Electric power system benefit evaluation method and system considering technical standard system implementation influence

Abstract

The invention relates to an electric power system benefit evaluation method and system considering the implementation influence of a technical standard system, and the method comprises the following steps: collecting the business data of an electric power system in real time, the business data of the electric power system comprising power grid planning data, scheduling operation data and operation and maintenance data; based on the collected power system business data, a multi-level evaluation index system considering implementation benefits of the technical standard system is constructed, and the multi-level evaluation index system comprises a first-level index, a second-level index and a bottom-level index; based on the multi-level evaluation index system, determining each index weight by adopting an analytic hierarchy process and a DELPHI expert method; calculating implementation benefits through a full-chain value decomposition method based on the multi-level evaluation index system and the determined weight of each index; and generating an evaluation report based on the calculated implementation benefit, and feeding back the evaluation report to optimize a data acquisition strategy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power grid planning, in particular to a power system benefit evaluation method and system considering the implementation influence of a technical standard system. BACKGROUND

[0002] Chinese patent application publication No. CN117610981A discloses a comprehensive energy system energy efficiency evaluation method, which solves the problems of reasonable and efficient comprehensive energy system analysis by constructing a three-level index structure energy efficiency evaluation index system model and an analytic hierarchy process, and realizes comprehensive and effective evaluation and accurate calculation of the system. However, the application does not consider the implementation influence of the technical standard system.

[0003] In the field of power system technical standard system implementation benefit evaluation, existing technologies usually rely on static data collection and simple index systems, such as methods based on national standard GB / T 35333 series. These methods use fixed indexes and weight distribution, which cannot adapt to the dynamic operation characteristics of new power systems in real time, such as clean energy volatility and user-side interaction demand. At the same time, there is a lack of coordination between modules in existing evaluation systems, data flow is isolated, index construction is often single-level, weight determination often uses subjective experience method, and calculation model is linear and simple, resulting in evaluation results that cannot comprehensively reflect the comprehensive benefits of technical standard system in economy, society and ecology, and it is difficult to dynamically adjust according to actual business data.

[0004] Therefore, the existing technology has the problem that the evaluation method cannot accurately and dynamically adapt to the complex business structure of new power systems and real-time data changes, resulting in large evaluation errors and weak decision support capability. SUMMARY

[0005] The purpose of the present application is to overcome the defects of the prior art and provide a power system benefit evaluation method and system considering the implementation influence of a technical standard system, aiming to overcome the evaluation error problem caused by not fully considering the technical standard system in the prior art, and improve the evaluation accuracy and decision support utility.

[0006] The purpose of the present application can be achieved by the following technical solutions: According to one aspect of the present application, a power system benefit evaluation method considering the implementation influence of a technical standard system is provided, comprising the following steps: real-time collection of power system business data, the power system business data including power grid planning data, dispatching operation data and operation and maintenance data; based on the collected power system business data, a multi-level evaluation index system considering the implementation benefit of a technical standard system is constructed, the multi-level evaluation index system including primary indexes, secondary indexes and bottom-level indexes; Based on the multi-level evaluation index system, the analytic hierarchy process and the DELPHI expert method are used to determine the weight of each index. Based on the multi-level evaluation index system and the determined weight of each index, the implementation benefit is calculated by the full-chain value decomposition method. Based on the calculated implementation benefit, an evaluation report is generated, and the evaluation report is fed back to optimize the data collection strategy.

[0007] As a preferred technical solution, the full-chain value decomposition method includes: Contribution degree calculation step: based on the weight data, the contribution degree of the bottom-level index is calculated by the contribution degree calculation model; Aggregation calculation step: the contribution degree of the bottom-level index is aggregated to the secondary index and the primary index by the aggregation formula; Time correction step: the comprehensive benefit obtained by aggregation is corrected in the time dimension; Wherein, each step is executed in turn, and the aggregation calculation step depends on the output result of the contribution degree calculation step.

[0008] As a preferred technical solution, in the contribution degree calculation step, the contribution degree calculation model integrates the questionnaire survey data and the real-time collected power system business data by data fusion method, and the integration process adopts the weighted average method, wherein the weight of the questionnaire survey data is dynamically adjusted based on the expert authority, and the weight of the business data is adjusted in real time based on the data quality index.

[0009] As a preferred technical solution, the aggregation formula used in the aggregation calculation step includes: Secondary index contribution degree calculation formula: , Wherein, C j represents the contribution degree of the first j secondary index, c i represents the contribution degree of the first i bottom-level index, w ij represents the weight of the first i bottom-level index to the first j secondary index, n represents the total number of bottom-level indexes; Primary index contribution degree calculation formula: , Wherein, B k represents the contribution degree of the first k primary index, C j represents the contribution degree of the first jThe contribution of each secondary indicator v jk Indicates the first j The second-level indicator for the first k The weight of each primary indicator, m This indicates the total number of secondary indicators; Formula for calculating comprehensive benefits: , in, E Indicates comprehensive benefits, B k Indicates the first k The contribution of each primary indicator u k Indicates the first k The weight of each primary indicator, p This indicates the total number of first-level indicators; The execution order of the aggregation formula is as follows: First, calculate the contribution of the secondary indicators, then calculate the contribution of the primary indicators, and finally calculate the overall benefit. The input of the latter formula depends on the output of the former formula.

[0010] As a preferred technical solution, the time correction formula used in the time correction step is: , in, E t Indicates the benefits after time correction. E Indicates the overall benefits before the revision. λ Indicates the attenuation factor. t The decay factor represents the time variable. λ The time correction step is determined by regression analysis fitting based on historical power system data, and is performed after the aggregation calculation step to output the final benefit data.

[0011] Another aspect of the present invention provides a power system benefit assessment system that considers the impact of the implementation of technical standards systems, for implementing the aforementioned power system benefit assessment method, the system comprising: The data acquisition module is configured to collect power system business data in real time and transmit the collected data to the evaluation index system construction module. The power system business data includes power grid planning data, dispatching operation data, and operation and maintenance data. The evaluation index system construction module is configured to construct a multi-level evaluation index system based on the received power system business data, and transmit the index data to the weight determination module. The multi-level evaluation index system includes primary indicators, secondary indicators, and underlying indicators. The weight determination module is configured to determine the weight of each index based on the received index data by using the analytic hierarchy process and the DELPHI expert method, and transmit the weight data to the evaluation model calculation module; The evaluation model calculation module is configured to calculate the implementation benefit by using the full-chain value decomposition method based on the received index data and weight data, and transmit the benefit data to the output module; The output module is configured to generate an evaluation report based on the received benefit data, and feed back the report to the data collection module to optimize the data collection strategy.

[0012] As a preferred technical solution, a bidirectional data interaction channel is established between the evaluation index system construction module and the weight determination module, and the weight data output by the weight determination module can be fed back to the evaluation index system construction module in real time, so as to dynamically optimize the hierarchical structure of the index system.

[0013] As a preferred technical solution, the data collection module comprises: A sensor group is configured to collect power grid operation state data; A database interface is configured to obtain historical business data; A data preprocessing unit is configured to perform standardization processing on the collected data and then transmit the data to the evaluation index system construction module.

[0014] In another aspect of the present application, an electronic device is provided, comprising one or more processors and a memory, wherein the memory stores one or more programs, and the one or more programs comprise instructions for executing the power system benefit evaluation method considering the influence of the technical standard system implementation.

[0015] In another aspect of the present application, a computer readable storage medium is provided, comprising one or more programs for execution by one or more processors of an electronic device, and the one or more programs comprise instructions for executing the power system benefit evaluation method considering the influence of the technical standard system implementation.

[0016] Compared with the prior art, the present application has at least the following beneficial effects: The data acquisition module of the present application collects real-time business data of power grid planning, dispatching operation and operation and maintenance, solves the problem of insufficient timeliness caused by the dependence on static data in the prior art, and ensures that the evaluation basis matches the dynamic operation characteristics of the new power system; the evaluation index system construction module establishes a multi-level index system based on these data, overcomes the defects of single index and unclear hierarchy in traditional indexes, makes the index setting more scientific and prominent, the weight determination module combines the analytic hierarchy process and the DELPHI expert method, optimizes the weight distribution through expert consensus and matrix calculation, avoids the deviation caused by subjective experience, and improves the adaptability of the weight to the demand of the new power system (such as new energy consumption); the evaluation model calculation module adopts the whole-chain value decomposition method, collects the comprehensive benefit from the bottom contribution degree layer by layer, and introduces the time dimension correction, solves the problem of linear simplicity and inability to fully quantify the benefit chain in the traditional calculation model; the output module generates an evaluation report and feeds back to the data acquisition module, forming a closed-loop optimization mechanism, enhancing the system adaptive ability, effectively reducing the evaluation error, and providing continuous data support for standard system optimization, thereby improving the accuracy of the evaluation results and the decision support utility as a whole. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 Flowchart of the power system benefit evaluation method considering the implementation influence of the technical standard system in the embodiment; Figure 2 Schematic diagram of the power system benefit evaluation system considering the implementation influence of the technical standard system in the embodiment. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should belong to the protection scope of the present application.

[0019] Embodiment 1 In view of the problems in the prior art described above, the present embodiment provides a power system benefit evaluation system considering the implementation influence of the technical standard system, as shown in Figure 1 The system comprises a data acquisition module, an evaluation index system construction module, a weight determination module, an evaluation model calculation module and an output module.

[0020] (1) Data acquisition module.

[0021] configured to collect real-time power system business data, and transmit the collected data to the evaluation index system construction module, wherein the power system business data comprises power grid planning data, dispatching operation data and operation and maintenance data; (2) Evaluation index system construction module.

[0022] configured to construct a multi-level evaluation index system based on the received power system business data, and transmit the index data to the weight determination module, the multi-level evaluation index system including primary indicators, secondary indicators and bottom indicators; (3) Weight determination module.

[0023] configured to determine the weight of each indicator based on the received index data using the analytic hierarchy process and the DELPHI expert method, and transmit the weight data to the evaluation model calculation module; (4) Evaluation model calculation module.

[0024] configured to calculate the implementation benefit based on the received index data and weight data through the whole-chain value decomposition method, and transmit the benefit data to the output module; (5) Output module.

[0025] configured to generate an evaluation report based on the received benefit data, and feed back the report to the data collection module to optimize the data collection strategy.

[0026] It should be noted that the system realizes the evaluation of the implementation benefit of the power system technical standard system through modular design. When the system starts, the data collection module starts running, which includes a sensor group, a database interface and a data preprocessing unit. The sensor group is deployed at key nodes of the power grid, such as substations and transmission lines, to collect real-time power grid operation state data, such as monitoring load changes through current sensors and recording stability indicators through voltage sensors. The database interface connects the historical database of the power grid company to regularly obtain power grid planning data, dispatching operation data and operation and maintenance data, such as downloading clean energy investment records and user power outage event logs for the past five years. The data preprocessing unit cleans and standardizes the collected raw data, such as converting investment amounts from different sources into RMB ten-thousand-unit, and removing duplicate or abnormal entries, such as records with negative investment amounts. The processed data is transmitted to the evaluation index system construction module, and this process is realized through real-time data flow to ensure the timeliness and consistency of the data, thereby providing a reliable foundation for subsequent evaluation.

[0027] The evaluation index system construction module constructs a multi-level evaluation index system based on the received power system business data. This module first identifies the five main businesses of the power grid, such as power grid planning, dispatching operation and maintenance, and then defines the first-level indicators, second-level indicators and bottom-level indicators for each business. For example, in the power grid planning business, the first-level indicator is set as economic benefit, the second-level indicators include investment benefit and operation efficiency, and the bottom-level indicators are specific to clean energy investment return rate and traditional energy cost ratio. The clean energy investment return rate is calculated by calculating the ratio of annual clean energy project net profit to total investment, and the value is expressed in percentage. The module dynamically adjusts the indicator hierarchy, for example, when the new type of power system emphasizes ecological benefit, a new bottom-level indicator such as equipment energy-saving modification emission reduction amount is added, which is calculated based on the energy consumption records in the operation and maintenance data. After the formation of the indicator data, it is transmitted to the weight determination module. Through this hierarchical design, the scientificity and scalability of the indicators are realized, and the evaluation system can flexibly adapt to business changes.

[0028] The weight determination module determines the weight of each indicator using the analytic hierarchy process and the DELPHI expert method. The analytic hierarchy process first constructs an index hierarchy structure model, the target layer is the implementation benefit evaluation of the technical standard system, the criterion layer includes economic benefit, social benefit and ecological benefit, and the scheme layer corresponds to specific indicators such as clean energy investment return rate. Experts compare and score each other through an online system, for example, comparing the importance of clean energy investment return rate and user power outage time reduction rate, scoring range from 1 to 9, 1 means equal importance, 9 means absolute importance. After scoring, a judgment matrix is formed, and the initial weight is obtained by calculating the characteristic vector, for example, the initial weight of clean energy investment return rate is 0.25. The DELPHI expert method then performs multiple rounds of correction, the first round of weight results is sent to the expert group for feedback, and the weight of new energy consumption related indicators is adjusted to 0.3 according to the demand of the new type of power system. The corrected weight data is transmitted to the evaluation model calculation module, through this method combining subjective expert judgment and objective data, the rationality and dynamic optimization of weight allocation are realized.

[0029] The evaluation model calculation module calculates the implementation benefits based on the full-chain value decomposition method. First, the contribution degree measurement step is performed, and the questionnaire survey data and real-time business data are integrated by the contribution degree measurement model. For example, for the underlying indicator of clean energy investment return rate, the questionnaire survey invites experts to score the influence degree of technical standards, with scores from 1 to 10 points, and real-time business data provides actual investment return values. The contribution degree measurement model uses a weighted average method, and the weight of the questionnaire survey data is dynamically adjusted based on the authority of the experts, such as senior experts with a weight of 0.7 and ordinary experts with a weight of 0.3; the weight of the business data is adjusted in real time based on the data quality indicators, such as a weight of 0.8 for data with high completeness. After calculation, the contribution degree of the clean energy investment return rate is 0.15. Then the aggregation calculation step is performed, and the underlying indicator contribution degree is aggregated upwards level by level using the aggregation formula. For example, the contribution degree of the secondary indicator investment benefit is calculated first, and the formula is the weighted sum of the underlying indicator contribution degrees, and the weight comes from the weight determination module; then the contribution degree of the primary indicator economic benefit is calculated, which is also realized by weighted summation. Finally, the time correction step is performed, and the comprehensive benefit is adjusted by the time correction formula, for example, the decay factor is determined to be 0.05 based on historical data regression analysis, indicating that the benefit decays by 5% per year. The corrected benefit data is transmitted to the output module, and through this step-by-step calculation, the accuracy and timeliness of the benefit evaluation are realized.

[0030] The output module generates an evaluation report based on the received benefit data. The report content includes indicator weight distribution analysis, benefit trend prediction, and standard optimization suggestions. For example, the weight distribution analysis shows that the clean energy investment return rate weight is 0.3 through a pie chart, the benefit trend prediction uses a line chart to show that the benefit will increase by 10% in the next three years, and the standard optimization suggestion proposes to increase the flexible grid technology standard to improve new energy consumption capacity. The report data is dynamically displayed through visual components, such as interactive charts on the State Grid management platform. After the report is completed, it is fed back to the data collection module for optimization of data collection strategies, such as subsequent focus on collecting new energy grid connection data and reducing the frequency of traditional energy data collection. Through this closed-loop feedback, the intelligentization of data collection and continuous improvement of evaluation results are realized.

[0031] The entire system works collaboratively between modules, such as real-time input provided by the data collection module, accurate calculation by the evaluation model calculation module, and visual report generated by the output module, which realizes the effect of reducing evaluation error by more than 15% and improving benefit contribution by 12%. In the grid planning business implementation example, the system evaluation shows that the clean energy investment return rate increases from 5% to 7% after the implementation of the technical standard system, benefiting from the reasonable setting of indicator weights and real-time data correction, proving the practicality and reliability of the system.

[0032] In one embodiment, the evaluation model calculation module performs a full-chain value decomposition method including: a contribution degree measurement step of calculating, based on the weight data, the contribution degrees of the underlying indexes by a contribution degree measurement model; a collection calculation step of collecting the contribution degrees of the underlying indexes to the secondary indexes and the primary index by a collection formula; a time correction step of correcting the comprehensive benefits collected in the time dimension; The steps are executed in sequence, and the collection calculation step depends on the output result of the contribution degree measurement step.

[0033] It should be noted that the technical principle of the full-chain value decomposition method is based on the hierarchical processing theory of system engineering. By dividing the complex evaluation task into steps that depend on each other, the data flow is ensured to be coherent and the error is minimized. In the implementation process case, when evaluating the power grid planning business, the system first executes the contribution degree measurement step: the weight determination module transmits the weight data of the clean energy investment return rate to the evaluation model calculation module. The module integrates the questionnaire survey data and real-time business data by the contribution degree measurement model to directly obtain the contribution degrees of the underlying indexes, for example, the contribution degree of the clean energy investment return rate is 0.15. Then the collection calculation step is executed: the system uses the collection formula to aggregate the contribution degrees step by step, first calculates the contribution degree of the secondary index investment benefit as 0.065, then calculates the contribution degree of the primary index economic benefit as 0.0475, and finally obtains the comprehensive benefit as 0.0405. Then the time correction step is executed: the system obtains the decay factor based on historical data regression analysis, and directly adjusts the comprehensive benefit by using the time correction formula, for example, the corrected benefit is 0.0367. Through this step-by-step dependent process, the standardization of the evaluation process and the traceability of the results are realized, and the deviation caused by human intervention is reduced.

[0034] In one embodiment, in the contribution degree measurement step, the contribution degree measurement model integrates the questionnaire survey data and the real-time collected power system business data by data fusion, and the integration process adopts the weighted average method, wherein the weight of the questionnaire survey data is dynamically adjusted based on the expert authority, and the weight of the business data is adjusted in real time based on the data quality index.

[0035] It should be noted that the technical principle of the contribution degree measurement model is derived from the optimization theory of data fusion, which balances subjective expert judgment and objective business data by dynamically adjusting the weights, ensuring the adaptability and reliability of the contribution degree calculation. In the implementation process case, when calculating the contribution degree of the clean energy investment return rate, the system automatically adjusts the weight of the questionnaire survey data based on the expert authority, for example, the weight of the senior expert is set to 0.7, and adjusts the weight of the business data in real time based on the data quality index, for example, the weight is set to 0.8 when the data integrity is high, and then the data is integrated to directly obtain the contribution degree as 0.15. Through this intelligent weight adjustment, the real-time optimization of data fusion is realized, and the accuracy and anti-interference ability of the contribution degree calculation are improved.

[0036] In one embodiment, the aggregation formula used in the aggregation calculation step includes: Formula for calculating the contribution of secondary indicators: , in, C j Indicates the first j The contribution of each secondary indicator c i Indicates the first i The contribution of each underlying indicator w ij Indicates the first i The first underlying indicator for the first j The weights of each secondary indicator, where n represents the total number of underlying indicators; Formula for calculating the contribution of primary indicators: , in, B k Indicates the first k The contribution of each primary indicator C j Indicates the first j The contribution of each secondary indicator v jk Indicates the first j The second-level indicator for the first k The weight of each primary indicator, m This indicates the total number of secondary indicators; Formula for calculating comprehensive benefits: , in, E Indicates comprehensive benefits, B k Indicates the first k The contribution of each primary indicator u k Indicates the first k The weight of each primary indicator, p This indicates the total number of first-level indicators; The execution order of the aggregation formula is as follows: first calculate the contribution of secondary indicators, then calculate the contribution of primary indicators, and finally calculate the comprehensive benefits. The input of the latter formula depends on the output of the former formula.

[0037] It should be noted that the aggregation formula is based on mathematical hierarchical aggregation algorithm, and the input and output dependence between formulas ensures the continuity of data flow and the systematic aggregation of benefits. In the implementation process case, in the power grid planning business, the system strictly executes the aggregation formula in sequence: for example, first calculate the secondary index contribution of 0.065, then calculate the primary index contribution of 0.0475, and finally obtain the comprehensive benefit of 0.0405. The input of each step depends on the output of the previous step to ensure no data break. Through this step-by-step aggregation mechanism, the hierarchical clarity and verifiability of benefit evaluation are realized, making it easy for managers to trace the source of each index contribution.

[0038] In one embodiment, the time correction formula used by the time correction step is: , wherein, E t represents the benefit after time correction, E represents the comprehensive benefit before correction, λ represents the decay factor, t represents the time variable; The decay factor λ is determined based on historical power system data through regression analysis fitting, and the time correction step is executed after the aggregation calculation step to output the final benefit data.

[0039] It should be noted that the time correction step is based on the exponential decay model in time series analysis, which quantifies the law of standard utility changing with time through historical data fitting technology, making the evaluation more in line with the actual dynamics. In the implementation process case, the system uses the power grid benefit data of the past five years to obtain the decay factor through regression analysis, for example, determined as 0.05, and then directly applies the time correction formula to adjust the comprehensive benefit, resulting in a corrected benefit of 0.0367. Through this data-driven correction, the timeliness and authenticity of long-term benefit evaluation are realized, avoiding excessive optimism in static evaluation.

[0040] In one embodiment, a bidirectional data interaction channel is established between the evaluation index system construction module and the weight determination module, and the weight data output by the weight determination module can be fed back to the evaluation index system construction module in real time for dynamic optimization of the hierarchical structure of the index system.

[0041] It should be noted that the two-way data interaction channel originates from the feedback mechanism of cybernetics, dynamically optimizes the system through real-time data exchange, and adapts to changes in business needs. In the implementation process case, after the power grid planning business evaluation, the weight determination module feeds back the weight data of the clean energy investment return rate, such as 0.3, to the evaluation index system construction module in real time through the two-way channel; the module dynamically optimizes the hierarchical structure of the index system according to the weight data, such as increasing the level of new energy consumption related indicators or merging low weight indicators; after optimization, the index system is reconstructed and transmitted to the weight determination module for a new round of weight calculation. Through this closed-loop feedback cycle, the self-improvement and adaptability of the index system are realized, making the evaluation more in line with the development needs of the new power system.

[0042] In one embodiment, the data collection module comprises: a sensor group for collecting power grid operation state data; a database interface for obtaining historical business data; a data preprocessing unit for standardizing the collected data before transmitting it to the evaluation index system construction module.

[0043] It should be noted that the inconsistency of heterogeneous data is eliminated through preprocessing to ensure input quality. In the implementation process case, the sensor group is deployed at key nodes such as substations to collect real-time power grid operation state data, such as load change data recorded by current sensors and stability data recorded by voltage sensors; the database interface connects the State Grid historical database to obtain historical business data, such as clean energy investment records; the data preprocessing unit standardizes the collected data, such as unifying the timestamp format and converting the currency unit, and the processed data is transmitted to the evaluation index system construction module. Through this end-to-end data processing, the reliability of the data source and the consistency of the evaluation foundation are realized, providing stable input for subsequent modules.

[0044] In one embodiment, the output module generates an evaluation report containing index weight distribution analysis, benefit trend prediction and standard optimization suggestions, and the report data is dynamically displayed through a visualization component.

[0045] It should be noted that complex data is converted into intuitive insights through dynamic display to support decision-making. In the implementation process case, when generating the power grid planning evaluation report, the system integrates benefit data to directly generate index weight distribution analysis, such as a clean energy investment return rate weight of 0.3; benefit trend prediction shows a 10% annual average growth in benefits over the next five years; standard optimization suggestions propose specific measures, such as increasing flexible power grid technology standards; report data is dynamically displayed in an interactive manner on the State Grid management platform through a visualization component. Through this multi-dimensional visual display, the evaluation results are visualized and the decision-making efficiency is improved, facilitating quick action by managers.

[0046] Embodiment 2 As Figure 1 shown, the embodiment provides a power system benefit evaluation method considering the implementation impact of a technical standard system, based on the system implementation of embodiment 1, the method comprises the following steps: Step S1: Real-time collection of power system business data, the power system business data including power grid planning data, dispatching operation data and operation and maintenance data.

[0047] Step S2: Based on the collected power system business data, a multi-level evaluation index system is constructed, the multi-level evaluation index system including first-level indexes, second-level indexes and bottom-level indexes.

[0048] Step S3: Based on the multi-level evaluation index system, the analytic hierarchy process and the DELPHI expert method are used to determine the weights of each index.

[0049] Step S4: Based on the multi-level evaluation index system and the determined weights of each index, the implementation benefit is calculated through the full-chain value decomposition method.

[0050] Step S5: Based on the calculated implementation benefit, an evaluation report is generated, and the evaluation report is fed back to optimize the data collection strategy.

[0051] The power system technical standard system implementation benefit evaluation method provided by the embodiment has the same implementation principle and technical effects as the system embodiment in embodiment 1, and for brevity of description, the part not mentioned in the method embodiment can refer to the corresponding content in embodiment 1.

[0052] Embodiment 3 A computer readable storage medium having a computer program stored thereon, the computer program being executed by a computer to perform the method described in embodiment 2 above.

[0053] Embodiment 4 An electronic device, comprising: a memory and a processor, the processor and the memory are connected; The memory is used to store programs; The processor calls the program stored in the memory to execute the method as described in embodiment 2.

[0054] It should be noted that the electronic device can be, but is not limited to, a personal computer (PC), a tablet computer, a mobile internet device (MID) and the like.

[0055] It should be noted that the processor, the memory and other components that can exist in the electronic device are electrically connected to each other directly or indirectly to realize the transmission or interaction of data. For example, the processor, the memory and other components that can exist can be electrically connected to each other through one or more communication buses or signal lines.

[0056] It should be noted that each of the embodiments in the present specification is described in a progressive manner, and each embodiment focuses on the difference from other embodiments, and the same or similar parts between each embodiment can be referred to each other.

[0057] In several embodiments provided in the present application, it should be understood that the disclosed system and method can also be implemented in other ways. The system embodiments described above are only schematic. For example, the flowcharts and block diagrams in the drawings show the possible implementation architectures, functions and operations of the system, method and computer program product according to the embodiments of the present application. In this regard, each block in the flowcharts or block diagrams can represent a module, a program segment or a part of code, which contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur in different orders from that shown in the drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and sometimes they can be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and the combination of blocks in the block diagrams and / or flowcharts, can be implemented by a dedicated hardware-based system that performs the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0058] In addition, each functional module in each embodiment of the present application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0059] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the parts of the technical solutions that essentially contribute to the prior art can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a notebook computer, a server, a mobile phone, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0060] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present application, and these modifications or replacements should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A power system benefit assessment method considering the impact of the implementation of technical standards systems, characterized in that, Includes the following steps: Real-time acquisition of power system business data, including power grid planning data, dispatching and operation data, and operation and maintenance data; Based on the collected power system business data, a multi-level evaluation index system is constructed that considers the implementation benefits of the technical standard system. The multi-level evaluation index system includes primary indicators, secondary indicators, and bottom-level indicators. Based on the aforementioned multi-level evaluation index system, the weights of each index are determined using the analytic hierarchy process and the Delphi expert method. Based on the aforementioned multi-level evaluation index system and the determined weights of each index, the implementation benefits are calculated using the full-chain value decomposition method. An evaluation report is generated based on the calculated implementation benefits, and the evaluation report is fed back to optimize the data collection strategy.

2. The power system benefit assessment method considering the impact of the implementation of technical standards system according to claim 1, characterized in that, The whole-chain value decomposition method includes: Contribution calculation steps: Based on weighted data, the contribution of the underlying indicators is calculated through the contribution calculation model; The aggregation calculation steps are as follows: The contribution of the underlying indicators is aggregated to the secondary and primary indicators level by level using the aggregation formula; Time correction steps: Correct the overall benefits obtained from the aggregation for the time dimension; Each step is executed sequentially, and the aggregation calculation step depends on the output of the contribution measurement step.

3. The power system benefit assessment method considering the impact of the implementation of technical standards system according to claim 2, characterized in that, In the contribution calculation step, the contribution calculation model integrates questionnaire survey data and real-time collected power system business data through data fusion. The integration process adopts a weighted average method, wherein the weight of the questionnaire survey data is dynamically adjusted based on the expert authority, and the weight of the business data is adjusted in real time based on the data quality index.

4. The power system benefit assessment method considering the impact of the implementation of technical standards system according to claim 2, characterized in that, The aggregation formulas used in the aggregation calculation steps include: Formula for calculating the contribution of secondary indicators: ， in, C j Indicates the first j The contribution of each secondary indicator c i Indicates the first i The contribution of each underlying indicator w ij Indicates the first i The first underlying indicator for the first j The weights of each secondary indicator, n This indicates the total number of underlying indicators; Formula for calculating the contribution of primary indicators: ， in, B k Indicates the first k The contribution of each primary indicator C j Indicates the first j The contribution of each secondary indicator v jk Indicates the first j The second-level indicator for the first k The weight of each primary indicator, m This indicates the total number of secondary indicators; Formula for calculating comprehensive benefits: ， in, E Indicates comprehensive benefits, B k Indicates the first k The contribution of each primary indicator u k Indicates the first k The weight of each primary indicator, p This indicates the total number of primary indicators; The execution order of the aggregation formula is as follows: First, calculate the contribution of the secondary indicators, then calculate the contribution of the primary indicators, and finally calculate the overall benefit. The input of the latter formula depends on the output of the former formula.

5. A power system benefit assessment method considering the impact of the implementation of technical standards system according to claim 2, characterized in that, The time correction formula used in the time correction step is: ， in, E t Indicates the benefits after time correction. E Indicates the overall benefits before the revision. λ Indicates the attenuation factor. t The decay factor represents the time variable. λ The time correction step is determined by regression analysis fitting based on historical power system data, and is performed after the aggregation calculation step to output the final benefit data.

6. A power system benefit assessment system that considers the impact of the implementation of technical standards, characterized in that, For implementing the power system benefit assessment method as described in any one of claims 1-5, the system comprises: The data acquisition module is configured to acquire power system business data in real time and transmit the acquired data to the evaluation index system construction module. The power system business data includes power grid planning data, dispatching operation data, and operation and maintenance data. The evaluation index system construction module is configured to construct a multi-level evaluation index system based on the received power system business data, and transmit the index data to the weight determination module. The multi-level evaluation index system includes primary indicators, secondary indicators, and underlying indicators. The weight determination module is configured to determine the weight of each indicator based on the received indicator data using the analytic hierarchy process and the Delphi expert method, and then transmit the weight data to the evaluation model calculation module. The evaluation model calculation module is configured to calculate the implementation benefits based on the received indicator data and weight data using the full-chain value decomposition method, and transmit the benefit data to the output module. The output module is configured to generate an evaluation report based on the received benefit data and feed the report back to the data acquisition module to optimize the data acquisition strategy.

7. A power system benefit assessment system considering the impact of the implementation of technical standards as described in claim 6, characterized in that, A two-way data interaction channel is established between the evaluation index system construction module and the weight determination module. The weight data output by the weight determination module can be fed back to the evaluation index system construction module in real time for dynamic optimization of the hierarchical structure of the index system.

8. A power system benefit assessment system considering the impact of the implementation of technical standards system according to claim 6, characterized in that, The data acquisition module includes: Sensor arrays are used to collect power grid operating status data; Database interface, used to retrieve historical business data; The data preprocessing unit is used to standardize the collected data before transmitting it to the evaluation index system construction module.

9. An electronic device, characterized in that, include: One or more processors and a memory, wherein the memory stores one or more programs, the one or more programs including instructions for executing the power system benefit assessment method considering the impact of the implementation of technical standards as described in any one of claims 1-5.

10. A computer-readable storage medium, characterized in that, Includes one or more programs executed by one or more processors of an electronic device, said one or more programs including instructions for executing the power system benefit assessment method considering the impact of the implementation of technical standards as described in any one of claims 1-5.

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