strategy can save 20–30% per year. | [76] | |
Operational Energy Consumption | 1 | Particle swarm optimization. | Study of system energy consumption and CO2 emissions. | The optimum capacity of the chiller is estimated to be 250 RT when the ice storage capacity is set to 1800 RT-h. | [77] |
2 | Model optimization. | A methodology is presented for determining the optimal chilled water storage (CWS) capacity and corresponding operating strategy for air conditioning loads at different electricity rates. | The optimal CWS strategy reduces peak demand by 38% under time-of-use (TOU) tariffs. Accordingly, customers can save 5.9% on operating costs. | [78] | |
3 | Strategy optimization. | The energy performance of a distributed energy system with a district cooling system (DES&DCS) was evaluated under four different control strategies. Comparisons were made with DCS and stand-alone cooling systems that are fully dependent on the grid. | Compared with the system that also adopts DCSs but only depends on the grid, the DES&DCS can save more than 10% of primary energy. Compared with the system that adopts an individual cooling system and only depends on the grid, the energy saving can be more than 16% and up to 19.1%. | [79] | |
4 | Software modeling. | Using EnergyPlus software modeling to validate a prediction methodology specifically designed to control multi-zone heating, ventilation, and air conditioning systems. | Regardless of the mode of operation (heating or cooling) and the time of year, energy consumption is significantly reduced by about 5 to 15%. | [80] | |
Development of a dynamic computer model for ice thermal storage systems to compare energy use in conventional air-cooled systems and ice thermal storage systems. | Full ice storage can save up to 55% of the monthly cost of electricity needed for cooling compared to traditional air-cooling systems. Using the full storage option can reduce the total energy consumption of a selected building by up to 5%. | [81] | |||
5 | Mathematical modeling. | A detailed mathematical model is proposed for the combination of heating and cooling chemistries in district energy to achieve the best performance of the whole system. | Effective savings in total annual costs and CO2 emissions. More than 67% of the CO2 reductions are achieved by mixing heat and district cooling. | [82] | |
External factors | 1 | Weather incorporated into the control system. | Consider the temperature parameters required for the indoor environment. | Significant increase in system operational capacity. | [83] |
A novel HVAC control method is proposed to minimize energy consumption while maintaining comfortable indoor temperatures based on short-term future predictions from weather forecasting models. | Energy savings of up to 58.79% have been demonstrated in EnergyPlus simulations. | [84] |
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Guo, X.; Xu, X.; Wang, Z.; Chang, Z.; Chang, C. Research Progress on the Performance Enhancement Technology of Ice-on-Coil Energy Storage. Energies 2025, 18, 1734. https://doi.org/10.3390/en18071734
Guo X, Xu X, Wang Z, Chang Z, Chang C. Research Progress on the Performance Enhancement Technology of Ice-on-Coil Energy Storage. Energies. 2025; 18(7):1734. https://doi.org/10.3390/en18071734
Chicago/Turabian StyleGuo, Xinxin, Xiaoyu Xu, Zhixin Wang, Zheshao Chang, and Chun Chang. 2025. "Research Progress on the Performance Enhancement Technology of Ice-on-Coil Energy Storage" Energies 18, no. 7: 1734. https://doi.org/10.3390/en18071734
APA StyleGuo, X., Xu, X., Wang, Z., Chang, Z., & Chang, C. (2025). Research Progress on the Performance Enhancement Technology of Ice-on-Coil Energy Storage. Energies, 18(7), 1734. https://doi.org/10.3390/en18071734