Long-term electricity consumption forecasting method based on system dynamics under the carbon-neutral target

Li, Jinghua; Luo, Yichen; Wei, Shanyang

doi:10.1016/j.energy.2021.122572

Cited by 36 publications

(12 citation statements)

References 35 publications

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“…However, to alleviate the increase in its energy consumption and accomplish the carbon peak emission goal, China requires the development of new zero-carbon emission technologies and the increase in the share of renewable energy sources. [35,36] For example, a higher percentage of aluminum metal would be supplied from the aluminum recycling process in the foreseeable future. In such a scenario, the demand for low-sulfur PC is expected to decline, which leads to an imbalance in PC cycles.…”

Section: Carbon-reduction Incentives To Sustainable Utilization Of Pcmentioning

confidence: 99%

High‐Purity Graphitic Carbon for Energy Storage: Sustainable Electrochemical Conversion from Petroleum Coke

Zhu

Song

et al. 2023

Advanced Science

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The petroleum coke (PC) has been widely used as raw materials for the preparation of electrodes in aluminium electrolysis and lithium‐ion batteries (LIB), during which massive CO2 gases are produced. To meet global CO2 reduction, an environmentally friendly route for utilizing PC is highly required. Here, a simple, scalable, catalyst‐free process that can directly convert high‐sulfur PC into graphitic nanomaterials under cathodic polarization in molten CaCl2‐LiCl at mild temperatures is proposed. The energy consumption of the proposed process is calculated to be 3 627.08 kWh t−1, half that of the traditional graphitization process (≈7,825.21 kWh t−1 graphite). When applied as a negative electrode for LIBs, the as‐converted graphite materials deliver a competitive specific capacity of ≈360 mAh g−1 (0.2 C) compared with commercial graphite. This approach has great potential to scale up for sustainably converting low‐value PC into high‐quality graphite for energy storage.

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Section: Carbon-reduction Incentives To Sustainable Utilization Of Pcmentioning

confidence: 99%

High‐Purity Graphitic Carbon for Energy Storage: Sustainable Electrochemical Conversion from Petroleum Coke

Zhu

Song

et al. 2023

Advanced Science

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show abstract

“…On the other hand, the approach presented in [17] covers the prediction of the behavior of an electrical network from the data obtained from the system, making use of deep learning. The main difference with this paper is one of use dynamic systems for long-term prediction, while our prediction is focused on the short term to be very accurate and to be able to get actions as close to reality as possible.…”

Section: Related Workmentioning

confidence: 99%

Maintaining flexibility in smart grid consumption through deep learning and deep reinforcement learning

et al. 2023

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“…Qiao et al (2019) emphasized the importance of indicators such as economic growth, urbanization, and trade development, among various coal demand factors. However, Li et al (2021) pointed out that the traditional influencing factor system is not suitable for the demand prediction research under the carbon neutrality target. The proposal of the carbon neutrality target will inevitably bring about new influencing factors such as technological progress and the growth rate of the proportion of renewable energy.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In addition to the complexity, the coal demand system also has the characteristics like nonlinear, dynamic, and time delay. It is affected by the insufficient data and the difficulty of quantifying some parameters or relations, making the system dynamics more suitable for the research of the coal demand system than the general econometric model (Li et al, 2021;Yang Q et al, 2021;Hosseinipooya et al, 2022). Therefore, this paper uses the system dynamics model and analyzes the causal relationship between system variables according to the influencing factors, clarifies the stocks, variables, auxiliary functions and parameters in the model, and then draws the coal demand system dynamics flow diagram and constructs the coal demand system dynamics model, so as to achieve the purpose of exploring the changing trend of coal demand in the future.…”

Section: System Dynamics Modelmentioning

confidence: 99%

Research on coal demand forecast and carbon emission reduction in Shanxi Province under the vision of carbon peak

Zhang²,

Qie³

et al. 2022

Front. Environ. Sci.

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Facing the increasingly severe climate situation, China strives to improve its Nationally Determined Contributions, promising to reach its carbon peak by 2030. Accurately predicting the future demand quantity and changing the trends of coal resources is the key to maintaining national energy security and achieving the goal of “carbon peak” and is also an important research topic in the future. To improve the prediction accuracy, this study sorts out eight common factors affecting the coal demand from the aspects of the economy, population, and energy. The grey relational analysis method was used to describe the degree of importance of each factor and screen out the relatively vital factors. The system dynamics model for coal demand in Shanxi Province was also established. The coal demand and carbon emission trends in Shanxi Province from 2021 to 2030 were predicted under business-as-usual, low-speed and high-speed development scenarios. The study results show that: 1) During the 14th Five-Year Plan to 15th Five-year Plan, the trend of coal demand has changed from increasing to decreasing. Under the three scenarios, the coal demand in 2021 is expected to be 372.03 million tons, 365.97 million tons, 360.04 million tons, and it is expected that the coal demand will peak in 2025, and will reach 394.77 million tons, 390.27 million tons, and 385.66 million tons under the three scenarios, respectively. 2) With the development of population and economy, the total energy demand shows a continuously increasing trend. It is estimated that the total energy consumption will reach 237.46-242.93 million tons of standard coal in 2030, and the total energy demand will continue to grow in the foreseeable future. 3) The carbon dioxide emission is closely related to the coal demand, showing a similar variation trend. Under the business-as-usual development scenario, carbon emissions in 2021 are 699.62 million tons, and carbon emissions are expected to reach 681.12 million tons in 2030. The low-speed development and high-speed development scenarios have different degrees of energy saving and emission reduction benefits. Compared with the business-as-usual development scenario in 2030, the carbon dioxide emissions will be reduced by 1.64 million tons and 3.56 million tons respectively. Finally, we put forward important policy measures to promote the economic transformation and upgrading of Shanxi Province and accelerate the realization of the “carbon peak” goal.

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Long-term electricity consumption forecasting method based on system dynamics under the carbon-neutral target

Cited by 36 publications

References 35 publications

High‐Purity Graphitic Carbon for Energy Storage: Sustainable Electrochemical Conversion from Petroleum Coke

High‐Purity Graphitic Carbon for Energy Storage: Sustainable Electrochemical Conversion from Petroleum Coke

Maintaining flexibility in smart grid consumption through deep learning and deep reinforcement learning

Research on coal demand forecast and carbon emission reduction in Shanxi Province under the vision of carbon peak

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