Operation effect evaluation method and system based on rural clean energy and load full power

Abstract

The invention is suitable for the technical field of index evaluation, and provides an operation effect evaluation method and system based on rural clean energy and load full electricity, and the method comprises the steps: constructing a power distribution network operation effect evaluation index system, determining a plurality of evaluation objects and a plurality of evaluation indexes in the power distribution network operation effect evaluation index system, the plurality of evaluation objects comprise rural clean energy and load full power; determining a target dimensionless processing method according to the plurality of evaluation objects and the plurality of evaluation indexes, and performing dimensionless processing on index data; respectively utilizing an entropy weight method and a CRITIC method to calculate a first weight and a second weight of each evaluation index corresponding to the plurality of evaluation objects; and determining a combined weight according to the first weight and the second weight, and determining an evaluation result of the operation effect of the power distribution network based on the combined weight, thereby providing an effective data basis by improving the evaluation accuracy of rural clean energy and load all-electricity substitution.

Description

Technical Field

[0001] The present invention relates to the technical field of index evaluation, and in particular to an operating effect evaluation method and system based on rural clean energy and full load electricity. Background Art

[0002] In recent years, many places have actively planned the development of rural clean energy, and promoted the development of rural industries through energy development. Photovoltaic is a necessary way to form a new power system for buildings and achieve low-carbon and zero-carbon operation of buildings. Among various renewable energy sources, the integration of photovoltaics and buildings based on building application scenarios has unique development advantages. With the development of the photovoltaic building industry and the maturity of technology, the replacement of clean energy and full-electricity replacement of loads in beautiful villages are becoming increasingly sound. Establishing a set of comprehensive and multi-angle evaluation index systems is an important basis for evaluating the operating effects of clean energy replacement and full-electricity replacement of loads in beautiful villages.

[0003] The existing technology generally uses regression analysis to simulate the indicator weights of nonlinear evaluation methods, and the regression coefficients are normalized to become simulation weights. This simulation method converts the nonlinear relationship between the indicator value and the evaluation value into a linear relationship, but the overall evaluation accuracy is not high, and some indicator regression coefficients may be negative, resulting in inaccurate evaluation results of the operating effect. Summary of the invention

[0004] The embodiments of the present application provide a method and system for evaluating operation effects based on rural clean energy and full load electricity, which are used to solve the problem of inaccurate operation effect evaluation results.

[0005] The first aspect of the embodiment of the present application provides an operation effect evaluation method based on rural clean energy and full load electricity, including:

[0006] Constructing a distribution network operation effect evaluation index system, determining a number of evaluation objects and a number of evaluation indicators in the distribution network operation effect evaluation index system, wherein the number of evaluation objects includes rural clean energy and full load electricity;

[0007] Determine a target dimensionless processing method according to the plurality of evaluation objects and the plurality of evaluation indicators, and perform dimensionless processing on the indicator data;

[0008] Calculate the first weight and the second weight of each evaluation index corresponding to the evaluation objects by using the entropy weight method and the CRITIC method respectively;

[0009] A combined weight is determined according to the first weight and the second weight, and an evaluation result of the operation effect of the distribution network is determined based on the combined weight.

[0010] Furthermore, the method of determining the target dimensionless processing method according to the plurality of evaluation objects and the plurality of evaluation indicators, and performing dimensionless processing on the indicator data, includes:

[0011] Taking the comprehensive evaluation stability of the distribution network operation effect evaluation index as the objective function and the dimensionless validity as the constraint condition, the first dimensionless mathematical model is constructed.

[0012] Taking the evaluation discrimination of the distribution network operation effect evaluation index as the objective function and the dimensionless validity as the constraint condition, the second dimensionless mathematical model is constructed.

[0013] A target dimensionless mathematical model is determined according to the first dimensionless mathematical model and the second dimensionless mathematical model.

[0014] Furthermore, the first dimensionless mathematical model is constructed by taking the comprehensive evaluation stability of the distribution network operation effect evaluation index as the objective function and taking dimensionless validity as the constraint condition, including:

[0015] Objective function:

[0016]

[0017] in: is the variance of the operation effect evaluation system, w is the weight of each evaluation index, F <F a The method is valid, --max(x j )≤a j ≤max(x j )and It indicates that the translation scale and the scaling scale can take values ​​within a larger range.

[0018] Furthermore, the evaluation discrimination of the distribution network operation effect evaluation index is used as the objective function, and the dimensionless validity is used as the constraint condition to construct a second dimensionless mathematical model, including:

[0019] Objective function:

[0020]

[0021] Constraints:

[0022]

[0023] Where: D is the discrimination of the comprehensive evaluation value, is the difference between two adjacent evaluation values, is the discrimination between two adjacent evaluation values.

[0024] Furthermore, the expression of the target dimensionless mathematical model is as follows:

[0025]

[0026] Among them: minU means that the difference between the system variance and the discrimination is the smallest.

[0027] Furthermore, the entropy weight method and the CRITIC method are used to calculate the first weight and the second weight of each evaluation index of the evaluation objects, respectively, including:

[0028] The entropy weight method calculates the first weight:

[0029]

[0030] Where: α j is the first weight of the jth indicator, n is the number of indicators, e j is the information entropy of the j-th indicator;

[0031] The CRITIC method calculates the second weight:

[0032]

[0033] Where: β j is the second weight of the j-th indicator, is the CRITIC weight calculated based on the standard deviation and conflict index value.

[0034] Furthermore, determining the combined weight according to the first weight and the second weight includes:

[0035] The combination weight is determined using the minimum discriminant information principle:

[0036]

[0037] Where: W j is the combined weight value.

[0038] The second aspect of the embodiment of the present application provides an operation effect evaluation system based on rural clean energy and full load electricity, including:

[0039] An index system construction unit is used to construct an index system for evaluating the operation effect of a distribution network, and determine a number of evaluation objects and a number of evaluation indicators in the index system for evaluating the operation effect of a distribution network, wherein the several evaluation objects include rural clean energy and full load electricity;

[0040] A dimensionless processing unit, used to determine a target dimensionless processing method according to the plurality of evaluation objects and the plurality of evaluation indicators, and perform dimensionless processing on the indicator data;

[0041] The first weight and the second weight calculation unit are used to calculate the first weight and the second weight of each evaluation index corresponding to the plurality of evaluation objects by using the entropy weight method and the CRITIC method respectively;

[0042] An operation effect evaluation result determination unit is used to determine a combined weight according to the first weight and the second weight, and determine an evaluation result of the operation effect of the distribution network based on the combined weight.

[0043] A third aspect of the embodiments of the present application provides a computer device, including:

[0044] memories, transceivers, processors, and bus systems;

[0045] Wherein, the memory is used to store programs;

[0046] The processor is used to execute the program in the memory, including executing the operation effect evaluation method based on rural clean energy and full load electricity as described in any one of the above items;

[0047] The bus system is used to connect the memory and the processor so that the memory and the processor can communicate with each other.

[0048] A fourth aspect of an embodiment of the present application provides a readable storage medium, including instructions, which, when executed on a computer, enables the computer to execute an operation effect evaluation method based on rural clean energy and full load electricity as described in any one of the above items.

[0049] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0050] This application constructs a distribution network operation effect evaluation index system based on rural clean energy and full-electric load. After determining the target dimensionless processing method, the optimal dimensionless method is selected for indicator processing, and then the first weight and the second weight of the indicator are calculated respectively. The combined weight of the evaluation object is determined based on the calculated first weight and second weight, and finally the combined weight is determined, that is, the score of the distribution network operation effect based on rural clean energy and full-electric load substitution is determined. This application provides an effective and accurate data basis for the high-quality low-carbon photovoltaic building demonstration project plan for beautiful villages by calculating the evaluation weights of rural clean energy and full-electric load substitution, providing operation and maintenance safety guarantees, and ensuring the normal operation of high-quality low-carbon photovoltaic buildings. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a schematic flow chart of an embodiment of an operation effect evaluation method based on rural clean energy and full load electricity in the present invention;

[0052] Figure 2It is a structural block diagram of an embodiment of an operation effect evaluation system based on rural clean energy and full load electricity in the present invention;

[0053] Figure 3 The figure is a structural block diagram of a computer device in the present invention. DETAILED DESCRIPTION

[0054] The embodiment of the present application provides a method and system for evaluating the operating effect based on rural clean energy and full-electric load, which solves the problem of errors in the current operating effect evaluation results considering rural clean energy and full-electric load replacement.

[0055] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein, for example. In addition, the terms "including" and "corresponding to" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0056] Embodiment 1

[0057] The implementation method in this embodiment can be implemented in the system, in the server, or in the terminal, and the specific implementation is not clearly limited. The following will introduce the operation effect evaluation method based on rural clean energy and full load electricity in this application from the perspective of the system. Figure 1 , the method provided in this application comprises the following steps:

[0058] S11. Construct a distribution network operation effect evaluation index system, determine several evaluation objects and several evaluation indicators in the distribution network operation effect evaluation index system, and several evaluation objects include rural clean energy and full load electricity;

[0059] In this embodiment, the distribution network operation effect evaluation index system takes rural clean energy and full load electricity as the evaluation objects, and uses electricity consumption, economy and environmental protection as evaluation indicators, among which the electricity consumption index can also be refined into standard coal consumption conversion and per capita electricity consumption, the economic index can be refined into distribution network loss, regional new energy power generation ratio, regional new energy utilization rate, and the environmental protection index can be refined into greenhouse gas emission calculation and pollutant emission calculation. It should be noted that when calculating the index weight, the electricity consumption index is taken together with the standard coal consumption conversion and per capita electricity consumption as the same index pool, and the economic index and environmental protection index are similar by analogy.

[0060] S12. Determine a target dimensionless processing method according to a number of evaluation objects and a number of evaluation indicators, and perform dimensionless processing on the indicator data;

[0061] The dimensionless transformation of indicators is an important part of the evaluation process. The rationality of the dimensionless transformation directly affects the accuracy of the evaluation indicators. In the data standardization process of multi-indicator evaluation, due to the different units, dimensions and magnitudes of each indicator, it is not convenient to analyze and may even affect the evaluation results. Therefore, in order to unify the indicators, all indicators must first be dimensionless, the dimensions must be eliminated, and they must be converted into dimensionless and magnitude-free standard scores, and then analyzed and evaluated.

[0062] In this embodiment, a target dimensionless processing method is determined according to a number of evaluation objects and a number of evaluation indicators, and dimensionless processing of indicator data includes the following steps:

[0063] 1. Taking the evaluation stability of the distribution network operation effect evaluation index as the objective function and the dimensionless validity as the constraint condition, the first dimensionless mathematical model is constructed;

[0064] Objective function:

[0065]

[0066] Constraints:

[0067]

[0068] in: is the variance of the operation effect evaluation system, w is the weight of each evaluation index, F <F a The method is valid, --max(x j )≤a j ≤max(x j )and It means that the translation scale and the telescopic scale can take values ​​in a larger range. Through this model, the optimal translation and telescopic scale with the highest stability can be solved.

[0069] 2. Taking the evaluation discrimination of the distribution network operation effect evaluation index as the objective function and the dimensionless validity as the constraint condition, the second dimensionless mathematical model is constructed;

[0070] Objective function:

[0071]

[0072] Constraints:

[0073]

[0074] Where: D is the discrimination of the comprehensive evaluation value, is the difference between two adjacent evaluation values,

[0075] is the discrimination between two adjacent evaluation values.

[0076] 3. Determine the target dimensionless mathematical model based on the first dimensionless mathematical model and the second dimensionless mathematical model.

[0077] Target dimensionless mathematical model:

[0078]

[0079] Among them: minU means that the difference between the system variance and the discrimination is the smallest.

[0080] Since the dimensions and magnitudes of different indicators will cause differences between the data, all indicator data need to be dimensionless before calculating the weights using the entropy weight method and the CRITIC method to eliminate the impact of the dimension. Here, the dimensionless processing method is selected taking into account the evaluation stability and the highest discrimination as influencing factors, and the dimensionless mathematical models are constructed respectively to select the optimal processing method.

[0081] S13. Calculate the first weight and second weight of each evaluation index corresponding to a number of evaluation objects by using the entropy weight method and the CRITIC method respectively;

[0082] S14. Determine a combined weight according to the first weight and the second weight, and determine an evaluation result of the distribution network operation effect based on the combined weight.

[0083] In this embodiment, the entropy weight method is used to calculate the index weights. There are two samples of objects to be evaluated, namely rural clean energy and full load electricity, represented by m, and n indicators to be evaluated, including electricity consumption indicators, standard coal consumption conversion, per capita electricity consumption, economic indicators, distribution network losses, regional new energy power generation ratio, regional new energy utilization rate, environmental protection indicators, greenhouse gas emission calculation, and pollutant emission calculation. The objects to be evaluated and the evaluation indicators form the original indicator data matrix X = (xij ) m×n , where 0≤i≤m, 0≤j≤n, the steps for calculating the first weight of the evaluation index system using the entropy weight method are as follows:

[0084] 1. Assuming that the optimal dimensionless processing method is normalization, let the jth index of the i-th evaluation object be x ij , then after normalization x ij 'for:

[0085] Positive indicators:

[0086]

[0087] Negative indicators:

[0088]

[0089] Where: min(x j ) is the minimum value of the j-th index, max(x j ) is the maximum value of the j-th indicator.

[0090] 2. Calculate the weight of the jth indicator of the i-th evaluation object sample:

[0091]

[0092] 3. According to the definition of entropy, calculate the entropy value of each indicator:

[0093]

[0094] Where: e j is the information entropy of the jth indicator, 0≤e j ≤1, k=1 / ln(m), k>0.

[0095] 4. Determine the weight of each indicator:

[0096]

[0097] Where: α j is the first weight of the jth indicator, n is the number of indicators, e j is the information entropy of the j-th indicator.

[0098] The sample data of the evaluation object that uses the entropy weight method to obtain the weights is similar to the method of using dimensionless processing for positive and negative indicators mentioned above, which will not be repeated here. The steps for CRITIC to calculate the second weight are as follows:

[0099] 1. Calculate the comparative strength of each evaluation object indicator, that is, the standard deviation σ j :

[0100]

[0101] Where: is the mean of the j-th indicator data.

[0102] 2. Calculate the correlation coefficient ρ of each object indicator ij :

[0103]

[0104] Where: x i and x j are the i-th and j-th columns of matrix X respectively.

[0105] 3. Calculate the conflict index value δ j :

[0106]

[0107] 4. Calculate the final weight:

[0108]

[0109] Where: β j is the second weight of the j-th indicator, is the CRITIC weight calculated based on the standard deviation and conflict index value.

[0110] Determine the combination weight w using the minimum discriminant information principle j :

[0111]

[0112] The final weight is not biased towards any of them and is close to both the first weight and the second weight.

[0113] The above embodiment selects the optimal dimensionless processing aspect based on the two aspects of comprehensive evaluation stability and evaluation discrimination. Under the premise of selecting the optimal dimensionless processing method, the entropy weight method is combined with the CRITIC method to calculate the weights respectively, and finally the comprehensive weights are calculated to obtain the evaluation results of the two indicators of rural clean energy and full electric replacement of loads. The calculation process of the method of the present invention is simple, the interference of outliers can be eliminated, and the problem of difficulty in statistical processing when the difference in index values ​​is too large is solved.

[0114] Embodiment 2

[0115] See also Figure 2 , an embodiment of an operation effect evaluation system based on rural clean energy and full load electricity in the present invention comprises the following steps:

[0116] The index system construction unit 101 is used to construct the distribution network operation effect evaluation index system, determine a number of evaluation objects and a number of evaluation indicators in the distribution network operation effect evaluation index system, and the number of evaluation objects includes rural clean energy and full load electricity;

[0117] The dimensionless processing unit 102 is used to determine a target dimensionless processing method according to a number of evaluation objects and a number of evaluation indicators, and perform dimensionless processing on the indicator data;

[0118] The first weight and the second weight calculation unit 103 is used to calculate the first weight and the second weight of each evaluation index corresponding to a plurality of evaluation objects by using the entropy weight method and the CRITIC method respectively;

[0119] The operation effect evaluation result determination unit 104 is used to determine a combined weight according to the first weight and the second weight, and determine an evaluation result of the operation effect of the distribution network based on the combined weight.

[0120] For the specific definition of the system, please refer to the definition of the method above, which will not be repeated here. Each module in the above system can be implemented in whole or in part by software, hardware and a combination thereof. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0121] Embodiment 3

[0122] See also Figure 3 The present invention provides a computer device, comprising:

[0123] memories, transceivers, processors, and bus systems;

[0124] Wherein, the memory is used to store programs;

[0125] The processor is used to execute the program in the memory, including executing the steps of the above method;

[0126] The bus system is used to connect the memory and the processor so that the memory and the processor can communicate with each other.

[0127] Those of ordinary skill in the art will appreciate that the units of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition of each example has been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0128] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored. In addition, each functional unit in each embodiment of the present invention can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units.

[0129] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-0nlyMemory), random access memory (RAM, RandomAccessMemory), mobile hard disk, magnetic disk or optical disk, etc., which can store program code.

[0130] It can be understood 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 they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A method for evaluating the operation effect of rural clean energy and full load electricity, characterized in that: include: Constructing a distribution network operation effect evaluation index system, determining a number of evaluation objects and a number of evaluation indicators in the distribution network operation effect evaluation index system, wherein the number of evaluation objects includes rural clean energy and full load electricity; Determine a target dimensionless processing method according to the plurality of evaluation objects and the plurality of evaluation indicators, and perform dimensionless processing on the indicator data; Calculate the first weight and the second weight of each evaluation index corresponding to the evaluation objects by using the entropy weight method and the CRITIC method respectively; A combined weight is determined according to the first weight and the second weight, and an evaluation result of the operation effect of the distribution network is determined based on the combined weight.

2. The operation effect evaluation method based on rural clean energy and full load electricity according to claim 1 is characterized in that: The method of determining the target dimensionless processing method according to the plurality of evaluation objects and the plurality of evaluation indicators, and performing dimensionless processing on the indicator data, comprises: Taking the comprehensive evaluation stability of the distribution network operation effect evaluation index as the objective function and the dimensionless validity as the constraint condition, the first dimensionless mathematical model is constructed. Taking the evaluation discrimination of the distribution network operation effect evaluation index as the objective function and the dimensionless validity as the constraint condition, the second dimensionless mathematical model is constructed. A target dimensionless mathematical model is determined according to the first dimensionless mathematical model and the second dimensionless mathematical model.

3. The operation effect evaluation method based on rural clean energy and full load electricity according to claim 2 is characterized in that: The first dimensionless mathematical model is constructed by taking the comprehensive evaluation stability of the distribution network operation effect evaluation index as the objective function and the dimensionless validity as the constraint condition, including: Objective function: Constraints: in: is the variance of the operation effect evaluation system, w is the weight of each evaluation index, F <F a The method is valid, --max(x j )≤a j ≤max(x j )and It indicates that the translation scale and the scaling scale can take values ​​within a larger range.

4. The operation effect evaluation method based on rural clean energy and full load electricity according to claim 2 is characterized in that: The second dimensionless mathematical model is constructed by taking the evaluation discrimination of the distribution network operation effect evaluation index as the objective function and taking dimensionless validity as the constraint condition, including: Objective function: Constraints: Where: D is the discrimination of the comprehensive evaluation value, is the difference between two adjacent evaluation values, is the discrimination between two adjacent evaluation values.

5. The operation effect evaluation method based on rural clean energy and full load electricity according to claim 2 is characterized in that: The expression of the target dimensionless mathematical model is as follows: Among them: minU means that the difference between the system variance and the discrimination is the smallest.

6. The operation effect evaluation method based on rural clean energy and full load electricity according to claim 1 is characterized in that: The entropy weight method and the CRITIC method are used to calculate the first weight and the second weight of each evaluation index of the evaluation objects, respectively, including: The entropy weight method calculates the first weight: Where: α j is the first weight of the jth indicator, n is the number of indicators, e j is the information entropy of the j-th indicator; The CRITIC method calculates the second weight: Where: β j is the second weight of the j-th indicator, is the CRITIC weight calculated based on the standard deviation and conflict index value.

7. The operation effect evaluation method based on rural clean energy and full load electricity according to claim 1 is characterized in that: The determining of the combined weight according to the first weight and the second weight comprises: The combination weight is determined using the minimum discriminant information principle: Where: w j is the combined weight value.

8. An operation effect evaluation system based on rural clean energy and full load electricity, characterized in that: include: An index system construction unit is used to construct an index system for evaluating the operation effect of a distribution network, and determine a number of evaluation objects and a number of evaluation indicators in the index system for evaluating the operation effect of a distribution network, wherein the several evaluation objects include rural clean energy and full load electricity; A dimensionless processing unit, used to determine a target dimensionless processing method according to the plurality of evaluation objects and the plurality of evaluation indicators, and perform dimensionless processing on the indicator data; The first weight and the second weight calculation unit are used to calculate the first weight and the second weight of each evaluation index corresponding to the plurality of evaluation objects by using the entropy weight method and the CRITIC method respectively; An operation effect evaluation result determination unit is used to determine a combined weight according to the first weight and the second weight, and determine an evaluation result of the operation effect of the distribution network based on the combined weight.

9. A computer device, characterized in that: include: memories, transceivers, processors, and bus systems; Wherein, the memory is used to store programs; The processor is used to execute the program in the memory, including executing the operation effect evaluation method based on rural clean energy and full load electricity according to any one of claims 1 to 7; The bus system is used to connect the memory and the processor so that the memory and the processor can communicate with each other.

10. A readable storage medium, characterized in that: It includes instructions, which, when running on a computer, enable the computer to execute the operation effect evaluation method based on rural clean energy and full load electricity as described in any one of claims 1 to 7.

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