Thermo-Economic Comparison and Parametric Optimizations among Two Compressed Air Energy Storage System Based on Kalina Cycle and ORC

Li, Ruixiong; Wang, Huanran; Yao, Erren; Zhang, Shuyu

doi:10.3390/en10010015

Cited by 19 publications

(25 citation statements)

References 42 publications

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“…The energy and mass conservation are applied to every component to obtain the thermodynamic properties of each key note, including the separating and mixing process of ammonia‐water. To simplify the calculation model, some assumptions are adopted: 1) The air is treated as an ideal gas in the combined system; 2) Ambient air consists of 78.12 % nitrogen, 20.96 % oxygen and 0.92 % argon; 3) The temperature and pressure of ambient atmospheric are 298.15 K and 101.325 kPa, respectively; 4) In the condenser, the hot medium is cooled by water coming from the atmospheric environment; and 5) The system is operated in the steady state; the local loss and frictional head loss appeared in heat exchanger and pipe are neglected …”

Section: Thermodynamic Modelmentioning

confidence: 99%

“…where δ and λ are the thickness of the plate and the thermal conductivity and h hot and h cold are the heat transfer coefficient of hot fluid and cold fluid:

true h_{med} = \frac{λ \cdot N u ((), L + b)}{2 L b},

…”

Section: Thermodynamic Modelmentioning

confidence: 99%

“…where L and b are the channel width and channel spacing, respectively; “med” represents hot or cold fluid; and Nu is the Nusselt number calculated by the ratio of convective to conductive heat transfer across (normal to) the boundary, which differs significantly between single‐phase heat transfer and multi‐phase heat transfer. To single‐phase heat transfer, the Nusselt number can be calculated:

true N u = 0.724 {(\frac{6 β}{π})}^{0.646} {(\frac{2 {\dot{m}}_{f}}{μ N ((), L + b)})}^{0.583} {(\frac{c_{p} μ}{λ})}^{1 / 3},

…”

Section: Thermodynamic Modelmentioning

confidence: 99%

“…Compressed air energy storage (CAES) systems, which compress the air into an air storage tank in the charge stage and output power using a gas turbine in discharge stage, are regarded as a workable solution for peaking shaving and load‐levelling of the electricity system . One of the biggest challenges faced by CAES systems, as compared with other energy storage systems (such as the hydraulic way, flywheel, and the electrochemical systems), is the consumption of fuel by the gas turbine, which may cause environment pollution and increase the energy loss for the system .…”

Section: Introductionmentioning

confidence: 99%

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Thermo‐Economic Analysis and Optimization of Adiabatic Compressed Air Energy Storage (A‐CAES) System Coupled with a Kalina Cycle

Wang

2018

Energy Tech

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An adiabatic compressed air energy storage system (A‐CAES) can improve the energy storage potential, compared with conventional CAES systems, by capturing the heat generated during the charge stage. However, a challenge remains in the utilization step of the stored thermal energy due to the energy loss from the thermal energy storage vessel, which has hindered commercialization of A‐CAES. To utilize the thermal energy produced by a compressor during the charge process efficiently, a novel system that couples A‐CAES system with the Kalina cycle (KC) is designed to recover the loss of heat. In the new system, which is characterized by a higher and more stable power output, a Kalina cycle and the expander absorb the heat released from thermal energy storage units to generate electricity. Theoretical thermodynamic analysis shows that the novel combined system can achieve more efficient operation than a single A‐CAES system. By examining the effects of key thermodynamic parameters on the exergy efficiency and overall capital cost, a Pareto frontier obtained shows the tradeoff between exergy efficiency and overall capital cost of the system, where 47.17 % and 1.455 k$ kW−1 are determined as the optimum values for the two objectives from the optimum design solutions.

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Section: Thermodynamic Modelmentioning

confidence: 99%

“…where δ and λ are the thickness of the plate and the thermal conductivity and h hot and h cold are the heat transfer coefficient of hot fluid and cold fluid:

true h_{med} = \frac{λ \cdot N u ((), L + b)}{2 L b},

…”

Section: Thermodynamic Modelmentioning

confidence: 99%

true N u = 0.724 {(\frac{6 β}{π})}^{0.646} {(\frac{2 {\dot{m}}_{f}}{μ N ((), L + b)})}^{0.583} {(\frac{c_{p} μ}{λ})}^{1 / 3},

…”

Section: Thermodynamic Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Thermo‐Economic Analysis and Optimization of Adiabatic Compressed Air Energy Storage (A‐CAES) System Coupled with a Kalina Cycle

Wang

2018

Energy Tech

Self Cite

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“…From Equation (15) we note that if . m a (1 + 1/α)c p2 3 ∆T ap < P out , or equivalently (T 3 − T 4 ) ∆T app , the power produced by the turbine is P out η cc η el P in .…”

Section: Regenerated Systemmentioning

confidence: 99%

Hybrid Hydrogen and Mechanical Distributed Energy Storage

2017

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Effective energy storage technologies represent one of the key elements to solving the growing challenges of electrical energy supply of the 21st century. Several energy storage systems are available, from ones that are technologically mature to others still at a research stage. Each technology has its inherent limitations that make its use economically or practically feasible only for specific applications. The present paper aims at integrating hydrogen generation into compressed air energy storage systems to avoid natural gas combustion or thermal energy storage. A proper design of such a hybrid storage system could provide high roundtrip efficiencies together with enhanced flexibility thanks to the possibility of providing additional energy outputs (heat, cooling, and hydrogen as a fuel), in a distributed energy storage framework. Such a system could be directly connected to the power grid at the distribution level to reduce power and energy intermittence problems related to renewable energy generation. Similarly, it could be located close to the user (e.g., office buildings, commercial centers, industrial plants, hospitals, etc.). Finally, it could be integrated in decentralized energy generation systems to reduce the peak electricity demand charges and energy costs, to increase power generation efficiency, to enhance the security of electrical energy supply, and to facilitate the market penetration of small renewable energy systems. Different configurations have been investigated (simple hybrid storage system, regenerate system, multistage system) demonstrating the compressed air and hydrogen storage systems effectiveness in improving energy source flexibility and efficiency, and possibly in reducing the costs of energy supply. Round-trip efficiency up to 65% can be easily reached. The analysis is conducted through a mixed theoretical-numerical approach, which allows the definition of the most relevant physical parameters affecting the system performance.

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Exergoeconomic analysis and multi‐objective whale optimization of an integrated solid oxide fuel cell and energy storage system using liquefied natural gas cold energy

Pan

Binama

et al. 2022

Intl J of Energy Research

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Summary A multigeneration system comprising solid oxide fuel cell, compressed air energy storage, gas turbine, supercritical CO2 recompression Brayton cycle (S‐CO2), and organic flash Rankine cycle (OFRC) based on liquefied natural gas cold energy is proposed. The system integrates peak shaving, heating, cooling, power generation, and energy storage, solving the imbalance between the supply and demand of renewable energy. It is comprehensively analyzed from the thermodynamic, economic, and exergoeconomic perspectives to evaluate the performance of the three phases (full time, charging period, and discharging period). The evaluation shows that the exergy analysis of the multigeneration system alone is not comprehensive, and the exergoeconomic analysis is more necessary. Additionally, the S‐CO2 coupled with the OFRC generates power in a more efficient way than the S‐CO2 coupled with the Organic Rankine cycle (ORC) or Organic Flash cycle (OFC) does. Finally, the NSGA‐II and multi‐objective whale algorithms are employed to optimize the system. The results show that the multi‐objective whale algorithm is slightly better than the NSGA‐II. Moreover, the optimized round‐trip efficiency (RTE) and levelized cost of electricity (LCOE) are 68.64% and 0.053 $ kWh−1, respectively. Compared with the base design point, the RTE improves by 1.93%, and the LCOE decreases by 3.64%.

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Thermo-Economic Comparison and Parametric Optimizations among Two Compressed Air Energy Storage System Based on Kalina Cycle and ORC

Cited by 19 publications

References 42 publications

Thermo‐Economic Analysis and Optimization of Adiabatic Compressed Air Energy Storage (A‐CAES) System Coupled with a Kalina Cycle

Thermo‐Economic Analysis and Optimization of Adiabatic Compressed Air Energy Storage (A‐CAES) System Coupled with a Kalina Cycle

Hybrid Hydrogen and Mechanical Distributed Energy Storage

Exergoeconomic analysis and multi‐objective whale optimization of an integrated solid oxide fuel cell and energy storage system using liquefied natural gas cold energy

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