Low-Carbon Economic Dispatch of an Integrated Energy System Based on Carbon Emission Flow Theory

Liu, Zheyuan; Xing, Haijun; Luo, Yangfan; Ye, Yujing; Shi, Yingchao

doi:10.1007/s42835-022-01298-7

Cited by 14 publications

(5 citation statements)

References 17 publications

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“…According to Krishna et al, AI algorithms and big data models are used to predict energy demand and price trends by analyzing historical data, current trends, and market factors. This can help companies make more accurate decisions for better production and operational planning [38,39]. Furthermore, Wang et al (2023) argued that utilizing AI algorithms and big data models enables intelligent analysis and optimization of various complex factors in energy systems, thereby improving the efficiency and reliability of the energy systems [40,41].…”

Section: E Discussionmentioning

confidence: 99%

The Optimization of Carbon Emission Prediction in Low Carbon Energy Economy Under Big Data

Luo,

Zhuo,

Liu

et al. 2024

IEEE Access

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With the intensification of global climate change, low-carbon energy has become a hot topic, and governments around the world are implementing corresponding policies to promote its use. This research first establishes a Multi-universe Quantum Harmony Search-Algorithm Dynamic Fuzzy System Ensemble (MUQHS-DMFSE) composite model for carbon emission prediction. This model combines the MUQHS algorithm with the DMFSE method by designing the workflow of the MUQHS algorithm, creating a DMFSE composite prediction model, introducing a sliding factor matrix, and using the MUQHS algorithm to search for the optimal sliding factors, thus obtaining optimized prediction values. In the research on low-carbon economic development, the research applies the Data Envelopment Analysis (DEA) method and establishes Charnes-Cooper-Rhodes (CCR) and Banker-Charnes-Cooper (BCC) models to assess the technical efficiency, pure technical efficiency, and scale efficiency of decision-making units. This research also uses the BCC model to project the production frontier and calculate input redundancy and output gap rates, and evaluate low-carbon economic development. Through the establishment and application of these two models, the research achieves carbon emission prediction and low-carbon economic analysis, validating the feasibility of the research methodology. The results show that the composite model can effectively predict carbon emissions, with a Mean Absolute Percentage Error (MAPE) below 3.5% and Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) below 200 tons, demonstrating the feasibility and accuracy of the model. The research on low-carbon economic development in S Province based on the DEA method reveals the need for energy structure adjustment, clean and renewable energy promotion, control of carbon emissions, and optimization of industrial structure with a focus on developing the tertiary industry. Therefore, the use of artificial intelligence and big data analysis can provide more precise insights into the trends and patterns of low-carbon economic development, as well as more effective predictions of future energy demand and resource supply, offering high practical value and scientific significance.INDEX TERMS low-carbon energy, MUQHS-DMFSE composite model, low-carbon economy, energy demand, resource supply

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Section: E Discussionmentioning

confidence: 99%

The Optimization of Carbon Emission Prediction in Low Carbon Energy Economy Under Big Data

Luo,

Zhuo,

Liu

et al. 2024

IEEE Access

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“…For this purpose, it is necessary to find the relationship between the carbon emissions and the power flow in the power system. This problem can be solved by the carbon emission flow theory (Wang C. et al, 2022;Liu et al, 2022;Huang et al, 2023), which establishes the correspondence between the carbon emission responsibilities and the flow of each bus and branch in the power system, as follows: According to the theory of flow tracing, each load is supplied by all the power sources in the network based on the principle of "proportional sharing", and then we can get the power contribution of any generating unit to any outgoing line load. For carbon emission flow analysis as well, the carbon emission flow of each incoming line of the node is uniformly mixed at the node, and the carbon emission flowing through each outgoing line is a proportional mix of each incoming line with equal branch carbon intensity.…”

Section: Carbon Flow Theorymentioning

confidence: 99%

A bi-layer wind-CCUS-battery expansion stochastic planning framework considering a source-load bilateral carbon incentive mechanism based on the carbon emission flow theory

Deng,

Nan,

Feng

et al. 2023

Front. Energy Res.

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The rapid development of low-carbon energy technologies and energy storage technologies has provided an important and feasible path to decarbonizing the power system. In this context, there is an increasing number of studies on renewable energy, carbon capture, utilization and storage (CCUS) and energy storage expansion planning. However, most of the existing studies attribute the carbon responsibilities to the source side and a small number to the load side. Expansion planning studies that consider the overall carbon emissions of the system to be shared between the source and the load side are still relatively few. Therefore, it is necessary for the source and the load side to share the responsibility for the total system carbon emissions. To fill this research gap, this paper proposes a source-load bilateral carbon incentive mechanism for wind-CCUS-battery power systems based on the carbon emission flow theory. Besides, a bi-layer wind-CCUS-battery expansion stochastic planning framework considering wind and load uncertainties is constructed. The first layer takes the minimum expectation of power generation costs, fixed investment costs of wind turbines and CCUS units and carbon incentive costs as the objective function from a source-side perspective. The second layer takes the minimum battery investment cost and the expectation of electricity purchasing costs and load-side carbon incentive costs as the objective function from a load-side perspective. Finally, the proposed model is tested on the IEEE 24 bus power system for validity and advantage. The results show that the current high investment cost is not favorable to CCUS construction. At this time, the bilateral carbon incentive mechanism is more conducive to promoting system carbon reduction than the unilateral carbon incentive mechanism. In the future, as the cost of CCUS decreases, the source-side carbon incentive mechanism is more conducive to system carbon reduction than the bilateral carbon incentive mechanism. Due to the consideration of the stochastic uncertainty of wind turbines and loads, the research in this paper is closer to the reality, which can provide a reference for the future carbon emission reduction path of the power system, especially for the quantitative analysis of carbon emission reduction of CCUS, which is an important guiding significance for the promotion of the engineering practice of CCUS.

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“…Reference [6] studied the computational properties of carbon flow to develop a recursive algorithm for its calculation and proposed a collaborative process for direct and recursive algorithms to select appropriate calculation methods based on the characteristics of transmission and distribution networks. Reference [7] introduces a two-stage lowcarbon scheduling model for a power system that uses carbon price as a pricing signal for demand response and achieves wind power consumption and carbon emission responsibility sharing among loads. Reference [8] considers that the essential characteristic of a power system is that the load side dominates the supply side.…”

Section: Introductionmentioning

confidence: 99%

“…It establishes a low-carbon optimization operation model for a distribution system. In summary, while the theory of carbon emission flow has been widely applied in low-carbon development in the power sector, previous studies [1][2][3][4][5][6][7][8][9][10][11][12][13] have not considered the full life-cycle carbon costs on the unit side, indicating a need for further improvements. This paper considers the full life-cycle carbon costs of generating units and utilizes electricity load data to establish a city-level electricity-to-carbon traceability model based on the theory of carbon emission flow in the power system.…”

Section: Introductionmentioning

confidence: 99%

A method to calculate the power-carbon emissions flow with the consideration of the full life-cycle carbon cost

Ye,

Zhang,

Huang

et al. 2024

Ninth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2023)

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The proposed "double carbon" target by the Chinese government underscores the importance of reducing carbon emissions in the power industry. The power industry, whose carbon emission accounts for about 43.1% of the overall energy-related carbon emissions, is one of the Chinese largest carbon-emitting industries. Calculating the carbon emissions flow in power systems helps to better reduce the use of conventional fossil fuel and complete the "double carbon" target. This paper first analyzes the full life-cycle carbon cost from the device perspective, thus setting a boundary for carbon accounting. Then the calculation method of power-carbon emissions flow is developed. Finally, the performance of the proposed method is evaluated based on the Lishui City power system.

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Low-Carbon Economic Dispatch of an Integrated Energy System Based on Carbon Emission Flow Theory

Cited by 14 publications

References 17 publications

The Optimization of Carbon Emission Prediction in Low Carbon Energy Economy Under Big Data

The Optimization of Carbon Emission Prediction in Low Carbon Energy Economy Under Big Data

A bi-layer wind-CCUS-battery expansion stochastic planning framework considering a source-load bilateral carbon incentive mechanism based on the carbon emission flow theory

A method to calculate the power-carbon emissions flow with the consideration of the full life-cycle carbon cost

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