Regional power system carbon emission influence factor analysis and carbon peak reaching prediction method

Abstract

The invention discloses a regional power system carbon emission influence factor analysis and carbon peak reaching prediction method, and relates to the technical field of carbon emission analysis. The method comprises the following steps: firstly, constructing a source-network-load carbon emission flow iterative algorithm, calculating a node carbon emission coefficient and branch carbon flow density, and verifying the effectiveness of the algorithm through an IEEE14 node system; then, based on an LMDI decomposition model, carbon emission driving factors are decomposed into eight influence factors from the perspective of the full life cycle, and the contribution degree and the action mechanism of each factor to carbon emission are clarified; according to the method, the carbon emission condition and influence factors of each link of the regional power system can be accurately quantified, a scientific basis is provided for formulating a carbon emission reduction strategy for the regional power industry, and a carbon peak reaching target is assisted to be realized.

Description

Technical Field

[0001] This invention belongs to the field of carbon emission analysis and prediction technology, specifically relating to a method for analyzing the influencing factors of carbon emissions in a regional power system and predicting carbon peaking. Background Technology

[0002] With the introduction of the "dual carbon" target, carbon emissions from regional power systems have received widespread attention. As a major sector of energy consumption and carbon emissions, the power system's carbon emission influencing factors are complex, involving multiple stages such as power generation, transmission and distribution, and electricity consumption. Currently, analyses of regional power system carbon emissions often focus on single stages or a few influencing factors, lacking a systemic decomposition from a life-cycle perspective. Furthermore, there is insufficient precision in calculating carbon emission flows and quantitative analysis of the contribution of each factor, making it difficult to effectively support carbon reduction decisions and carbon peak prediction in the regional power industry. Therefore, a method capable of comprehensively and accurately analyzing the influencing factors of regional power system carbon emissions is urgently needed. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method for analyzing the influencing factors of carbon emissions in regional power systems and predicting carbon peak, so as to achieve a comprehensive analysis and accurate quantification of carbon emissions in regional power systems.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: A method for analyzing the influencing factors of carbon emissions in a regional power system and predicting carbon peaking includes the following steps: Constructing an iterative algorithm for source-grid-load carbon emission flow 1.1 Define the nodal carbon emission factor and branch carbon flux density. The formula for calculating the nodal carbon emission factor is as follows: Where Ii and Ti represent the sets of all branches of a node into which functional energy is injected; and represent the set of branches from branch P. b Inflowing active power; ρ b G represents the branch carbon flux density of branch b; s This represents the active power of the generator connected to node i; e s This indicates the carbon dioxide coefficient of the generator set.

[0005] 1.2 For a power system consisting of n nodes, the active power can be represented by a matrix as follows: The main diagonal element pii is the active power injected into node i by the generator connected to node i; pij is the active power injected into node j by node i connected to node j.

[0006] 1.3 Initial generator's power carbon factor vector Each time the nodal carbon potential is calculated, the i-th element of the nodal carbon potential vector is updated, as shown in the formula: .

[0007] 1.4 Sort the network nodes. If aij is negative, swap nodes i and j and related nodes. Swap and reverse elements whose row index is greater than their column index, as shown in the equation. in, These are the swapped matrix elements.

[0008] By eliminating the negative elements of the matrix, we obtain the swapped node power matrix and the initial electric carbon factor vector.

[0009] Analysis of influencing factors based on LMDI decomposition model 2.1 From a life-cycle perspective, carbon emission drivers are decomposed into eight influencing factors, and the decomposition formula is as follows: The parameters are defined as described in claim 1.

[0010] 2.2 Calculate the change in total carbon emissions in period t relative to the base period, as shown in the formula. and through formula Furthermore, the fuel carbon emission factor is a fixed value. It can be ignored.

[0011] Verify the effectiveness of the algorithm The IEEE 14-node system was used for verification. The system contains 14 nodes, including 2 pure power nodes and 3 pure load nodes. Based on the given unit parameters, the electric carbon factor vector was obtained after 14 iterations to verify the accuracy of the source-grid-load carbon emission flow iterative algorithm.

[0012] Analysis of the contribution of each influencing factor Based on the results of the LMDI decomposition model, we analyze the contribution and mechanism of each influencing factor on carbon emissions from the power generation, transmission and distribution, and power consumption sides, providing a basis for the formulation of carbon emission reduction strategies. Detailed Implementation

[0013] The present invention will be further described in detail below with reference to specific embodiments.

[0014] Example: Taking the Xinjiang regional power system as an example Validation of the iterative algorithm for carbon emission flow from source to network to load An IEEE 14-node system was adopted, with nodes 1 and 8 as pure power nodes and nodes 3, 10, and 14 as pure load nodes. The unit parameters are as follows: G1 is a coal-fired unit with a carbon emission factor of 0.875 kgCO2 / (kW·h); G2 and G4 are gas-fired units with carbon emission factors of 0.525 and 0.520 kgCO2 / (kW·h), respectively; G3 and G5 are wind and solar units with a carbon emission factor of 0. After 14 iterations, the electric carbon factor vector was obtained, verifying that the algorithm can effectively calculate carbon emission flows.

[0015] The iterative carbon flow algorithm proposed in this study is applicable to power systems with relatively complex topologies. This paper employs... Figure 1 The IEEE 14-node system shown verifies the method and theory proposed in this paper. The system has 2 pure power nodes, including node 1 and node 8; 3 pure load nodes, including node 3, node 10 and node 14; and a total of 14 nodes.

[0016] According to the unit parameters given by DING et al. [4], G1 is a coal-fired unit with a carbon emission coefficient of 0.875 kgCO2 / (kW·h). G2 and G4 are gas-fired units with carbon emission coefficients of 0.525 and 0.520 kgCO2 / (kW·h), respectively. G3 and G5 are distributed wind turbine units and hydropower units, respectively, with a carbon emission coefficient of 0.

[0017] After 14 iterations, the electric carbon factor vector for:

[0018] In the subsequent analysis and decomposition of factors affecting carbon emissions in the power system and the carbon peak prediction model, the carbon potential formula after the above 14 iterations is used to calculate the carbon emission flow of each link of the source, grid and load.

[0019] Application of LMDI decomposition model Based on the decomposition of statistical data using the LMDI model, the contribution of various factors influencing carbon emissions from the power industry in Xinjiang Uygur Autonomous Region from 2016 to 2023 was obtained. The decomposition results are as follows: Figure 3 As shown.

[0020] from Figure 3It can be seen that among the factors influencing carbon emissions in the power industry of Xinjiang Uygur Autonomous Region from 2016 to 2023, the industrial electricity consumption intensity effect and the regional GDP effect have the most significant driving effect on carbon emissions in the power industry, with contribution rates of 55.25% and 71.22%, respectively. The power generation and consumption conversion effect, total population effect, and fuel consumption structure effect have very small driving effects on carbon emissions, at 3.04%, 11.00%, and 0.1%, respectively. The inhibitory effects are thermal power fuel conversion rate, power supply structure, and industrial structure effect, among which the most significant inhibitory effect is the industrial structure effect, with a contribution rate of -19.32%.

[0021] To gain a more detailed understanding of the annual changes in the contribution rate of each influencing factor, this paper further decomposes the influencing factors for each year, obtaining the following results: Figure 4 and Figure 5 As shown.

[0022] This invention, by constructing an iterative algorithm for source-grid-load carbon emission flow and an LMDI decomposition model, achieves accurate calculation of carbon emissions from regional power systems and comprehensive analysis of influencing factors, providing scientific support for carbon emission reduction and carbon peaking efforts in the regional power industry. Beneficial effects

[0023] Compared with the prior art, the present invention has the following advantages: An iterative algorithm for carbon emission flow from source to grid to load was developed, which can accurately calculate the carbon emission situation of each node and branch of the power system, providing a reliable tool for analyzing carbon emission distribution.

[0024] Based on the LMDI decomposition model, eight influencing factors were decomposed from a life cycle perspective, comprehensively quantifying the contribution of each factor to carbon emissions and providing a basis for formulating targeted emission reduction strategies.

[0025] The effectiveness of the algorithm was verified using the IEEE 14-node system, ensuring the accuracy and reliability of the analysis results. Attached Figure Description

[0026] Figure 1: IEEE 14-node network topology diagram, power system structure used for algorithm verification; Figure 2: Carbon potential distribution of nodes in the IEEE 14-node lossless network, showing the calculation results of the iterative algorithm; Figure 3: Bar chart of contribution of carbon emission influencing factors from 2016 to 2023, showing the LMDI decomposition results; Figure 4: View of the contribution value of each influencing factor (unit: 10,000 tons); Figure 5: Contribution rate (%) of each influencing factor.

Claims

1. A method for analyzing the influencing factors of carbon emissions in a regional power system and predicting carbon peaking, characterized in that, Includes the following steps: 1.1 Construct an iterative algorithm for source-grid-load carbon emission flow to calculate node carbon emission coefficients and branch carbon flow densities. The formula for calculating the node carbon emission coefficient is as follows: In the formula, Ii and Ti represent the set of all branches of the node that inject active power into node i, respectively; represents the active power flowing in from branch Pb; ρb represents the branch carbon flow density of branch b; Gs represents the active power of the generator connected to node i; and es represents the carbon coefficient of the generator set. 1.2 Based on the LMDI decomposition model, from the perspective of the entire life cycle of the power system, the carbon emission drivers are decomposed into eight influencing factors: fuel consumption structure effect, thermal power fuel conversion effect, and power supply structure effect on the generation side; power generation-consumption conversion effect on the transmission and distribution side; and industrial electricity intensity effect, industrial structure effect, regional GDP effect, and total population effect on the consumption side. The decomposition formula is as follows: Where C represents the total carbon emissions of the power industry, Ci represents the carbon emissions of the i-th fuel in power production, F represents the total fuel consumption for power production, Fi represents the consumption of the i-th fuel in power production, H represents thermal power generation, E represents total power generation, X represents total electricity consumption, CFi represents the carbon emission coefficient effect, FFi represents the fuel consumption structure effect, FH represents the thermal power fuel conversion rate effect, HE represents the power supply structure effect, EX represents the power generation-consumption conversion effect, XG represents the industrial electricity consumption intensity effect, IG represents the industrial structure effect, G represents the regional GDP effect, and Pall represents the total population effect. 1.3 The source-grid-load carbon emission flow iterative algorithm was verified using the IEEE 14-node system. The system has 2 pure power nodes, 3 pure load nodes, and a total of 14 nodes. The iterative electric carbon factor vector was calculated based on the unit parameters. 1.4 Based on the decomposition results of the LMDI decomposition model and combined with the actual characteristics of the regional power system, we analyze the contribution and mechanism of each influencing factor to carbon emissions.

2. The method for analyzing the influencing factors of carbon emissions in a regional power system and predicting carbon peaking according to claim 1, characterized in that, In step 1.1, for a power system consisting of n nodes, the active power is represented by a matrix as follows: Wherein, the main diagonal element pii is the active power injected into node i by the generator connected to node i; pij is the active power injected into node j by node i connected to node j.

3. The method for analyzing the influencing factors of carbon emissions in a regional power system and predicting carbon peaking according to claim 1, characterized in that, In step 1.1, the initial generator's power carbon factor vector ECF is: ECF = [e1 e2…e m ] T (3); Each time the nodal carbon potential is calculated, the i-th element of the nodal carbon potential vector is updated, using the following formula: r (k+1) =Ar (k) +B (5).

4. The method for analyzing the influencing factors of carbon emissions in a regional power system and predicting carbon peaking according to claim 1, characterized in that, In step 1.1, when listing and writing the node power matrix, the network nodes are sorted. If aij is negative, nodes i and j are swapped, and other nodes related to nodes i and j are also swapped. When the row number of an element is greater than the column number, the row number and column number are swapped and reversed, as shown in equations (8) and (9). a' ij =a ji (8); a′ ji =a ij (9). Among them, a' ij These are the swapped matrix elements.

5. The method for analyzing the influencing factors of carbon emissions in a regional power system and predicting carbon peaking according to claim 1, characterized in that, In step 1.3, the unit parameters include: G1 is a coal-fired unit with a carbon emission coefficient of 0.875 kgCO2 / (kW·h); G2 and G4 are gas-fired units with carbon emission coefficients of 0.525 and 0.520 kgCO2 / (kW·h), respectively; G3 and G5 are distributed wind turbine units and hydro turbine units, respectively, with a carbon emission coefficient of 0. The electric carbon factor vector is obtained after 14 iterations.

6. The method for analyzing the influencing factors of carbon emissions in a regional power system and predicting carbon peaking according to claim 1, characterized in that, In step 1.2, let C0 and Ct represent the total carbon emissions of the regional power industry in the base period and period t, respectively. Then, the change in total carbon emissions in period t relative to the base period is expressed as: ΔC=C t -C0=ΔC FF +ΔC FH +ΔC HE +ΔC EX +ΔC XG +ΔC IG +ΔC G +ΔC P (11) The formulas for calculating the changes in each influencing factor are as follows: Furthermore, the fuel carbon emission factor is a fixed value, ΔC. CF It can be ignored.

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