An evaluation method for power grid enterprises under the background of a new power system

Abstract

This invention discloses an evaluation method for power grid enterprises under the background of a new power system, comprising: obtaining basic data of evaluation indicators for the enterprise to be evaluated based on a pre-constructed evaluation indicator system for power grid enterprises; performing value-based processing on the basic data of evaluation indicators using set-valued statistics to obtain attribute values ​​of the evaluation indicators; calculating the weight values ​​of the evaluation indicators using the attribute hierarchy model method based on the attribute values ​​of the evaluation indicators; and solving the relative closeness between the evaluation indicators and the ideal indicators of the enterprise to be evaluated using the grey relational degree-ideal point method based on the weight values ​​of the evaluation indicators, so as to obtain the evaluation results based on the relative closeness. This scheme comprehensively considers evaluation indicators from multiple dimensions such as the power source side, the grid side, and the load side. By assigning weights to the evaluation indicator system and solving the closeness between each evaluation indicator and the ideal indicator, it improves the rationality and effectiveness of the evaluation results, making the evaluation indicators of power grid enterprises better adapt to the requirements of the development of the new power system.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid enterprise evaluation, and in particular to an evaluation method for power grid enterprises under the background of a new type of power system. Background Art

[0002] In the context of the new power system, with the continuous emergence of new forms and models in the power industry, the demand for power services and consumption concepts are becoming increasingly diversified and low-carbon, and the strategic positioning, business development, and management methods of power grid companies are also undergoing major changes. How to establish a scientific and complete comprehensive evaluation index system for power grid companies to effectively achieve accurate matching of supply and demand of power grid companies, meet energy needs, reduce social energy consumption, and promote industrial upgrading is an urgent problem to be solved.

[0003] Domestic and foreign scholars have conducted extensive research on the evaluation index system of power grid enterprises. For example, Zeng Ming et al. constructed a design and model of a renewable energy policy evaluation system for power grid enterprises based on the ubiquitous power Internet of Things platform. Yuan Yiping et al. considered the multi-period coordination of the new power system and constructed an evaluation index system for multi-period coordinated panoramic operation simulation. Bie Chaohong et al. established a multi-level index and weak link identification system for different risk types based on the segmented multi-objective risk analysis theory. The research methods of these power grid enterprise evaluation index systems often only target one aspect of the power system operation characteristics and cannot fully adapt to the power system with new energy as the main body. In order to reflect the common and long-term direction of the development of the power industry and follow the principles of scientificity and continuity, it is necessary to construct a comprehensive evaluation system for power grid enterprises under the background of a new power system. Summary of the invention

[0004] To this end, the present invention analyzes the internal factors of the power system's power supply side, grid side, load side, etc., and refines and screens out core indicators with "green electricity", "reliable electricity", "efficient electricity" and "intelligent electricity" as evaluation effects, constructs a comprehensive evaluation index system for power grid enterprises, which can adapt to the "green, safe and economical" operation requirements of the new power system, and verifies the effectiveness and applicability of the comprehensive evaluation system through examples.

[0005] The present invention provides an evaluation method for power grid enterprises under the background of a new type of power system. In the method, first, based on a pre-constructed evaluation index system for power grid enterprises, basic evaluation index data of the enterprise to be evaluated is obtained, wherein the evaluation index system for power grid enterprises includes a dimension layer, a process index, a process index group and a key index, the process index is a refinement index of the dimension layer, the process index group is a refinement index of the process index, the key index is a screening index of the process index, and the evaluation index of the enterprise to be evaluated is a key index. The key index at least includes the utilization of new energy inside and outside the area, the comprehensive utilization rate of new energy, the proportion of new energy installed capacity, the proportion of new energy electricity, the system frequency support capacity, the system reserve rate, the overload level of the transmission line, the probability of power shortage, the contribution rate of new energy peak regulation, the utilization rate of renewable energy, the penetration rate of distributed wind and solar, the rate of abandoned wind and solar, the application scale of the plant-grid coordination device, the application ratio of new transmission technology, the capacity ratio of smart substations, and the collection rate of the power information system. Then, the set value statistics method is used to quantify the basic data of the evaluation indicators to obtain the attribute values ​​of the evaluation indicators; then, based on the attribute values ​​of the evaluation indicators, the attribute hierarchy model method is used to calculate the weight values ​​of the evaluation indicators; finally, based on the weight values ​​of the evaluation indicators, the grey correlation-ideal point method is used to solve the relative proximity between the evaluation indicators of the enterprises to be evaluated and the ideal indicators, so as to rank the enterprises to be evaluated based on the relative proximity and obtain the evaluation results.

[0006] Optionally, in the step of using the set value statistical method to perform valuation processing on the evaluation index to obtain the attribute value of the evaluation index, first, set the expert's scoring interval for the evaluation index They represent the lower limit and upper limit of the k-th expert's approval of the l-th evaluation indicator. Then, the expert score of the evaluation indicator is calculated based on the score interval:

[0007]

[0008] In the formula, g l,k is the score of the kth expert on the lth evaluation indicator, k = 1, 2, ..., K, indicating that there are K experts in total, and δ is the interval estimation error of the scoring interval. Next, the credibility of the expert score is tested to obtain the scoring results of each evaluation indicator. The credibility of the expert score of the lth evaluation indicator η can be calculated by the following formula: l :

[0009]

[0010] Finally, the scoring results are normalized and uniformly mapped to the interval [0,1] to obtain the attribute value of the evaluation index.

[0011] Optionally, in the step of calculating the weight value of the evaluation index using the attribute hierarchy model method based on the attribute value of the evaluation index, first, determine the association relationship between the key index and the process index in the evaluation index system and the affiliation relationship between the dimension layer and the process index. Then, based on the association relationship and the affiliation relationship, establish a hierarchical structure to divide the evaluation index system into a target layer, a criterion layer, and a solution layer; then, construct a judgment matrix between the target layer and the criterion layer, and between the criterion layer and the solution layer; then, calculate the eigenvalue and eigenvector of the judgment matrix to obtain the weight value of the evaluation index; finally, perform a weight sensitivity analysis on the evaluation index to determine the importance of the evaluation index.

[0012] Optionally, in the above method, a correlation coefficient matrix between process indicators and key indicators can be constructed:

[0013]

[0014] In the formula, Y is the correlation coefficient matrix, Y ii is a diagonal submatrix. When i≠j, Y ij is a non-diagonal submatrix 1≤i,j≤a, a represents the number of dimensional layers in the evaluation index system, m represents the number of process indicators, n represents the number of key indicators, M(i) represents the number of process indicators belonging to the i-th dimensional layer, and N(i) represents the number of key indicators belonging to the i-th dimensional layer. Then, calculate the correlation degree of each submatrix in the correlation matrix:

[0015]

[0016] Among them, y ij Represents the correlation coefficient between the process indicator element and the key indicator element. If there is a correlation, then y ij =1, if there is no correlation, then y ij = 0. Finally, the objective function is calculated based on the correlation of each submatrix, and the membership relationship with the largest objective function value is determined as the membership relationship between the dimension layer and the process indicator. The objective function is:

[0017]

[0018] In the formula, sum[Y ii ]、sum[Y ij ] represent the correlation degree of the diagonal submatrix and non-diagonal submatrix in the correlation matrix respectively.

[0019] Optionally, in the step of using the grey correlation degree-ideal point method to solve the relative proximity between the evaluation index and the ideal index of the enterprise to be evaluated based on the weight value of the evaluation index, so as to sort the enterprises to be evaluated based on the relative proximity to obtain the evaluation result, first, construct the attribute decision matrix of the evaluation index, and normalize the attribute decision matrix to obtain the standardized matrix. Then, the weight value of the evaluation index is weighted and multiplied with the corresponding attribute value in the standardized matrix to obtain the weighted matrix. Subsequently, the positive ideal solution and the negative ideal solution of the evaluation index are determined based on the weighted matrix; the Euclidean space distance from the evaluation index to the positive ideal solution and the Euclidean space distance from the evaluation index to the negative ideal solution are calculated; the grey correlation degree and proximity between the evaluation index and the positive ideal solution, and the grey correlation degree and proximity between the evaluation index and the negative ideal solution are calculated. Finally, based on the proximity of the evaluation index to the positive ideal solution and the proximity of the evaluation index to the negative ideal solution, the relative proximity between the evaluation index and the ideal index of the enterprise to be evaluated is solved, and the enterprises to be evaluated are sorted based on the relative fit to obtain the evaluation result of the enterprise to be evaluated.

[0020] Optionally, in the above method, the grey correlation degree between the evaluation index and the positive ideal solution is calculated by the following formula:

[0021] in,

[0022]

[0023] The grey correlation degree between the evaluation index and the negative ideal solution is calculated by the following formula:

[0024] in,

[0025]

[0026] In the formula, are the grey correlation between the p-th evaluation index attribute value and the positive ideal solution, and the grey correlation between the evaluation index attribute value and the negative ideal solution of the q-th power grid enterprise to be evaluated, are the degree of convergence between the p-th evaluation index attribute value of the q-th power grid enterprise to be evaluated and the positive ideal solution, and the degree of convergence between the evaluation index attribute value and the negative ideal solution, respectively. and They represent the approach limits of positive ideal solution and negative ideal solution respectively, μ is the approach coefficient, represents the positive ideal solution of the pth evaluation index of the qth power grid enterprise to be evaluated; represents the negative ideal solution of the pth evaluation index of the qth power grid enterprise to be evaluated,

[0027]

[0028]

[0029]

[0030] is the weighted attribute value of the pth evaluation index of the qth power grid enterprise to be evaluated, is the weight value of the pth evaluation index, represents the attribute value of the pth evaluation index of the qth power grid enterprise to be evaluated, P is the total number of evaluation indicators, and Q is the total number of enterprises to be evaluated;

[0031] The relative closeness between the evaluation index of the enterprise to be evaluated and the ideal index is calculated by the following formula:

[0032] in,

[0033]

[0034]

[0035]

[0036]

[0037] In the formula, is the relative proximity of the enterprise to be evaluated, and are the proximity between the pth evaluation index attribute value of the qth power grid enterprise to be evaluated and the positive ideal solution, and the proximity between the evaluation index attribute value and the negative ideal solution, γ1 and γ2 represent the proximity coefficients, and They are respectively the Euclidean space distance from the pth evaluation index attribute value of the qth power grid enterprise to the positive ideal solution and the Euclidean space distance from the evaluation index attribute value to the negative ideal solution.

[0038] According to another aspect of the present invention, a computing device is provided, comprising: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a method for executing an evaluation method for a power grid enterprise in the context of a new type of power system according to the present invention.

[0039] According to another aspect of the present invention, there is provided a computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions which, when executed by a computing device, enable the computing device to execute an evaluation method for a power grid enterprise in the context of a novel power system according to the present invention.

[0040] The present invention constructs a comprehensive evaluation index system for power grid enterprises with "green electricity", "reliable electricity", "efficient electricity" and "smart electricity" as evaluation effects, constructs an attribute hierarchy model empowerment-grey correlation ideal point method comprehensive evaluation model, and verifies the effectiveness and applicability of the proposed evaluation index system and model method through examples, which provides theoretical support for deepening the transformation and upgrading of my country's energy and power industry and promoting the high-quality development of power grid enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] To achieve the above and related purposes, certain illustrative aspects are described herein in conjunction with the following description and accompanying drawings, which indicate various ways in which the principles disclosed herein can be practiced, and all aspects and their equivalents are intended to fall within the scope of the claimed subject matter. The above and other purposes, features and advantages of the present disclosure will become more apparent by reading the following detailed description in conjunction with the accompanying drawings. Throughout the present disclosure, the same reference numerals generally refer to the same parts or elements.

[0042] Figure 1 It shows the logical relationship diagram of the comprehensive evaluation system of power grid enterprises under the background of new power system;

[0043] Figure 2 A block diagram of a computing device 200 according to an embodiment of the present invention is shown;

[0044] Figure 3 A schematic flow chart of a method 300 for evaluating a power grid enterprise in a new power system context according to an embodiment of the present invention is shown;

[0045] Figure 4 A schematic diagram of a power grid enterprise evaluation index system according to an embodiment of the present invention is shown;

[0046] Figure 5 A hierarchical structure diagram of an evaluation index system according to an embodiment of the present invention is shown;

[0047] Figure 6 A sensitivity analysis diagram of the weight value of "green electricity" is shown;

[0048] Figure 7 A sensitivity analysis diagram of the weight value of "reliable electricity" is shown;

[0049] Figure 8 A sensitivity analysis diagram of the weight value of "high efficiency electricity" is shown;

[0050] Fig. 9 A sensitivity analysis diagram of the weight value of "Smart Electricity" is shown. DETAILED DESCRIPTION

[0051] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0052] In the context of the new power system, power grid companies should comprehensively lead green and low-carbon development in terms of ideology and theory, action practice, technological innovation and business model. When constructing a comprehensive evaluation index system, power grid companies should reflect the common and long-term direction of the development of the new power system and follow the requirements of scientificity, continuity and pertinence. Among them, scientificity means that the constructed comprehensive evaluation index system of the new power system complies with the principles of specificity, measurability, achievability, reality and time limit. Continuity means that the evaluation system should fully draw on existing research plans and evaluation systems. Pertinence means that the evaluation system should highlight the current status and development characteristics of my country's power grid.

[0053] Based on the characteristics of my country's power industry and the development situation of the industry, this plan combines the basic principles of building a comprehensive evaluation index system, studies from the perspectives of evaluation effects (external factors), KPI key indicators, classification dimensions (internal factors) and exploratory practice, and proposes the logical relationship of a new type of comprehensive evaluation index system for power systems. Figure 1 The figure shows the logical relationship diagram of the comprehensive evaluation system of power grid enterprises under the background of the new power system. Figure 1 As shown in the figure, the comprehensive evaluation index system of power grid enterprises can measure the effects of the new power system from the aspects of "green electricity", "reliable electricity", "efficient electricity" and "smart electricity". The dimensional classification can refine the evaluation indicators from internal factors such as the grid side, power supply side and load side to screen out key indicators that characterize the characteristics of the new power system.

[0054] Figure 2 FIG. 2 shows a block diagram of a computing device 200 according to an exemplary embodiment of the present invention. Figure 2 As shown, in a basic configuration 202, computing device 200 typically includes a system memory 206 and one or more processors 204. A memory bus 208 may be used for communication between processor 204 and system memory 206.

[0055] Depending on the desired configuration, the processor 204 can be any type of processor, including but not limited to: a microprocessor (μP), a microcontroller (μC), a digital information processor (DSP), or any combination thereof. The processor 204 can include one or more levels of cache such as a primary cache 210 and a secondary cache 212, a processor core 214, and registers 216. An example processor core 214 can include an arithmetic logic unit (ALU), a floating point unit (FPU), a digital signal processing core (DSP core), or any combination thereof. An example memory controller 218 can be used with the processor 204, or in some implementations, the memory controller 218 can be an internal part of the processor 204.

[0056] Depending on the desired configuration, the system memory 206 may be any type of memory, including but not limited to: volatile memory (such as RAM), non-volatile memory (such as ROM, flash memory, etc.), or any combination thereof. The system memory 206 may include an operating system 220, one or more programs 222, and program data 228. In some embodiments, the program 222 may be arranged to be executed by one or more processors 204 on the operating system using the program data 228 to execute the instructions 223 of the method 300 according to the present invention.

[0057] The computing device 200 may also include a storage interface bus 234. The storage interface bus 234 enables communication from storage devices 232 (e.g., removable storage 236 and non-removable storage 238) to the basic configuration 202 via the bus / interface controller 230. At least a portion of the operating system 220, the program 222, and the data 224 may be stored on the removable storage 236 and / or the non-removable storage 238, and when the computing device 200 is powered on or the program 222 is to be executed, it is loaded into the system memory 206 via the storage interface bus 234 and executed by the one or more processors 204.

[0058] The computing device 200 may also include an interface bus 240 that facilitates communication from various interface devices (e.g., output devices 242, peripheral interfaces 244, and communication devices 246) to the basic configuration 202 via the bus / interface controller 230. Example output devices 242 include a graphics processing unit 248 and an audio processing unit 250. They can be configured to facilitate communication with various external devices such as a display or speakers via one or more A / V ports 252. Example peripheral interfaces 244 may include a serial interface controller 254 and a parallel interface controller 256, which may be configured to facilitate communication with external devices such as input devices (e.g., keyboards, mice, pens, voice input devices, touch input devices) or other peripherals (e.g., printers, scanners, etc.) via one or more I / O ports 258. Example communication devices 246 may include a network controller 260, which may be arranged to communicate with one or more other computing devices 262 via a network communication link via one or more communication ports 264.

[0059] A network communication link can be an example of a communication medium. Communication media can generally be embodied as computer-readable instructions, data structures, program modules in a modulated data signal such as a carrier wave or other transmission mechanism, and can include any information delivery medium. A "modulated data signal" can be a signal in which one or more of its data sets or its changes can be carried out in a manner that encodes information in the signal. As non-limiting examples, communication media can include wired media such as a wired network or a dedicated line network, and various wireless media such as sound, radio frequency (RF), microwave, infrared (IR) or other wireless media. The term computer-readable medium used herein can include both storage media and communication media.

[0060] The computing device 200 can be implemented as a server, such as a file server 240, a database 250, a server, an application server, etc. These electronic devices can be such as a personal digital assistant (PDA), a wireless network browsing device, an application-specific device, or a hybrid device that can include any of the above functions. It can be implemented as a personal computer including a desktop computer and a notebook computer configuration. In some embodiments, the computing device 200 is configured to execute the evaluation method 300 of the power grid enterprise in the context of the new power system.

[0061] Figure 3 FIG. 3 is a flow chart showing a method 300 for evaluating a power grid enterprise in a new power system context according to an embodiment of the present invention. Figure 3 As shown, the method 300 starts at step S310, and based on a pre-constructed grid enterprise evaluation index system, basic evaluation index data of the enterprise to be evaluated is obtained.

[0062] Among them, the evaluation index system of power grid enterprises includes dimension layer, process index, process index group and key index. The process index is the detailed index of dimension layer, the process index group is the detailed index of process index, the key index is the screening index of process index, and the evaluation index of the enterprise to be evaluated is the key index. The key index at least includes the utilization of new energy inside and outside the area, the comprehensive utilization rate of new energy, the proportion of new energy installed capacity, the proportion of new energy electricity, the frequency support capacity of the system, the system reserve rate, the overload level of transmission lines, the probability of power shortage, the contribution rate of new energy peak regulation, the utilization rate of renewable energy, the penetration rate of distributed wind and solar, the rate of wind and solar abandonment, the application scale of plant-grid coordination devices, the application ratio of new transmission technologies, the capacity ratio of smart substations, and the collection rate of electricity information systems. Figure 4 FIG. 2 is a schematic diagram showing a power grid enterprise evaluation index system according to an embodiment of the present invention. Figure 4 As shown in the figure, on the power supply side, the focus is on the contribution of new energy to power balance and grid security, as well as the utilization efficiency of new energy and conventional power sources; on the grid side, the regional exchange capacity of cross-regional power transmission of the grid, the grid elasticity performance that characterizes the control characteristics of the grid side of the new power system, and the grid's ability to accept new energy that reflects the macro transmission capacity are considered; on the load side, the demand response effect that reflects the load side regulation capacity, the development trend of distributed energy, and the implementation effect of load side electric energy substitution are considered. A series of process indicators are obtained by refining the dimensional layer, and the process indicators are further refined. More critical indicators are selected from them to form four process indicator groups, that is, the indicators of the process indicator group are further refined from a series of process indicators. The core indicators in the process indicator group are taken as the key KPI indicators with "green electricity", "reliable electricity", "efficient electricity" and "smart electricity" as the evaluation effect.

[0063] Among them, "green electricity" mainly includes a high proportion of new energy output, reduction of abandoned solar and wind power, terminal power substitution and energy conservation and emission reduction supervision. "Reliable electricity" includes system structure optimization, lean operation management, high-quality power products and safe and reliable power supply. "Efficient electricity" includes intelligent technology and equipment, optimized investment management, comprehensive energy services and optimized energy structure. "Smart electricity" includes highly integrated and shared power grids, high-speed multi-directional communications, a strong power grid skeleton and fault isolation and self-healing.

[0064] In the calculation example of the present invention, considering the differences in economic development, energy supply and demand, resource endowment, power grid structure and other aspects in different regions of my country, a multidimensional cluster analysis can be conducted on the provincial power grid enterprises to be evaluated, and the basic data of the corresponding evaluation indicators can be collected. The enterprises to be evaluated can be divided into three situations. The first category is the eastern region, which belongs to the receiving-end power grid, has a developed economy, high load density, and overloaded atmospheric environmental capacity. The second category is the central region, where the cross-regional exchange of loads accounts for a small proportion, there is no obvious receiving-end and sending-end power grid characteristics, the load density is general, and the atmospheric environmental capacity is slightly surplus. The third category is the western and northeastern regions, which belong to the sending-end power grid, with an underdeveloped economy, low load density, and relatively sufficient atmospheric environmental capacity. Quantitative basic data can be obtained directly, and data that cannot be directly obtained can be obtained indirectly by calculating related data, or by statistical analysis of historical data; for qualitative and subjective data, it can be obtained by surveying and consulting relevant experts or decision makers.

[0065] Then, step S320 is executed to perform valuation processing on the basic data of the evaluation index using a set value statistical method to obtain the attribute value of the evaluation index.

[0066] In order to quantify some qualitative indicators, the evaluation indicators can be valued by using the Set-Valued Statistical Method (SVSM). First, set the expert scoring range for the evaluation indicators. They represent the lower limit and upper limit of the score of the k-th expert's approval of the l-th evaluation indicator. Then, let the interval estimation error of the evaluation interval be δ, and construct a set-valued statistical sample. The score of the k-th expert on the l-th evaluation indicator can be expressed as:

[0067]

[0068] In the formula, g l,k is the score of the kth expert on the lth evaluation indicator, k = 1, 2, ..., K, indicating that there are K experts in total. Next, the credibility of the expert scores is tested to obtain the score results of each evaluation indicator. The credibility of the expert score of the lth evaluation indicator η l for:

[0069]

[0070] When the credibility η of each evaluation indicator is greater than 0.9, it indicates that the experts have a high degree of recognition of the judgment results of the evaluation indicators. Finally, the scoring results are normalized to eliminate the magnitude differences between the indicator data, and the scoring results are uniformly mapped to the [0,1] interval to obtain the attribute value of the evaluation indicator. In the example of the present invention, the results of the preprocessing of the basic data of the evaluation indicators are shown in Table 1.

[0071] Table 1. Results of preprocessing of basic data for evaluation indicators

[0072]

[0073]

[0074] Then, step S330 is executed, and the weight value of the evaluation index is calculated using the attribute hierarchy model method based on the attribute value of the evaluation index. The attribute hierarchy model is a decision-making method based on the ball game model. According to the mutual correlation and affiliation between the factors, the factors are clustered and combined at different levels to form a multi-level analysis structure model, so that the problem is ultimately reduced to the determination of the relative importance weight of the lowest level (schemes, measures, etc. for decision-making) relative to the highest level (overall goal) or the ranking of the relative order of merit. In one embodiment of the present invention, first, the correlation relationship between the key indicators and the process indicators in the evaluation index system and the affiliation relationship between the dimension layer and the process indicators are determined. Logical verification analysis can be performed by constructing a correlation coefficient matrix. The larger the correlation coefficient, the higher the degree of correlation between the indicators. The relevant indicators in the KPI key indicator set and the process indicator set are numbered in turn, and a correlation coefficient matrix between the process indicators and the key indicators is constructed:

[0075]

[0076] In the formula, Y is the correlation coefficient matrix, Y ii is a diagonal submatrix. When i≠j, Y ij is a non-diagonal submatrix 1≤i,j≤a, a represents the number of dimensional layers in the evaluation index system, m represents the number of process indicators, n represents the number of key indicators, M(i) represents the number of process indicators belonging to the i-th dimensional layer, and N(i) represents the number of key indicators belonging to the i-th dimensional layer. Then, calculate the correlation degree of each submatrix in the correlation matrix:

[0077]

[0078] Among them, y ij Represents the correlation coefficient between the process indicator element and the key indicator element. If there is a correlation, then y ij =1, if there is no correlation, then y ij = 0. Finally, the objective function is calculated based on the correlation of each submatrix, and the membership relationship with the largest objective function value is determined as the membership relationship between the dimension layer and the process indicator. The objective function is:

[0079]

[0080] In the formula, sum[Y ii ]、sum[Y ij] respectively represent the correlation between the diagonal submatrix and the non-diagonal submatrix in the correlation matrix. According to the objective function, the permutation and combination method can be used to calculate the correlation between the process indicator and the dimension layer under different affiliations, and the affiliation combination with the largest target value is selected to determine the final affiliation between the dimension layer and the process indicator layer in the indicator system. If there are multiple combinations with the maximum target values ​​at the same time, the final combination is selected based on the fit between the specific process indicator and the dimension to which it belongs. Then, based on the correlation and affiliation of the evaluation indicator system, a hierarchical structure can be established to divide the evaluation indicator system into the target layer, the criterion layer and the scheme layer. Figure 5 FIG. 4 shows a hierarchical structure diagram of an evaluation index system according to an embodiment of the present invention. Figure 5 As shown in the figure, the target layer is the evaluation index system of the new power system, the criterion layer includes green electricity, reliable electricity, efficient electricity and smart electricity, and the solution layer includes various key indicators. On the basis of the hierarchical structure, the judgment matrices of the target layer and the criterion layer, and each criterion layer and the subordinate solution layer are constructed respectively. Considering the logical relationship and association relationship between the various levels of the comprehensive evaluation index system of the new power system, the indicators of each layer are compared and judged with the upper and lower layers respectively, and the constructed judgment matrix is:

[0081]

[0082]

[0083]

[0084]

[0085]

[0086] The elements of each judgment matrix are the ratios of the attribute values ​​of the row elements of each layer and the attribute values ​​of the column elements of each layer. The consistency ratio C of each judgment matrix is ​​tested. R Does it meet C R =C I / R I <0.1, λ max is the maximum eigenvalue of the judgment matrix, r is the order of the judgment matrix, if it is satisfied, the consistency test is passed. I Represents consistency index; R I Represents the random consistency index. Then, the maximum eigenvalue of the judgment matrix at each level and its corresponding eigenvector are calculated, and the obtained eigenvector is normalized to obtain the required weight. Finally, the weight values ​​of each evaluation index are shown in Table 2:

[0087] Table 2 Weight values ​​of comprehensive evaluation index system

[0088]

[0089]

[0090]

[0091] From the weight values ​​obtained by the attribute hierarchy model method, it can be seen that the weight value of "reliable electricity" is the largest, and the weight value of "efficient electricity" is the smallest, which means that the most fundamental goal of power grid companies is to ensure stable power supply and reliable and safe power grid operation, which also reflects the social responsibility of power grid companies.

[0092] Finally, the weight sensitivity analysis of the evaluation index is carried out to determine the importance of the evaluation index. The MATLAB software can be used to perform case simulation analysis on the new power system comprehensive evaluation index system. In the case of 100 sampling points, the weight value sensitivity curves between the target layer and the criterion layer, and between the criterion layer and the solution layer are obtained by simulation. Figure 6-Figure 9 The weight value sensitivity analysis diagrams of green electricity, reliable electricity, efficient electricity, and smart electricity are shown respectively. The evaluation system, evaluation effect, and correlation between evaluation indicators of the new power system are more intuitively displayed. Based on the simulation results, it can be seen that the sensitivity changes of each evaluation effect ("green electricity", "reliable electricity", "efficient electricity", and "smart electricity") in the comprehensive evaluation system of the new energy power system are basically consistent. Figure 6-Figure 9 As shown in the figure, the most sensitive indicators are 16 electricity information system collection rate, 3 transmission line overload level, 10 new energy peak load contribution rate, and 13 plant network coordination device application scale. In other words, the more sensitive the indicator, the greater the weight and the most important it is in the entire evaluation system.

[0093] Finally, step S340 is executed to calculate the relative proximity between the evaluation index and the ideal index of the enterprise to be evaluated based on the weight value of the evaluation index by using the grey correlation degree-ideal point method, so as to sort the enterprises to be evaluated based on the relative proximity and obtain the evaluation result.

[0094] Among them, the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) uses the positive and negative ideal solutions of the evaluation objects to sort the evaluation objects. This method is widely used in risk assessment, multi-objective optimization and other fields. In order to make the comprehensive evaluation results more realistic, this scheme adopts the grey correlation-TOPSIS method to improve the rationality and effectiveness of the evaluation results by solving the degree of closeness between each evaluation index and the ideal index. The steps of the grey correlation-ideal point method are as follows:

[0095] 1) Construct the attribute decision matrix of the evaluation index and normalize the attribute decision matrix to obtain a standardized matrix. Assuming there are Q power grid companies to be evaluated, the new power system comprehensive evaluation index system includes P indicators, represents the attribute value of the pth evaluation index of the qth power grid enterprise to be evaluated, then the attribute decision matrix constructed is It can be expressed as:

[0096]

[0097] Standardize each attribute indicator in the attribute decision matrix to obtain a standardized matrix

[0098]

[0099] In the example, four provincial power grid enterprises to be evaluated are given. The constructed new power system comprehensive evaluation index system includes four secondary indicators, namely four evaluation effects, and 16 tertiary indicators, namely key evaluation indicators. represents the first evaluation index attribute value of provincial power grid enterprise 1, then the constructed normalized attribute decision matrix can be expressed as:

[0100]

[0101] 2) Multiply the weight value of the evaluation index by the corresponding attribute value in the standardized matrix to obtain a weighted matrix. is the weighted attribute value of the pth evaluation index of the qth power grid enterprise to be evaluated, is the weight value of the pth evaluation index, represents the attribute value of the pth evaluation index of the qth power grid enterprise to be evaluated, P is the total number of evaluation indicators, and Q is the total number of enterprises to be evaluated. The weighted matrix obtained in this example is for:

[0102]

[0103] 3) Determine the positive ideal solution and negative ideal solution of the evaluation index based on the weighted matrix. represents the positive ideal solution of the pth evaluation index of the qth power grid enterprise to be evaluated; It represents the negative ideal solution of the pth evaluation index of the qth power grid enterprise to be evaluated:

[0104]

[0105]

[0106] Taking provincial power grid enterprise 1 as an example, a case analysis is carried out. The positive ideal solution determined based on the weighted matrix is ​​0.2626; the negative positive ideal solution is 0.0308.

[0107] 4) Calculate the Euclidean space distance from the evaluation index to the positive ideal solution and the Euclidean space distance from the evaluation index to the negative ideal solution. and They are the Euclidean space distance from the p-th evaluation index attribute value of the q-th power grid enterprise to the positive ideal solution and the Euclidean space distance from the evaluation index attribute value to the negative ideal solution:

[0108]

[0109]

[0110] Through calculation, the Euclidean space distances between the provincial power grid enterprise 1 to be evaluated and the positive ideal solution and the negative ideal solution are 0.0529 and 0.0374 respectively.

[0111] 5) Calculate the grey correlation degree and proximity between the evaluation index and the positive ideal solution, and the grey correlation degree and proximity between the evaluation index and the negative ideal solution. The grey correlation degree between the evaluation index and the positive ideal solution is calculated by the following formula:

[0112] in,

[0113]

[0114] The grey correlation degree between the evaluation index and the negative ideal solution is calculated by the following formula:

[0115] in,

[0116]

[0117] In the formula, are the grey correlation between the p-th evaluation index attribute value and the positive ideal solution, and the grey correlation between the evaluation index attribute value and the negative ideal solution of the q-th power grid enterprise to be evaluated, are the degree of convergence between the p-th evaluation index attribute value of the q-th power grid enterprise to be evaluated and the positive ideal solution, and the degree of convergence between the evaluation index attribute value and the negative ideal solution, respectively. and They represent the convergence limits of the positive ideal solution and the negative ideal solution, respectively, and μ is the convergence coefficient, which is usually taken as 0.5. The grey correlation between the provincial power grid enterprise 1 to be evaluated and the positive and negative ideal solutions is calculated to be 0.6704 and 0.5545, respectively. The closeness of the provincial power grid enterprise 1 to be evaluated to the positive and negative ideal solutions is 0.5211 and 0.4059, respectively.

[0118] 6) Based on the proximity of the evaluation index to the positive ideal solution and the proximity of the evaluation index to the negative ideal solution, the relative proximity between the evaluation index and the ideal index of the enterprise to be evaluated is solved, and the enterprises to be evaluated are ranked based on the relative fit to obtain the evaluation results of the enterprises to be evaluated. The relative proximity between the evaluation index and the ideal index of the enterprise to be evaluated is calculated by the following formula:

[0119] in,

[0120]

[0121]

[0122] In the formula, is the relative proximity of the enterprise to be evaluated, and They are the proximity between the pth evaluation index attribute value of the qth grid enterprise to be evaluated and the positive ideal solution, and the proximity between the evaluation index attribute value and the negative ideal solution. γ1 and γ2 represent the proximity coefficients, both of which are usually 0.5. The larger the relative proximity value of the grid enterprise to be evaluated, the better the effect of the new power system evaluation index, and vice versa. The relative proximity of the provincial grid enterprise 1 to be evaluated is calculated to be 0.5213. By analogy, the evaluation results of the provincial grid enterprises 2-4 to be evaluated can be obtained according to the above steps, as shown in Table 3.

[0123] Table 3 Results of the new power system evaluation system

[0124]

[0125] The evaluation results are ranked, and the relative proximity analysis shows that: Provincial Grid Company 3> Provincial Grid Company 4> Provincial Grid Company 2> Provincial Grid Company 1. Therefore, the new power system operation effect of Provincial Grid Company 3 is rated as the best or the most excellent.

[0126] In the context of building a new power system, establishing a scientific and complete comprehensive evaluation index system for power grid enterprises is a key measure to comprehensively build a safe, reliable, green, efficient and intelligent modern power grid. The comprehensive evaluation results of the grey correlation-ideal point method show that: First, based on the guarantee of "reliable electricity", by strengthening the construction of grid structure, improving the system regulation and demand-side response capabilities, enhancing the overload level of transmission lines, reducing the probability of power shortage, supporting the power grid to accept large-scale new energy power generation, ensuring the safe and stable operation of the large power grid system, and providing reliable and continuous power supply. Second, with the goal of absorbing "green electricity", the proportion of new energy installed capacity and electricity share will be increased, the quality and efficiency of new energy grid connection will be improved, the clean energy absorption capacity will be improved, and the optimization of energy and electricity allocation will be realized in a larger range. Third, with the pursuit of "efficient electricity" as development, the efficiency of new energy utilization will be improved, the development of peak-shaving and frequency-regulating power sources and new energy storage technologies will be accelerated, and a modern power supply service system will be built, while solving the contradiction between the rising cost of the energy supply side and the low cost of the demand side. Fourth, using "smart electricity" as a means to advance digital empowerment and digital grid construction, promote the integrated development of sources, networks, loads and storage, strengthen major technological research, make the new power system "observable, measurable and controllable", and use digital grids to promote the construction of a new power system with new energy as the main body.

[0127] In addition, from the perspective of the current development of my country's power industry, there is still a large gap in the operation effect of the new power system of power grid companies in different regions. On the one hand, this gap is related to the economic development and resource endowment of the region, and on the other hand, it is closely related to the power installed capacity (power side), power grid architecture (power side), and load demand (load side) in the region. Therefore, when considering the construction of a comprehensive evaluation index system for power grid companies, the design should be considered from more dimensions to make the evaluation system more scientific and complete.

[0128] The present invention refines and screens the evaluation indicators of power grid enterprises in multiple dimensions such as the power supply side, the power grid side and the load side, and constructs a new power system comprehensive evaluation indicator system with green electricity, reliable electricity, intelligent electricity and efficient electricity as the evaluation effects. By constructing a comprehensive evaluation model of attribute hierarchy model empowerment-grey correlation ideal point method, the effectiveness and applicability of the constructed comprehensive evaluation indicator system and model method are verified by example, the rationality and effectiveness of the evaluation results are improved, and the evaluation indicators of power grid enterprises are better adapted to the requirements of the development of new power systems. It provides theoretical support for power grid enterprises to accelerate the implementation and evaluation of the construction of new power systems.

[0129] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0130] Although the present invention has been described according to a limited number of embodiments, it will be apparent to those skilled in the art, with the benefit of the above description, that other embodiments may be envisioned within the scope of the invention thus described. In addition, it should be noted that the language used in this specification is selected primarily for readability and teaching purposes, rather than for explaining or defining the subject matter of the present invention. Therefore, many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the appended claims. The disclosure of the present invention is illustrative, not restrictive, with respect to the scope of the present invention, which is defined by the appended claims.

Claims

1. A method for evaluating a power grid enterprise in the context of a power system, suitable for execution in a computing device, wherein: The method comprises: Based on the pre-constructed power grid enterprise evaluation index system, the basic data of the evaluation index of the enterprise to be evaluated is obtained, wherein the power grid enterprise evaluation index system includes a dimension layer, process indicators, a process indicator group and key indicators, wherein the dimension layer includes the power supply side, the power grid side and the load side, wherein on the power supply side, the contribution of new energy to power balance and power grid security, as well as the utilization efficiency of new energy and conventional power sources are focused on; on the power grid side, the regional exchange capacity of cross-regional power transmission of the power grid, the power grid elasticity performance that characterizes the control characteristics of the power grid side of the power system, and the power grid's ability to accept new energy that reflects the macro transmission capacity are considered; on the load side, the demand response effect that reflects the load side regulation capacity, the development trend of distributed energy, and the implementation effect of load side electric energy substitution are considered; the process indicator is a refined indicator of the dimension layer, the process indicator group is a refined indicator of the process indicator, the key indicator is a screening indicator of the process indicator group, and the evaluation index of the enterprise to be evaluated is a key indicator , the key indicators at least include the utilization of new energy inside and outside the area, the comprehensive utilization rate of new energy, the proportion of new energy installed capacity, the proportion of new energy electricity, the system frequency support capacity, the system reserve rate, the overload level of the transmission line, the probability of power shortage, the contribution rate of new energy peak regulation, the utilization rate of renewable energy, the penetration rate of distributed wind and solar power, the rate of wind and solar power abandonment, the application scale of plant-grid coordination devices, the application ratio of new transmission technologies, the capacity ratio of smart substations, and the collection rate of electricity information systems; the key indicators are evaluated by green electricity, reliable electricity, efficient electricity, and smart electricity. The green electricity includes a high proportion of new energy output, relief of wind and solar power abandonment, terminal electricity substitution, and energy conservation and emission reduction supervision. The reliable electricity includes system structure optimization, lean operation management, high-quality electric energy products, and safe and reliable power supply. The efficient electricity includes intelligent technology and equipment, optimized investment management, comprehensive energy services, and optimized energy structure. The smart electricity includes highly integrated and shared power grids, high-speed multi-directional communications, a strong power grid skeleton, and fault isolation and self-healing. Using set value statistics method to perform valuation processing on the basic data of the evaluation index to obtain the attribute value of the evaluation index; Based on the attribute values ​​of the evaluation indicators, the weight values ​​of the evaluation indicators are calculated using the attribute hierarchy model method, including: determining the association relationship between the key indicators and the process indicators in the evaluation indicator system and the affiliation relationship between the dimension layer and the process indicators; based on the association relationship and the affiliation relationship, establishing a hierarchical structure, dividing the evaluation indicator system into a target layer, a criterion layer and a solution layer; constructing a judgment matrix between the target layer and the criterion layer, and between the criterion layer and the solution layer; calculating the eigenvalue and eigenvector of the judgment matrix to obtain the weight value of the evaluation indicator; performing a weight sensitivity analysis on the evaluation indicator to determine the importance of the evaluation indicator; Based on the weight value of the evaluation index, the grey correlation degree-ideal point method is used to solve the relative proximity between the evaluation index and the ideal index of the enterprise to be evaluated, so as to sort the enterprises to be evaluated based on the relative proximity to obtain the evaluation result, including: constructing an attribute decision matrix of the evaluation index, and normalizing the attribute decision matrix to obtain a standardized matrix; weighting and multiplying the weight value of the evaluation index with the corresponding attribute value in the standardized matrix to obtain a weighted matrix; determining the positive ideal solution and the negative ideal solution of the evaluation index based on the weighted matrix; calculating the Euclidean space distance from the evaluation index to the positive ideal solution and the Euclidean space distance from the evaluation index to the negative ideal solution; calculating the grey correlation degree and proximity between the evaluation index and the positive ideal solution, and the grey correlation degree and proximity between the evaluation index and the negative ideal solution; based on the proximity of the evaluation index to the positive ideal solution and the proximity of the evaluation index to the negative ideal solution, solving the relative proximity between the evaluation index and the ideal index of the enterprise to be evaluated, and sorting the enterprises to be evaluated based on the relative proximity to obtain the evaluation result of the enterprise to be evaluated; wherein, the grey correlation degree between the evaluation index and the positive ideal solution is calculated by the following formula: in, The grey correlation degree between the evaluation index and the negative ideal solution is calculated by the following formula: in, In the formula, are the grey correlation between the p-th evaluation index attribute value and the positive ideal solution, and the grey correlation between the evaluation index attribute value and the negative ideal solution of the q-th power grid enterprise to be evaluated, are the degree of convergence between the p-th evaluation index attribute value of the q-th power grid enterprise to be evaluated and the positive ideal solution, and the degree of convergence between the evaluation index attribute value and the negative ideal solution, respectively. and They represent the approach limits of positive ideal solution and negative ideal solution respectively, μ is the approach coefficient, represents the positive ideal solution of the pth evaluation index of the qth power grid enterprise to be evaluated, represents the negative ideal solution of the pth evaluation index of the qth power grid enterprise to be evaluated, is the weighted attribute value of the pth evaluation index of the qth power grid enterprise to be evaluated, is the weight value of the pth evaluation index, represents the attribute value of the pth evaluation index of the qth power grid enterprise to be evaluated, P is the total number of evaluation indicators, and Q is the total number of enterprises to be evaluated; The relative closeness between the evaluation index of the enterprise to be evaluated and the ideal index is calculated by the following formula: in, In the formula, is the relative proximity of the enterprise to be evaluated, and are the proximity between the pth evaluation index attribute value of the qth power grid enterprise to be evaluated and the positive ideal solution, and the proximity between the evaluation index attribute value and the negative ideal solution, γ1 and γ2 represent the proximity coefficients, and They are the Euclidean space distance from the p-th evaluation index attribute value of the q-th power grid enterprise to the positive ideal solution and the Euclidean space distance from the evaluation index attribute value to the negative ideal solution; The determining of the association between the key indicators and the process indicators in the evaluation indicator system and the affiliation between the dimension layer and the process indicators includes: Construct the correlation coefficient matrix between process indicators and key indicators: In the formula, Y is the correlation coefficient matrix, Y ii is a diagonal submatrix. When i≠j, Y ij is a non-diagonal submatrix 1≤i,j≤a, a represents the number of dimensional layers in the evaluation index system, m represents the number of process indicators, n represents the number of key indicators, M(i) represents the number of process indicators belonging to the i-th dimensional layer, and N(i) represents the number of key indicators belonging to the i-th dimensional layer; Calculate the correlation degree of each sub-matrix in the correlation coefficient matrix: Among them, y ij Represents the correlation coefficient between the process indicator element and the key indicator element. If there is a correlation, then y ij =1, if there is no correlation, then y ij =0; The objective function is calculated based on the correlation of each submatrix, and the membership relationship with the largest objective function value is determined as the membership relationship between the dimension layer and the process indicator. The objective function is: In the formula, sum[Y ii ]、sum[Y ij ] represent the correlation degree of the diagonal submatrix and the correlation degree of the non-diagonal submatrix in the correlation matrix respectively.

2. The method of claim 1, wherein: The step of using the set value statistics method to perform valuation processing on the basic data of the evaluation index to obtain the attribute value of the evaluation index includes: Set the expert scoring range for the evaluation indicators; Calculate expert scores of the evaluation indicators based on the scoring intervals; Testing the credibility of the expert scores to obtain the score results of each evaluation indicator; and The scoring results are normalized and uniformly mapped to the interval [0, 1] to obtain the attribute value of the evaluation index.

3. The method of claim 2, wherein: The expert score of the evaluation indicator is calculated by the following formula: The credibility of the expert rating is calculated using the following formula: Where η l is the credibility of the expert rating of the lth evaluation indicator, g l,k is the score of the kth expert on the lth evaluation indicator, represents the lower limit of the score of the kth expert's approval of the lth evaluation indicator, The upper limit of the score of the kth expert's approval of the lth evaluation indicator, K is the total number of experts, and δ is the interval estimation error of the score interval.

4. A computing device comprising: one or more processors; Memory; as well as One or more devices comprising instructions for executing the method according to any one of claims 1-3.

5. A computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform the method according to any one of claims 1 to 3.

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