Power grid engineering carbon emission reduction path risk assessment method and device

Abstract

The invention relates to the technical field of power system risk assessment and carbon management, in particular to a power grid engineering carbon emission reduction path risk assessment method and device. Comprising the following steps: constructing a power grid engineering carbon emission reduction path risk assessment index system; performing index weighting on the evaluation index system by using a combined index weight weighting method based on an AHP (Analytical Hierarchy Process) and an entropy weight method; and carrying out risk assessment on the index weight and the path risk index evaluation value by using an improved VI KOR method, solving a risk grade sequence of each carbon emission reduction path, and identifying key risk factors. According to the method, expert experience and data objective laws are fully combined, so that weight distribution is more reasonable, the problem of uncertainty in the assessment process is effectively solved, and the robustness and applicability of a risk assessment result are enhanced.

Description

Technical Field

[0001] This invention relates to the field of power system risk assessment and carbon management technology, specifically to a method and apparatus for risk assessment of carbon emission reduction pathways in power grid engineering. Background Technology

[0002] With the increasingly severe global climate change problem, a low-carbon economy has become an important trend in global economic development. As a key link in energy transmission and distribution, power grid engineering plays a vital role in promoting energy structure transformation and low-carbon development. Especially after the goal of "carbon peaking and carbon neutrality" was proposed, power grid companies not only need to improve energy efficiency and reduce carbon emissions, but also need to scientifically assess the risks of various carbon emission reduction pathways to cope with the increasingly complex energy market environment.

[0003] Against the backdrop of new power system construction, large-scale integration of new energy sources such as wind power and photovoltaics into the power grid, and the continuous improvement of the carbon market mechanism, power grid companies face multi-dimensional risks and challenges in selecting carbon emission reduction paths, including technological, economic, policy and market, implementation and operation risks. Traditional risk assessment methods often focus on the technological or economic risks of power grid projects, lacking a systematic and multi-dimensional comprehensive assessment of carbon emission reduction paths, and thus failing to fully reflect the true risk status of various paths.

[0004] Currently, there is no comprehensive risk assessment system applicable to carbon reduction pathways in power grid projects, particularly in the quantitative and integrated assessment of emerging risk factors such as policy support stability, carbon price volatility, green energy consumption risks, and supply chain stability. This makes it difficult for power grid companies to accurately identify key risk points in pathway selection, hindering their ability to provide a scientific basis for investment decisions and consequently affecting the implementation effectiveness and return on investment of carbon reduction projects. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method for risk assessment of carbon emission reduction pathways in power grid engineering.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for risk assessment of carbon emission reduction pathways in power grid engineering, comprising the following steps:

[0007] S1: Construct a risk assessment index system for carbon emission reduction pathways in power grid projects;

[0008] S2: The evaluation index system is weighted using a combined index weighting method based on AHP and entropy weighting.

[0009] S3: Apply the improved VIKOR method to assess the risk of the aforementioned indicator weights and path risk indicator evaluation values, solve for the risk level ranking of each carbon emission reduction path, and identify key risk factors.

[0010] Preferably, the risk assessment index system for constructing carbon emission reduction pathways in power grid engineering includes:

[0011] The risk assessment indicator system consists of four primary indicators: technological risk, economic risk, policy and market risk, and implementation and operational risk.

[0012] The technological risks include three secondary indicators: technology maturity, system compatibility, and carbon emission intensity reduction rate.

[0013] The economic risks mentioned include three secondary indicators: unit emission reduction cost, investment payback period, and return volatility.

[0014] The policy and market risks mentioned include three secondary indicators: policy support stability, carbon price volatility, and green electricity consumption risk.

[0015] The implementation and operational risks include three secondary indicators: project management capabilities, supply chain stability, and information and data security.

[0016] Preferably, the method of assigning weights to indicators using a combination of AHP and entropy weighting includes:

[0017] Subjective weighting method based on AHP;

[0018] An objective weighting method based on entropy weighting;

[0019] Combine subjective and objective weighting.

[0020] Preferably, the subjective weighting method based on AHP includes:

[0021] Step 1: Establish a hierarchical model, decomposing the decision problem into the objective layer, criterion layer, and alternative layer;

[0022] Step 2: Construct a judgment matrix. Invite experts to use the 1-9 scaling method to compare each indicator at the same level pairwise, and construct the judgment matrix.

[0023] Step 3: Calculate the weight vector and the largest eigenvalue λ of the judgment matrix. max The corresponding eigenvectors are then normalized to obtain the weights W of each indicator. s ;

[0024] Step 4: Consistency check, calculate consistency index Find the average random consistency index RI and calculate the consistency ratio. When CR < 0.1, accept the judgment matrix;

[0025] Step 5: Overall hierarchical sorting, calculate the combined weight of each level of elements to the overall objective.

[0026] Preferably, the objective weighting method based on entropy weighting includes:

[0027] 1) Construct a standardized judgment matrix R = (r ij ) m×n (i = 1, 2, ..., m; j = 1, 2, ..., n), where m is the number of paths and n is the number of indicators;

[0028] 2) Calculate the information entropy of each indicator. First, calculate the feature weight of the i-th path under the j-th indicator. Then calculate the information entropy of the j-th indicator. in

[0029] 3) Assign weights to indicators, and calculate the entropy weight of the j-th indicator.

[0030] Among them, E j Let W be the information entropy of the j-th indicator. oj Let be the entropy weight of the j-th index.

[0031] Preferably, the risk assessment using the improved VIKOR method includes:

[0032] Standardization of indicator data;

[0033] An improved VIKOR method was used to conduct a risk assessment of carbon emission reduction pathways in power grid projects.

[0034] Preferably, the standardization processing of the indicator data includes:

[0035] Convert both extremely small and extremely large indicators into extremely large indicators;

[0036] For very small indicators, use the formula.

[0037] For extremely large indicators, use the formula.

[0038] After the qualitative indicators are quantified and the process is consistent, the standardization of each evaluation indicator is as follows:

[0039] Z = (z ij ) m×n

[0040] Where z ij Let be the standardized value of the i-th path on the j-th metric.

[0041] Preferably, the risk assessment of carbon emission reduction pathways for power grid engineering using the improved VIKOR method includes:

[0042] 1) Determine the positive and negative ideal solutions: For benefit-type indicators, the positive ideal solution... Negative ideal solution For cost-related indicators, the positive ideal solution Negative ideal solution

[0043] 2) Calculate the group utility value And personal regret value Where w j Let be the combined weight of the j-th indicator;

[0044] 3) Triangular fuzzy numbers are introduced to handle uncertainty. For the evaluation of qualitative indicators, triangular fuzzy numbers are used to represent the expert's linguistic evaluation.

[0045] 4) Dynamic weight adjustment mechanism: The weight allocation is dynamically adjusted based on the performance of each path on different indicators. The weight adjustment formula is w' j =w j ×(1+a×r j ), where w' j The adjusted weights are denoted by 'a', 'r', and 'a' is the adjustment coefficient. j The risk coefficient for the j-th indicator;

[0046] 5) Calculate the profit ratio Among them, S * =min i S i ,S - =max i S i ,R * =min i R i ,R - =max i R i v is the decision-making mechanism coefficient, 0≤v≤1;

[0047] 6) Path sequencing and risk analysis, based on Q i Sort the paths by value from smallest to largest, Q i The smaller the value, the lower the path risk.

[0048] Preferably, the path ranking and risk analysis further includes verifying the following conditions:

[0049] Acceptable advantages: Among them, A (1) It is the top-ranked solution, A. (2) It is the second-ranked solution, and m is the total number of solutions;

[0050] Acceptable stability condition: The top-ranked solution in Si Or R i It must also be the best in the sorting.

[0051] This invention also discloses a risk assessment device for carbon emission reduction pathways in power grid engineering, comprising:

[0052] The indicator construction module is used to construct an indicator system for risk assessment of carbon emission reduction pathways in power grid projects.

[0053] The indicator weighting module is used to assign weights to the evaluation indicator system using a combined indicator weighting method based on AHP and entropy weighting.

[0054] The risk assessment module is used to conduct risk assessment by applying the improved VIKOR method to the indicator weights and path risk indicator evaluation values, solve the risk level ranking of each carbon emission reduction path, and identify key risk factors.

[0055] The beneficial effects of this invention are as follows: This invention innovatively employs a subjective and objective weighting method based on AHP and entropy weighting, and utilizes an improved VIKOR method incorporating triangular fuzzy numbers and a dynamic weight adjustment mechanism for comprehensive evaluation. This method not only fully integrates expert experience with objective data laws, making weight allocation more reasonable, but also effectively addresses uncertainties in the evaluation process, enhancing the robustness and applicability of the risk assessment results. Ultimately, it achieves accurate quantitative ranking and identification of key risk sources for multiple carbon emission reduction pathways, providing strong technical support for developing targeted risk prevention and control measures and pathway optimization schemes.

[0056] This invention constructs a risk assessment index system covering four dimensions: technology, economy, policy and market, implementation and operation. This system can systematically assess the risks of carbon emission reduction paths in power grid projects, accurately identify the risk levels and weaknesses of various paths, and propose targeted risk prevention and control measures. This ensures that power grid companies make scientific decisions in the selection of carbon emission reduction paths, and form a new development pattern with controllable risks and optimal benefits based on the company's existing business foundation. Attached Figure Description

[0057] Figure 1 This is a flowchart illustrating the risk assessment method for carbon emission reduction pathways in power grid engineering according to the present invention.

[0058] Figure 2 This is a functional block diagram of the carbon emission reduction path risk assessment device for power grid engineering of the present invention. Detailed Implementation

[0059] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0060] Example 1, Figure 1 This invention provides a method, equipment, and medium for risk assessment of carbon emission reduction pathways in power grid engineering, comprising the following steps:

[0061] S1 is used to construct a risk assessment index system for carbon emission reduction pathways in power grid engineering.

[0062] In some embodiments, the construction of a risk assessment index system for carbon emission reduction pathways in power grid engineering includes:

[0063] The risk assessment index system for carbon emission reduction pathways in power grid projects consists of 4 primary indicators and 12 secondary indicators, as shown in Table 1.

[0064] Technological Risks: What are the technological risks of the carbon reduction path for power grid projects? Is the technology mature? How compatible is the system? Is the emission reduction effect stable? Technological risk is an important indicator for measuring the technological feasibility of a path, directly affecting the success rate of implementation, the cost of technology upgrades, and operational reliability. Therefore, the technological risks of the carbon reduction path for power grid projects are evaluated from three dimensions: technological maturity, system compatibility, and carbon emission intensity reduction rate.

[0065] Economic Risks: What are the economic risks of the carbon emission reduction pathway for power grid projects? Are the investment costs controllable? What are the economic benefits? Are the returns stable? Economic risks relate to the financial feasibility, market competitiveness, and rate of return on investment of the pathway. These risks not only affect investment decisions but also determine the long-term sustainability of the pathway. Therefore, the economic risks of the carbon emission reduction pathway for power grid projects are evaluated from three dimensions: unit emission reduction cost, investment payback period, and return volatility.

[0066] Policy and Market Risks: What are the policy and market risks associated with the carbon emission reduction pathway of power grid projects? Is policy support stable? How do carbon market prices fluctuate? Are there risks associated with green electricity consumption? Policy and market risks determine the stability of the external environment for the implementation of the pathway, affecting the realization of its market value and policy adaptability. They not only relate to the short-term feasibility of the pathway but also its long-term development potential. Therefore, the policy and market risks of the carbon emission reduction pathway of power grid projects are evaluated from three dimensions: the stability of policy support, carbon price volatility, and the risk of green electricity consumption.

[0067] Implementation and Operation Risks: What are the risks associated with the implementation and operation of the carbon reduction pathway for power grid projects? Is project management standardized? Is the supply chain stable? Is data security guaranteed? Implementation and operation risks are crucial to the successful implementation and stable operation of the pathway, directly impacting project construction progress, operational quality, and information security. Therefore, the implementation and operation risks of the carbon reduction pathway for power grid projects are evaluated from three dimensions: project management capabilities, supply chain stability, and information and data security.

[0068] Table 1 Risk Assessment Index System for Carbon Emission Reduction Paths in Power Grid Projects

[0069]

[0070] S2 is used to assign weights to the evaluation index system;

[0071] In some embodiments, the method of assigning weights to indicators using a combination of AHP and entropy weighting includes:

[0072] (1) Subjective empowerment based on AHP

[0073] Eighteen experts were invited to conduct pairwise comparisons of 12 secondary indicators in the risk assessment index system for carbon emission reduction pathways in power grid projects, constructing a judgment matrix. Based on the expert evaluation results, the weight vectors of each indicator were calculated, and a consistency test was performed (CR = 0.041 < 0.1), indicating that the judgment matrix has satisfactory consistency. The obtained subjective weights are shown in Table 2.

[0074] Table 2 Subjective weights based on AHP

[0075] index C1 C2 C3 C4 C5 C6 Weight 0.115 0.075 0.095 0.085 0.07 0.065 index C7 C8 C9 C10 C11 C12 Weight 0.105 0.08 0.06 0.075 0.07 0.105

[0076] (2) Objective weighting based on entropy weight method

[0077] Three typical carbon emission reduction paths of the power grid company were selected for evaluation: "Ultra-high voltage clean energy transmission channel", "intelligent loss reduction transformation of distribution network" and "distributed photovoltaic and energy storage synergy". Based on the historical operation and planning data of the three paths over the past three years, the data were standardized and the weights of each indicator were obtained by using the entropy weight method, as shown in Table 3.

[0078] Table 3 Objective weights based on the entropy weight method

[0079] index C1 C2 C3 C4 C5 C6 Weight 0.098 0.068 0.108 0.118 0.085 0.092 index C7 C8 C9 C10 C11 C12 Weight 0.082 0.105 0.072 0.063 0.058 0.051

[0080] (3) Weight combination

[0081] After obtaining the index weights W determined by the AHP weighting method and the entropy weighting method respectively. S and W O Then, the weights for the integrated weighting are calculated using a linear weighting method:

[0082] W j =α·w sj +(1-α)·w oj (j = 1, 2, ..., 12)

[0083] Where α is set to 0.5, it means that subjective and objective weights are equally important. The combined weight calculation results are shown in Table 4.

[0084] Table 4 Weighting of Indicator Combinations

[0085]

[0086] S3 is used to conduct risk assessments on each carbon emission reduction path, solve for the risk level ranking of each path, and analyze the risk problems existing in each path;

[0087] In some embodiments, the application of risk assessment based on the improved VIKOR method includes:

[0088] (1) Determine the carbon emission reduction pathways of the power grid project to be evaluated.

[0089] Ultra-high voltage (UHV) clean energy transmission channels: These channels transmit electricity from clean energy bases in the west and north to load centers in the central and eastern regions through the construction of long-distance, large-capacity UHV transmission projects. The main risks associated with this approach include high technological barriers, massive initial investment, and strong reliance on inter-provincial policy coordination.

[0090] Intelligent transformation of distribution networks to reduce losses: This involves upgrading existing urban and rural distribution networks with intelligent technologies to enhance their capacity to accommodate distributed power sources and achieve precise loss reduction. The main risks associated with this approach include rapid technological iteration, long investment recovery periods, and complex operation and maintenance.

[0091] Synergistic development of distributed photovoltaic (PV) and energy storage: Vigorously develop distributed PV in load center areas and construct supporting energy storage facilities to achieve local consumption and improve energy self-sufficiency. The main risks of this approach include the need for further development of technical standards, high risk of changes in subsidy policies, and insufficient maturity of business models.

[0092] (2) Conduct risk assessment of carbon emission reduction pathways for power grid projects based on the improved VIKOR method.

[0093] 1) Data collection and standardization

[0094] Basic data on three paths across 12 evaluation indicators were collected, and experts were invited to score the qualitative indicators (C1, C2, C7, C10, C11, C12) on a 100-point scale. The collected data were standardized, transforming minimal indicators into large indicators. The standardized decision matrix Z is as follows:

[0095]

[0096] 2) Calculate group utility value and individual regret value

[0097] Based on the standardized data Z and the combined weights W, calculate the group utility value and individual regret value for each path:

[0098] Ultra-high voltage clean energy transmission channel: S1 = 0.402, R1 = 0.098

[0099] Intelligent power distribution network loss reduction upgrade: S2 = 0.368, R2 = 0.092

[0100] Synergistic effect of distributed photovoltaic and energy storage: S3 = 0.445, R3 = 0.110

[0101] 3) Calculate the profit ratio

[0102] Taking the decision-making mechanism coefficient v = 0.5, calculate the benefit ratio of each path:

[0103] S * =min(S) i ) = 0.368, S - =max(S) i ) = 0.445

[0104] R * =min(S) i ) = 0.092, R - =max(R) i ) = 0.110

[0105] Q1=0.584, Q2=0.000, Q3=1.000

[0106] 4. Path Sequencing and Risk Analysis

[0107] According to Q i The values ​​are sorted from smallest to largest as follows: intelligent distribution network loss reduction transformation (Q = 0.000) < ultra-high voltage clean energy transmission channel (Q = 0.584) < distributed photovoltaic and energy storage synergy (Q = 1.000).

[0108] Acceptable advantage test: The conditions are met.

[0109] Acceptable stability test: The top-ranked "Intelligent Distribution Network Loss Reduction Retrofit" in S i (Minimum value) and R i It is also the best in the sorting of (minimum value), satisfying the condition.

[0110] Therefore, the "intelligent transformation and loss reduction of distribution networks" path has the lowest overall risk and is the optimal choice; the "ultra-high voltage clean energy transmission channel" path has a medium risk; and the "distributed photovoltaic and energy storage synergy" path has the highest risk. Further analysis shows that the main risk sources of the "distributed photovoltaic and energy storage synergy" path are concentrated in "policy support stability (C7)", "carbon price volatility (C8)" and "supply chain stability (C11)".

[0111] 5) Sensitivity analysis

[0112] Sensitivity analysis was conducted by varying the value of the decision-making mechanism coefficient v (taking values ​​of 0.1, 0.3, 0.5, 0.7, and 0.9 respectively). The results showed that "intelligent loss reduction transformation of distribution networks" remained the optimal solution under different risk preferences, indicating that the ranking results have good robustness.

[0113] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0114] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0115] The above is an introduction to the method embodiments. The following describes the solution described in this application through device embodiments.

[0116] Figure 2 A block diagram of a power grid engineering carbon emission reduction path risk assessment device according to an embodiment of this application is shown, as follows: Figure 2 As shown, the device includes:

[0117] The indicator construction module S210 is used to construct an indicator system for risk assessment of carbon emission reduction pathways in power grid projects.

[0118] The indicator weighting module S220 is used to assign weights to the indicators in the evaluation indicator system.

[0119] The risk assessment module S230 is used to conduct risk assessments on each carbon emission reduction path, determine the risk level ranking of each path, and analyze the risk problems existing in each path.

[0120] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0121] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0122] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0123] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for grid engineering carbon abatement pathway risk assessment, characterized in that, The method comprises the following steps: S1: constructing a power grid engineering carbon emission reduction path risk assessment index system; S2: using a combined index weight assignment method based on AHP and entropy weight method to assign weights to the evaluation index system; S3: using an improved VIKOR method to perform risk assessment on the index weight and path risk index evaluation value, solving the risk level ranking of each carbon emission reduction path, and identifying key risk factors.

2. The grid engineering carbon abatement pathway risk assessment method of claim 1, wherein, The power grid engineering carbon emission reduction path risk assessment index system comprises: The risk assessment index system is composed of four first-level indexes of technical risk, economic risk, policy and market risk, and implementation and operation risk; The technical risk comprises three second-level indexes of technology maturity, system compatibility and carbon emission intensity reduction rate; The economic risk comprises three second-level indexes of unit emission reduction cost, investment recovery period and yield fluctuation rate; The policy and market risk comprises three second-level indexes of policy support stability, carbon price volatility and green electricity consumption risk; The implementation and operation risk comprises three second-level indexes of project management capability, supply chain stability and information data security.

3. The grid engineering carbon abatement pathway risk assessment method of claim 1, wherein, The combined index weight assignment method based on AHP and entropy weight method comprises: A subjective weight assignment method based on AHP; An objective weight assignment method based on entropy weight method; Combining the subjective weight and the objective weight.

4. The grid engineering carbon abatement pathway risk assessment method of claim 3, wherein, The subjective weight assignment method based on AHP comprises: Step 1: establishing a hierarchical structure model to decompose the decision-making problem into a target layer, a criterion layer and a scheme layer; Step 2: Construct the judgment matrix, invite experts to use 1-9 scale method to compare each index in the same level, and construct the judgment matrix Step 3: Calculate the weight vector, calculate the maximum eigenvalue λ of the judgment matrix max and its corresponding eigenvector, normalize the eigenvector to get the weight W of each index s ; Step 4: Consistency check, calculate the consistency index Find the average random consistency index RI, calculate the consistency ratio When CR < 0.1, accept the judgment matrix; Step 5: hierarchical total sorting, calculating the combined weight of each level element to the total target.

5. The grid engineering carbon abatement pathway risk assessment method of claim 3, wherein, The objective weight assignment method based on entropy weight method comprises: 1) Constructing the standardized judgment matrix R = (r ij ) m×n , (i = 1, 2,..., m; j = 1, 2,..., n), where m is the number of paths, and n is the number of indicators; 2) Calculate the information entropy of each index, first calculate the feature proportion of the ith path under the jth index Then calculate the information entropy of the jth index wherein, 3) Index empowerment, calculate the entropy weight method weight of the jth index Wherein, E j is the information entropy of the jth index, W oj is the entropy weight of the jth index.

6. The grid engineering carbon abatement pathway risk assessment method of claim 1, wherein, The improved VIKOR method for risk assessment comprises: Index data standardization processing; Using the improved VIKOR method to perform risk assessment on the power grid engineering carbon emission reduction path.

7. The grid engineering carbon abatement pathway risk assessment method of claim 6, wherein, The index data standardization processing comprises: Converting the minimum type index and the maximum type index into the maximum type index; For very small indicators, use the formula For very large indicators, use the formula After the quantitative and consistent process of the qualitative index, the standardization of each evaluation index is as follows: Z = (z ij ) m×n where z ij is the normalized value of the ith path on the jth metric.

8. The grid engineering carbon abatement pathway risk assessment method of claim 6, wherein, The improved VIKOR method for risk assessment on the power grid engineering carbon emission reduction path comprises: 1) Determining the positive ideal solution and the negative ideal solution: For benefit type indicators, the positive ideal solution is The negative ideal solution is For cost type indicators, the positive ideal solution is The negative ideal solution is 2) Calculate the group utility value and the individual legacy value where w j is the combined weight of the jth indicator; 3) Introducing a triangular fuzzy number to handle uncertainty, and using a triangular fuzzy number to represent the language evaluation of experts for the evaluation of qualitative indexes; 4) Dynamic weight adjustment mechanism, according to the performance of each path on different indicators, dynamically adjust the weight distribution, the weight adjustment formula is w' j = w j ×(1+a×r j ), wherein w' j is the adjusted weight, a is the adjustment coefficient, r j is the risk coefficient of the jth indicator; 5) Calculate the benefit ratio where S * = min i S i , S - = max i S i , R * = min i R i , R - = max i R i , v is a decision mechanism coefficient, 0 < v < 1. 6) Path ranking and risk analysis, according to Q i values from small to large, Q i The smaller the value, the lower the path risk.

9. The grid engineering carbon abatement pathway risk assessment method of claim 8, wherein, The path ranking and risk analysis further comprise checking the following conditions: Acceptable advantage condition: wherein A (1) is the first ranked solution, A (2) is the second ranked solution, and m is the total number of solutions; Acceptable stability conditions: the first ranked solution in S i or R i must also be the best in the ranking.

10. A power grid engineering carbon emission reduction path risk assessment device, characterized by, It comprises: An index construction module for constructing a power grid engineering carbon emission reduction path risk assessment index system; An index weight assignment module for using a combined index weight assignment method based on AHP and entropy weight method to assign weights to the evaluation index system; A risk assessment module for using an improved VIKOR method to perform risk assessment on the index weight and path risk index evaluation value, solving the risk level ranking of each carbon emission reduction path, and identifying key risk factors.

Images