Research method for risk identification of energy supply chain

Abstract

The invention discloses a research method for risk identification of an energy supply chain, and relates to the field of energy. The method comprises the following steps: constructing an energy supply chain elasticity measurement index system to define an energy supply chain risk, quantifying the energy supply chain elasticity measurement index system by means of a Bayesian model, and identifying key nodes of the energy supply chain risk; selecting coal power energy supply chain risk influence factor indexes, judging whether the risk influence factors have a binary relationship or not, and constructing an adjacent matrix containing the binary relationship of each factor; and calculating the adjacent matrix to obtain a reachable matrix, decomposing the reachable matrix at different levels, distinguishing the structural relevance of various coal-electricity energy supply chain risk influence factor indexes, and clarifying the risk infection path of the coal-electricity energy supply chain. According to the method, the energy supply chain risk is taken as an entry point, the structural relevance of various risks is identified, key blocking points restricting transformation and upgrading of the Chinese energy industry can be broken through, construction of a modern energy system is assisted, and high-quality development of energy is promoted.

Description

Technical Field

[0001] The present invention relates to the field of energy, and in particular to a research method for identifying risks in an energy supply chain. Background Art

[0002] Clarifying the risk path of the energy supply chain is the fundamental solution to promote the transformation and upgrading of the energy industry and eliminate energy risks. Therefore, starting from the energy supply chain risk and identifying the structural correlation of various risks will help to clear the key bottlenecks that restrict the transformation and upgrading of China's energy industry, help build a modern energy system, and promote high-quality energy development. Summary of the invention

[0003] A research method for energy supply chain risk identification, characterized by comprising the steps of: 1) Construct an energy supply chain resilience measurement index system to define energy supply chain risks, quantify the energy supply chain resilience measurement index system with the help of the Bayesian model, and identify the key nodes of energy supply chain risks; 2) Select the risk influencing factor indicators of the coal-fired power energy supply chain, determine whether there is a binary relationship between the risk influencing factors, and thus construct an adjacency matrix containing the binary relationships of each factor; 3) Operate the adjacency matrix to obtain the reachable matrix, decompose the reachable matrix at different levels, identify the structural correlation of various coal-fired power energy supply chain risk influencing factor indicators, and clarify the risk transmission path of the coal-fired power energy supply chain; As a preferred technical means: Step 1) includes: 101) Construct an energy supply chain resilience measurement indicator system; 102) Quantify the energy supply chain resilience measurement index system with the help of Bayesian model; 103) Identify key points of energy supply chain risk; As a preferred technical means: In step 101), the method for constructing an energy supply chain elasticity measurement index system is: Energy supply chain resilience measurement mainly covers impact factors, which are divided into primary impact factors and secondary impact factors: the main indicators of primary impact factors are energy input , Energy production , Energy imports , Energy transfer and energy exports The secondary influencing factors are classified according to the three major fossil energy sources in primary energy consumption, coal, oil and natural gas, which are coal input , Oil transfer volume , Natural gas transfer volume , coal production , Oil production , Natural gas production , coal imports , Oil imports , natural gas imports , coal output , Oil transfer volume , Natural gas output , coal export volume , Oil export volume , Natural gas export volume ; As a preferred technical means: in step 102), the steps of quantifying the energy supply chain elasticity measurement index system with the help of the Bayesian model are as follows: A. Find the annual growth rate of each secondary impact factor : : ; in, Represents the value of the secondary impact factor in a certain year; B. Measure the probability of occurrence of influencing factors at all levels: First, the occurrence probability is defined as the probability that the secondary and primary influencing factors are impacted by exogenous risks; Secondly, the probability of occurrence of the first-level impact factor and the second-level impact factor is measured respectively; The probability measurement method of secondary impact factors is: ; in Years with negative growth rates of secondary impact factors, is the total year; The method for normalizing the probability of occurrence of secondary impact factors is: ; The probability measurement method of the first-level impact factor is: ; ; in, To calculate the probability of the three major fossil energy sources in primary energy consumption, coal, oil and natural gas, is the weight of each secondary influencing factor converted into standard coal; C. Measure the prior probability of influencing factors at all levels: First, the prior probability is defined as the probability that the previous level of impact factors are impacted by exogenous risks when the impact factors at each level occur; Secondly, the prior probabilities of the secondary impact factors and the primary impact factors are measured respectively; The prior probability measurement method of the secondary impact factor is: ; ; in, In order to convert the average amount of the three major fossil energy sources in primary energy consumption, coal, oil and natural gas, into the average standard coal amount, is the prior probability of the secondary impact factor Normalized values; The prior probability measurement method of the first-level impact factor is: ; ; in, is the sum of the weight of standard coal converted from the secondary impact factors corresponding to each primary impact factor. is the prior probability of the first-level impact factor Normalized values; D. Measure the posterior probability of influencing factors at all levels: First, the posterior probability of the first-level influencing factors is defined as the probability of each first-level influencing factor occurring when the supply chain is hit by a risk, and the probability of the second-level influencing factor being at risk when each first-level influencing factor occurs; Secondly, the posterior probabilities of the first-level impact factors and the second-level impact factors are measured respectively; The posterior probability measurement method of each level of influencing factors is: ; ; ; ; in, and is the total probability formula and conditional probability formula, is the posterior probability of the first-level impact factor, is the posterior probability of the secondary impact factor; As a preferred technical means: Step 2) includes: 201) Select the risk influencing factors of coal power energy supply chain; 202) Construct an adjacency matrix containing the binary relationships of each factor; As a preferred technical means: in step 201), the risk influencing factor indicators of the coal power energy supply chain are: Moral and Collaborative Risks , Information transmission risks , production (supply) risks , Procurement Risks , Economic Cycle Risk , Financial Risk , Differences in corporate culture , Policy risks , Market (demand) risk , production technology risks , Legal risks , Logistics Risk , Risk of unexpected events ; As a preferred technical means: in step 202), an adjacency matrix is ​​formed : ; ; Among them, the adjacency matrix yes The matrix of Represents the number of indicators of risk influencing factors of coal power energy supply chain; As a preferred technical means: Step 3) includes: 301) Operate the adjacency matrix to obtain the reachability matrix; 302) Decomposing the reachability matrix at different levels; In step 301), a reachability matrix is ​​formed : ; ; ; Among them, the reachability matrix yes The matrix of Represents the number of indicators of risk influencing factors of coal power energy supply chain; is the identity matrix; For the road is long; Beneficial results: Compared with the prior art, the present invention first defines the energy supply chain risk by constructing an energy supply chain elasticity measurement index system, quantifies the energy supply chain elasticity measurement index system with the help of a Bayesian model, and identifies the key nodes of the energy supply chain risk; secondly, selects indicators of risk influencing factors of the coal-fired power energy supply chain to determine whether there is a binary relationship between the risk influencing factors, and thereby constructs an adjacency matrix containing the binary relationships of each factor; finally, the adjacency matrix is ​​operated to obtain a reachable matrix, and the reachable matrix is ​​decomposed at different levels to identify the structural correlation of various coal-fired power energy supply chain risk influencing factor indicators, and clarify the risk transmission path of the coal-fired power energy supply chain. BRIEF DESCRIPTION OF THE DRAWINGS

[0004] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0005] The technical solution of the present invention is further described in detail below in conjunction with the accompanying drawings: like Figure 1 As shown, the present invention comprises the steps of: Construct an energy supply chain resilience measurement index system to define energy supply chain risks, quantify the energy supply chain resilience measurement index system with the help of Bayesian model, and identify the key nodes of energy supply chain risks; Select the risk influencing factor indicators of coal power energy supply chain, determine whether there is a binary relationship between the risk influencing factors, and thus construct an adjacency matrix containing the binary relationships of each factor; The adjacency matrix is ​​calculated to obtain the reachable matrix, and the reachable matrix is ​​decomposed at different levels to identify the structural correlation of various indicators of coal-fired power energy supply chain risk influencing factors, and clarify the risk transmission path of the coal-fired power energy supply chain; This embodiment selects data from the China Energy Statistical Yearbook from 2000 to 2022, and converts raw coal, crude oil, and natural gas into standard coal according to the reference coefficients of various energy sources converted to standard coal in the China Energy Statistical Yearbook; :Build an energy supply chain resilience measurement index system to define energy supply chain risks, quantify the energy supply chain resilience measurement index system with the help of Bayesian model, and identify the key nodes of energy supply chain risks; :Build an energy supply chain resilience measurement indicator system; Energy supply chain resilience measurement mainly covers impact factors, which are divided into primary impact factors and secondary impact factors: the main indicators of primary impact factors are energy input , Energy production , Energy imports , Energy transfer and energy exports The secondary influencing factors are classified according to the three major fossil energy sources in primary energy consumption, coal, oil and natural gas, which are coal input , Oil transfer volume , Natural gas transfer volume , coal production , Oil production , Natural gas production , coal imports , Oil imports , natural gas imports , coal output , Oil transfer volume , Natural gas output , coal export volume , Oil export volume , Natural gas export volume ; :Using the Bayesian model to quantify the energy supply chain resilience measurement index system; A. Find the annual growth rate of each secondary impact factor : : ; in, Represents the value of the secondary impact factor in a certain year; B. Measure the probability of occurrence of influencing factors at all levels: First, the occurrence probability is defined as the probability that the secondary and primary influencing factors are impacted by exogenous risks; Secondly, the probability of occurrence of the first-level impact factor and the second-level impact factor is measured respectively; The probability measurement method of secondary impact factors is: ; in Years with negative growth rates of secondary impact factors, is the total year; The method for normalizing the probability of occurrence of secondary impact factors is: ; The probability measurement method of the first-level impact factor is: ; ; in, To calculate the probability of the three major fossil energy sources in primary energy consumption, coal, oil and natural gas, is the weight of each secondary influencing factor converted into standard coal; C. Measure the prior probability of influencing factors at all levels: First, the prior probability is defined as the probability that the previous level of impact factors are impacted by exogenous risks when the impact factors at each level occur; Secondly, the prior probabilities of the secondary impact factors and the primary impact factors are measured respectively; The prior probability measurement method of the secondary impact factor is: ; ; in, In order to convert the average amount of the three major fossil energy sources in primary energy consumption, coal, oil and natural gas, into the average standard coal amount, is the prior probability of the secondary impact factor Normalized values; The prior probability measurement method of the first-level impact factor is: ; ; in, is the sum of the weight of standard coal converted from the secondary impact factors corresponding to each primary impact factor. is the prior probability of the first-level impact factor Normalized values; D. Measure the posterior probability of influencing factors at all levels: First, the posterior probability of the first-level influencing factors is defined as the probability of each first-level influencing factor occurring when the supply chain is hit by a risk, and the probability of the second-level influencing factor being at risk when each first-level influencing factor occurs; Secondly, the posterior probabilities of the first-level impact factors and the second-level impact factors are measured respectively; The posterior probability measurement method of each level of influencing factors is: ; ; ; ; in, and is the total probability formula and conditional probability formula, is the posterior probability of the first-level impact factor, is the posterior probability of the secondary impact factor; Table 1 Prior and posterior probability measurement results of Shanghai energy supply chain elasticity ; Table 2 Prior and posterior probability measurement results of Jiangsu energy supply chain elasticity ; Table 3 Prior and posterior probability measurement results of Zhejiang energy supply chain elasticity ; Table 4 Prior and posterior probability measurement results of Anhui energy supply chain elasticity ; In general, the nodes that occupy an important position in the energy supply chain are energy transfer, energy import and energy transfer. Among them, the first-level influencing factor, energy transfer, is the main source and key node of energy supply chain risks in the Yangtze River Delta region. In terms of energy categories, controlling the risks of coal and oil in the energy supply chain among the second-level influencing factors is of great significance for steadily improving the resilience of the energy supply chain and ensuring the stability of the energy supply chain. :Select the risk influencing factor indicators of coal power energy supply chain, determine whether there is a binary relationship between the risk influencing factors, and thus construct an adjacency matrix containing the binary relationships of each factor; :The risk influencing factors of coal power energy supply chain are Moral and Collaborative Risks , Information transmission risks , production (supply) risks , Procurement Risks , Economic Cycle Risk , Financial Risk , Differences in corporate culture , Policy risks , Market (demand) risk , production technology risks , Legal risks , Logistics Risk , Risk of unexpected events ; Forming the adjacency matrix ; ; ; Among them, the adjacency matrix yes The matrix of Represents the number of indicators of risk influencing factors of coal power energy supply chain; Table 5 Adjacency matrix of risk influencing factors of coal power energy supply chain ; The adjacency matrix is ​​calculated to obtain the reachable matrix, and the reachable matrix is ​​decomposed at different levels to identify the structural correlation of various indicators of coal-fired power energy supply chain risk influencing factors, and clarify the risk transmission path of the coal-fired power energy supply chain; Forming a reachability matrix : ; ; ; Among them, the reachability matrix yes The matrix of Represents the number of indicators of risk influencing factors of coal power energy supply chain; is the identity matrix; For the road is long; Table 6 Accessibility matrix of risk influencing factors of coal power energy supply chain ; The reachability matrix is ​​decomposed at different levels; Table 7 The reachable set, the preceding set and their intersection ; in, , , They are the reachable set, predecessor set and common set of the reachable matrix respectively. The common set is the intersection of the reachable set and the predecessor set. Table 8 Hierarchical decomposition of factors affecting coal energy supply chain risks ; Among them, if a risk factor can reach the set With common set If the elements are the same, the risk factor belongs to the first level and the risk factor is crossed out. Repeat the above operation and iterate back and forth until all risk factors find the corresponding level.

Claims

1. A research method for energy supply chain risk identification, characterized by Includes steps: 1) Construct an energy supply chain resilience measurement index system to define energy supply chain risks, quantify the energy supply chain resilience measurement index system with the help of the Bayesian model, and identify the key nodes of energy supply chain risks; 2) Select the risk influencing factor indicators of the coal-fired power energy supply chain, determine whether there is a binary relationship between the risk influencing factors, and thus construct an adjacency matrix containing the binary relationships of each factor; 3) Operate the adjacency matrix to obtain the reachable matrix, decompose the reachable matrix at different levels, identify the structural correlation of various indicators of coal-fired power energy supply chain risk influencing factors, and clarify the risk transmission path of the coal-fired power energy supply chain.

2. The research method for energy supply chain risk identification according to claim 1 is characterized by: Step 1) includes: 101) Construct an energy supply chain resilience measurement indicator system; 102) Quantify the energy supply chain resilience measurement index system with the help of Bayesian model; 103) Identify key points of energy supply chain risk; In step 101), the method for constructing an energy supply chain resilience measurement index system is: Energy supply chain resilience measurement mainly covers impact factors, which are divided into primary impact factors and secondary impact factors: the main indicators of primary impact factors are energy input , Energy production , Energy imports , Energy transfer and energy exports The secondary influencing factors are classified according to the three major fossil energy sources in primary energy consumption, coal, oil and natural gas, which are coal input , Oil transfer volume , Natural gas transfer volume , coal production , Oil production , Natural gas production , coal imports , Oil imports , natural gas imports , coal output , Oil transfer volume , Natural gas output , coal export volume , Oil export volume , Natural gas export volume .

3. The research method for energy supply chain risk identification according to claim 2 is characterized by: In step 102), the steps of quantifying the energy supply chain elasticity measurement index system with the help of the Bayesian model are as follows: A. Find the annual growth rate of each secondary impact factor ： ； in, Represents the value of the secondary impact factor in a certain year; B. Measure the probability of occurrence of influencing factors at all levels: First, the occurrence probability is defined as the probability that the secondary and primary influencing factors are impacted by exogenous risks; Secondly, the probability of occurrence of the first-level impact factor and the second-level impact factor is measured respectively; The probability measurement method of secondary impact factors is: ； in Years with negative growth rates of secondary impact factors, is the total year; The method for normalizing the probability of occurrence of secondary impact factors is: ； The probability measurement method of the first-level impact factor is: ； ； in, To calculate the probability of the three major fossil energy sources in primary energy consumption, coal, oil and natural gas, is the weight of each secondary influencing factor converted into standard coal; C. Measure the prior probability of influencing factors at all levels: First, the prior probability is defined as the probability that the previous level of impact factors are impacted by exogenous risks when the impact factors at each level occur; Secondly, the prior probabilities of the secondary impact factors and the primary impact factors are measured respectively; The prior probability measurement method of the secondary impact factor is: ； ； in, In order to convert the average amount of the three major fossil energy sources in primary energy consumption, coal, oil and natural gas, into the average standard coal amount, is the prior probability of the secondary impact factor Normalized values; The prior probability measurement method of the first-level impact factor is: ； ； in, is the sum of the weight of standard coal converted from the secondary impact factors corresponding to each primary impact factor. is the prior probability of the first-level impact factor Normalized values; D. Measure the posterior probability of influencing factors at all levels: First, the posterior probability of the first-level influencing factors is defined as the probability of each first-level influencing factor occurring when the supply chain is hit by a risk, and the probability of the second-level influencing factor being at risk when each first-level influencing factor occurs; Secondly, the posterior probabilities of the first-level impact factors and the second-level impact factors are measured respectively; The posterior probability measurement method of each level of influencing factors is: ； ； ； ； in, and is the total probability formula and conditional probability formula, is the posterior probability of the first-level impact factor, is the posterior probability of the secondary impact factor.

4. The research method for energy supply chain risk identification according to claim 1 is characterized by: Step 2) includes: 201) Select the risk influencing factors of coal power energy supply chain; 202) Construct an adjacency matrix containing the binary relationships of each factor; In step 201), the risk influencing factor indicators of coal power energy supply chain are: Moral and Collaborative Risks , Information transmission risks , production (supply) risks , Procurement Risks , Economic Cycle Risk , Financial Risk , Differences in corporate culture , Policy risks , Market (demand) risk , production technology risks , Legal risks , Logistics Risk , Risk of unexpected events .

5. The research method for energy supply chain risk identification according to claim 4 is characterized by: In step 202), an adjacency matrix is ​​formed : ； ； Among them, the adjacency matrix yes The matrix of Represents the number of indicators of risk influencing factors of coal power energy supply chain.

6. The research method for energy supply chain risk identification according to claim 1 is characterized by: Step 3) includes: 301) Operate the adjacency matrix to obtain the reachability matrix; 302) Decomposing the reachability matrix at different levels; In step 301), a reachability matrix is ​​formed : ； ； ； Among them, the reachability matrix yes The matrix of Represents the number of indicators of risk influencing factors of coal power energy supply chain; is the identity matrix; For the road is long.

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