Energy structure optimization and carbon emission management method based on carbon pinch technology

Abstract

The invention discloses an energy structure optimization and carbon emission management method based on a carbon pinch technology, and the method comprises the following steps: collecting the energy consumption data of a target region and the multi-dimensional original data of each energy performance index, and determining the replacement energy through the carbon pinch technology; determining an energy replacement weight by the entropy weight-TOPSIS model; clustering is carried out through a K-means clustering algorithm; an energy substitution model based on clustering contribution degree; the invention relates to an energy dynamic competition model based on a lotka-Voltena equation. The method has three advantages of multi-dimensional fusion, dynamic adaptation and regional adaptation: the entropy weight-TOPSIS model is coupled with carbon emission, economic cost and environmental synergistic effect, and collaborative optimization of multi-attribute decision and carbon pinch point rigid constraint is realized; the adaptability of the model to a policy scene is enhanced through logic progression from static substitution to dynamic competition; based on clustering analysis and alternative weight dynamic allocation, a quantifiable and replicable technical path is provided for differentiated transformation of the high-carbon dependent region.

Description

Technical Field

[0001] The present invention relates to the field of low-carbon development technology, and in particular to an energy structure optimization and carbon emission management method based on carbon pinch technology. Background Art

[0002] Global climate change, the most serious challenge of the 21st century, is driven by excessive greenhouse gas emissions since the Industrial Revolution. This has caused atmospheric carbon dioxide concentrations to surge from 280 ppm to 420 ppm, leading to a global temperature rise of 1.1°C over the past century. This has also triggered systemic crises such as glacier melt (Greenland ice sheet is losing 279 billion tons annually), a 3.3 mm annual sea level rise, and a surge in extreme climate disasters (costs exceeding $350 billion from 2020 to 2022). Transforming the energy structure is crucial to resolving this challenge.

[0003] In the existing literature [Linnhoff B. & Hindmarsh E., "The pinch design method for heat exchanger networks," Chemical Engineering Science, Vol. 38, No. 5 (1983), pp. 745-763.], Professor Linnhoff et al. proposed a carbon pinch heat exchanger network optimization design method in the late 1970s. This method represents a methodology for energy integration technology in chemical processes. Its innovation lies in starting the design from the pinch point and identifying a substantially matching integrated curve and optional extension structure. However, its drawbacks are that its heuristic approach sometimes leads to overuse of a single program and is not applicable to problems with AT problem constraints (ATmin values).

[0004] In the existing literature [Tan RR & Foo DCY, "Pinch analysis approach to carbon-constrained energy sector planning," Energy, Vol. 32, No. 8 (2007), pp. 1422-1429.], Tan et al. expanded on this basis and used pinch point technology to establish a composite curve to implement carbon pinch analysis for energy sector planning. However, the method is simple, has low accuracy, and is only applicable to single emissions.

[0005] In the existing literature [Cossutta M., Foo DCY & Tan R.R., "Carbon emission pinch analysis (CEPA) for planning the decarbonization of the UK power sector," Sustainable Production and Consumption, Vol. 25 (2021), pp. 259-270.], Cossutta et al. analyzed various options for the UK to achieve net zero emissions based on graphical CEPA technology, taking into account limiting factors such as power plant conditions, renewable energy and carbon transition, but ignoring the impact of hydrogeography and nuclear energy.

[0006] In the existing literature [Lu Biao, Wang Suojun, Chen Demin, et al., "Empirical Study on Regional Energy Structure Optimization Based on Carbon Constraints," Journal of Anhui University of Technology (Natural Science Edition), No. 1, 2022, pp. 86-90], Lu Biao et al. applied the carbon pinch method to the field of regional energy structure based on the basic principle of carbon constraints. They set up two energy optimization scenarios, solved the minimum demand for clean energy, and optimized the energy structure of the entire region and each industrial sector.

[0007] The carbon pinch approach described above provides a system-level carbon constraint boundary criterion for energy policy. Its advantage lies in identifying minimum clean energy demand thresholds through a thermodynamic framework, supporting multi-scenario pathway planning and structural optimization. However, its common limitation lies in its static model, which struggles to simulate the dynamic interactions between policy, technology, and the market. It also ignores the constraints of geographic resource endowments on the spatial distribution of carbon flows, and lacks analysis of the coupling of technology substitution cost curves and cross-sector carbon transfer mechanisms. This restricts the policy adaptability of complex energy systems. This is why we propose this present invention. Summary of the Invention

[0008] The present invention aims to provide a method for energy structure optimization and carbon emission management based on carbon pinch technology to solve the problem that existing technologies are limited to static models, have difficulty in simulating the dynamic interaction of policy, technology and market, ignore the constraints of geographical resource endowment on the spatial distribution of carbon flow, and lack coupling analysis of technology substitution cost curves and cross-sector carbon transfer mechanisms, which restricts the policy adaptability of complex energy systems.

[0009] In order to achieve the above object, the present invention provides the following technical solutions:

[0010] A method for energy structure optimization and carbon emission management based on carbon pinch technology, comprising the following steps:

[0011] S1. Collect the target area's energy consumption data and multi-dimensional raw data of various energy performance indicators, perform standardized preprocessing, and output a dimensionless data set;

[0012] S2. Carbon pinch technology determines replacement energy: Determine the carbon intersection point in the target area. Energy below the carbon pinch point is low-emission energy that does not need to be replaced, while energy above the carbon pinch point is high-emission energy that needs to be replaced. Determine the total amount of high-carbon energy that needs to be reduced and build a zero-carbon energy replacement strategy.

[0013] S3. Determine energy substitution weights using the entropy weight-TOPSIS model: Use the entropy weight method to determine the weights of each energy indicator, calculate its comprehensive utility value, determine the ideal solution and negative ideal solution of each energy source using the TOPSIS rule, and calculate the distance between each energy source and its ideal solution and negative ideal solution to obtain the energy weight ranking;

[0014] S4. K-means clustering algorithm clustering: The K-means clustering algorithm is used to analyze the clustering characteristics of each energy source, minimize the intra-cluster square error, and construct multi-level energy clustering;

[0015] S5. Energy substitution model based on cluster contribution: Construct a substitution demand model under carbon constraints, quantify cluster contribution and dynamically assign substitution weights;

[0016] S6. Energy dynamic competition model based on the Lotka-Voltena equation: Quantify the competitive evolution law of zero-carbon energy through time-varying growth rate and policy synergy coefficient, and provide dynamic decision-making support for medium- and long-term energy structure optimization.

[0017] Furthermore, in step S2, the carbon intersection point in the target area is determined using an image method, and the specific steps include:

[0018] S201. Draw the supply and demand curves for each energy source: Construct a coordinate system with energy demand on the horizontal axis and cumulative carbon emissions on the vertical axis. Connect the supply curves of each energy source end to end according to the carbon emission factor from small to large to form a stepped energy supply curve. Use the actual total energy demand of the region, i.e., the rightmost end of the energy supply curve, as the endpoint and draw a vertical line segment to represent the energy demand curve.

[0019] S202. Determine the carbon emission target: determine the carbon emission target value based on the carbon emission demand and the carbon emission limit, and draw a target carbon saving curve through the origin and the carbon emission target value;

[0020] S203. Determine the carbon pinch point: Shift the energy supply curve horizontally to the right until it intersects with the carbon emission target value point. This point is the carbon pinch point.

[0021] S204. Determine the supply and demand of each energy source and the amount of zero-carbon energy used: the distance the energy supply curve moves horizontally to the right is the required zero-carbon energy supply;

[0022] S205. High-emission energy above the pinch point that will exceed the limit is replaced with zero-carbon energy in proportion to form a new target energy emission curve.

[0023] Furthermore, in step S205, the supply and demand of energy is calculated by constructing a mathematical model, and the constructed mathematical model is as follows:

[0024] The objective function is to minimize the supply of zero-carbon energy that replaces energy sources with excessive carbon emissions:

[0025] min∑F i

[0026] The constraints are as follows:

[0027] Assume that there are different energy supplies S in the area being sought i , where i is the type of energy, and the corresponding different carbon emission coefficients are C s，i , then the total carbon emissions E of energy i can be expressed as:

[0028] E=∑E s,i =S i *C s,i

[0029] The formula for calculating the amount of zero-carbon energy substitution is:

[0030] Q 零碳 =∑(S 高碳 -S 夹点 )

[0031] Among them, S 高碳 is the original high-carbon energy supply, S 夹点 is the energy demand corresponding to the pinch point;

[0032] Energy demand balance conditions Source demand balance conditions are:

[0033]

[0034] Carbon emission limits:

[0035]

[0036] Non-negative constraints:

[0037] F j ,D j ,S i ≥0

[0038] Where: i, j: energy supply coefficient and energy demand coefficient respectively, F j is the zero-emission energy supply of required energy j, S i is the supply of energy i, C s，iis the emission factor of energy i, C D，j is the calorific value ratio of energy j, D j is the demand for energy j.

[0039] Furthermore, in step S3, the steps of determining the energy replacement weight using the entropy weight-TOPSIS model include:

[0040] S301, normalized matrix: using the index data of each energy source to form the matrix x′ ij , perform normalization on each column of data so that the indicator data are in the same dimension:

[0041]

[0042] S302, entropy calculation: Calculate the entropy value p of each indicator ij :

[0043]

[0044] Calculate the entropy value e of the jth indicator j :

[0045]

[0046] S303, weight w j Calculation: Determine the weight of each indicator based on the entropy calculation results to reflect the relative importance of each indicator in energy evaluation:

[0047]

[0048] S304, weighted normalization matrix: Use the weights of each indicator to perform weighted processing on the normalized data to obtain a weighted energy data matrix:

[0049] v ij =w j ·x′ ij

[0050] S305. Determine the ideal solution and the negative ideal solution:

[0051] Ideal solution A + :

[0052] A + =(max(v 1j ),max(v 2j ),...,max(v mj ))

[0053] Negative ideal solution A - :

[0054] A - =(min(v1j ),min(v 2j ),...,min(v mj ))

[0055] S306, Euclidean distance calculation: Calculate the Euclidean distance between each energy source and the ideal solution and the negative ideal solution respectively, so as to measure the degree of deviation between the energy source and the ideal state;

[0056] The distance D from the ideal solution i+ :

[0057]

[0058] Distance D from the negative ideal solution i- :

[0059]

[0060] S307, relative proximity C i calculate:

[0061]

[0062] The energy sources are sorted according to their relative proximity values. The smaller the relative proximity, the closer the energy source is to the ideal solution.

[0063] Furthermore, in step S4, the mathematical expression of the K-means clustering algorithm is:

[0064]

[0065] Where: J represents the squared error within the cluster; k represents the number of cluster categories; C i represents the i-th cluster; x represents the sample point in the cluster; μ i Represents the center point (center of mass) of the i-th cluster.

[0066] Furthermore, in step S5, the mathematical model construction of the energy substitution model based on cluster contribution includes the following steps:

[0067] S501, Setting of total carbon emissions and reductions: Assume that the total carbon emissions of the regional energy system is capped at C max , the current total carbon emissions are:

[0068]

[0069] Among them, S i : consumption of energy i (10,000 tons); C s,i : Carbon emission factor of energy i (gCO2 / MJ); H i : calorific value ratio of energy i (MJ / kg);

[0070] The carbon emissions that need to be reduced are:

[0071] ΔC=C current -C max ←

[0072] S502. Calculation of clustered carbon emission contribution

[0073] Based on the K-means clustering results, the carbon emission contribution of each cluster is calculated:

[0074]

[0075] Allocate emission reduction targets to each cluster according to their contribution:

[0076] ΔC k =ΔC·Contribution k

[0077] S503, substitution ratio calculation

[0078] For each cluster, calculate its unit energy carbon emission intensity:

[0079]

[0080] The replacement ratio is:

[0081]

[0082] Constraints:

[0083] 0≤Replacement ratio k ≤1

[0084] S504. Set up a multi-objective optimization model: Combine the TOPSIS comprehensive score and define the dynamic substitution weight:

[0085]

[0086] in, The average TOPSIS score of cluster k; The average carbon emission factor of cluster k; α: comprehensive performance; β: balance weight of carbon emissions;

[0087] The final replacement ratio is adjusted to:

[0088] Optimize replacement ratio k = Replacement ratio k w k .

[0089] Furthermore, in step S6, the mathematical expression of the Lotka-Volterra equation is:

[0090]

[0091] Among them, P i represents the market share of the i-th energy source; r i is the intrinsic growth rate; α ij is the competitive inhibition coefficient matrix; K(t) = K0 + vt is the time-varying carrying capacity; β i is the synergistic growth coefficient.

[0092] The principle and beneficial effects of this technical solution:

[0093] 1. The present invention constructs a multi-dimensional dynamic energy optimization framework through the deep coupling of carbon pinch analysis and entropy weight-TOPSIS model, breaking through the single-dimensional limitation of traditional static models. In view of the shortcomings of carbon pinch analysis in dynamic interaction and multi-objective coordination, the entropy weight method is innovatively introduced to objectively weight carbon emission intensity (weight 0.0914), environmental synergy effect (N2O emission coefficient weight 0.175) and other multi-dimensional indicators, and combined with the TOPSIS model to quantify the comprehensive utility value of different energy sources (such as natural gas Ci = 0.872, significantly higher than coke 0.384), forming a "total control-structure optimization" decision-making closed loop. The K-means clustering algorithm reveals the heterogeneity of energy, providing a three-in-one quantitative tool of "rigid constraints-dynamic adaptation-regional differences" for step-by-step emission reduction under the 1.5°C target of the Paris Agreement.

[0094] 2. This paper addresses the multi-objective collaborative optimization challenges of complex energy systems and constructs a three-level progressive methodology of "classification-substitution-co-opetition." First, the heterogeneous characteristics of the energy system are classified in multiple dimensions using the K-means clustering algorithm, breaking through the single-dimensional carbon emission orientation of traditional carbon pinch analysis. Second, based on cluster contribution and TOPSIS comprehensive scores, a substitution demand model under carbon constraints is proposed to quantify the substitution threshold of high-carbon energy and dynamically assign regional weights. Finally, an improved Lotka-Volterra dynamic competition model is introduced to characterize the long-term nonlinear laws of zero-carbon energy penetration. The innovation of this methodological system is reflected in its triple advantages of multi-dimensional integration, dynamic adaptation and regional adaptation: the entropy weight-TOPSIS model couples carbon emissions, economic costs and environmental synergy effects (N2O emission coefficient weight is 0.175) to achieve the coordinated optimization of multi-attribute decision-making and carbon pinch rigid constraints; the logical progression from static substitution (gap of 1.23 billion tons of standard coal) to dynamic competition enhances the model's adaptability to policy scenarios; based on cluster analysis and dynamic allocation of substitution weights (such as a 15% increase in the weight of coal-dependent regions in the north), it provides a quantifiable and replicable technical path for the differentiated transformation of high-carbon-dependent regions. BRIEF DESCRIPTION OF THE DRAWINGS

[0095] Figure 1 This is a schematic diagram of the energy carbon emission reduction framework analysis proposed in this invention;

[0096] Figure 2 Schematic diagram of energy supply curve and demand curve;

[0097] Figure 3 This is a schematic diagram of the target carbon saving curve;

[0098] Figure 4 Schematic diagram for determining the carbon pinch point;

[0099] Figure 5 This is a schematic diagram of the energy substitution curve;

[0100] Figure 6 is the target energy emission curve. DETAILED DESCRIPTION

[0101] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments and the accompanying drawings. Here, the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0102] It should also be noted that, in order to avoid obscuring the present invention due to unnecessary details, only the processing steps closely related to the solution according to the present invention are shown in the drawings, while other details that are not closely related to the present invention are omitted.

[0103] It should be emphasized that the term “include / comprises” when used in the present invention refers to the existence of features, elements or steps, but does not exclude the existence or addition of one or more other features, elements or steps.

[0104] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. In the accompanying drawings, the same reference numerals represent the same or similar components, or the same or similar steps.

[0105] It should be emphasized here that the step marks mentioned below do not limit the order of the steps, but it should be understood that the steps can be executed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be executed simultaneously.

[0106] Example 1

[0107] refer to Figure 1 , a method for energy structure optimization and carbon emission management based on carbon pinch technology, comprising the following steps:

[0108] S1. Collect the target area energy consumption data and multi-dimensional raw data of various energy performance indicators, and output a dimensionless data set after standardized preprocessing.

[0109] S2. Carbon pinch analysis method and its mathematical model

[0110] (1) Draw the energy supply curve and demand curve.

[0111] Construction of energy supply curve: By multiplying the calorific value coefficient by the amount of each energy used, we can get the energy demand of each energy, which is used as the horizontal axis. Then, by multiplying the carbon factor by the amount of each energy used, we can get the cumulative emission value of each energy. According to the energy type (such as nuclear power, wind power, natural gas, coal), the energy supply curves are connected end to end according to the CO2 emission factor from small to large to form a step-shaped curve. The horizontal length of each segment represents the available amount of the energy, and the vertical height represents its carbon emission contribution. The effect is referenced Figure 2 .

[0112] Constructing the energy demand curve: Draw a vertical line segment with the region's actual total energy demand (the rightmost end of the energy supply curve) as the right endpoint. If the demand curve shifts right (energy demand increases), the supply curve position needs to be readjusted to meet carbon emission constraints.

[0113] (2) Determine carbon emission targets.

[0114] Based on regional carbon reduction policies or international agreements (such as the Paris Agreement), a cap on total carbon emissions is determined. This is plotted as a horizontal line on a coordinate system, with energy demand on the horizontal axis and cumulative carbon emissions on the vertical axis.

[0115] Define the coordinate point (x = energy demand, y = carbon emission limit) as the carbon emission target value point. Connect the carbon emission target value point and the origin to draw the target carbon saving curve. Its slope represents the carbon emission intensity constraint per unit energy demand. Figure 3

[0116] (3) Determine the location of the carbon pinch point.

[0117] refer to Figure 2 , move the energy supply curve horizontally to the right until it intersects with the carbon emission target value point, then this point is the carbon pinch point, refer to Figure 4 By calculating the position of the curve, we can get the changes in the supply and demand of each energy source. Energy sources below the carbon pinch point can meet the carbon emission limit, while energy sources above the carbon pinch point have carbon emissions exceeding the limit.

[0118] (4) Determine the supply and demand of each energy source and the use of zero-carbon energy.

[0119] refer to Figure 5 , by analyzing the carbon emission limit, the image is divided into areas where the emission exceeds the prescribed amount (reference Figure 5 blue area) and the emission area within the regulations (reference Figure 5 yellow area).

[0120] Below the carbon pinch point: low-emission energy (such as natural gas) can be used directly, and its total carbon emissions do not exceed the target value.

[0121] Above the carbon pinch point: High-emission energy sources (such as coal) that exceed the limit need to be replaced.

[0122] To meet both energy demand and carbon emission targets, zero-carbon energy must be used to replace energy sources that exceed carbon emission targets. The distance moved horizontally to the right represents the required zero-carbon energy supply.

[0123] (5) Determine zero-carbon energy alternative strategies.

[0124] Replace the high-emission energy above the pinch point that will exceed the limit with zero-carbon energy (such as wind power and photovoltaic power) in proportion to form a new target energy emission curve. Figure 6 .

[0125] To make the calculation more accurate, we can calculate the supply and demand of energy by building a mathematical model. The mathematical model is as follows:

[0126] The objective function is to minimize the supply of zero-carbon energy that replaces energy with excessive carbon emissions, as shown in formula (1):

[0127] min∑F i (1)

[0128] The constraints are as follows:

[0129] Assume that there are different energy supplies S in the area being sought i , where i is the type of energy, and the corresponding different carbon emission coefficients are C s，i , then the total carbon emissions E of energy i can be expressed as:

[0130] E=∑E s,i =S i *C s,i (2)

[0131] The calculation formula for zero-carbon energy substitution is as follows:

[0132] Q 零碳 =∑(S 高碳 -S 夹点 ) (3)

[0133] Among them, S 高碳 is the original high-carbon energy supply, S 夹点 is the energy demand corresponding to the pinch point.

[0134] Energy demand balance conditions Source demand balance conditions:

[0135]

[0136] Carbon emission limits:

[0137]

[0138] Non-negative constraints:

[0139] F j ,D j ,S i ≥0 (6)

[0140] Where:

[0141] i and j are the energy supply coefficient and energy demand coefficient respectively;

[0142] F j is the zero-emission energy supply of required energy j;

[0143] S i is the supply of energy i;

[0144] C s，i is the emission factor of energy i;

[0145] C D，j is the calorific value ratio of energy j;

[0146] D j is the demand for energy j.

[0147] S3. Comprehensive evaluation model based on entropy weight-TOPOSIS method

[0148] In carbon pinch analysis, the substitution threshold of high-carbon energy (such as the amount of coal supply that needs to be reduced) is quantified through graphical methods. However, substitution decisions need to consider multi-dimensional indicators such as carbon emission factors, energy prices, and technological maturity. The traditional single-indicator evaluation system is difficult to meet the needs of comprehensive optimization.

[0149] Therefore, this paper proposes to introduce the entropy weight method - Topsis model into carbon pinch analysis to improve the problem of traditional carbon pinch analysis methods in selecting energy optimization priorities. Among them, the rigid constraint of the carbon pinch defines the "quantity" of energy substitution - the total amount of high-carbon energy that needs to be reduced. The entropy weight method - Topsis model quantifies the "quality" of different zero-carbon energy sources through multi-attribute decision-making - calculating their comprehensive utility value based on weighted indicators (such as carbon emission weight 0.0914, price weight 0.314), and then sorting the substitution priority.

[0150] The entropy weight method, based on information entropy theory, determines the weights of various indicators from the perspective of data variability and information content. This effectively avoids subjective bias in weighting, making weight determination more objective and scientific. The TOPSIS method, by constructing ideal and negative ideal solutions, can intuitively assess the relative strengths and weaknesses of various energy sources across comprehensive indicators, ultimately providing a clear ranking basis for energy selection and substitution.

[0151] The mathematical model is defined as follows:

[0152] 1. Normalized matrix:

[0153] First, perform normalized matrix processing. For the collected energy data x ij , such as the carbon emission factor, calorific value ratio, released calorific value, CO2 emission, N2O emission coefficient and other indicator data of various energy sources such as raw coal and washed coal, forming the matrix x′ ij .

[0154] Normalize each column of data so that the data of each indicator are in the same dimension to facilitate subsequent analysis.

[0155]

[0156] 2. Entropy calculation:

[0157] The entropy value reflects the degree of information uncertainty of the indicator. The larger the entropy value, the more uncertain the information of the indicator is and the smaller its weight is. The entropy value p of each indicator is calculated using the information entropy theory. ij .

[0158]

[0159] Calculate the entropy value e of the jth indicator j :

[0160]

[0161] 3. Weight calculation w j :

[0162] The weight of each indicator is determined according to the entropy calculation results, which objectively reflects the relative importance of each indicator in energy evaluation.

[0163]

[0164] 4. Weighted normalization matrix:

[0165] Then, a weighted normalization matrix is ​​constructed. The normalized data is weighted using the calculated weights of each indicator to obtain a weighted energy data matrix.

[0166] v ij =wj ·x′ ij (11)

[0167] The TOPSIS method (Technique for Order Preference by Similarity to Ideal Solution) is a distance-based multi-attribute decision-making method. Its core idea is to determine the quality of each solution by calculating its distance from the ideal solution and the negative ideal solution. The specific steps are as follows:

[0168] 5. Ideal solution and negative ideal solution: On this basis, for each indicator, determine its ideal solution (the benefit-type indicator takes the maximum value, and the cost-type indicator takes the minimum value) and negative ideal solution (the benefit-type indicator takes the minimum value, and the cost-type indicator takes the maximum value).

[0169] Ideal solution A + :

[0170] A + =(max(v 1j ),max(v 2j ),...,max(v mj )) (12)

[0171] Negative ideal solution A - :

[0172] A - =(min(v 1j ),min(v 2j ),...,min(v mj )) (13)

[0173] 6. Euclidean distance calculation:

[0174] The Euclidean distance between each energy source and the ideal solution and the negative ideal solution is calculated to measure the degree of deviation of the energy source from the ideal state.

[0175] The distance from the ideal solution D_{i+}:

[0176]

[0177] The distance from the negative ideal solution D_{i-}:

[0178]

[0179] 7. Relative Proximity Calculation: Energy sources are ranked based on their relative proximity values. A smaller relative proximity value indicates a source closer to the ideal solution. This series of steps comprehensively considers multiple factors, providing a more scientific and comprehensive basis for energy selection and substitution decisions.

[0180]

[0181] The relationship between the items in each category and the ideal distance is calculated, and the relevant ranking is obtained.

[0182] S4. Multidimensional clustering model based on K-means algorithm

[0183] The comprehensive evaluation model of the entropy weight-TOPOSIS method not only breaks through the limitations of traditional single-dimensional analysis, but also provides a scientific and operational decision-making basis for regional energy transformation through weighted indicator evaluation (such as carbon emission factor, economic cost, and technical feasibility), which can effectively achieve carbon emission targets.

[0184] However, traditional homogenization substitution strategies are difficult to meet the precise needs of regional emission reduction. To this end, this paper introduces the K-means clustering algorithm to analyze the clustering characteristics of various energy sources, laying the foundation for formulating a step-by-step emission reduction policy.

[0185] The K-means clustering algorithm is an unsupervised learning method that can divide data into several categories so that data within the same category have high similarity and data between different categories have large differences. Its core goal is to minimize the within-cluster sum of squares (WCSS), which is expressed as:

[0186]

[0187] in:

[0188] J represents the intra-cluster squared error;

[0189] k represents the number of cluster categories;

[0190] C i represents the i-th cluster;

[0191] x represents the sample point in the cluster;

[0192] μ i Represents the center point (centroid) of the i-th cluster.

[0193] S5. Energy substitution model based on cluster contribution

[0194] Based on the cluster analysis in 2.3, this section constructs a substitution demand model under carbon constraints, quantifies cluster contributions, and dynamically allocates substitution weights to achieve “classified policy implementation.”

[0195] The mathematical model is defined as follows:

[0196] (1) Setting the total amount of carbon emissions and reductions

[0197] Assume that the total carbon emission limit of the regional energy system is C max (Unit: gCO2), the current total carbon emissions are:

[0198]

[0199] Where: -S i :Energy i consumption (10,000 tons)-C s,i :Carbon emission factor of energy i (gCO2 / MJ)-H i : Calorific value ratio of energy i (MJ / kg)

[0200] The carbon emissions that need to be reduced are:

[0201] ΔC=C current -C max ← (19)

[0202] (2) Calculation of cluster carbon emission contribution

[0203] Based on the K-means clustering results, the carbon emission contribution of each cluster is calculated:

[0204]

[0205] Allocate emission reduction targets to each cluster according to their contribution:

[0206] ΔC k =ΔC·Contribution k (twenty one)

[0207] (3) Calculation of substitution ratio

[0208] For each cluster, calculate its unit energy carbon emission intensity:

[0209]

[0210] The replacement ratio is:

[0211]

[0212] Constraints:

[0213] 0≤Replacement ratio k ≤1 (25)(4) Setting up a multi-objective optimization model

[0214] Combined with the TOPSIS comprehensive score, dynamic alternative weights are defined:

[0215]

[0216] Where: Average TOPSIS score for cluster k - Average carbon emission factor of cluster k -α=0.6,β=0.4: Balance weight between comprehensive performance and carbon emissions

[0217] The final replacement ratio is adjusted to:

[0218] Optimize replacement ratio k = Replacement ratio k w k (27)

[0219] S6. Energy Dynamic Competition Model Using the Lotka-Volterra Equation

[0220] The energy substitution model based on cluster contribution transforms the qualitative conclusions of the carbon pinch into operational substitution thresholds and path planning. However, all the above methods only use zero-carbon energy substitution for a general description, and do not specifically express the use categories of zero-carbon energy. Especially in the context of accelerated technological iteration and dynamic interaction between policy and market, this paper will introduce an improved Lotka-Volterra competition equation to solve the problem of which zero-carbon energy to use to replace supersaturated carbon energy. By simulating the competitive evolution of multiple energy sources through time-varying growth rate (r), carrying capacity (K) and synergy coefficient (α), it connects short- and medium-term substitution goals with long-term transformation logic, and provides a decision-making tool for the dynamic optimization of energy systems under carbon constraints.

[0221] The Lotka-Volterra equation, originating in ecology, is commonly used to describe the dynamic evolution of resource competition among species. Based on a modified Lotka-Volterra equation, this paper applies this model to quantify the competitive evolution of zero-carbon energy sources, such as wind, solar, hydrogen, and nuclear, by combining time-varying growth rates with policy synergy coefficients. This provides dynamic decision-making support for medium- and long-term energy structure optimization.

[0222] The model equations are shown in formula (29):

[0223]

[0224] Where: -P i represents the market share of the i-th energy source (i=1,2,...,5 corresponds to solar energy, wind energy, hydrogen energy, nuclear fusion energy and biomass energy respectively) - r i is the inherent growth rate (reflecting the speed of technological progress) - α ij is the competition inhibition coefficient matrix - K(t) = K0 + vt is the time-varying carrying capacity (v = 0.5 / year reflects the growth of total demand) - β i is the synergistic growth coefficient (representing the strength of policy support).

[0225] This paper addresses the multi-objective collaborative optimization challenges of complex energy systems and constructs a three-level progressive methodology: classification, substitution, and competition. First, the heterogeneous characteristics of energy systems are classified in multiple dimensions using the K-means clustering algorithm, breaking through the single-dimensional carbon emission focus of traditional carbon pinch analysis. Second, based on cluster contribution and TOPSIS comprehensive scores, a carbon-constrained substitution demand model is proposed to quantify the substitution threshold for high-carbon energy and dynamically assign regional weights. Finally, an improved Lotka-Volterra dynamic competition model is introduced to characterize the long-term nonlinear laws of zero-carbon energy penetration.

[0226] The innovation of this methodological system is reflected in its triple advantages of multi-dimensional integration, dynamic adaptation and regional adaptation: the entropy weight-TOPSIS model couples carbon emissions, economic costs and environmental synergy effects (N2O emission coefficient weight is 0.175) to achieve the coordinated optimization of multi-attribute decision-making and carbon pinch rigid constraints; the logical progression from static substitution (gap of 1.23 billion tons of standard coal) to dynamic competition enhances the model's adaptability to policy scenarios; based on cluster analysis and dynamic allocation of substitution weights (such as a 15% increase in the weight of coal-dependent regions in the north), it provides a quantifiable and replicable technical path for the differentiated transformation of high-carbon-dependent regions.

[0227] It should be understood that the present invention is not limited to the specific configurations and processes described above and illustrated in the figures. For the sake of brevity, a detailed description of known methods is omitted. In the above embodiments, several specific steps are described and illustrated as examples. However, the method of the present invention is not limited to the specific steps described and illustrated. Those skilled in the art may make various changes, modifications, and additions, or change the order of the steps after understanding the spirit of the present invention.

[0228] In the present invention, features described and / or illustrated for one embodiment may be used in the same or similar manner in one or more other embodiments, and / or combined with or replace features of other embodiments.

[0229] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations to the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A method for energy structure optimization and carbon emission management based on carbon pinch technology, characterized in that: The steps include: S1. Collect the target area's energy consumption data and multi-dimensional raw data of various energy performance indicators, perform standardized preprocessing, and output a dimensionless data set; S2. Carbon pinch technology determines replacement energy: Determine the carbon intersection point in the target area. Energy below the carbon pinch point is low-emission energy that does not need to be replaced, while energy above the carbon pinch point is high-emission energy that needs to be replaced. Determine the total amount of high-carbon energy that needs to be reduced and build a zero-carbon energy replacement strategy. S3. Determine energy substitution weights using the entropy weight-TOPSIS model: Use the entropy weight method to determine the weights of each energy indicator, calculate its comprehensive utility value, determine the ideal solution and negative ideal solution of each energy source using the TOPSIS rule, and calculate the distance between each energy source and its ideal solution and negative ideal solution to obtain the energy weight ranking; S4. K-means clustering algorithm clustering: Using the K-means clustering algorithm, we analyze the clustering characteristics of each energy source, minimize the intra-cluster square error, and construct multi-level energy clustering; S5. Energy substitution model based on cluster contribution: Construct a substitution demand model under carbon constraints, quantify cluster contribution and dynamically assign substitution weights; S6. Energy dynamic competition model based on the Lotka-Voltena equation: Quantify the competitive evolution law of zero-carbon energy through time-varying growth rate and policy synergy coefficient, and provide dynamic decision-making support for medium- and long-term energy structure optimization.

2. The method for energy structure optimization and carbon emission management based on carbon pinch technology according to claim 1 is characterized in that: In step S2, the carbon intersection point in the target area is determined using an image method, and the specific steps include: S201. Draw the supply and demand curves for each energy source: Construct a coordinate system with energy demand on the horizontal axis and cumulative carbon emissions on the vertical axis. Connect the supply curves of each energy source end to end according to the carbon emission factor from small to large to form a stepped energy supply curve. Use the actual total energy demand of the region, i.e., the rightmost end of the energy supply curve, as the endpoint and draw a vertical line segment to represent the energy demand curve. S202. Determine the carbon emission target: determine the carbon emission target value based on the carbon emission demand and the carbon emission limit, and draw a target carbon saving curve through the origin and the carbon emission target value; S203. Determine the carbon pinch point: Shift the energy supply curve horizontally to the right until it intersects with the carbon emission target value point. This point is the carbon pinch point. S204. Determine the supply and demand of each energy source and the amount of zero-carbon energy used: the distance the energy supply curve moves horizontally to the right is the required zero-carbon energy supply; S205. High-emission energy above the pinch point that will exceed the limit is replaced with zero-carbon energy in proportion to form a new target energy emission curve.

3. The method for energy structure optimization and carbon emission management based on carbon pinch technology according to claim 2 is characterized in that: In step S205, the supply and demand of energy is calculated by constructing a mathematical model, and the constructed mathematical model is as follows: The objective function is to minimize the supply of zero-carbon energy that replaces energy sources with excessive carbon emissions: min∑F i The constraints are as follows: Assume that there are different energy supplies S in the area being sought i , where i is the type of energy, and the corresponding different carbon emission coefficients are C s，i , then the total carbon emissions E of energy i can be expressed as: E=∑E s,i =S i *C s,i The formula for calculating the amount of zero-carbon energy substitution is: Q 零碳 =∑(S 高碳 -S 夹点 ) Among them, S 高碳 is the original high-carbon energy supply, S 夹点 is the energy demand corresponding to the pinch point; Energy demand balance conditions Source demand balance conditions are: Carbon emission limits: Non-negative constraints: F j ,D j ,S i ≥0 Where: i, j: energy supply coefficient and energy demand coefficient respectively, F j is the zero-emission energy supply of required energy j, S i is the supply of energy i, C s，i is the emission factor of energy i, C D，j is the calorific value ratio of energy j, D j is the demand for energy j.

4. The method for energy structure optimization and carbon emission management based on carbon pinch technology according to claim 1 is characterized in that: In step S3, the steps of determining the energy replacement weight using the entropy weight-TOPSIS model include: S301, normalized matrix: using the index data of each energy source to form the matrix x′ ij , perform normalization on each column of data so that the indicator data are in the same dimension: S302, entropy calculation: Calculate the entropy value p of each indicator ij : Calculate the entropy value e of the jth indicator j : S303, weight w j Calculation: Determine the weight of each indicator based on the entropy calculation results to reflect the relative importance of each indicator in energy evaluation: S304, weighted normalization matrix: Use the weights of each indicator to perform weighted processing on the normalized data to obtain a weighted energy data matrix: v ij =w j ·x′ ij S305. Determine the ideal solution and the negative ideal solution: Ideal solution A + : A + =(max(v 1j ),max(v 2j ),...,max(v mj )) Negative ideal solution A - : A - =(min(v 1j ),min(v 2j ),...,min(v mj )) S306, Euclidean distance calculation: Calculate the Euclidean distance between each energy source and the ideal solution and the negative ideal solution respectively, so as to measure the degree of deviation between the energy source and the ideal state; The distance D from the ideal solution i+ : Distance D from the negative ideal solution i- : S307, relative proximity C i calculate: The energy sources are sorted according to their relative proximity values. The smaller the relative proximity, the closer the energy source is to the ideal solution.

5. The method for energy structure optimization and carbon emission management based on carbon pinch technology according to claim 1 is characterized in that: In step S4, the mathematical expression of the K-means clustering algorithm is: Where: J represents the squared error within the cluster; k represents the number of cluster categories; C i represents the i-th cluster; x represents the sample point in the cluster; μ i Represents the center point (centroid) of the i-th cluster.

6. The method for energy structure optimization and carbon emission management based on carbon pinch technology according to claim 1 is characterized in that: In step S5, the mathematical model construction of the energy substitution model based on cluster contribution includes the following steps: S501, Setting of total carbon emissions and reductions: Assume that the total carbon emissions of the regional energy system is capped at C max , the current total carbon emissions are: Among them, S i : consumption of energy i (10,000 tons); C s,i : Carbon emission factor of energy i (gCO2 / MJ); H i : calorific value ratio of energy i (MJ / kg); The carbon emissions that need to be reduced are: ΔC=C current -C max ← S502. Calculation of clustered carbon emission contribution Based on the K-means clustering results, the carbon emission contribution of each cluster is calculated: Allocate emission reduction targets to each cluster according to their contribution: ΔC k =ΔC·Contribution k S503, substitution ratio calculation For each cluster, calculate its unit energy carbon emission intensity: The replacement ratio is: Constraints: 0≤Replacement ratio k ≤1 S504. Set up a multi-objective optimization model: Combine the TOPSIS comprehensive score and define the dynamic substitution weight: in, The average TOPSIS score of cluster k; The average carbon emission factor of cluster k; α: comprehensive performance; β: balance weight of carbon emissions; The final replacement ratio is adjusted to: Optimize replacement ratio k = Replacement ratio k w k .

7. The method for energy structure optimization and carbon emission management based on carbon pinch technology according to claim 1 is characterized in that: In step S6, the mathematical expression of the Lotka-Volterra equation is: Among them, P i represents the market share of the i-th energy source; r i is the intrinsic growth rate; α ij is the competition inhibition coefficient matrix; K(t) = K0 + vt is the time-varying carrying capacity; β i is the synergistic growth coefficient.

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