Novel power system industry chain technology performance evaluation method and device and medium

Abstract

The invention provides a novel power system industrial chain technology performance evaluation method, device and equipment and a medium, and the method comprises the steps: obtaining an input variable and an output variable of each link of an industrial chain and corresponding data, and constructing an industrial chain index evaluation system according to the input variable and the output variable; analyzing a plurality of efficiency indexes of each link on the industry chain by using an input-oriented DEA-BCC model; based on each input slack variable, utilizing an SFA model to calculate and obtain a corrected input variable and a corrected slack variable which are excluded from external environmental factor influence and random interference error; and finally, analyzing multiple efficiency indexes of each link on the industrial chain by using an input-oriented DEA-BCC model to obtain the technical efficiency of the novel power system industrial chain to be evaluated. According to the invention, a more objective and more accurate industrial chain performance evaluation result can be obtained based on an input-oriented three-stage DEA method.

Description

Technical Field

[0001] The present invention relates to the technical field of power system performance evaluation, and in particular to a novel power system industry chain technical performance evaluation method, device and medium. Background Art

[0002] The new power system is a power system that takes green and low-carbon renewable energy as the main supply, maximizes consumption as the main task, and highlights the characteristics of green, clean, intelligent, efficient, safe and controllable. At present, related research work mainly focuses on the analysis and definition of theoretical aspects such as the concepts and characteristics of each link of the new power system, while there is little attention and research on the entire new power system industry chain. In previous studies, different analysis methods were used to evaluate the performance of the traditional power system industry chain based on different research objects and links. The research objects here mainly refer to power generation industries such as thermal power, wind power, photovoltaic power, and the entire power industry. The links in the industry chain include power generation, transmission, distribution, and power consumption.

[0003] For the new power system, its industrial chain can also be divided into power generation, transmission, distribution and consumption. There are many methods for evaluating the performance of the industrial chain. Commonly used methods include DuPont analysis, fuzzy comprehensive evaluation, balanced scorecard and data envelopment analysis (DEA). Among them, DuPont analysis uses the relationship between several major financial ratios to comprehensively analyze the financial status of the enterprise. This method is a classic method for evaluating corporate performance from a financial perspective; fuzzy comprehensive evaluation is a method based on the membership theory of fuzzy mathematics to transform qualitative evaluation into quantitative evaluation. This method is suitable for evaluation of non-deterministic problems; balanced scorecard mainly realizes strategic planning through graphs, cards and tables. This method can reflect the comprehensive operating status of the organization and make the performance evaluation balanced and perfect; DEA method is a method of evaluating the relative effectiveness of comparable companies of the same type based on multiple input and output indicators using linear programming methods. Obviously, compared with other methods, DEA method can efficiently analyze the efficiency of multiple inputs and multiple outputs without dimensionless data and weight setting. It is an ideal evaluation method with strong objectivity.

[0004] The existing DEA method has a significant effect on the performance evaluation of the power system industry chain, and can effectively measure the performance of the same type of unit industry. At the same time, many optimized and improved DEA methods have been born, such as fuzzy DEA method and two-stage DEA method. Although these methods can effectively analyze the technical efficiency of multiple inputs and multiple outputs, and can also effectively measure the performance of the same type of units, they cannot avoid the impact of external environmental factors and random interference factors on technical efficiency, resulting in the prediction results being not objective and accurate enough. Summary of the invention

[0005] In order to overcome the problems existing in the related technologies, the present disclosure provides a new method, device, equipment and medium for evaluating the technical performance of the industrial chain of a power system, so as to solve the technical problem that the existing performance evaluation method of the industrial chain of a power system cannot avoid the influence of external environmental factors and random interference factors on the technical efficiency, resulting in the prediction results being not objective and accurate enough.

[0006] One or more embodiments of this specification provide a novel method for evaluating the technical performance of an electric power system industry chain, including the following steps:

[0007] Establishment of index evaluation system: Obtain the input variables and output variables of each link of the new power system industrial chain to be analyzed and their corresponding data, and build an industrial chain index evaluation system based on the input variables and output variables;

[0008] The first stage of the three-stage DEA: Based on the industrial chain indicator evaluation system, the input-oriented DEA-BCC model is used to analyze multiple efficiency indicators of each link in the industrial chain, where multiple efficiency indicators include SE, TE, PTE and various input slack variables;

[0009] The second stage of the three-stage DEA: Based on each input slack variable, the SFA model is used to calculate the corrected input variables and corrected slack variables that exclude the influence of external environmental factors and random interference errors; and

[0010] The third stage of the three-stage DEA: Based on the corrected input variables and the corrected slack variables, the input-oriented DEA-BCC model is used to analyze multiple efficiency indicators of each link in the industrial chain to obtain the technical efficiency of the new power system industrial chain to be evaluated.

[0011] One or more embodiments of this specification provide a novel power system industry chain technology performance evaluation device, including:

[0012] The indicator evaluation system construction module is used to obtain the input variables and output variables of each link of the new power system industrial chain to be analyzed and their corresponding data, and to construct the industrial chain indicator evaluation system based on the input variables and output variables;

[0013] The first stage evaluation module is used to analyze multiple efficiency indicators of each link in the industrial chain based on the industrial chain indicator evaluation system using the input-oriented DEA-BCC model. The multiple efficiency indicators include SE, TE, PTE and various input slack variables.

[0014] The second stage correction module is used to calculate the corrected input variables and corrected slack variables excluding the influence of external environmental factors and random interference errors based on each input slack variable using the SFA model; and

[0015] The third stage evaluation module is used to analyze multiple efficiency indicators of each link in the industrial chain based on the corrected input variables and the corrected slack variables using the input-oriented DEA-BCC model in the first stage evaluation module to obtain the technical efficiency of the new power system industrial chain to be evaluated.

[0016] One or more embodiments of the present specification provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-described novel power system industry chain technology performance evaluation method when executing the computer program.

[0017] One or more embodiments of the present specification provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the novel power system industry chain technical performance evaluation method as described above.

[0018] The present disclosure provides a new type of power system industrial chain technical efficiency evaluation method, device, equipment and medium, which has the advantage of conducting a new type of power system industrial chain technical efficiency evaluation based on an input-oriented three-stage DEA method. For the new type of power system that is currently rarely studied, the technical efficiency of each link in its industrial chain is evaluated from the overall perspective, and the impact of environmental factors and random variables on the industrial chain market efficiency evaluation is fully considered to obtain a more objective and accurate industrial chain performance evaluation result. The method provided in this embodiment uses a three-stage method to remove these external influences, and the various efficiency indicators obtained can objectively and accurately reflect the technical efficiency of the new type of power system industrial chain, and further put forward effective suggestions for improving and enhancing the market performance of the new type of power system industrial chain, so as to assist in the design of the new type of power system industrial chain operation plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate one or more embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1 A flowchart of a novel method for evaluating the technical performance of the power system industry chain provided for one or more embodiments of this specification;

[0021] Figure 2 A three-stage DEA evaluation process flow chart of the technical efficiency of the new power system industry chain provided in one or more embodiments of this specification;

[0022] Figure 3 A block diagram of a novel power system industry chain technology performance evaluation device provided for one or more embodiments of this specification;

[0023] as well as

[0024] Figure 4 A schematic diagram of the structure of a computer device provided for one or more embodiments of this specification. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the technical solutions in one or more embodiments of this specification will be clearly and completely described below in conjunction with the drawings in one or more embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0026] The present invention is described in detail below in conjunction with specific implementation methods and the accompanying drawings.

[0027] Method Embodiment

[0028] According to an embodiment of the present invention, a new method for evaluating the technical performance of the power system industry chain is provided. Figure 1 As shown, it is a flow chart of the new power system industry chain technology efficiency evaluation method provided in this embodiment. The new power system industry chain technology efficiency evaluation method according to the embodiment of the present invention includes:

[0029] Step S1, establishment of an index evaluation system: obtaining the input variables and output variables of each link of the new power system industrial chain to be analyzed and their corresponding data, and constructing an industrial chain index evaluation system based on the input variables and output variables.

[0030] Step S2, the first stage of the three-stage DEA: Based on the industrial chain indicator evaluation system, the input-oriented DEA-BCC model is used to analyze multiple efficiency indicators of each link in the industrial chain, and multiple efficiency indicators are obtained including scale efficiency (SE), technical efficiency (TE), pure technical efficiency (PTE) and various input slack variables.

[0031] Step S3, the second stage of the three-stage DEA: Based on each input slack variable, the SFA (Stochastic Frontier Analysis) model is used to calculate and obtain the corrected input variables and corrected slack variables by eliminating the influence of external environmental factors and random interference errors.

[0032] Step S4, the third stage of the three-stage DEA: Based on the corrected input variables and the corrected slack variables, the input-oriented DEA-BCC model in step S1 is used to analyze multiple efficiency indicators of each link in the industrial chain to obtain the technical efficiency of the new power system industrial chain to be evaluated.

[0033] The method provided in this embodiment is based on an input-oriented three-stage DEA method to evaluate the technical efficiency of the new power system industrial chain. For the new power system, which is currently rarely studied, the technical efficiency of each link in the industrial chain is evaluated from the overall perspective, and the impact of environmental factors and random variables on the market efficiency evaluation of the industrial chain is fully considered to obtain a more objective and accurate industrial chain performance evaluation result. The method provided in this embodiment uses a three-stage method to remove these external influences, and the various efficiency indicators obtained can objectively and accurately reflect the technical efficiency of the new power system industrial chain, and further put forward effective suggestions for improving and enhancing the market performance of the new power system industrial chain, so as to assist in the design of the new power system industrial chain operation plan.

[0034] In this embodiment, reference Figure 2 As shown, the three-stage DEA evaluation process flowchart of the technical efficiency of the new power system industrial chain provided in this embodiment divides the industrial chain into three parts, that is, the upstream of the industrial chain is defined as the power generation side, the midstream of the industrial chain is defined as the power transmission and distribution side, and the downstream of the industrial chain is defined as the power consumption side, and the input and output variables of each link are as follows.

[0035] In this embodiment, the upstream of the industrial chain is the birthplace of electric energy, involving the construction of various power stations and various power generation equipment. Therefore, the upstream of the industrial chain uses the installed capacity of power generation and the investment in power engineering construction as input variables, which can better meet its characteristics and background. The output indicator of the power generation side of the upstream of the industrial chain is set as power generation, which can measure the development speed and level of productivity. In addition, considering that the new power system has the characteristics of green and low carbon, the standard coal consumption of power generation can also be used as the output indicator of the environmental performance of the upstream of the industrial chain.

[0036] The midstream of the industrial chain is the transmission and distribution side of electric energy, and is a bridge connecting the upstream and downstream of the industrial chain. The construction of a new power system requires strengthening the expansion and transformation capabilities, improving the flexibility of the power system, and actively promoting the vigorous development of smart grids and ultra-high voltage technology industries. Compared with traditional power systems, new power systems have a larger scale and scope of investment, and the investment is mainly concentrated in the construction of transmission lines and power grid projects. Therefore, the midstream of the industrial chain can use the length of 220KV and above transmission lines and the construction investment of power grid projects as input variables. The output of power transmission and distribution is mainly reflected in the sales volume of electricity. This indicator is calculated by the annual report of the power department, and the indicator results are relatively objective; the technical benefits are mainly reflected in the line loss rate, where the line loss rate can be obtained by calculating the difference between the total power supply and the total power sales as a percentage of the total power supply. This indicator has a high correlation with market performance. The lower the line loss rate, the better the market performance. Therefore, the output variables of the midstream of the industrial chain are sales volume and line loss rate.

[0037] The downstream of the industrial chain is the electricity users of various industries, so the input variable of this part is set as the power supply, and the output variable is set as the power consumption. In addition, considering that the number of new energy vehicles is increasing and the development prospects are good, charging piles are expected to be further constructed and deployed in the downstream of the industrial chain. Therefore, this embodiment uses the number of charging piles as the second output variable of the downstream of the industrial chain.

[0038] In another embodiment, environmental factors are potential influencing factors in the process of industrial chain performance evaluation. Their existence causes the performance evaluation results to be not completely objective and accurate. Therefore, the external environmental influencing factors of each link in step S3 of this embodiment are specifically as follows.

[0039] The upstream of the new power system industry chain has the characteristics of numerous power generation companies, complex market structure, and fierce competition. According to the SCP theory, the structure of the market will affect market performance, and the market structure is reflected by the market share. Therefore, the market share of each power group is used as the environmental variable in the upstream of the industry chain; for the midstream of the industry chain, due to the large scale of this part, it is easily affected by environmental factors such as the number of users and the social and economic level. In order to comprehensively consider users and the economy, the midstream of the industry chain uses per capita GDP as the environmental variable. This indicator combines population and regional GDP, which can more objectively reflect the impact of environmental factors on the transmission and distribution network. For the downstream of the industry chain, the most obvious environmental influencing factor is the country's support policy for the new energy industry. Therefore, the downstream of the industry chain uses the degree of policy support for the new energy industry as the environmental variable.

[0040] In this embodiment, the constructed industrial chain index evaluation system is specifically shown in Table 1 below.

[0041] Table 1. Industrial chain links and three-stage DEA model indicators

[0042]

[0043] In this embodiment, the data obtained above needs to be preprocessed to remove outlier data and perform normalization.

[0044] In this embodiment, the establishment of the first-stage BCC model of the three-stage DEA and the determination of multiple efficiency indicators of each link in the industrial chain are specifically achieved through the following steps.

[0045] Based on the input-oriented BCC (Banker, Charnes, and Cooper) model, the original input variable and output variable data are loaded into the classic DEA model using DEAP2.1 software, with the aim of comprehensively measuring and evaluating multiple efficiency indicators of each link in the industrial chain, including SE, TE, PTE and various input slack variables. There is a certain relationship between the above efficiency indicators, namely TE = SE*PTE; the objective function and constraints of the DEA-BCC model in this embodiment are as follows:

[0046]

[0047]

[0048] Among them, θ is the overall efficiency of each decision-making unit, and its value range is [0,1]. When θ=1, the decision-making unit is located on the frontier and is in a DEA effective state; when 0≤θ<1, the decision-making unit is in a non-DEA effective state. ε is the introduced non-Archimedean infinitesimal; j=1,2,...,n is the decision-making unit; X is the input vector; λ j represents the combination ratio of effective units constructed relative to each decision-making unit (DMU); Y is the output vector; S + is the input slack variable, S - is the output slack variable.

[0049] Specifically, it is necessary to first select appropriate input and output variables based on the specific conditions of each link in the new power system industrial chain, and then combine the industrial chain indicator evaluation system, analyze the statistical data of various indicator variables within a certain time interval, and obtain the TE mean, PTE mean, SE mean and other parameters (industry chain performance) of the upstream, midstream and downstream of the industrial chain in each time period. PTE refers to the impact of input variables on comprehensive efficiency without considering the influence of scale efficiency factors. If PTE is less than 1, it means that the utilization of input variables still has room for improvement, and production technology needs to be further improved; if PTE is equal to 1, it means that the utilization of input variables has been maximized and no improvement is needed. SE reflects the coordination relationship between input and output. When SE is equal to 1, it means that the input and output have reached the optimal state.

[0050] Next, the input redundancy obtained by the DEA-BCC model in the first stage was used as the explained variable, and the environmental variables of the upstream, midstream and downstream were selected as the explanatory variables. The maximum likelihood method (MLE) of Frontier 4.1 software was used to estimate the regression coefficients and perform SFA regression analysis. In this stage, the SFA regression operation was performed by relying on the mixed error random frontier model and the cost function model to remove the influence of external environmental variables and random errors, and to achieve efficiency measurement of all research objects under the same environment. The SFA regression operation formula is as follows:

[0051] S ik =f(Z k β i )+V ik +μ ik (2);

[0052] Where i = 1, 2, ....., m; k = 1, 2, ...., n; S ik Z represents the slack variable of the i-th input of the k-th decision-making unit; k =(Z 1k ,Z 2k ,.....Z pk ), represents the environment variable; β i represents the parameter to be estimated of the environmental variable; f(Z k β i ) represents the environment variable Z k For S ik The influence of V ik +μ ik The mixed error can be expressed as ik represents the random error estimate, μ ik Represents the inefficiency term of management.

[0053] By adjusting the environmental variables and regression coefficients, the original input indicators can be adjusted, and the adjusted input indicators are as follows:

[0054] X A ik =X ik +[max k {z k β i}-z k β i ]+[max k {V ik}-V ik ](3);

[0055] Among them, X Aik Represents the actual input value after adjustment; β i represents the parameter estimate of the environmental variable; V ik represents an estimate of the random error.

[0056] Specifically speaking about the analysis results, if the regression coefficient of the environmental variable on its input slack variable is negative, it means that the environmental variable in this link of the industrial chain has a negative feedback effect on the input slack variable, that is, the increase in the influence of the environmental variable will reduce the redundancy of the input slack, thereby increasing TE; if the regression coefficient of the environmental variable on its input slack variable is approximately zero, it means that the impact of the environmental variable on TE is very small; if the regression coefficient of the environmental variable on its input slack variable is positive, it means that the environmental variable has a positive feedback effect on its input slack variable, that is, excessive input will cause redundant waste, thereby reducing TE.

[0057] Finally, the third stage: reanalysis with replacement variables.

[0058] The original input variables are replaced with new input variables adjusted in the second stage, and then the first stage calculation is repeated with the new input variables and the original output variables to obtain the technical efficiency of the new power system industrial chain to be evaluated. The purpose is to remove the performance values ​​affected by environmental factors and random errors to ensure the objectivity and accuracy of the results, thereby obtaining performance values ​​that fully reflect the management level of the decision-making unit.

[0059] Specifically speaking, for the analysis results of this stage, if the TE of a certain link in the industrial chain increases after removing external environmental variables and random errors, it means that the link is constrained by unfavorable environmental factors, thereby limiting its own TE; if the TE remains unchanged before and after adjustment, it means that the impact of environmental variables on TE is very small; if the TE decreases after adjustment, it means that the TE of this link was overestimated in the first stage.

[0060] The method provided in this embodiment regulates the input-output relationship of each link of the new power system industrial chain through the three-stage DEA method, so as to realize the ability of the new power system industrial chain to produce the optimal proportion of some input factors under the given asset input and production technology. In other words, it is the ability to achieve maximum output under the conditions of given various input factors, or the ability to minimize input under a given output level, so as to ensure the safe and stable supply of electricity and the high-quality development of new energy.

[0061] Device Embodiment

[0062] According to an embodiment of the present invention, a novel power system industry chain technology performance evaluation device is provided. Figure 3 As shown, it is a block diagram of a new power system industry chain technology efficiency evaluation device provided in this embodiment. The new power system industry chain technology efficiency evaluation device according to the embodiment of the present invention includes:

[0063] The indicator evaluation system construction module 10 is used to obtain the input variables and output variables of each link of the new power system industrial chain to be analyzed and their corresponding data, and to construct the industrial chain indicator evaluation system according to the input variables and output variables.

[0064] The first-stage evaluation module 20 is used to analyze multiple efficiency indicators of each link in the industrial chain based on the industrial chain indicator evaluation system using the input-oriented DEA-BCC model, where the multiple efficiency indicators include SE, TE, PTE and various input slack variables.

[0065] The second stage correction module 30 is used to calculate the corrected input variables and corrected slack variables excluding the influence of external environmental factors and random interference errors based on each input slack variable using the SFA model;

[0066] The third stage evaluation module 40 is used to analyze multiple efficiency indicators of each link in the industrial chain based on the corrected input variables and the corrected slack variables using the input-oriented DEA-BCC model in the first stage evaluation module 20 to obtain the technical efficiency of the new power system industrial chain to be evaluated.

[0067] The method provided in this embodiment is based on an input-oriented three-stage DEA method to evaluate the technical efficiency of the new power system industrial chain. For the new power system, which is currently rarely studied, the technical efficiency of each link in the industrial chain is evaluated from the overall perspective, and the impact of environmental factors and random variables on the market efficiency evaluation of the industrial chain is fully considered to obtain a more objective and accurate industrial chain performance evaluation result. The method provided in this embodiment uses a three-stage method to remove these external influences, and the various efficiency indicators obtained can objectively and accurately reflect the technical efficiency of the new power system industrial chain, and further put forward effective suggestions for improving and enhancing the market performance of the new power system industrial chain, so as to assist in the design of the new power system industrial chain operation plan.

[0068] In this embodiment, the four major links of the industrial chain are divided into three parts, that is, the upstream of the industrial chain is defined as the power generation side, the midstream of the industrial chain is defined as the power transmission and distribution side, and the downstream of the industrial chain is defined as the power consumption side, and the input and output variables of each link are specifically as follows.

[0069] The upstream of the industrial chain uses installed power generation capacity and power project construction investment as input variables, power generation and standard coal consumption as output variables, and the environmental variable is the market share of each power group.

[0070] In the midstream of the industrial chain, the length of transmission lines of 220KV and above and the construction investment of power grid projects are used as input variables, the power sales volume and line loss rate are used as output variables, and the environmental variable is GDP per capita.

[0071] The downstream of the industrial chain uses power supply as the input variable, the total electricity consumption and the number of charging piles in society as the output variables, and the environmental variable is the degree of policy support for the new energy industry by the state.

[0072] In this embodiment, the first stage evaluation module 20 is configured to specifically perform the following steps.

[0073] Based on the input-oriented BCC model, the original input variable and output variable data are loaded into the classic DEA model using DEAP2.1 software to evaluate multiple efficiency indicators of each link in the industrial chain, including SE, TE, PTE and various input slack variables. There is a certain relationship between the above efficiency indicators, that is, TE = SE*PTE; the objective function and constraints of the DEA-BCC model in this embodiment are as follows:

[0074]

[0075]

[0076] Among them, θ is the overall efficiency of each decision-making unit, and its value range is [0,1]. When θ=1, the decision-making unit is located on the frontier and is in a DEA effective state; when 0≤θ<1, the decision-making unit is in a non-DEA effective state. ε is the introduced non-Archimedean infinitesimal; j=1,2,...,n is the decision-making unit; X is the input vector; λ j represents the combined proportion of effective units constructed relative to each DMU; Y is the output vector; S + is the input slack variable, S - is the output slack variable.

[0077] Specifically, it is necessary to first select appropriate input and output variables based on the specific conditions of each link in the new power system industrial chain, and then combine the industrial chain indicator evaluation system, analyze the statistical data of various indicator variables within a certain time interval, and obtain the TE mean, PTE mean, SE mean and other parameters (industry chain performance) of the upstream, midstream and downstream of the industrial chain in each time period. PTE refers to the impact of input variables on comprehensive efficiency without considering the influence of scale efficiency factors. If PTE is less than 1, it means that the utilization of input variables still has room for improvement, and production technology needs to be further improved; if PTE is equal to 1, it means that the utilization of input variables has been maximized and no improvement is needed. SE reflects the coordination relationship between input and output. When SE is equal to 1, it means that the input and output have reached the optimal state.

[0078] In this embodiment, the second stage correction module 30 is configured to specifically perform the following steps.

[0079] The input redundancy obtained by the BCC model in the first stage was used as the explained variable, and then the environmental variables of the upstream, midstream and downstream were selected as the explanatory variables. The maximum likelihood method (MLE) of Frontier 4.1 software was used to estimate the regression coefficients and perform SFA regression analysis. In this stage, the SFA regression operation was performed using the mixed error random frontier model and the cost function model to remove the influence of external environmental variables and random errors, and to achieve efficiency measurement for all research objects under the same environment. The SFA regression operation formula is as follows:

[0080] S ik =f(Z k β i )+V ik +μ ik (5);

[0081] Where i = 1, 2, ....., m; k = 1, 2, ...., n; S ik Z represents the slack variable of the i-th input of the k-th decision-making unit; k =(Z 1k ,Z 2k ,.....Z pk ), represents the environment variable; β i represents the parameter to be estimated of the environmental variable; f(Z k β i ) represents the environment variable Z k For S ik The influence of V ik +μ ik The mixed error can be expressed as ik represents the random error estimate, μ ik Represents the inefficiency term of management.

[0082] By adjusting the environmental variables and regression coefficients, the original input indicators can be adjusted, and the adjusted input indicators are as follows:

[0083] X A ik =X ik +[max k {z k β i}-z k β i ]+[max k {V ik}-V ik ](6);

[0084] Among them, X A ik Represents the actual input value after adjustment; β irepresents the parameter estimate of the environmental variable; V ik represents an estimate of the random error.

[0085] Specifically speaking about the analysis results, if the regression coefficient of the environmental variable on its input slack variable is negative, it means that the environmental variable in this link of the industrial chain has a negative feedback effect on the input slack variable, that is, the increase in the influence of the environmental variable will reduce the redundancy of the input slack, thereby increasing TE; if the regression coefficient of the environmental variable on its input slack variable is approximately zero, it means that the impact of the environmental variable on TE is very small; if the regression coefficient of the environmental variable on its input slack variable is positive, it means that the environmental variable has a positive feedback effect on its input slack variable, that is, excessive input will cause redundant waste, thereby reducing TE.

[0086] Finally, the third-stage evaluation module 40 replaces the original input variables with new input variables after the second-stage correction module 30, and then uses the new input variables and the original output variables to calculate the technical performance of the new power system industrial chain to be evaluated through the input-oriented DEA-BCC model in the first-stage evaluation module 20. The purpose is to remove the performance values ​​affected by environmental factors and random errors to ensure the objectivity and accuracy of the results, thereby obtaining performance values ​​that fully reflect the management level of the decision-making unit.

[0087] Specifically speaking, for the analysis results of this stage, if the TE of a certain link in the industrial chain increases after removing external environmental variables and random errors, it means that the link is constrained by unfavorable environmental factors, thereby limiting its own TE; if the TE remains unchanged before and after adjustment, it means that the impact of environmental variables on TE is very small; if the TE decreases after adjustment, it means that the TE of this link was overestimated in the first stage.

[0088] The embodiment of the present invention is an apparatus embodiment corresponding to the above-mentioned method embodiment. The specific operations of the processing steps of each module can be understood by referring to the description of the method embodiment, which will not be repeated here.

[0089] like Figure 4 As shown, the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the new power system industrial chain technology efficiency evaluation method in the above-mentioned embodiment is implemented.

[0090] The present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for evaluating the technical efficiency of the new power system industrial chain in the above embodiment is implemented. Alternatively, when the computer program is executed by a processor, the method for evaluating the technical efficiency of the new power system industrial chain in the above embodiment is implemented. When the computer program is executed by the processor, the following method steps are implemented:

[0091] Step S1: Obtain input variables and output variables of each link of the new power system industrial chain to be analyzed and their corresponding data, and construct an industrial chain indicator evaluation system based on the input variables and output variables.

[0092] Step S2: Based on the industrial chain indicator evaluation system, the input-oriented DEA-BCC model is used to analyze multiple efficiency indicators of each link in the industrial chain, thereby obtaining the DEA efficiency value of each link in the new power system industrial chain, and obtaining multiple efficiency indicators including SE, TE, PTE and various input slack variables.

[0093] Step S3: Based on each input slack variable, the SFA model is used to calculate the corrected input variable and the corrected slack variable that exclude the influence of external environmental factors and random interference errors;

[0094] Step S4: Based on the corrected input variables and the corrected slack variables, the input-oriented DEA-BCC model in step S1 is used to analyze multiple efficiency indicators of each link in the industrial chain to obtain the technical efficiency of the new power system industrial chain to be evaluated.

[0095] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0096] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device or system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The device and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0097] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and the contents not described in detail in the specification of the present invention belong to the common knowledge of those skilled in the art.

Claims

1. A new method for evaluating the technical performance of the power system industry chain, characterized in that The following steps are involved: Establishment of index evaluation system: Obtain the input variables and output variables of each link of the new power system industrial chain to be analyzed and their corresponding data, and build the industrial chain index evaluation system based on the input variables and output variables; The first stage of the three-stage DEA: Based on the industrial chain indicator evaluation system, the input-oriented DEA-BCC model is used to analyze multiple efficiency indicators of each link in the industrial chain, where multiple efficiency indicators include SE, TE, PTE and various input slack variables; The second stage of the three-stage DEA: Based on each input slack variable, the SFA model is used to calculate the corrected input variables and corrected slack variables that exclude the influence of external environmental factors and random interference errors; as well as The third stage of the three-stage DEA: Based on the corrected input variables and the corrected slack variables, the input-oriented DEA-BCC model is used to analyze multiple efficiency indicators of each link in the industrial chain to obtain the technical efficiency of the new power system industrial chain to be evaluated.

2. The new power system industry chain technology performance evaluation method according to claim 1, characterized in that: The various links of the industrial chain are divided into three parts, including the upstream power generation side of the industrial chain, the midstream power transmission and distribution side of the industrial chain, and the downstream power consumption side of the industrial chain; The upstream power generation side of the industrial chain uses power generation installed capacity and power engineering construction investment as input variables, power generation and power generation standard coal consumption as output variables, and the environmental variable is the market share of each power group; The transmission and distribution side of the midstream of the industrial chain uses the length of 220KV and above transmission lines and the construction investment of power grid projects as input variables, the power sales volume and line loss rate as output variables, and the environmental variable is GDP per capita; and The downstream electricity consumption side of the industrial chain uses power supply as the input variable, the total electricity consumption and the number of charging piles in society as the output variables, and the environmental variable is the degree of policy support for the new energy industry by the state.

3. The new power system industry chain technology performance evaluation method according to claim 1, characterized in that: The objective function and constraints of the input-oriented DEA-BCC model are as follows: Where θ is the overall efficiency of each decision-making unit, and its value range is [0,1]. When θ=1, the decision-making unit is located on the frontier surface and is in a DEA effective state; when 0≤θ<1, the decision-making unit is in a non-DEA effective state; ε is the introduced non-Archimedean infinitesimal; j=1,2,...,n is the decision-making unit; X is the input vector; λ j represents the combination ratio of effective units constructed relative to each decision-making unit; Y is the output vector; S + is the input slack variable, S - is the output slack variable.

4. The new power system industry chain technology performance evaluation method according to claim 1, characterized in that: The specific implementation steps of using the SFA model to calculate the corrected input variables and corrected slack variables that exclude the influence of external environmental factors and random interference errors are as follows: The input redundancy obtained by the DEA-BCC model in the first stage is used as the explained variable, and then the environmental variables of the upstream, midstream and downstream of the industrial chain are selected as explanatory variables. The regression coefficients are estimated using the maximum likelihood method, and SFA regression analysis is performed. The SFA model is used to calculate the corrected input variables and corrected slack variables that exclude the influence of external environmental factors and random interference errors. The SFA model is specifically as follows: S ik =f(Z k ;β i )+V ik +μ ik (2); Where i = 1, 2, ....., m; k = 1, 2, ...., n; S ik Z represents the slack variable of the i-th input of the k-th decision-making unit; k =(Z 1k ,Z 2k ,.....Z pk ), represents the environment variable; β i represents the parameter to be estimated of the environmental variable; f(Z k β i ) represents the environment variable Z k For S ik The influence of V ik +μ ik The mixed error can be expressed as ik represents the random error estimate, μ ik represents the inefficiency term of management; By adjusting the environmental variables and regression coefficients, the original input indicators are adjusted, and the adjusted input indicators are as follows: X A ik =X ik +[max k {With k β i }-With k β i ]+[max k {V ik }-V ik ](3); Among them, X A ik Represents the actual input value after adjustment; β i represents the parameter estimate of the environmental variable; V ik represents an estimate of the random error.

5. A new type of power system industry chain technology performance evaluation device, characterized by include: The indicator evaluation system construction module is used to obtain the input variables and output variables of each link of the new power system industrial chain to be analyzed and their corresponding data, and to construct the industrial chain indicator evaluation system based on the input variables and output variables; The first stage evaluation module is used to analyze multiple efficiency indicators of each link in the industrial chain based on the industrial chain indicator evaluation system using the input-oriented DEA-BCC model. The multiple efficiency indicators include SE, TE, PTE and various input slack variables; The second stage correction module is used to calculate the corrected input variables and corrected slack variables based on each input slack variable using the SFA model to exclude the influence of external environmental factors and random interference errors; as well as The third stage evaluation module is used to analyze multiple efficiency indicators of each link in the industrial chain based on the corrected input variables and the corrected slack variables using the input-oriented DEA-BCC model in the first stage evaluation module to obtain the technical efficiency of the new power system industrial chain to be evaluated.

6. The new power system industry chain technology performance evaluation device according to claim 5, characterized in that: The various links of the industrial chain are divided into three parts, including the upstream power generation side of the industrial chain, the midstream power transmission and distribution side of the industrial chain, and the downstream power consumption side of the industrial chain; The upstream power generation side of the industrial chain uses power generation installed capacity and power engineering construction investment as input variables, power generation and power generation standard coal consumption as output variables, and the environmental variable is the market share of each power group; The transmission and distribution side of the midstream of the industrial chain uses the length of 220KV and above transmission lines and the construction investment of power grid projects as input variables, the power sales volume and line loss rate as output variables, and the environmental variable is GDP per capita; and The downstream electricity consumption side of the industrial chain uses power supply as the input variable, the total electricity consumption and the number of charging piles in society as the output variables, and the environmental variable is the degree of policy support for the new energy industry by the state.

7. The new power system industry chain technology performance evaluation device according to claim 5, characterized in that: The objective function and constraints of the input-oriented DEA-BCC model in the first-stage evaluation module are as follows: Where θ is the overall efficiency of each decision-making unit, and its value range is [0,1]. When θ=1, the decision-making unit is located on the frontier surface and is in a DEA effective state; when 0≤θ<1, the decision-making unit is in a non-DEA effective state; ε is the introduced non-Archimedean infinitesimal; j=1,2,...,n is the decision-making unit; X is the input vector; λ j represents the combination ratio of effective units constructed relative to each decision-making unit; Y is the output vector; S + is the input slack variable, S - is the output slack variable.

8. The new power system industry chain technology performance evaluation device according to claim 5, characterized in that: The second stage correction module is configured to perform the following steps: The input redundancy obtained by the DEA-BCC model in the first stage is used as the explained variable, and then the environmental variables of the upstream, midstream and downstream of the industrial chain are selected as explanatory variables. The regression coefficients are estimated using the maximum likelihood method, and SFA regression analysis is performed. The SFA model is used to calculate the corrected input variables and corrected slack variables that exclude the influence of external environmental factors and random interference errors; the SFA model is specifically as follows: S ik =f(Z k ;β i )+V ik +μ ik (2); Where i = 1, 2, ....., m; k = 1, 2, ...., n; S ik Z represents the slack variable of the i-th input of the k-th decision-making unit; k =(Z 1k ,Z 2k ,.....Z pk ), represents the environment variable; β i represents the parameter to be estimated of the environmental variable; f(Z k β i ) represents the environment variable Z k For S ik The influence of V ik +μ ik The mixed error can be expressed as ik represents the random error estimate, μ ik represents the inefficiency term of management; By adjusting the environmental variables and regression coefficients, the original input indicators are adjusted, and the adjusted input indicators are as follows: X A ik =X ik +[max k {With k β i }-With k β i ]+[max k {V ik }-V ik ](3); Among them, X A ik Represents the actual input value after adjustment; β i represents the parameter estimate of the environmental variable; V ik represents an estimate of the random error.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the new power system industry chain technical efficiency evaluation method as described in any one of claims 1 to 4 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for evaluating the technical efficiency of the new power system industrial chain as claimed in any one of claims 1 to 4 is implemented.

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