CAES Systems Integrated into a Gas-Steam Combined Plant: Design Point Performance Assessment

Salvini, Coriolano

doi:10.3390/en11020415

Cited by 15 publications

(11 citation statements)

References 27 publications

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“…f(x,y) = 28.87 +1.63x + 15.47y -8.334x 2 + 5.496xy -2.678y 2 + 0.6549x 3 -3.215x 2 y -0.3593xy 2 + 0.8707y 3 -0.1285x 3 y + 0.6649x 2 y 2 + 0.5406xy 3 -0.1293y 4 (7) Equation of Prediction Curve 2: f(x,y) = 7.777 + 8.027x -9.332y -3.915x 2 + 6.99xy -9.751y 2 + 2.65x 3 -0.01228x 2 y -2.42xy 2 -0.9329y 3 The extracted equations from the regression process (Equations 7 to 11) will use to represent the regression predictions. The value of regression predictions can be known by entering the value of power curves parameter according to the parameters that describe each variable of power curves (f for power, x for air pressure and y for air motor speed).…”

Section: Equation Of Prediction Curvementioning

confidence: 99%

“…Technological developments in energy storage are growing due to the high energy use derived from renewable energy sources that can be combined to support peak loads on the power grid [1][2][3][4][5], one of which is the use of compressed air energy storage (CAES). This technology is one of the promising technologies because it is characterized by high reliability and low environmental impact [6][7][8][9][10]. The main disadvantage of big systems of this technologies is the reliance on geological formations required for energy storage such as A-CAES, D-CAES, and I-CAES [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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Power curves prediction using empirical data regression on small scale compressed air energy storage

Widjonarko

Soenoko

Wahyudi

et al. 2019

JMES

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The key to optimizing the system is to know the operating point of the system at the time of loading, or it is known as the power curve. However, to identify the power curve, the existing method is to model the mathematical of the system. Therefore some component characteristics need to be known and need additional observations if the component variable is unknown, and it becomes a long identification process. So, in this exploratory research will be presented the way to find out the power curve of a system without modeling mathematical of the system, but by using the polynomial regression technique. This regression technique form is using the empirical data of the power curve form parameter on SS-CAES prototype. The method is based on five approach model in which is the variation of loading sampling data to be used with the purpose is to find the best sampling of prediction. The data will be analyzed in the form of statistical parameters and the graph to show the evaluation process of this technique. From the results of the regression can be concluded that the power curve of SS-CAES can be identified with a high correlation value of 0.997 (99,745% accuracy) and the best way to take samples of data to be used in this technique is presented in the paper.

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Section: Equation Of Prediction Curvementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Power curves prediction using empirical data regression on small scale compressed air energy storage

Widjonarko

Soenoko

Wahyudi

et al. 2019

JMES

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show abstract

“…In addition, p ou C , p a and η p C are the outlet pressure, the inlet atmospheric pressure and the polytropic efficiency of the compressor, respectively. P C also can be expressed as the wind power surplus P WT ex adjusted with respect to the electric efficiency η e WT of the WT and the transmission efficiency η tr between the electric motor and the compressor, as presented in Equation (9).…”

Section: Diesel Generatormentioning

confidence: 99%

“…Salvini [9] conducted a techno-economic analysis of a CAES system integrated into a small-size gas steam combined cycle plant. The economic analysis focused on plant performance and investments costs.…”

Section: Introductionmentioning

confidence: 99%

Computer Model for Financial, Environmental and Risk Analysis of a Wind–Diesel Hybrid System with Compressed Air Energy Storage

et al. 2019

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Remote and isolated communities in Canada experience gaps in access to stable energy sources and must rely on diesel generators for heat and electricity. However, the cost and environmental impact resulting from the use of fossil fuels, especially in local energy production, heating, industrial processes and transportation are compelling reasons to support the development and deployment of renewable energy hybrid systems. This paper presents a computer model for economic analysis and risk assessment of a wind-diesel hybrid system with compressed air energy storage. The proposed model is developed from the point of view of the project investor and it includes technical, financial, risk and environmental analysis. Robustness is evaluated through sensitivity analysis. The model has been validated by comparing the results of a wind-diesel case study against those obtained using HOMER (National Renewable Energy Laboratory, Golden, CO, United States) and RETScreen (Natural Resources Canada, Government of Canada, Canada) software. The impact on economic performance of adding energy storage system in a wind-diesel hybrid system has been discussed. The obtained results demonstrate the feasibility of such hybrid system as a suitable power generator in terms of high net present value and internal rate of return, low cost of energy, as well as low risk assessment. In addition, the environmental impact is positive since less fuel is used.Energies 2019, 12, 4054 2 of 23 making well suited for the deployment of hybrid systems. Wind-diesel hybrid system with compressed air energy storage (WDCAS) appears as a cost-effective alternative to supply electricity and to ensure diesel generator operational efficiency. The combination of these sources with an energy storage system (ESS) helps to balance variations in power supply and demand.A wind-diesel hybrid system is a stand-alone power system with wind and diesel generators. These systems are recommended for medium scale applications when the use of diesel generators is unavoidable. ESS integration facilitates a hybrid stand-alone system to optimize energy usage while maintaining efficient demand response. Compressed air energy storage (CAES) appears as an economically mature technology. Cost, simplicity, lifespan, fuel consumption and greenhouse gas (GHG) emissions are all influential factors that determine the competitiveness of this technology for wind-diesel hybridization. Moreover, CAES represents a promising solution to store electricity derived from kinetic energy of air at a particular time for further utilization.WDCAS operates according to compression-decompression cycles. The overall performance is subject to load power requirements, energy sources availability and energy storage level. The principle consists of two main phases. During high wind speed conditions, the excess of air is compressed and stored in a reservoir, when the energy is needed, the stored compressed air is released to drive a turbo-generator for electricity production. During low wind speed conditions,...

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“…CAES technology consists of two storage tank constructions: namely construction for large-scale and small scale technology. The constructions of large-scale CAES technologies are A-CAES, D-CAES, and I-CAES [22], [23]. This large scale system has a weakness; it has dependence on geological formations because it requires a large air storage space to store energy.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Power Characteristic Curve on Small Scale Compressed Air Energy Storage by Using Regression Analysis

Widjonarko¹,

Soenoko²,

Wahyudi³

et al. 2018

Preprint

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The research on small scale compressed air energy storage (SS-CAES) becomes an interesting topic especially in optimizing the performance of the system. In this topic, the characteristic curve of the energy storage system is the key to control the system to reach optimum power to the load. In previous research, mathematical equations were used to get the characteristic curve. This paper proposes the polynomial regression based on the actual output data from the prototype to model the characteristic curve of the SS-CAES prototype. The authors have compared the use of mathematical models and polynomial regression in modeling the power curve with actual observational data and determining the level of accuracy of modeling. The results showed that by using polynomial regression, the characteristics of the SS-CAES prototype power curve could only be obtained by using the sample data from the system output with accuracy value 0.967 for R-square. Thus, an approach using this method would facilitate researchers to obtain the characteristics of the curve of the system.

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CAES Systems Integrated into a Gas-Steam Combined Plant: Design Point Performance Assessment

Cited by 15 publications

References 27 publications

Power curves prediction using empirical data regression on small scale compressed air energy storage

Power curves prediction using empirical data regression on small scale compressed air energy storage

Computer Model for Financial, Environmental and Risk Analysis of a Wind–Diesel Hybrid System with Compressed Air Energy Storage

Prediction of Power Characteristic Curve on Small Scale Compressed Air Energy Storage by Using Regression Analysis

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