Dynamic optimization of a hybrid system of energy-storing cryogenic carbon capture and a baseline power generation unit

Safdarnejad, Seyed Mostafa; Hedengren, John D.; Baxter, Larry

doi:10.1016/j.apenergy.2016.03.074

Cited by 37 publications

(20 citation statements)

References 40 publications

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“…Countries around the world have developed many technologies and proposed various policies to promote the development of renewable energy such as large-scale energy storage [1], feed-in tariffs (FIT) and renewable portfolio standards (RPS). Large-scale energy storage is a practical solution in adopting renewable energy resources in power grid as an important technology [2], and many countries have considered it as a viable solution to increase the contribution of renewable energy resources [3,4]. Renewable energy policies can also significantly contribute to the expansion of domestic industrial The model not only clearly shows the complex logical relationship between the factors but also reveals the process of coordination between the two policy tools in renewable energy power industry's development.…”

Section: Introductionmentioning

confidence: 99%

The Development of the Renewable Energy Power Industry under Feed-In Tariff and Renewable Portfolio Standard: A Case Study of China’s Photovoltaic Power Industry

et al. 2017

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Among the regulatory policies, feed-in tariffs (FIT) and renewable portfolio standards (RPS) are the most popular to promote the development of renewable energy power industry. They can significantly contribute to the expansion of domestic industrial activities in terms of sustainable energy. In this paper, we synthetically consider various important factors with the analysis of the existing literature, and use system dynamics (SD) to establish models of long-term development of the renewable energy power industry under FIT and RPS schemes. The model not only clearly shows the complex logical relationship between the factors but also reveals the process of coordination between the two policy tools in the development of the renewable energy power industry. In addition, as an example of development of renewable energy industry, the paper studies the development of China's photovoltaic power industry under different scenarios. The models proposed in this paper can provide a reference for scholars to study development of the renewable energy power industry in different countries, thereby facilitating an understanding of the renewable energy power's long-term sustainable development pattern under FIT and RPS schemes, and helping to provide references for policy-making institutions. The results show that in the perfect competitive market, the implementation of RPS can promote long-term and rapid development of China's photovoltaic power industry given the constraints and actions of the mechanisms of RPS quota proportion, the TTGC valid period, and fines, compared with FIT. At the end of the paper, policy implications are offered as references for the government.Sustainability 2017, 9, 532 2 of 23 activities in terms of sustainable energy [5]. Among the regulatory policies, FIT and RPS are the most popular. More than 60 countries and regions worldwide have implemented one or other of the two policies [6]. FIT and RPS have common attributes, in that both are policy tools with dual characteristics of government intervention and market regulation.FIT policy, represented by China, South America, and most European countries, is a scheme designed to accelerate investment in renewable energy technologies. It is a government-led regulatory mechanism that requires power grid enterprises to buy electricity from renewable energy producers at government-specified prices. In the early stages of renewable energy development, it ensured the sale of renewable energy at a protected price, ensuring that the high costs of electricity generation associated with certain renewable energy technologies do not prohibit the development and use of those technologies, eliminating the usual uncertainties and risks of renewable energy [7]. The goal of the FIT is to offer cost-based compensation to renewable energy producers, providing them with price certainty and long-term contracts that help finance renewable energy investments [8].

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Section: Introductionmentioning

confidence: 99%

The Development of the Renewable Energy Power Industry under Feed-In Tariff and Renewable Portfolio Standard: A Case Study of China’s Photovoltaic Power Industry

et al. 2017

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“…Various CO 2 capture and sequestration technologies have been developed to reduce CO 2 emissions and the International Energy Agency (IEA) has announced that CO 2 capture and sequestration (CCS) technology will cover 19% of the total CO 2 reductions by 2050 [1][2][3][4][5][6]. Among them, geological sequestration will be deeply considered to reduce CO 2 emissions at low cost [7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

The Use of the Surface Roughness Value to Quantify the Extent of Supercritical CO2 Involved Geochemical Reaction at a CO2 Sequestration Site

et al. 2017

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Changes in the physical properties of the supercritical CO 2 (scCO 2 ) reservoir rock is one of the most important factors in controlling the storage safety at a scCO 2 sequestration site. According to recent studies, it is probable that geochemical reactions influence changes in the rock properties after a CO 2 injection in the subsurface, but quantitative data that reveal the interrelationship of the factors involved and the parameters needed to evaluate the extent of scCO 2 -rock-groundwater reactions have not yet been presented. In this study, the potential for employing the surface roughness value (SR RMS ) to quantify the extent of the scCO 2 involved reaction was evaluated by lab-scale experiments. For a total of 150 days of a simulation of the scCO 2 -sandstone-groundwater reaction at 100 bar and 50 • C, the trends in changes in the physical rock properties, pH change, and cation concentration change followed similar logarithmic patterns that were significantly correlated with the logarithmic increase in the SR RMS value. These findings suggest that changes in surface roughness can quantify the extent of the geochemical weathering process and can be used to evaluate leakage safety due to the progressive changes in rock properties at scCO 2 storage sites.

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“…The MSFLA-LSSVM model is used to forecast the CO 2 The relative error of each prediction point is shown in Table 2. From Figure 3 and Table 2, it can be concluded that using the MSFLA-LSSVM model to forecast CO 2 emissions in China can obtain good forecasting results, and the forecasting curve has a good fitting degree with the actual curve.…”

Section: Forecasting Effect Test For Msfla-lssvm Modelmentioning

confidence: 99%

“…CO 2 is one of the main gases that cause the greenhouse effect. Carbon capture and storage is the process of capturing and storing the CO 2 in industrial production by various means, such as cryogenic carbon capture [1,2], chemical absorption [3], oxyfuel combustion [4] and so on. Carbon capture technologies that result in significant reduction in energy-related CO 2 emissions, specifically from coal-fired power plants, can relieve the greenhouse effect.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting of Energy-Related CO2 Emissions in China Based on GM(1,1) and Least Squares Support Vector Machine Optimized by Modified Shuffled Frog Leaping Algorithm for Sustainability

2018

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Presently, China is the largest CO 2 emitting country in the world, which accounts for 28% of the CO 2 emissions globally. China's CO 2 emission reduction has a direct impact on global trends. Therefore, accurate forecasting of CO 2 emissions is crucial to China's emission reduction policy formulating and global action on climate change. In order to forecast the CO 2 emissions in China accurately, considering population, the CO 2 emission forecasting model using GM(1,1) (Grey Model) and least squares support vector machine (LSSVM) optimized by the modified shuffled frog leaping algorithm (MSFLA) (MSFLA-LSSVM) is put forward in this paper. First of all, considering population, per capita GDP, urbanization rate, industrial structure, energy consumption structure, energy intensity, total coal consumption, carbon emission intensity, total imports and exports and other influencing factors of CO 2 emissions, the main driving factors are screened according to the sorting of grey correlation degrees to realize feature dimension reduction. Then, the GM(1,1) model is used to forecast the main influencing factors of CO 2 emissions. Finally, taking the forecasting value of the CO 2 emissions influencing factors as the model input, the MSFLA-LSSVM model is adopted to forecast the CO 2 emissions in China from 2018 to 2025.

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Dynamic optimization of a hybrid system of energy-storing cryogenic carbon capture and a baseline power generation unit

Cited by 37 publications

References 40 publications

The Development of the Renewable Energy Power Industry under Feed-In Tariff and Renewable Portfolio Standard: A Case Study of China’s Photovoltaic Power Industry

The Development of the Renewable Energy Power Industry under Feed-In Tariff and Renewable Portfolio Standard: A Case Study of China’s Photovoltaic Power Industry

The Use of the Surface Roughness Value to Quantify the Extent of Supercritical CO2 Involved Geochemical Reaction at a CO2 Sequestration Site

Forecasting of Energy-Related CO2 Emissions in China Based on GM(1,1) and Least Squares Support Vector Machine Optimized by Modified Shuffled Frog Leaping Algorithm for Sustainability

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