Electric energy substitution evolution path prediction method and system considering carrying capacity of power distribution network

Abstract

An electric energy substitution evolution path prediction method and system considering a power distribution network bearing capability, and the method comprises the steps: carrying out the calculation based on the data of each enterprise in a certain time period in combination with an industry space lag model, and obtaining the scale value of the industry where the electric energy substitution of the region is located in a next time period; based on enterprise clusters with different configurations, simulating an electric energy substitution evolution path to obtain the diffusivity of electric energy substitution of the region in a certain period of time; calculating based on the diffusivity and the scale value to obtain a scale value of electric energy substitution of the region in the next time period; correcting the scale value of electric energy substitution based on the electric energy substitution maximum load access amount calculated by combining the power distribution network parameters with the power distribution network bearing capacity evaluation index to obtain the electric energy substitution amount accessed to the power distribution network; the method simulates an electric energy substitution evolution path, and enables the prediction result to be more comprehensive and accurate. The maximum bearing capacity of the power distribution network is considered by calculating the electric energy replacement load access amount, and a more practical and operable prediction result is provided for the power grid.

Description

Technical Field

[0001] The present application relates to the field of electric energy substitution development prediction, and specifically to a method and system for predicting the evolution path of electric energy substitution taking into account the carrying capacity of the distribution network. Background Art

[0002] With the rapid development of new energy, the coal-based energy structure of the power industry has gradually improved, and the carbon emissions contained in electricity have been reduced. Promoting electricity substitution and improving the cleanliness of terminal energy use are important measures for my country's low-carbon transformation. However, while reducing the end-users' consumption of primary energy, electricity substitution has increased the pressure on regional power grid power supply. Orderly promotion of the development of electricity substitution is of great significance to alleviating the pressure on power supply and promoting the realization of dual carbon goals. Carrying out research on the prediction of the evolutionary path of electricity substitution will help evaluate the development potential of the local electricity substitution market, formulate development goals and promotion policies that match the development potential, and create a good external environment for the orderly development of electricity substitution in the region; on the other hand, it will help to clarify the development scale of local electricity substitution projects, clarify electricity demand according to the evolutionary path, and provide users with high-quality electricity supply guarantees. At present, it is urgent to solve the problem that the current electricity substitution scale prediction ignores the expansion of technology and the carrying capacity of the distribution network. Summary of the invention

[0003] In order to solve the problem that the prediction of electric energy substitution scale in the prior art ignores the technology expansion and the carrying capacity of the distribution network, this application proposes a prediction method for the evolution path of electric energy substitution taking into account the carrying capacity of the distribution network, including:

[0004] Based on the data of each enterprise in a region during a certain period of time, combined with a pre-built industry space hysteresis model, calculation is performed to obtain the scale value of the industry in which the electric energy substitution in the region is located in the next period of the certain period of time;

[0005] Based on the data of enterprises in a region during a certain period of time, the enterprise clusters with different configurations are generated by using the fuzzy set qualitative comparative analysis method to simulate the evolution path of electric energy substitution and obtain the diffusion rate of electric energy substitution in the region during a certain period of time.

[0006] Calculate based on the diffusion rate of electric energy substitution in the region during the certain period and the scale value of the industry in which the electric energy substitution in the region is located during the next period to obtain the scale value of electric energy substitution in the region during the next period;

[0007] Based on the distribution network parameters and the distribution network carrying capacity evaluation index, the maximum load access amount of electric energy substitution is calculated, and the scale value of electric energy substitution in the region in the next period is corrected to obtain the electric energy substitution amount connected to the distribution network.

[0008] Preferably, the data of each enterprise in a region during a certain period is combined with a pre-built industry space hysteresis model to calculate and obtain the scale value of the industry where the electric energy substitution in the region is located in the next period of the certain period, including:

[0009] Based on the data of each enterprise in a certain period of time and combined with the industry spatial lag model, the scale growth rate of the industry where the electric energy substitution is located in the region during a certain period of time is obtained;

[0010] The scale value of the industry in which electric energy substitution is located in the region in a certain period of time is calculated based on the scale growth rate to obtain the scale value of the industry in which electric energy substitution is located in the region in the next period of the certain period of time.

[0011] Preferably, the enterprise clusters with different configurations generated by using the fuzzy set qualitative comparative analysis method based on the data of each enterprise in a region during a certain period of time are simulated to simulate the evolution path of electric energy substitution to obtain the diffusion rate of electric energy substitution in the region during a certain period of time, including:

[0012] Based on the data of enterprises in a certain period of time, the fuzzy set qualitative comparative analysis method is used to extract the influencing factors of each enterprise's choice of electric energy substitution;

[0013] The configuration of each enterprise is determined based on the influencing factors of each enterprise's choice of electric energy substitution, and two enterprise clusters of evolutionary games are formed according to the configurations of enterprises with different proportions;

[0014] In each game cycle, one enterprise is selected from each of the two enterprise clusters for game play, and the next round of game strategy is selected based on the calculated self-income and the income of neighboring enterprises;

[0015] After the game is over, the diffusion rate of electricity substitution technology in the region is calculated based on the proportion of the number of enterprises that choose electricity substitution to the total number of enterprises.

[0016] Preferably, in each game cycle, one enterprise is selected for game based on the two enterprise clusters respectively, and the next round of game strategy is selected according to the calculated self-income and the income of neighboring enterprises, including:

[0017] The configurations of the enterprises selected based on the two enterprise clusters choose whether to participate in the electricity substitution game, and the generalized cost model and the evolutionary game model are used for calculation to obtain the enterprise's own benefits; based on the enterprise's own benefits and the benefits of its neighboring enterprises, the Fermi function is used to calculate and obtain the probability that the enterprises in the next round of the game imitate the game strategies of their neighboring enterprises.

[0018] Preferably, the maximum load access amount of electric energy substitution calculated based on the distribution network parameters combined with the distribution network carrying capacity evaluation index is used to correct the scale value of electric energy substitution in the region in the next period to obtain the electric energy substitution amount connected to the distribution network, including:

[0019] Calculate based on distribution network parameters combined with distribution network carrying capacity assessment indicators to obtain the values ​​of each indicator;

[0020] Based on the values ​​of each indicator and the thresholds set for each indicator, a threshold of the load access capacity of the distribution network is obtained;

[0021] Based on the threshold of the load access capacity of the distribution network minus the load capacity of other industries, the maximum load access capacity of electric energy substitution is obtained;

[0022] The scale value of electric energy substitution in the region in the next period is corrected based on the maximum load access amount of electric energy substitution, and a smaller value is selected as the electric energy substitution amount for access to the distribution network.

[0023] Preferably, the calculation formula of the industry space hysteresis model is as follows:

[0024]

[0025] Where Y it is the scale growth rate of a certain electric energy substitution industry in region i during period t; β0 is the 0th coefficient; β1 is the 1st coefficient; β2 is the 2nd coefficient; β3 is the 3rd coefficient; β4 is the 4th coefficient; β5 is the 5th coefficient; SPe it The specialization effect is represented by the deviation between the ratio of the industry output value to the local total output value and the regional average ratio; Div it The Herfindhal clustering index can be used to represent the diversification effect; Com it The competitiveness of enterprises can be expressed by the deviation between the ratio of the number of enterprises to the added value of the industry and the regional average ratio; Siz it is the average size of the enterprise; Den it is the local economic scale, which can be expressed by local GDP; ρ∑ j≠i W ij LqCy jt is the spatial spillover effect of the industry; W ij is the spatial weight matrix; ρ is the coefficient; ε it is the error term; Y jt is the growth rate of a certain electric energy substitution industry in region j during period t.

[0026] Based on the same application concept, this application also proposes a power substitution evolution path prediction system considering the carrying capacity of the distribution network, including:

[0027] The industry scale value prediction module is used to calculate based on the data of various enterprises in a region during a certain period of time in combination with a pre-built industry space hysteresis model to obtain the scale value of the industry in which the electric energy substitution in the region is located in the next period of the certain period of time;

[0028] The electric energy substitution diffusion rate calculation module is used to simulate the evolution path of electric energy substitution based on the enterprise clusters with different configurations generated by the fuzzy set qualitative comparative analysis method based on the data of each enterprise in a certain period of time, and obtain the diffusion rate of electric energy substitution in the region in a certain period of time;

[0029] An electric energy substitution scale value prediction module is used to calculate based on the diffusion rate of electric energy substitution in the region during the certain period and the scale value of the industry in which the electric energy substitution in the region is located during the next period, to obtain the scale value of electric energy substitution in the region during the next period;

[0030] The electric energy substitution scale value correction module is used to correct the scale value of electric energy substitution in the region in the next period based on the maximum load access amount of electric energy substitution calculated based on the distribution network parameters combined with the distribution network carrying capacity evaluation index, so as to obtain the electric energy substitution amount connected to the distribution network.

[0031] Preferably, the industry scale value prediction module is specifically used for:

[0032] Based on the data of each enterprise in a certain period of time and combined with the industry spatial lag model, the scale growth rate of the industry where the electric energy substitution is located in the region during a certain period of time is obtained;

[0033] The scale value of the industry in which electric energy substitution is located in the region in a certain period of time is calculated based on the scale growth rate to obtain the scale value of the industry in which electric energy substitution is located in the region in the next period of the certain period of time.

[0034] Preferably, the electric energy substitution diffusion rate calculation module comprises:

[0035] Extracting influencing factors submodule, which is used to extract the influencing factors of each enterprise's choice of electric energy substitution based on the data of each enterprise in a certain period of time by using the fuzzy set qualitative comparative analysis method;

[0036] The enterprise cluster formation submodule is used to determine the configuration of each enterprise based on the influencing factors of each enterprise's choice of electric energy substitution, and form two enterprise clusters of evolutionary games according to the configurations of enterprises with different proportions;

[0037] The evolutionary game submodule is used for each game cycle, selecting one enterprise for game based on the two enterprise clusters, and selecting the next round of game strategy based on the calculated self-income and the income of neighboring enterprises;

[0038] The diffusion rate calculation submodule is used to calculate the diffusion rate of electric energy substitution technology in the region based on the proportion of the number of enterprises that choose electric energy substitution to the total number of enterprises after the game is over.

[0039] Preferably, the evolutionary game submodule is specifically used for:

[0040] Based on the configuration of the enterprises selected from the two enterprise clusters, whether to participate in the electric energy substitution game is played, and the generalized cost model and the evolutionary game model are used for calculation to obtain the enterprise's own benefits;

[0041] Based on the enterprise's own benefits and the benefits of its neighboring enterprises, the Fermi function is used to calculate the probability that the enterprise in the next round of the game will imitate the game strategy of its neighboring enterprises.

[0042] Preferably, the electric energy substitution scale value correction module is specifically used for:

[0043] Calculate based on distribution network parameters combined with distribution network carrying capacity assessment indicators to obtain the values ​​of each indicator;

[0044] Based on the values ​​of each indicator and the thresholds set for each indicator, a threshold of the load access capacity of the distribution network is obtained;

[0045] Based on the threshold of the load access capacity of the distribution network minus the load capacity of other industries, the maximum load access capacity of electric energy substitution is obtained;

[0046] The scale value of electric energy substitution in the region in the next period is corrected based on the maximum load access amount of electric energy substitution, and a smaller value is selected as the electric energy substitution amount for access to the distribution network.

[0047] Preferably, the calculation formula of the industry space hysteresis model in the industry scale value prediction module is as follows:

[0048]

[0049] Where Y it is the scale growth rate of a certain electric energy substitution industry in region i during period t; β0 is the 0th coefficient; β1 is the 1st coefficient; β2 is the 2nd coefficient; β3 is the 3rd coefficient; β4 is the 4th coefficient; β5 is the 5th coefficient; SPe it The specialization effect is represented by the deviation between the ratio of the industry output value to the local total output value and the regional average ratio; Div it The Herfindhal clustering index can be used to represent the diversification effect; Com it The competitiveness of enterprises can be expressed by the deviation between the ratio of the number of enterprises to the added value of the industry and the regional average ratio; Siz it is the average size of the enterprise; Den it is the local economic scale, which can be expressed by local GDP; ρ∑ j≠i W ij LqCy jt is the spatial spillover effect of the industry; W ij is the spatial weight matrix; ρ is the coefficient; ε it is the error term; Y jtis the growth rate of a certain electric energy substitution industry in region j during period t.

[0050] On the other hand, the present application also proposes an electronic device, comprising: at least one processor and a memory; the memory and the processor are connected via a bus;

[0051] The memory is used to store one or more programs;

[0052] When the one or more programs are executed by the at least one processor, a method for predicting an evolutionary path of electric energy substitution taking into account the carrying capacity of a distribution network as described above is implemented.

[0053] On the other hand, the present application also proposes a readable storage medium having an execution program stored thereon, which, when executed, implements a method for predicting the evolution path of electric energy substitution that takes into account the carrying capacity of the distribution network as described above.

[0054] Compared with the prior art, the beneficial effects of this application are:

[0055] A method and system for predicting the evolutionary path of electric energy substitution considering the carrying capacity of a distribution network, comprising: calculating based on the data of each enterprise in a region during a certain period in combination with a pre-constructed industry space hysteresis model to obtain the scale value of the industry in which the electric energy substitution in the region is located in the next period of the certain period; simulating the evolutionary path of electric energy substitution based on the data of each enterprise in the region during a certain period using a fuzzy set qualitative comparative analysis method to generate enterprise clusters with different configurations to obtain the diffusion rate of electric energy substitution in the region during a certain period; calculating based on the diffusion rate of electric energy substitution in the region during the certain period and the scale value of the industry in which the electric energy substitution in the region is located in the next period , obtain the scale value of electric energy substitution in the region in the next period; based on the distribution network parameters combined with the distribution network carrying capacity evaluation index, the maximum load access amount of electric energy substitution is calculated, and the scale value of electric energy substitution in the region in the next period is corrected to obtain the electric energy substitution amount connected to the distribution network; the application simulates the evolution path of electric energy substitution, and can obtain the technology expansion process, so that the prediction results are more comprehensive and accurate, and provide a more scientific basis for the formulation of electric energy substitution strategies; the application calculates the maximum load access amount of electric energy substitution taking into account the carrying capacity of the distribution network, and provides a more practical and operational prediction result in combination with the actual carrying capacity of the power grid;

[0056] This application uses the fsQCA method to extract influencing factors to form game clusters to capture the differences between different game populations, so as to more accurately predict the decision-making of enterprises in the selection of electric energy substitution technologies and make the evolutionary game process more in line with the actual situation of technology expansion;

[0057] This application takes into account the expansion of industry scale, technological expansion and the maximum carrying capacity of the distribution network, and can more comprehensively and accurately evaluate the development trend of electric energy substitution. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 A flow chart of a method for predicting the evolutionary path of electric energy substitution taking into account the carrying capacity of the distribution network in this application;

[0059] Figure 2 A flowchart of this application;

[0060] Figure 3 A schematic diagram of the enterprise's selection of electric energy alternatives under the configuration considered in this application;

[0061] Figure 4 A schematic diagram of the probability of selecting electric energy substitution for each configuration of the enterprise in this application;

[0062] Figure 5 This is a schematic diagram of the impact of the innovation changes in Chengdu on network diffusion for this application;

[0063] Figure 6 This is a schematic diagram of the impact of asset specificity and executive dominance on network diffusion for this application;

[0064] Figure 7 This is a schematic diagram of the impact of industry agglomeration and executive dominance on network diffusion for this application;

[0065] Figure 8 A schematic diagram of the load and capacity development path for this application;

[0066] Fig. 9 This is a structural diagram of an electric energy substitution evolution path prediction system considering the carrying capacity of the distribution network in this application;

[0067] Fig.10 A diagram of an electronic device for the present application. DETAILED DESCRIPTION

[0068] On the basis of predicting the natural development scale of the industry, this application uses the evolutionary game method to simulate the expansion process of electric energy substitution technology, obtains the evolution process of the natural development scale of electric energy substitution, and then proposes the distribution network carrying capacity evaluation index, corrects the natural development scale of electric energy substitution, and obtains the electric energy substitution evolution path that takes into account the distribution network carrying capacity. This comprehensive consideration makes the prediction results more comprehensive and accurate, and can provide a more scientific basis for the formulation of electric energy substitution strategies. In addition, in establishing the evolutionary game model of technology expansion, this application first uses the fuzzy set qualitative comparative analysis fSQCA method to extract the key factors that affect users' choice of electric energy substitution technology, and generates user groups with different configurations, so that the evolutionary game process is more in line with the actual situation of technology expansion. In order to better understand this application, the content of this application is further explained below in conjunction with the drawings and embodiments of the specification.

[0069] Embodiment 1:

[0070] A method for predicting the evolution path of electric energy substitution considering the carrying capacity of distribution network. The specific process is as follows: Figure 1 As shown, including:

[0071] Step 1: Based on the data of each enterprise in a region during a certain period of time, combined with a pre-built industry space hysteresis model, a calculation is performed to obtain the scale value of the industry in which the electric energy substitution in the region is located in the next period of the certain period of time;

[0072] Step 2: Based on the data of enterprises in a region during a certain period of time, the enterprise clusters with different configurations are generated by using the fuzzy set qualitative comparative analysis method to simulate the evolution path of electric energy substitution and obtain the diffusion rate of electric energy substitution in the region during a certain period of time;

[0073] Step 3, calculating based on the diffusion rate of the electric energy substitution in the region in the certain period and the scale value of the industry in which the electric energy substitution in the region is located in the next period, to obtain the scale value of the electric energy substitution in the region in the next period;

[0074] Step 4: Based on the distribution network parameters and the distribution network carrying capacity evaluation index, the maximum load access amount of electric energy substitution is calculated, and the scale value of electric energy substitution in the region in the next period is corrected to obtain the electric energy substitution amount connected to the distribution network.

[0075] Before step 1, the construction of the industry space lag model is also included:

[0076] According to the impact of regional economic structure on industrial development, an industry spatial lag model is constructed to calculate the industry growth rate. The formula is as follows:

[0077]

[0078] Where Y it is the scale growth rate of a certain electric energy substitution industry in region i during period t; β0 is the 0th coefficient; β1 is the 1st coefficient; β2 is the 2nd coefficient; β3 is the 3rd coefficient; β4 is the 4th coefficient; β5 is the 5th coefficient; SPe it The specialization effect is represented by the deviation between the ratio of the industry output value to the local total output value and the regional average ratio; Div it The Herfindhal clustering index can be used to represent the diversification effect; Com it The competitiveness of enterprises can be expressed by the deviation between the ratio of the number of enterprises to the added value of the industry and the regional average ratio; Siz it is the average size of the enterprise; Den it is the size of the local economy, which can be expressed by local GDP;

[0079] ρ∑ j≠i W ijLqCy jt is the spatial spillover effect of the industry; W ij is the spatial weight matrix; ρ is the coefficient; ε it is the error term; Y jt is the growth rate of a certain electric energy substitution industry in region j during period t.

[0080] In step 1, the data of each enterprise in a region in a certain period of time is combined with the pre-built industry space hysteresis model to calculate the scale value of the industry where the power substitution in the region is located in the next period of the certain period of time, specifically including:

[0081] Based on the data of each enterprise in a certain period of time and combined with the industry spatial lag model, the scale growth rate of the industry where the electric energy substitution is located in the region during a certain period of time is obtained;

[0082] Based on the scale value of the industry where the electric energy substitution is located in a certain period of time and the scale growth rate, the scale value of the industry where the electric energy substitution is located in the region in the next period of the certain period of time is obtained. The calculation formula is as follows:

[0083]

[0084] In the formula, is the scale value of the industry where the electric energy substitution is located in region i during period t+1; Y is the scale value of the industry where the electric energy substitution is located in region i during period t; it is the growth rate of a certain electric energy substitution industry in region i during period t.

[0085] In step 2, based on the data of enterprises in a region during a certain period of time, the enterprise clusters with different configurations are generated by using the fuzzy set qualitative comparative analysis method to simulate the evolution path of electric energy substitution, and obtain the diffusion rate of electric energy substitution in the region during a certain period of time, including:

[0086] Based on the data of enterprises in a certain period of time, the fuzzy set qualitative comparative analysis method is used to extract the influencing factors of each enterprise's choice of electric energy substitution, including:

[0087] Firstly, the fuzzy set qualitative comparative analysis method fsQCA is used to analyze the relationship between corporate production decisions and technological innovation, that is, the amount of funds invested in research and development, and asset specificity, that is, assets that are locked in for specific purposes and are difficult to use for other purposes. It is used to measure the correlation between industry barriers, the degree of executive dominance, and the degree of industrial agglomeration. It analyzes the degree of affiliation of high-expansion companies with high technological innovation, strong asset specificity, strong executive dominance, and strong industrial agglomeration, thereby analyzing whether various characteristics play a positive role in corporate decision-making.

[0088] The definitions of the variables representing technological innovation, asset specificity, executive dominance, and industry agglomeration are shown in the following table:

[0089]

[0090]

[0091] Table 1 Variable definition table Position coefficient Qx n,r The calculation formula is as follows:

[0092]

[0093] In the formula, Qx n,r is the position coefficient; X n,r is the total number of employees in the industry n related to the enterprise's main business in region r; X r is the total number of employees in region r; Xn is the total number of employees in industry n; X is the total number of employees in the industry.

[0094] First, the variables are normalized and then a necessity test is performed to test whether a single conditional variable has a necessary relationship with the outcome variable. When the consistency between the two is greater than 0.9, this conditional variable can be regarded as a necessary condition for the occurrence of the outcome variable.

[0095] The results of the necessity test are shown in the following table:

[0096]

[0097] Table 2 Necessity test results

[0098] As can be seen from Table 2, the degree of technological innovation plays a decisive role in the expansion of enterprise scale, and other variables cannot play a decisive role. Next, we will conduct a configuration analysis of the variables.

[0099] Condition variables are divided into core conditions and marginal conditions. Condition variables that appear in both the simplified solution and the intermediate solution are considered core conditions, and those that appear only in the intermediate solution are considered marginal conditions. Core conditions are factors that have a greater impact on corporate decision-making. In general, the consistency threshold is set to 0.8. The original coverage measures the proportion of membership of the results explained by each solution, such as the proportion of configuration 1 in all cases belonging to expansion.

[0100] The results of the explanatory power analysis based on the high and low expansion cases of enterprise scale are shown in the following table:

[0101]

[0102] Table 3 Explanation and analysis of enterprise scale

[0103] The configuration of each enterprise is determined based on the influencing factors of each enterprise's choice of electric energy substitution, and two enterprise clusters of evolutionary games are formed according to the configurations of enterprises with different proportions, including:

[0104] Select typical condition configuration schemes that have strong explanatory power for enterprise production decisions; R t Indicates the degree of innovation, T t Indicates asset specificity, A t Indicates the degree of executive dominance, H t Indicates industrial agglomeration. *Indicates that the enterprise does not have this characteristic.

[0105] Enterprise Configuration 1: R t ~*T t ~*A t ~*H t . It indicates that the enterprise has high innovation, but does not meet other conditions. According to the consistency results, the enterprises in this configuration are high-expansion enterprises, and they tend to make positive decisions in production decisions.

[0106] Enterprise Configuration 2: *R t ~T t ~A t ~*H t This indicates that the enterprise has both asset specificity and executive dominance, but not the other conditions. The consistency results prove that enterprises belonging to this configuration also tend to make active decisions.

[0107] Enterprise Configuration 3: *R t ~*T t ~H t . It indicates that enterprises are not innovative, have low asset specificity, and are in areas where industries are over-concentrated, regardless of the degree of executive dominance. The consistency results show that enterprises belonging to this configuration tend to be conservative in their decision-making.

[0108] There is no obvious trend in the innovation level, asset specificity, executive dominance, and industrial agglomeration of other configuration options.

[0109] Based on the above-mentioned enterprise configurations with different proportions, two enterprise clusters of evolutionary games are formed.

[0110] In each game cycle, one enterprise is selected for game based on the two enterprise clusters, and the next round of game strategy is selected based on the calculated self-income and the income of neighboring enterprises, including:

[0111] (1) Establish a group game profit model that takes into account important influencing factors:

[0112] Taking heat pump as an example, its income is PH income Income from heating sale With government subsidiesI sub , the specific calculation formula is as follows:

[0113] PH income =I sale +I sub

[0114] Where PH income I is the broad income; sale I is the income from heating; sub Subsidized by the government.

[0115] The actual production costs of enterprises include fuel costs, carbon emission costs, and electricity costs. Since the traditional evolutionary game model makes behavioral decisions entirely based on the results of benefits, it ignores the fact that enterprise decisions are affected by many factors other than costs, such as the aforementioned degree of innovation and the degree of executive dominance. In order to take into account the selection tendencies of enterprises with different configurations, a generalized cost model that considers various influencing factors is established:

[0116]

[0117] Where M t is the generalized operating cost of the enterprise in period t, p t is the fuel cost required for the enterprise to produce in period t; e t is the electricity cost required for the enterprise to produce in period t; C m,t C is the enterprise's converted installation and operation cost during period t; c,t is the carbon emission cost generated in period t; is the first impact coefficient of the variable on generalized returns; is the second impact coefficient of the variable on generalized returns; is the third impact coefficient of the variable on generalized returns. According to the necessity test results of fsQCA, f(R t ) is the first normalized index value of the enterprise in period t; f(T t ,A t ) is the second normalized index value of the enterprise in period t; f(H t ) is the third normalized index value of the enterprise in period t. t is the value of the technological innovation variable in period t; T t is the value of the asset specificity variable in period t; A t is the value of the executive dominance variable in period t; H t is the value of the industrial agglomeration variable in period t.

[0118] The first normalized index value of the enterprise in period t f(R t ) is calculated as follows:

[0119]

[0120] In the formula, R t is the value of the technological innovation variable in period t; R min is the minimum value of the technological innovation variable; R maxis the maximum value of the technological innovation variable;

[0121] The second normalized index value f(T t ,A t ) is calculated as follows:

[0122]

[0123] Where, T t is the value of the asset specificity variable in period t; T min is the minimum value of the asset specificity variable; T max is the maximum value of the asset specificity variable; A t is the value of the executive dominance variable in period t;

[0124] The third normalized index value f(H t ) is calculated as follows:

[0125]

[0126] In the formula, H t is the value of the industrial agglomeration variable in period t; H min is the minimum value of the industrial agglomeration variable; H max is the maximum value of the industrial agglomeration variable.

[0127] The formula for calculating corporate income is as follows:

[0128] Q t =PH income -M t

[0129] In the formula, Q t is the profit of the enterprise in period t; PH income is the broad income; M t is the generalized operating cost of the enterprise in period t;

[0130] (2) Establish an evolutionary game model that takes into account differences in group benefits:

[0131] The game matrix of whether an enterprise chooses electric energy replacement technology is shown in the following table:

[0132]

[0133] Table 4 Game Matrix

[0134] Enterprises A and B represent two enterprises in the same industry.

[0135] Strategy 1: Both companies A and B decide to adopt the electric energy alternative as their development path. In this scenario, both companies will experience a series of economic activities, including an increase in revenue and generalized costs caused by adopting the new strategy:

[0136] P a1 =R alter,a,t

[0137] P b1 =R alter,b,t

[0138] Where P a1 is the profit of enterprise a under strategy 1; R alter,a,t is the income of enterprise a from participating in electricity substitution; P b1 is the profit of enterprise b under strategy 1; R alter,b,t The benefits of enterprise b participating in electricity substitution.

[0139] Strategy 2: When enterprise b decides not to participate in the electricity substitution in the current game, it will obtain R tra.b,t However, if enterprise a chooses to change from its previous non-participation status to active participation, it will have to bear additional transformation costs X a In order to measure the transformation cost more accurately, we introduced the transformation cost coefficient ρ a , according to the equipment life and simulation step length, if the equipment life is 20 years and the simulation step length is 1 month, then It is 1 / 240.

[0140]

[0141] Where P a2 is the profit of enterprise a under strategy 2; a is the transformation coefficient of enterprise a; X a is the transformation cost of enterprise a; R alter,a,t is the income of enterprise a from participating in electricity substitution; t is the time period; K0 is the K0th game of enterprise; K is the Kth game of enterprise; R tra.b,t is the profit of enterprise b not participating in electricity substitution; P b2 is the profit of enterprise b under strategy 2.

[0142] Strategy 3: Enterprise a maintains its position of not participating in electricity substitution, and the corresponding game payoff is recorded as P a3 , R tra,a,t Specifically refers to the accumulated benefits of enterprise a after t rounds of game without participating in decision-making. If enterprise b did not participate in the previous round of game and participates in this round, it needs to pay an additional transformation cost δ b X b , whose game payoff is Pb3 .

[0143]

[0144] Where P a3 is the profit of enterprise a under strategy 3; R tra,a,t is the profit of enterprise a not participating in electricity substitution; P b3 is the profit of enterprise b under strategy 3; R alter,b,t is the income of enterprise b from participating in electricity substitution; b is the transformation coefficient of enterprise b; X b is the transformation cost of enterprise b.

[0145] Strategy 4: When the enterprise still insists on using traditional heating methods, the profits of enterprise a and enterprise b are P a4 and P b4 .

[0146] P a4 =R tra,a,t

[0147] P b4 =R tra,b,t

[0148] Where P a4 is the profit of enterprise a under strategy 4; R tra,a,t is the profit of enterprise a not participating in electricity substitution; P b4 is the profit of enterprise b under strategy 4; R tra,b,t The benefit of enterprise b not participating in electricity substitution.

[0149] (3) Establish an individual decision-making model:

[0150] The scale-free network is a very important model in the study of complex networks. It explains the self-growth and optimization characteristics of the self-organization evolution process of complex networks, which matches the evolution process of the selection of electric energy replacement technologies. A few companies in the cluster have a large number of connections, while most companies have only a small number of connections. When making decisions, companies tend to learn from high-yield companies with connections. This patent uses a scale-free network to represent the enterprise cluster network.

[0151] In each evolution cycle, enterprises a and b are randomly selected from the enterprise group to play games. The individual enterprise's game profit is the sum of its profits after K games. The individual enterprise compares its own profit with the profits of the connected enterprises, hereinafter referred to as neighboring enterprises, and learns and updates the strategy of the neighbor enterprise with the largest profit. The Fermi function is now used to calculate the probability of imitating the neighbor's strategy:

[0152]

[0153] In the formula, is the probability that enterprise z imitates the strategy of its neighbor enterprise w; U z is the cumulative revenue of enterprise z; U w is the cumulative profit sum of enterprise w; m is the irrational parameter.

[0154] After the game is over, the diffusion rate of electric energy substitution technology in the region is calculated based on the proportion of enterprises that choose electric energy substitution to the total number of enterprises, including:

[0155] After the game is over, the proportion of enterprises that choose electric energy substitution to the total number of enterprises is η t , is called the diffusion rate of electric energy substitution technology in this round of game during period t.

[0156] In step 3, the scale value of the electric energy substitution in the region in the next period is obtained by calculating based on the diffusion rate of the electric energy substitution in the region in the certain period and the scale value of the industry in which the electric energy substitution in the region is located in the next period, specifically including:

[0157] Based on the multiplication of the diffusion rate of electricity substitution in a certain period of time and the scale value of the industry in which electricity substitution is located in the next period of time, the scale value of electricity substitution in the next period of time is calculated.

[0158] In step 4, based on the distribution network parameters and the distribution network carrying capacity evaluation index, the maximum load access amount of electric energy substitution is calculated, and the scale value of electric energy substitution in the region in the next period is corrected to obtain the electric energy substitution amount connected to the distribution network, which specifically includes:

[0159] The distribution network carrying capacity assessment indicators include: power quality indicators, technical and economic indicators, annual average load rate of feeder and distribution transformer capacity, harmonic distortion rate and line loss rate;

[0160] Power quality indicators:

[0161]

[0162] Where V q is the voltage qualification rate, which refers to the ratio of the number of nodes with qualified voltage at a certain time point to the total number; n q is the number of nodes with qualified voltage; n is the total number of nodes.

[0163] Technical and economic indicators:

[0164]

[0165] In the formula, F load is the maximum load rate, the percentage of maximum load to maximum capacity; S is the line or transformer capacity; is the maximum active power of load c; φ c is the power factor of load c; c is the load ranking; m is the total number of loads on the line or transformer.

[0166] Annual average load rate of feeder and distribution transformer capacity:

[0167]

[0168] In the formula, F m is the annual average load factor, the ratio of the annual average load of feeders and distribution transformers to their capacity;

[0169] P ave is the average load; S is the line or transformer capacity.

[0170] Harmonic distortion:

[0171]

[0172] Where, THD is the harmonic distortion rate, which reflects the proportion of the voltage's higher harmonics to the fundamental wave; U1 is the fundamental wave voltage; U h is the hth harmonic of voltage; H max is the highest harmonic taken into account; h is the number of the higher harmonic.

[0173] Line loss rate:

[0174]

[0175] In the formula, F loss is the line loss rate, which is the percentage of active power loss to the input power at its starting point; is the input power at the beginning; I o is the current value of branch o; r o is the resistance value of branch o; o is the order of branches; L is the set of branches; I x is the current value of the distribution transformer line x; r x is the resistance value of the distribution transformer line x; x is the order of the distribution transformer line; T is the set of distribution transformer lines.

[0176] The maximum carrying capacity of the power grid is the maximum load access capacity under the condition that all indicators are less than the set threshold. The calculation formula is as follows:

[0177] S max =max S load (V q ≤V q,set ,F load ≤F load,set ,F m ≤F m,set ,THD

[0178] ≤THDset ,F loss ≤F loss,set )

[0179] In the formula, S max is the threshold of the maximum carrying capacity of the power grid; S load is the grid carrying capacity; V q is the voltage qualification rate; V q,set is the maximum voltage qualified rate; F load is the maximum load rate; F load,set F is the maximum load factor setting value; m is the annual average load factor; F m,set is the maximum annual average load rate; THD is the harmonic distortion rate; THD set is the maximum harmonic distortion rate; F loss is the line loss rate; F loss,set is the maximum line loss rate.

[0180] From the maximum load access capacity S max，t Deduct the load capacity of other industries S rest，t , the remaining space S max，t -S rest，t The maximum access capacity of electric energy substitution is calculated by dividing the maximum access capacity of electric energy substitution by the scale value of electric energy substitution in the next period. For comparison, the smaller value is the amount of electricity replaced by the grid:

[0181]

[0182] Where, Er is the amount of electric energy replacement connected to the distribution network; is the scale value of electric energy substitution in the region in the next period; is the scale value of the industry where the electricity is substituted in the region in the next period; η t is the diffusion rate of electric energy substitution in the region during period t; S max，t -S rest，t is the maximum access capacity of electric energy substitution; S max，t is the maximum load access capacity; S rest，t Load capacity for other industries.

[0183] Combination Figure 2 The flowchart of the embodiment is verified, which specifically includes: the parameters taken are shown in the following table:

[0184]

[0185] Table 5 Parameter value table

[0186] Estimate the overall scale of district heating in five years (unit: GJ), convert the total energy into power load (MWh), assume that the annual operating hours of heating equipment are 1000h, and estimate the heating load power (kW), as shown in the following table:

[0187] First, the collected enterprise data were analyzed by fsQCA. The results are shown in Table 3. It can be seen that the consistency of configurations 1, 2, and 3 is greater than the consistency threshold of 0.8, indicating that the proposed configurations have good explanatory power.

[0188] According to the original coverage of the configuration, assuming that the proportion of enterprises in each configuration in the cluster is similar to the original coverage, two slightly different enterprise groups A and B are generated. In cluster A, configuration 1 accounts for 35%, configuration 2 accounts for 10%, configuration 3 accounts for 17%, and the rest belongs to other configurations, with no obvious tendency. In cluster B, configuration 1 accounts for 30%, configuration 2 accounts for 15%, configuration 3 accounts for 21%, and the rest belongs to other configurations, with no obvious tendency. According to the proportion of enterprise configurations, the population of evolutionary game is generated. Under ideal circumstances, there are 150 enterprises in each group. By comparing the evolution process of clusters with different two configurations, the probability of choosing electric energy substitution in enterprise clusters A and B is obtained as follows: Figure 3 As shown, it can be seen that the different configuration compositions of enterprises in the cluster will have a significant impact on the diffusion process of electric energy substitution. Figure 3-Figure 7 The horizontal axis is time and the vertical axis is probability.

[0189] The probability of enterprises choosing electric energy substitution for each configuration is as follows: Figure 4 As shown, where X:P a represents the probability of choosing electric energy substitution in group A, a∈{1,2,3} represents configurations 1, 2, and 3 in enterprise group A; Y:P b represents the probability of choosing electric energy substitution in group B, b∈{1,2,3} represents configurations 1, 2, and 3 in enterprise group B, and the horizontal axis represents the time required for n rounds of game.

[0190] Path 1: When the enterprise's innovation level is high, the enterprise's generalized cost will be low. The higher the innovation level of the enterprise, the faster the probability of choosing electric energy substitution. The control variable innovation level is taken as 0.1, 0.3, 0.5, 0.7, and 0.9 in turn. The final benefit increases, making the probability of enterprises choosing electric energy substitution gradually increase over time. Its evolution path is as follows: Figure 5 shown.

[0191] Path 2: When asset specificity and senior management dominance are high, the generalized cost is higher than that of Path 1, but the development of electric energy substitution is still relatively fast. At the same time, different transformation degrees are taken, namely 0.1, 0.3, 0.5, 0.7,

[0192]

[0193] Table 6 Heating load power table 0.9 shows that although the probability of enterprises choosing electric energy substitution in different time periods is slightly different, the final evolution path of enterprises with strong expansion is similar. Figure 6 shown.

[0194] Path 3: When the enterprise's innovation level is low, the expansion speed of electric energy substitution is slow. The different transformation levels are 0.1, 0.3, 0.5, 0.7, and 0.9 respectively. The evolution path is as follows: Figure 7 As shown in Figure 2, the conservative enterprises in configuration 3 will continue to choose not to participate in electricity substitution. The game process of paths 1, 2, and 3 is mutually verified with the results of the enterprise decision-making factor analysis of fsQCA.

[0195] Assuming an average annual growth rate of 5%, the peak load of natural development of electric energy substitution and the peak load of other industries are shown in the following table:

[0196]

[0197] Table 7 Load peak table

[0198] The electric energy replacement load value is estimated based on the average value of the overall industry load, the simulation results of the enterprise cluster A of the electric energy replacement technology diffusion rate, and the peak-to-peak coefficient of the electricity consumption curve of 1.12. Assuming that the average value of the industry load is The proportion of electrical energy replacement equipment is the diffusion rate η, and the electrical load is The peak power load of the electric energy replacement equipment is

[0199] According to the indicators, the total load carrying capacity of a single ring network is 13,000 kW. As the load of other industries continues to increase over time, while the total load carrying capacity is a fixed value, the grid's load carrying capacity for heat pumps decreases. Figure 8 The curve shows that in 2027, the heat pump load of the distribution network is shown in the following table:

[0200]

[0201] Table 8 Distribution network heat pump load table ring network carrying capacity evaluation indicators are shown in the following table:

[0202]

[0203] Table 9 Single-ring network carrying capacity evaluation table Grid carrying capacity evaluation index thresholds are shown in the following table:

[0204]

[0205] Table 10 Grid carrying capacity evaluation table

[0206] It can be seen that the average load rate of large enterprises has reached the upper limit allowed by the indicator, that is, the development of heat pumps will exceed the carrying capacity of the power grid by 2027. Due to the limitation of carrying capacity, the development path of heat pump electric energy substitution in 2027 and beyond is as follows: Figure 8 As shown in the curve.

[0207] Assume that in 2027, when the heat pump load carrying capacity is insufficient, the power grid is transformed from a single-ring network to a double-ring network, the heavy-load line load is cut, the line operation efficiency is improved, the grid structure is gradually simplified, and the power supply area is clarified; then the heat pump load carrying capacity after the transformation is as follows: Figure 8 As shown by the grey dotted line, according to the carrying capacity index, the maximum load bearing capacity of the power grid is estimated to be 15,000 kW. At this time, the power grid's carrying capacity for heat pumps increases, exceeding the natural growth capacity of the heat pumps, and the heat pumps return to their natural development path.

[0208] In this application, fsQCA is a fuzzy set qualitative comparative analysis method, which is a case-oriented research method. It is based on set theory and configurational thinking, and effectively connects qualitative analysis with quantitative analysis. The basic idea is to use architecture theory and Boolean algebra operations to examine the relationship between antecedent conditions and condition combinations and results from the perspective of sets, so as to explain the complex causal relationship behind the phenomenon.

[0209] The spatial lag model adds spatial influence to the ordinary autoregressive model. The spatial lag value is introduced into the ordinary autoregressive model to characterize the spatial influence of the surrounding areas on the local area. The spatial lag model is an autoregressive model that takes spatial variables into account, also called a spatial autoregressive model.

[0210] Herfindhal is a clustering index, which is a comprehensive index for measuring industry concentration. It refers to the sum of the squares of the percentage of total industry revenue or total assets held by each market competitor in an industry, and is used to measure changes in market share, that is, the dispersion of the scale of manufacturers in the market.

[0211] Spatial spillover effect refers to the phenomenon that the state and efficiency of regional economic activities change due to the flow of factors between regions. Whether there is a spatial spillover effect between two regions can be judged by the spatial correlation test model. By analyzing the correlation of the observed values ​​of the two regions, such as GDP and other indicators, it is judged whether the two regions affect each other in a certain characteristic, such as economic development. A positive correlation between the two is called a positive spillover effect, and a negative correlation is called a negative spillover effect.

[0212] Based on the prediction of the natural development scale of the industry, this application uses the evolutionary game method to simulate the expansion process of electric energy substitution technology, obtains the evolution process of the natural development scale of electric energy substitution, and then proposes a distribution network carrying capacity evaluation index, corrects the natural development scale of electric energy substitution, and obtains the electric energy substitution evolution path that takes into account the distribution network carrying capacity. This comprehensive consideration makes the prediction results more comprehensive and accurate, and can provide a more scientific basis for the formulation of electric energy substitution strategies.

[0213] The method used in the technology expansion of this application is based on fsQCA, which analyzes the affiliation of high-expansion enterprises with high technological innovation, strong asset specificity, strong executive dominance, and industrial agglomeration, and deeply analyzes whether enterprises choose electric energy substitution under different conditions and configurations, extracts factors that have a greater impact on users' choice of electric energy substitution technology, and further divides different types of groups. Compared with the previous research method that only considers a single group, the evolutionary game results of this invention are closer to the actual situation in the region.

[0214] Embodiment 2:

[0215] A prediction system for the evolutionary path of electric energy substitution considering the carrying capacity of distribution network, with the structure as follows Fig. 9 As shown, including:

[0216] The industry scale value prediction module is used to calculate based on the data of various enterprises in a region during a certain period of time in combination with a pre-built industry space hysteresis model to obtain the scale value of the industry in which the electric energy substitution in the region is located in the next period of the certain period of time;

[0217] The electric energy substitution diffusion rate calculation module is used to simulate the evolution path of electric energy substitution based on the enterprise clusters with different configurations generated by the fuzzy set qualitative comparative analysis method based on the data of each enterprise in a certain period of time, and obtain the diffusion rate of electric energy substitution in the region in a certain period of time;

[0218] An electric energy substitution scale value prediction module is used to calculate based on the diffusion rate of electric energy substitution in the region during the certain period and the scale value of the industry in which the electric energy substitution in the region is located during the next period, to obtain the scale value of electric energy substitution in the region during the next period;

[0219] The electric energy substitution scale value correction module is used to correct the scale value of electric energy substitution in the region in the next period based on the maximum load access amount of electric energy substitution calculated based on the distribution network parameters combined with the distribution network carrying capacity evaluation index, so as to obtain the electric energy substitution amount connected to the distribution network.

[0220] Industry scale value prediction module, specifically used for:

[0221] Based on the data of each enterprise in a certain period of time and combined with the industry spatial lag model, the scale growth rate of the industry where the electric energy substitution is located in the region during a certain period of time is obtained;

[0222] The scale value of the industry in which electric energy substitution is located in the region in a certain period of time is calculated based on the scale growth rate to obtain the scale value of the industry in which electric energy substitution is located in the region in the next period of the certain period of time.

[0223] Electric energy substitution diffusion rate calculation module, including:

[0224] Extracting influencing factors submodule, which is used to extract the influencing factors of each enterprise's choice of electric energy substitution based on the data of each enterprise in a certain period of time by using the fuzzy set qualitative comparative analysis method;

[0225] The enterprise cluster formation submodule is used to determine the configuration of each enterprise based on the influencing factors of each enterprise's choice of electric energy substitution, and form two enterprise clusters of evolutionary games according to the configurations of enterprises with different proportions;

[0226] The evolutionary game submodule is used for each game cycle, selecting one enterprise for game based on the two enterprise clusters, and selecting the next round of game strategy based on the calculated self-income and the income of neighboring enterprises;

[0227] The diffusion rate calculation submodule is used to calculate the diffusion rate of electric energy substitution technology in the region based on the proportion of the number of enterprises that choose electric energy substitution to the total number of enterprises after the game is over.

[0228] The evolutionary game submodule is specifically used for:

[0229] Based on the configuration of the enterprises selected from the two enterprise clusters, whether to participate in the electric energy substitution game is played, and the generalized cost model and the evolutionary game model are used for calculation to obtain the enterprise's own benefits;

[0230] Based on the enterprise's own benefits and the benefits of its neighboring enterprises, the Fermi function is used to calculate the probability that the enterprise in the next round of the game will imitate the game strategy of its neighboring enterprises.

[0231] The electric energy substitution scale value correction module is specifically used for:

[0232] Calculate based on distribution network parameters combined with distribution network carrying capacity assessment indicators to obtain the values ​​of each indicator;

[0233] Based on the values ​​of each indicator and the thresholds set for each indicator, a threshold of the load access capacity of the distribution network is obtained;

[0234] Based on the threshold of the load access capacity of the distribution network minus the load capacity of other industries, the maximum load access capacity of electric energy substitution is obtained;

[0235] The scale value of electric energy substitution in the region in the next period is corrected based on the maximum load access amount of electric energy substitution, and a smaller value is selected as the electric energy substitution amount for access to the distribution network.

[0236] The calculation formula of the industry space lag model in the industry scale value prediction module is as follows:

[0237]

[0238] Where Y it is the scale growth rate of a certain electric energy substitution industry in region i during period t; β0 is the 0th coefficient; β1 is the 1st coefficient; β2 is the 2nd coefficient; β3 is the 3rd coefficient; β4 is the 4th coefficient; β5 is the 5th coefficient; SPe it The specialization effect is represented by the deviation between the ratio of the industry output value to the local total output value and the regional average ratio; Div it The Herfindhal clustering index can be used to represent the diversification effect; Com it The competitiveness of enterprises can be expressed by the deviation between the ratio of the number of enterprises to the added value of the industry and the regional average ratio; Siz it is the average size of the enterprise; Den it is the local economic scale, which can be expressed by local GDP; ρ∑ j≠i W ij LqCy jt is the spatial spillover effect of the industry; W ij is the spatial weight matrix; ρ is the coefficient; ε it is the error term.

[0239] Embodiment 3:

[0240] like Fig.10 As shown, the present invention also provides an electronic device, which may be a computer device, a single-chip device, an intelligent mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, the processor, and the transceiver component are connected via a bus; the memory may be used to store an execution program, and an exemplary execution program may include instructions; the processor is used to execute the instructions stored in the memory. The memory may also be used to store data, which may be called and / or modified when the instructions are executed.

[0241] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in a storage medium to implement the corresponding method flow or corresponding functions, so as to implement the steps of a method for predicting the evolution path of electric energy substitution considering the carrying capacity of the distribution network in the above-mentioned embodiment.

[0242] Example 4

[0243] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory), which is a memory device in an electronic device for storing programs and data. It can be understood that the storage medium here can include both built-in storage media in electronic devices and, of course, extended storage media supported by electronic devices. The storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more execution programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk storage. The processor loads and executes one or more instructions stored in the storage medium, which can implement the steps of a method for predicting the evolution path of electric energy substitution considering the carrying capacity of the distribution network in the above embodiment.

[0244] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0245] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0246] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0247] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0248] The above are merely embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application are included in the scope of the claims of the present application to be approved.

Claims

1. A method for predicting the evolutionary path of electric energy substitution considering the carrying capacity of the distribution network, characterized in that: include: Based on the data of each enterprise in a region during a certain period of time, combined with a pre-built industry space hysteresis model, calculation is performed to obtain the scale value of the industry in which the electric energy substitution in the region is located in the next period of the certain period of time; Based on the data of enterprises in a region during a certain period of time, the enterprise clusters with different configurations are generated by using the fuzzy set qualitative comparative analysis method to simulate the evolution path of electric energy substitution and obtain the diffusion rate of electric energy substitution in the region during a certain period of time. Calculate based on the diffusion rate of electric energy substitution in the region during the certain period and the scale value of the industry in which the electric energy substitution in the region is located during the next period to obtain the scale value of electric energy substitution in the region during the next period; Based on the distribution network parameters and the distribution network carrying capacity evaluation index, the maximum load access amount of electric energy substitution is calculated, and the scale value of electric energy substitution in the region in the next period is corrected to obtain the electric energy substitution amount connected to the distribution network.

2. The method according to claim 1, characterized in that The data of each enterprise in a region during a certain period is calculated in combination with a pre-built industry space hysteresis model to obtain the scale value of the industry where the electric energy substitution in the region is located in the next period of the certain period, including: Based on the data of each enterprise in a certain period of time and combined with the industry spatial lag model, the scale growth rate of the industry where the electric energy substitution is located in the region during a certain period of time is obtained; The scale value of the industry in which electric energy substitution is located in the region in a certain period of time is calculated based on the scale growth rate to obtain the scale value of the industry in which electric energy substitution is located in the region in the next period of the certain period of time.

3. The method according to claim 1, characterized in that The enterprise clusters with different configurations generated by the fuzzy set qualitative comparative analysis method based on the data of each enterprise in a certain period of time are simulated to simulate the evolution path of electric energy substitution, and the diffusion rate of electric energy substitution in the region in a certain period of time is obtained, including: Based on the data of enterprises in a certain period of time, the fuzzy set qualitative comparative analysis method is used to extract the influencing factors of each enterprise's choice of electric energy substitution; The configuration of each enterprise is determined based on the influencing factors of each enterprise's choice of electric energy substitution, and two enterprise clusters of evolutionary games are formed according to the configurations of enterprises with different proportions; In each game cycle, one enterprise is selected from each of the two enterprise clusters for game play, and the next round of game strategy is selected based on the calculated self-income and the income of neighboring enterprises; After the game is over, the diffusion rate of electricity substitution technology in the region is calculated based on the proportion of the number of enterprises that choose electricity substitution to the total number of enterprises.

4. The method according to claim 3, characterized in that: In each game cycle, one enterprise is selected for game based on the two enterprise clusters, and the next round of game strategy is selected according to the calculated self-income and the income of neighboring enterprises, including: The configurations of the enterprises selected based on the two enterprise clusters choose whether to participate in the game of electric energy substitution, and the generalized cost model and the evolutionary game model are used for calculation to obtain the enterprise's own benefits; the Fermi function is used to calculate the probability that the enterprises in the next round of the game imitate the game strategies of neighboring enterprises based on the enterprise's own benefits and the benefits of neighboring enterprises.

5. The method according to claim 1, characterized in that The maximum load access amount of electric energy substitution calculated based on the distribution network parameters and the distribution network carrying capacity evaluation index is used to correct the scale value of electric energy substitution in the region in the next period of time to obtain the electric energy substitution amount connected to the distribution network, including: Calculate based on distribution network parameters combined with distribution network carrying capacity assessment indicators to obtain the values ​​of each indicator; Based on the values ​​of each indicator and the thresholds set for each indicator, a threshold of the load access capacity of the distribution network is obtained; Based on the threshold of the load access capacity of the distribution network minus the load capacity of other industries, the maximum load access capacity of electric energy substitution is obtained; The scale value of electric energy substitution in the region in the next period is corrected based on the maximum load access amount of electric energy substitution, and a smaller value is selected as the electric energy substitution amount for access to the distribution network.

6. The method according to claim 2, characterized in that The calculation formula of the industry space lag model is as follows: Where Y it is the scale growth rate of a certain electric energy substitution industry in region i during period t; β0 is the 0th coefficient; β1 is the 1st coefficient; β2 is the 2nd coefficient; β3 is the 3rd coefficient; β4 is the 4th coefficient; β5 is the 5th coefficient; SPe it The specialization effect is represented by the deviation between the ratio of the industry output value to the local total output value and the regional average ratio; Div it The Herfindhal clustering index can be used to represent the diversification effect; Com it The competitiveness of enterprises can be expressed by the deviation between the ratio of the number of enterprises to the added value of the industry and the regional average ratio; Siz it is the average size of the enterprise; Den it is the local economic scale, which can be expressed by local GDP; ρ∑ j≠i W ij LqCy jt is the spatial spillover effect of the industry; W ij is the spatial weight matrix; ρ is the coefficient; ε it is the error term; Y jt is the growth rate of a certain electric energy substitution industry in region j during period t.

7. A prediction system for the evolutionary path of electric energy substitution considering the carrying capacity of the distribution network, characterized in that: include: The industry scale value prediction module is used to calculate based on the data of various enterprises in a region during a certain period of time in combination with a pre-built industry space hysteresis model to obtain the scale value of the industry in which the electric energy substitution in the region is located in the next period of the certain period of time; The electric energy substitution diffusion rate calculation module is used to simulate the evolution path of electric energy substitution based on the enterprise clusters with different configurations generated by the fuzzy set qualitative comparative analysis method based on the data of each enterprise in a certain period of time, and obtain the diffusion rate of electric energy substitution in the region in a certain period of time; An electric energy substitution scale value prediction module is used to calculate based on the diffusion rate of electric energy substitution in the region during the certain period and the scale value of the industry in which the electric energy substitution in the region is located during the next period, to obtain the scale value of electric energy substitution in the region during the next period; The electric energy substitution scale value correction module is used to correct the scale value of electric energy substitution in the region in the next period based on the maximum load access amount of electric energy substitution calculated based on the distribution network parameters combined with the distribution network carrying capacity evaluation index, so as to obtain the electric energy substitution amount connected to the distribution network.

8. The system according to claim 7, characterized in that The industry scale value prediction module is specifically used for: Based on the data of each enterprise in a certain period of time and combined with the industry spatial lag model, the scale growth rate of the industry where the electric energy substitution is located in the region during a certain period of time is obtained; The scale value of the industry in which electric energy substitution is located in the region in a certain period of time is calculated based on the scale growth rate to obtain the scale value of the industry in which electric energy substitution is located in the region in the next period of the certain period of time.

9. The system according to claim 7, characterized in that The electric energy substitution diffusion rate calculation module comprises: Extracting influencing factors submodule, which is used to extract the influencing factors of each enterprise's choice of electric energy substitution based on the data of each enterprise in a certain period of time by using the fuzzy set qualitative comparative analysis method; The enterprise cluster formation submodule is used to determine the configuration of each enterprise based on the influencing factors of each enterprise's choice of electric energy substitution, and form two enterprise clusters of evolutionary games according to the configurations of enterprises with different proportions; The evolutionary game submodule is used for each game cycle, selecting one enterprise for game based on the two enterprise clusters, and selecting the next round of game strategy based on the calculated self-income and the income of neighboring enterprises; The diffusion rate calculation submodule is used to calculate the diffusion rate of electric energy substitution technology in the region based on the proportion of the number of enterprises that choose electric energy substitution to the total number of enterprises after the game is over.

10. The system according to claim 9, characterized in that The evolutionary game submodule is specifically used for: Based on the configuration of the enterprises selected from the two enterprise clusters, whether to participate in the electric energy substitution game is played, and the generalized cost model and the evolutionary game model are used for calculation to obtain the enterprise's own benefits; Based on the enterprise's own benefits and the benefits of its neighboring enterprises, the Fermi function is used to calculate the probability that the enterprise in the next round of the game will imitate the game strategy of its neighboring enterprises.

11. The system according to claim 7, characterized in that The electric energy substitution scale value correction module is specifically used for: Calculate based on distribution network parameters combined with distribution network carrying capacity assessment indicators to obtain the values ​​of each indicator; Based on the values ​​of each indicator and the thresholds set for each indicator, a threshold of the load access capacity of the distribution network is obtained; Based on the threshold of the load access capacity of the distribution network minus the load capacity of other industries, the maximum load access capacity of electric energy substitution is obtained; The scale value of electric energy substitution in the region in the next period is corrected based on the maximum load access amount of electric energy substitution, and a smaller value is selected as the electric energy substitution amount for access to the distribution network.

12. The system according to claim 8, characterized in that The calculation formula of the industry space lag model in the industry scale value prediction module is as follows: Where Y it is the scale growth rate of a certain electric energy substitution industry in region i during period t; β0 is the 0th coefficient; β1 is the 1st coefficient; β2 is the 2nd coefficient; β3 is the 3rd coefficient; β4 is the 4th coefficient; β5 is the 5th coefficient; SPe it The specialization effect is represented by the deviation between the ratio of the industry output value to the local total output value and the regional average ratio; Div it The Herfindhal clustering index can be used to represent the diversification effect; Com it The competitiveness of enterprises can be expressed by the deviation between the ratio of the number of enterprises to the added value of the industry and the regional average ratio; Siz it is the average size of the enterprise; Den it is the local economic scale, which can be expressed by local GDP; ρ∑ j≠i W ij LqCy jt is the spatial spillover effect of the industry; W ij is the spatial weight matrix; ρ is the coefficient; ε it is the error term; Y jt is the growth rate of a certain electric energy substitution industry in region j during period t.

13. An electronic device, characterized in that: include: at least one processor and memory; The memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, a method for predicting an evolutionary path of electric energy substitution taking into account the carrying capacity of a distribution network as described in any one of claims 1 to 6 is implemented.

14. A readable storage medium, characterized in that: An execution program is stored thereon, and when the execution program is executed, a method for predicting the evolutionary path of electric energy substitution taking into account the carrying capacity of the distribution network as described in any one of claims 1 to 6 is implemented.

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