A Power System Optimal Dispatch Strategy Considering the Flow of Carbon Emissions and Large Consumers

Yang, Jun; Feng, Xin; Tang, Youxi; Yan, Jun; He, Haibo; Luo, Chao

doi:10.3390/en8099087

Cited by 9 publications

(4 citation statements)

References 29 publications

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“…In actual production, factors such as unit capacity specifications, fuel quality, operating status, and load rate all can affect the carbon emission intensity. The carbon emission intensity of coal-fired units E G [32] is calculated as follows:…”

Section: Modeling Of Carbon Emissions From Source-side Unitsmentioning

confidence: 99%

Coupled Model and Node Importance Evaluation of Electric Power Cyber-Physical Systems Considering Carbon Power Flow

Yang

Sun

et al. 2022

Energies

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To improve the distributed carbon emission optimization control capability of the smart distribution network system, thereby reducing the carbon emissions in the distribution process, it is a very important issue to comprehensively analyze the importance of the node carbon emission flow of the smart distribution network. This paper transforms the power grid into a carbon emission flow network through power flow calculations: Based on the complex network theory, it determines the coupling scale of the two networks by means of the correlation coefficient method and the correlation matrix method, and establishes a coupling network model based on the carbon emission flow network; Combining the different business characteristics of carbon emission flow and information flow, an evaluation index system considering the dual-network coupling scale is established, and a multi-indicator comprehensive evaluation method that combines the Topsis and grey relational analysis method, that can objectively evaluate indicators that contain subjective components was proposed; The obtained node importance values can be used to determine the relative key line, greater sum node importance values represent a greater carbon emission impact of the line, providing a sequential basis for the carbon reduction and restructuring of the distribution network; Taking the 3-machine 9-node system as an example, the carbon flow distribution in the corresponding network is calculated, and the comprehensive importance value of the coupling node is calculated to analyze the rationality of this method.

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Section: Modeling Of Carbon Emissions From Source-side Unitsmentioning

confidence: 99%

Coupled Model and Node Importance Evaluation of Electric Power Cyber-Physical Systems Considering Carbon Power Flow

Yang

Sun

et al. 2022

Energies

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“…However, such obligations need to provide proper information to the demand side. The influence of a change on the generation or demand sides has been investigated via marginal carbon intensity [23], but it neither provides suitable information for the generation side nor for the demand side. In [24], two issues such as the carbon accounting at the regional level and locational carbon intensity assessment at the user level were addressed.…”

Section: B Parametersmentioning

confidence: 99%

“…Consequently, via a distribution process, → can be taken away, by adjusting it to zero, and distributing it over according to the component ratio of using (23) and (24). In (23), the UPR at iteration k+1 from each bus h to bus i is equal to the UPR at the previous iteration k plus the UPR from bus h to j (for each bus h adjacent to i) multiplied by the UPR from bus j to bus i. It is worth mentioning that this distribution is done if there is any bus h that satisfies → .…”

Section: ) Traced and Untraced Power Ratiosmentioning

confidence: 99%

Carbon Footprint Management: A Pathway Toward Smart Emission Abatement

Pourakbari‐Kasmaei

Lehtonen

Contreras

et al. 2020

IEEE Trans. Ind. Inf.

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There is an increasing concern about controlling and reducing carbon emissions in power systems. In this regard, researchers have focused on managing emissions on the generation side, which is the main source of emissions. Considering emission limits on the generation side results in an increase in locational marginal prices that negatively affects social welfare. However, carbon emissions are a by-product of electricity generation that is used to satisfy the demands on the consumer side. Consequently, demand side emission control may not be achieved if only generation is taken into account. In order to fill this existing gap, in this paper, a demand-side management approach aiming at carbon footprint control is proposed. First, the carbon footprint is allocated among the consumers using an improved proportional sharing theorem method. Each consumer learns about their real-time carbon footprint, excess carbon footprint, and the incurred surcharge tax. Then, demands are adjusted via a proper adjustment procedure. This provides enough information for consumers where demand management may result in carbon footprint and demand reductions as well as the exemption of incurred taxes. Profit analysis for both generation and demand sides is carried out to show the effectiveness of the proposed framework using two illustrative case studies. The results obtained, compared with existing policies such as carbon cap, cap-and-trade, and carbon tax, prove the fairness and the advantages of the proposed model for both the demand and the generation sides.Index Terms-Carbon footprint allocation, carbon abatement, demand side management, power tracing, tax exemption.

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“…As one of the primary sources of carbon emissions, the power industry has a dramatic potential for carbon emissions reduction and obvious scope for optimization. In the past two decades, numerous studies have been conducted regarding power system planning and dispatching under market circumstances that take into account carbon trading and, in particular, future carbon prices [2][3][4]. Therefore, the development of a reliable carbon price forecasting and analysis approach is the key to anticipating the changing trends of the energy market, and, thus, to provide a valid reference for establishing power industry policy.…”

Section: Introductionmentioning

confidence: 99%

A Carbon Price Forecasting Model Based on Variational Mode Decomposition and Spiking Neural Networks

et al. 2016

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Accurate forecasting of carbon price is important and fundamental for anticipating the changing trends of the energy market, and, thus, to provide a valid reference for establishing power industry policy. However, carbon price forecasting is complicated owing to the nonlinear and non-stationary characteristics of carbon prices. In this paper, a combined forecasting model based on variational mode decomposition (VMD) and spiking neural networks (SNNs) is proposed. An original carbon price series is firstly decomposed into a series of relatively stable components through VMD to simplify the interference and coupling across characteristic information of different scales in the data. Then, a SNN forecasting model is built for each component, and the partial autocorrelation function (PACF) is used to determine the input variables for each SNN model. The final forecasting result for the original carbon price can be obtained by aggregating the forecasting results of all the components. Actual InterContinental Exchange (ICE) carbon price data is used for simulation, and comprehensive evaluation criteria are proposed for quantitative error evaluation. Simulation results and analysis suggest that the proposed VMD-SNN forecasting model outperforms conventional models in terms of forecasting accuracy and reliability.

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A Power System Optimal Dispatch Strategy Considering the Flow of Carbon Emissions and Large Consumers

Cited by 9 publications

References 29 publications

Coupled Model and Node Importance Evaluation of Electric Power Cyber-Physical Systems Considering Carbon Power Flow

Coupled Model and Node Importance Evaluation of Electric Power Cyber-Physical Systems Considering Carbon Power Flow

Carbon Footprint Management: A Pathway Toward Smart Emission Abatement

A Carbon Price Forecasting Model Based on Variational Mode Decomposition and Spiking Neural Networks

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