Advanced Adiabatic Compressed Air Energy Storage Research Articles

Performance analysis and configuration method optimization of AA-CAES-based air storage tanks

To improve the performance of the compressed air energy storage (CAES) system, flow and heat transfer in different air storage tank (AST) configurations are investigated using numerical simulations after the numerical model has been experimentally validated. System performance for different AST placement methods is analyzed through numerical simulations integrated with the thermodynamic model of advanced adiabatic compressed air energy storage (AA-CAES). An in-depth study examines the impact of key system parameters on system performance with different AST configurations. Based on these analyses, the AA-CAES system with a constant volume of AST is optimized. The results indicate that horizontal placement of the AST improves heat transfer capability within the same working pressure range but results in slightly lower energy storage efficiency, achieving 64.61% compared to 65.50% for vertical placement. However, horizontal placement offers higher energy storage density, achieving 3.54 kW h/m3 under specific conditions, compared to 3.14 kW h/m3 for vertical placement. As the energy storage flow rate increases, exceeding the critical flow rate significantly improves heat transfer in vertically placed ASTs, thus narrowing the energy storage density gap between configurations. Increased turbine efficiency, additional external heat sources, and further utilization of compression heat provide more significant performance improvements for the AA-CAES with the AST placed horizontally compared to vertically. Compared to the AA-CAES with vertically placed ASTs, the configuration of the ASTs is optimized to enhance the electrical output of the AA-CAES by 76.4 MW h and reduce the input by 78.9 MW h at a storage flow rate of 0.5 kg/s.

Optimal planning of electricity-gas coupled coordination hub considering large-scale energy storage

Large-scale long-duration energy storage (LDES), like compressed air energy storage (CAES) and liquid air energy storage (LAES), is promising for high-penetration renewable energy consumption in the city-scale integrated energy system (IES). Due to high installation costs and challenges in decentralized control of distributed energy storage, this paper proposes an optimal planning model for an urban electricity-gas coordination hub (UEGCH) with centralized energy storage using large-scale LDES. Firstly, the UEGCH with energy cascade utilization is designed, where process-based linearized models of advanced adiabatic compressed air energy storage (AACAES) and LAES are established to make a trade-off between solving accuracy and speed. Then, a city-scale integrated electricity and gas system (IEGS) is constructed with multiple regions considering line-packing. Finally, a multi-scenario planning method is proposed based on X-means for scenario reduction, and the global optimum is obtained through linearization. Results show that compared with traditional gas storage tank (GST) in the UEGCH, the utilization of AACAES and LAES requires as little as 7 % and 0.7 % of the construction volume with lower carbon emissions and can be cost-competitive in terms of future advancement. As for different EESs, in contrast to the energy-based energy storage (EBES) (i.e., battery (BT)), the levelized cost of energy (LCOE) of the capacity-based energy storage (CBES), which includes AACAES and LAES, can be reduced by nearly 0.1 $/kWh, bringing an annual economic benefit of $ 3 million. Besides, the discharge duration of CBES can be increased to 13 h, demonstrating the application prospect in 100 % renewable consumption and islanding operation. Compared with AACAES, LAES can reach 11.67 times the energy density, with a drop of 6.11 % in operating cost. The abilities of EBES and CBES to locally consume renewables are respectively weaker and stronger than that of power-to-gas (P2G) combined with line-packing. Meanwhile, this paper further analyzes the impacts of temperature, discharge duration, renewable penetration, energy tariff, and demand on the operation and sizing of centralized energy storage.

Optimal design and performance assessment of a proposed constant power operation mode for the constant volume discharging process of advanced adiabatic compressed air energy storage

A power operation mode of constant volume discharging process for advanced adiabatic compressed air energy storage (AA-CAES), called compensation mode (C mode), is proposed. The dynamic model of the proposed C mode discharging process is established based on the conservations of mass, momentum, and energy. The reliabilities of compressor, air tank, and air turbine are verified separately. The optimal design is carried out to reduce the power consumed by the compressor. The optimal design pressure ratio and reduced flow rate are 2.48 and 9.292 × 10−5 m·s·K0.5, respectively. This finding proves that the proposed dynamic model obeys the first and second laws of thermodynamics. The exergy distribution of the entire discharging process is studied. Results show that the two largest exergy losses occur in MT and MR. The performance of the proposed C mode is compared with that of the two other operation modes. The proposed C mode enhances the energy storage efficiency of the AA-CAES system by 14.71 %. This study provides a relatively efficient and feasible technical solution for the constant power operation of AA-CAES.

Optimal Dispatch of Integrated Energy Systems Considering Advanced Adiabatic Compressed Air Energy Storage with a High Percentage of Fluctuating Energy Consumption

Aiming at the integrated energy system (IES) of the region with a multi-energy coupling of cold, heat, and power (CHP), this research establishes the IES model with the advanced adiabatic compressed air energy storage (AA-CAES) as the energy hub. Further, to increasingly improve the system’s consumption ratio of fluctuating energy sources such as wind power, this paper designs the AA-CAES wide operating condition operation scheme based on the compressor adjustable inlet guide vanes (VIGVs) regulation technology. Finally, it is validated with a real application Case. Simulation results indicate that the operating costs of the newly modeled IES can be decreased by 20% compared to a regular integrated energy system, which demonstrates the effectiveness of AA-CAES-wide operating conditions in promoting the economic operation of integrated energy systems (IES).

Design and Thermodynamic Investigation of a Waste Heat‐Assisted Compressed Air Energy Storage System Integrating Thermal Energy Storage and Organic Rankine Cycle

Compressed air energy storage (CAES) technology has attracted growing attention because of the demand for load shifting and electricity cost reduction in energy‐intensive industries. To increase the round‐trip efficiency and energy storage density and simplify the structure of advanced adiabatic CAES (AA‐CAES) systems, a waste heat‐assisted CAES (WH‐CAES) design integrating a tube‐in‐tube thermal energy storage unit and an organic Rankine cycle (ORC) is described herein. Thermodynamic models of the proposed WH‐CAES system are established, and the effects of waste heat temperature, ratio of compression ratios, ratio of expansion ratios, and air storage cavern pressure on the performance of different systems, including AA‐CAES and WH‐CAES with and without ORC, are investigated. The results show that when the waste heat temperature is 500 °C, the input work of the compressors reaches its minimum value at a ratio of compression ratios of 1.14, and the optimal ratio of expansion ratios is 0.39. Under these optimal operating conditions, compared with the AA‐CAES system, the round‐trip storage efficiency, energy storage density, and system energy efficiency of the WH‐CAES system can be increased significantly. The findings validate the potential of the proposed WH‐CAES system.

Comparative evaluation of advanced adiabatic compressed gas energy storage systems

Compressed air energy storage (CAES) is a mature electrical energy storage option among different types of energy storage technologies. The positive environmental attributes of the advanced adiabatic compressed air energy storage (AA-CAES) arise from a lack of the need for a combustion chamber. Taking into account the thermodynamic properties and economic considerations, a new approach has been adopted to investigate the effect of various working fluid specifications on the overall performance of the AA-CAES system. This approach involves utilizing hydrogen and nitrogen as working fluid. The technical evaluation includes energy and exergy analysis supported by economic and parametric analysis for advanced adiabatic compressed hydrogen storage (AA-CHES) systems and in addition, an advanced adiabatic compressed nitrogen storage (AA-CNES) is also considered. The results of the thermodynamic analysis illustrate the performance measurements parameters for round-trip efficiency and exergy round-trip efficiency for AA-CHES, AA-CNES, and AA-CAES are equal to 55.05 % - 63.47 %, 54.74 % - 63.16 %, and 54.5 % - 62.91 %, respectively. It is clear that changing the working fluid improves the performance of the studied system. The payback period for AA-CNES, AA-CAES, and AA-CHES are 1.2, 1.3, and 5.4 years, respectively. Additionally, the parametric investigations reveal the influential behaviors of the design parameters on the main output of the system.

A novel constant power operation mode of constant volume expansion process for AA-CAES: Regulation strategy, dynamic simulation, and comparison

A novel operation mode of constant volume discharging process for advanced adiabatic compressed air energy storage (AA-CAES) is proposed, which achieves a constant output power by throttling and air supplementation. The dynamic model of the expansion process was developed based on the conservation of mass and energy of the open system. The reliability of the turbine and air storage tank models was verified. The results of the energy and exergy analysis of the expansion process show.

Photothermal-assisted scheme design and thermodynamic analysis of advanced adiabatic compressed air energy storage system

The conventional photothermal-assisted scheme adopted by advanced adiabatic compressed air energy storage (AA-CAES) has equal stages of expanders and high-temperature reheaters, and is equipped with a regenerator to waste heat recovery, which is relatively complex and requires high solar heat supply and solar irradiance. In this paper, a novel photothermal-assisted AA-CAES (PT-AA-CAES) with a simpler structure and suitable for low solar irradiance is designed, which adopts 3-stage expansion and 2-stage high-temperature reheat. The thermodynamic model of PT-AA-CAES system is established, and a comprehensive evaluation coefficient combining exergy efficiency and energy storage density is proposed. The numerical results demonstrate that the compressor unit outlet pressure and expansion unit inlet pressure with the optimal comprehensive performance are 10 MPa and 4 MPa. Furthermore, when the solar irradiance is below 690 W/m2, 660 W/m2, and 600 W/m2, the output work, exergy efficiency, and energy storage density of the novel PT-AA-CAES system will surpass those of the conventional PT-AA-CAES system with the same solar heat consumption, and at a solar irradiance of 580 W/m2, the exceeded values are 44.3 kW h, 2.03%, and 0.15 kW h/m3, respectively. These conclusions can assist the AA-CAES system in the selection of unit pressures and photothermal-assisted scheme.

Optimal bidding strategies of advanced adiabatic compressed air energy storage based energy hub in electricity and heating markets

Integration of power and heating systems can not only improve energy efficiency but also reduce the peak generation capacity by narrowing the gap between peak and valley demands. Advanced adiabatic compressed air energy storage (AA-CAES) is a large-scale and environmental-friendly storage technology that can supply heat and power. It can be adopted as an energy hub that integrates electricity and heating systems. This paper studies the strategic behavior of an AA-CAES based energy hub in deregulated electricity and heat markets. Firstly, a heat market clearing model with elastic thermal demands is developed, and the impact of demand elasticity on the stability of the heat market is analyzed. Secondly, based on the proposed heat market model, a bi-level optimization model is established to explore the strategic bidding and scheduling of the merchant AA-CAES based energy hub in both markets. Through exploiting the weakly coupling structure of the bi-level problem, a special decomposition method is proposed to compute the market equilibrium. The optimal response of the lower-level power/heat market clearing problem is analytically expressed by multi-parametric programming techniques, where the bidding strategies are parameters. With the lower-level parameterized solution, the original complex joint optimization problem is transformed into a small-scale mixed integer quadratic program with only a few binary variables, and thus can be solved efficiently. Case studies verify the proposed model and method. Results show that the AA-CAES based energy hub/heat market is conducive to an 8.6%/22.5% reduction in the operation cost of the heat market/energy hub.

Exergy analysis and particle swarm optimization of clean energy router based on a solar‐thermal‐assisted advanced adiabatic compressed air energy storage system

AbstractThe clean Energy router based on advanced adiabatic compressed air energy storage (AA‐CAES) has the characteristics of large capacity, high efficiency and zero carbon emission which are an effective mitigation scheme for the integration of renewables and peak‐shaving and a new clean energy technology for storing energy in the world. A novel solar‐thermal‐assisted AA‐CAES (ST‐AA‐CAES) is proposed in this paper, integrating variable thermal energy storage to improve the system electric to electric (E2E) and round‐trip efficiency (RTE). The efficiency and exergy evaluation of ST‐AA‐CAES are carried out to determine the performance of ST‐AA ‐CAES. The results illustrate that E2E, RTE, and exergy efficiency can reach 56.4%, 95.5%, and 55.9%, respectively. Meanwhile, the details of exergy efficiency and destruction of each subsystem are demonstrated. Particle swarm optimization algorithm is applied to analyse the economy of optimally integrated energy systems which has the advantages of high accuracy, convenient implementation and fast convergence. The system can be applied in abundant solar energy resources area with high efficiency and multi‐energy supply capability.

Two-timescale coordinated operation of wind-advanced adiabatic compressed air energy storage system: A bilevel stochastic dynamic programming method

Renewable energy is a promising solution to address the energy crisis and environmental issues, but it comes with challenges due to its inherent volatility and limited dispatchability. Advanced adiabatic compressed air energy storage (AA-CAES) is a favorable partner for centralized renewable integration, due to its numerous benefits, such as large capacity, long lifetime, fast response capability, and zero carbon emissions. For the economic management of a wind-AACAES system, a battery-like AA-CAES dispatch model is proposed, where the charging/discharging efficiencies and capacities are dependent on the operating power and state-of-charge (SoC) to capture the part-load characteristics. A hybrid control strategy is proposed to enhance the flexibility and efficiency of the compression side under off-design conditions caused by simultaneous changes in back pressure and load. To provide dispatch and control strategies of AA-CAES, a multi-timescale optimization problem is established. The slow timescale determines the baseline output scheduling on an hourly basis to maximize total revenue, while the fast timescale updates the real-time adjustment every five minutes to minimize the penalty of tracking error. A bi-level stochastic dynamic programming (SDP) framework is formulated to incorporate the wind uncertainties and multi timescale coordination, where terminal SoCs and value functions are essential to integrate decisions across slow and fast timescales. A non-iterative parametric programming-based method is proposed to solve the slow-timescale SDP, and a simulation-based rollout algorithm is applied to extract the fast-timescale actions, which yields evident improvement over any heuristic base policy that is sequentially consistent. The numerical results demonstrate that the proposed method has a performance gap of approximately 7.1%, which outperforms the rule-based method by 32.6%. The installation of AA-CAES improves the total revenue by 11.9%. Additionally, the hybrid control strategy enhances the charging flexibility and efficiency by 30.7% and 4.3%, respectively, resulting in a 3.9% reduction in tracking deviation penalty.

Optimal Scheduling of Multi-energy System Considering Economy and Energy Conservation

In recent years, the optimal scheduling of multienergy has become the focus of the research. Against this background, this paper builds a model of a multienergy flow system of cooling, heating, and power, and advanced adiabatic compressed air energy storage (AA-CAES) is introduced to smooth wind power generation (WPG) and supply heating/cooling energy. Simulated annealing algorithm (SAA) is employed to energy-saving scheduling of the system with “exergy assessment” method. The energy-saving index and the exergy efficiency are compared in different cases. SAA is compared with the particle swarm optimization (PSO) algorithm in solving the optimal scheduling strategy. The cooling, heating, and power demands of an industrial park and WPG in typical days are employed to the simulation. The scheduling results of exergy input of SAA are far less than those of PSO in typical days of different seasons. The multienergy system without AA-CAES is also modeled, and energy-saving economic scheduling is carried out. The exergy efficiency of the system with AA-CAES is between 38% and 58% while the exergy efficiency of the system without AA-CAES is merely between 27% and 48%.

Analysis of Voltage Control Strategies for DC Microgrid with Multiple Types of Energy Storage Systems

Direct-current (DC) microgrids have gained worldwide attention in recent decades due to their high system efficiency and simple control. In a self-sufficient energy system, voltage control is an important key to dealing with upcoming challenges of renewable energy integration into DC microgrids, and thus energy storage systems (ESSs) are often employed to suppress the power fluctuation and ensure the voltage stability. In this paper, the performances of three voltage control strategies for DC microgrids are compared, including the proportion integration (PI) control, the fuzzy PI control and particle swarm optimization (PSO) PI control. Particularly, two kinds of ESSs including battery and advanced adiabatic compressed air energy storage (AA-CAES) with different operational characteristics are installed in the microgrid, and their impacts on voltage control are investigated. The control performances are comprehensively compared under different control schemes, various scenarios of renewable energy fluctuations, participation in the control of the two ESSs or not, and different fault conditions. Additionally, the dynamic performances of the ESSs are exhibited. The results verify the validity of the control schemes and the feasibility of the configuration of the ESSs into the DC microgrid.

Economic dispatch of CAES in an integrated energy system with cooling, heating, and electricity supplies

AbstractFlexible combined cooling, heating, and power (CCHP) systems are effective in integrating wind sources. As an attractive, clean, and large‐scale energy storage technique, the advanced adiabatic compressed air energy storage (AA‐CAES) can store and generate both electricity and heating, and also provide cooling during expansion under certain conditions. Although AA‐CAES has immense potential in multi‐energy supply systems, CCHP dispatch with AA‐CAES and wind power generation (WPG) is yet to be systematically studied. In this study, the economic dispatch of an AA‐CAES system equipped with WPG is addressed. The AA‐CAES system is comprehensively modelled by considering its thermal characteristics, air‐temperature changes due to heating exchange, air storage constraint, and other factors, particularly the heat supply to the air for expansion, which is a key factor that influences the cooling supply. Subsequently, the cooling, heating, and power of the AA‐CAES system are dispatched to minimise the operating cost under different supply modes. In conclusion, the proposed method is demonstrated using an integrated energy system in an industrial park, and the operation cost of the AA‐CAES system is minimised. The numerical results demonstrate that the participation of AA‐CAES in CCHP dispatch can curtail WPG and reduce operation costs. The economics of the different supply modes of AA‐CAES are also discussed.

Optimal dispatch of an energy hub with compressed air energy storage: A safe reinforcement learning approach

With the advancements in renewable energy and energy storage technologies, the energy hubs (EH) have been emerging in recent years. The scheduling of EH is a challenging task due to the need to incorporate uncertainties at energy supply and load side. The existing model-based optimization approaches proposed for the above purpose have limitations in terms of solution accuracy and computational efficiency, which makes hinders their applications. This paper proposes a model-free, safe deep reinforcement learning (DRL) approach, using primal-dual optimization and imitation learning, for optimal scheduling of an EH that includes a tri-generative advanced adiabatic compressed air energy storage (AA-CAES). First, the operation of an AA-CAES under off-design conditions is modeled and linearized using a mixed-integer linear programming (MILP). Then, a safe DRL approach is proposed with training and testing steps considering a case study. The performance of the proposed approach in reducing operational cost and satisfying constraints is compared to the state-of-the-art DRL algorithms as well as a deterministic MILP approach. In addition, the generalization of the proposed approach is examined on a test-set. Finally, the effect of off-design conditions of a tri-generative AA-CAES on the optimal dispatch strategy is investigated. The results indicate that the proposed approach can effectively reduce the operational cost and satisfy the operational constraints.

Exploiting the operational flexibility of AA-CAES in energy and reserve optimization scheduling by a linear reserve model

Advanced Adiabatic Compressed Air Energy Storage (AA-CAES) is expected to play a crucial role in providing energy shifting and fast regulation to variable renewable energy generation because of its large capacity and fast response. However, due to the complex operation characteristics of AA-CAES, there are few studies on AA-CAES optimal operation providing energy and reserve for power systems. Therefore, a new robust scheduling approach is proposed, in which the operation of AA-CAES is precisely characterized by the synergetic energy and reserve market. To fully exploit the operational flexibility of AA-CAES, this paper proposes a linear reserve model of AA-CAES by quickly switching working modes. The energy storage-reserve constraint is established to ensure the deliverability of reserve services. Then, an enhanced column and constraint generation (C&CG) algorithm is proposed to solve the proposed model. Finally, the proposed method is applied to the Garver-6 bus and IEEE-118 bus systems in the MATLAB operating environment. Numerical simulation results indicate that AA-CAES participating in power system scheduling can reduce the generation and reserve cost of conventional units, and improve wind power accommodation. By quickly switching the working mode, the adjustable reserve margin of AA-CAES can be effectively improved. In addition, compared with the existing nested C&CG algorithm, the enhanced C&CG algorithm effectively improves the solution efficiency of the proposed model.

First and second law analysis and operational mode optimization of the compression process for an advanced adiabatic compressed air energy storage based on the established comprehensive dynamic model

Compressed air energy storage (CAES) possesses great application potential. The dynamic characteristic of the compression process is meaningful for the parameter optimization and control design of the CAES system. A dynamic model of the compression process for an advanced adiabatic CAES (AA-CAES) system is created on the basis of the principles of conservations of mass, momentum, and energy of an opening system. The reliabilities of the proposed model are verified from two aspects of the compressor model and air storage device model. Energy and exergy analysis indicates that the proposed model follows the first and second laws of thermodynamics. The exergy losses of each component are calculated for the whole dynamic process. Calculation results show that the exergy losses in the compressors are higher than those in the heat exchangers. A multi-objective optimization is conducted by Genetic Algorithm. The mass optimum solution can improve ηex and mair by 0.014% and 0.660%, respectively. Four operational modes are proposed and optimized to improve the compression efficiency. The thermal performances of the design condition and four operational modes are simulated and compared. The comparison results show that modes N2 and N1 are the better operational modes, with high efficiency and low power consumptions, respectively.

Advanced exergy and exergo-economic analyses of an advanced adiabatic compressed air energy storage system

It is important to determine the source of the exergy destruction of the system components and their relations with each other in order to increase the working performance of energy charging systems. In this paper, conventional/advanced exergy and exergo-economic analyses of an advanced adiabatic compressed air energy storage (AA-CAES) system with a power output of 10 MW were performed. The results show that the proposed system accounts for 44 % of the exergy destruction and 31 % of the exergy destruction cost avoidable while 10 % of investment cost is avoidable. These percentage values represent the improvement potential for the system. Conventional exergy analysis results show that three most effective system components in exergy destruction as wind turbine, regulator and heat exchanger-3, respectively, while this order changes as wind turbine, compressor-1 and turbine-2 in advanced exergy analysis. This difference is due to technological limitations. In summary, advanced exergy analysis provides the designer with a detailed path regarding the improvement potential and cost savings of the system.

Optimal Dispatching of Ladder-Type Carbon Trading in Integrated Energy System With Advanced Adiabatic Compressed Air Energy Storage

Advanced adiabatic compressed air energy storage (AA-CAES) is a promising form of CAES technology, which can realize multi-energy cascade storage and supply. Given the characteristics of cooling, heating, and power (CHP) load demand of regionally integrated energy system (IES) and the overall need to control the total carbon emission, this study first establishes the AA-CAES multi-energy storage model; second, based on analyzing the multi-energy characteristics of AA-CAES, a ladder-type carbon trading mechanism is introduced; furthermore, aiming for minimal system operating costs and carbon trading costs, an optimal dispatch model of an integrated energy system with AA-CAES as the energy hub for coupling multiple energy forms is established. Finally, an empirical study was conducted based on the energy use scenario of a provincial and ministerial university in Qinghai Province. This study analyzes the impact of the operation of AA-CAES and the introduction of ladder-type carbon trading on the operating costs and carbon trading costs of IES. The simulation results show that the total operating cost of the proposed model is reduced by 23.9%, and carbon emission is decreased by 14.5% compared to the conventional IES. It proves the validity of coupling AA-CAES and stepped carbon trading mechanisms to facilitate low-carbon economy in IES.

Stochastic optimal dispatch of combined heat and power integrated AA-CAES power station considering thermal inertia of DHN

In order to improve the peak shaving capability of combined heat and power system and cope with uncertainties, a stochastic optimal dispatch model of combined heat and power system integrated Advanced Adiabatic Compressed Air Energy Storage (AA-CAES) power station considering the thermal inertia of District Heating Network (DHN) is proposed in this paper. The proposed model has the following characteristics: 1) AA-CAES power stations are configured in combined heat and power system, i.e., AA-CAES can supply both power and heat to the system, and the linearized model of AA-CAES power stations is established in this paper; 2) considering the thermal inertia of heating pipelines and buildings, the DHN model is established; 3) in order to deal with uncertainties in the system, the stochastic optimal (SO) dispatch model is established. The case study shows that compared with AA-CAES power stations which only provides power for the system, the combined heat and power supply of AA-CAES power stations can reduce the operation cost and the wind power curtailment. The thermal inertia of DHN cause the time shift of heat load, which effectively improves the operation flexibility of the combined electric-thermal system. In addition, configuring AA-CAES power station and considering the thermal inertia of DHN can enhance the ability of the system to cope with uncertainties.