Layered multi-dimensional comprehensive evaluation system and method based on novel power system

Abstract

The invention relates to the technical field of electric power system analysis, in particular to a hierarchical multi-dimensional comprehensive evaluation system and method based on a novel electric power system, and the system comprises a classification module which is configured to carry out feature hierarchical classification on the electric power system and extract a feature hierarchical classification result; the electric power system evaluation module is configured to calculate relevant index data of the electric power system by adopting different visual angle dimensions according to different feature level units; the normalization module is configured to perform normalization processing on related index data of the power system under different feature level units; and the result output module is configured to output a power system evaluation result according to the power system related index data normalized by the different feature level units. The objective of the invention is to realize synchronous quantitative comparison of construction processes of novel electric power systems in different areas and complete evaluation work of the novel electric power systems oriented to new energy as a main body.

Description

Technical Field

[0001] The present invention relates to the technical field of power system analysis, and in particular to a hierarchical multi-dimensional comprehensive evaluation system and method based on a new power system. Background Art

[0002] In order to reduce global carbon emissions, the penetration rate of new energy in the power system has increased significantly, which has triggered research on the construction and management of new power systems with new energy as the main body. In this context, it is particularly important to ensure the rationality of the construction and management of new power systems. Therefore, the accuracy of the evaluation method for new power systems with new energy as the main body is particularly critical.

[0003] At present, the evaluation system for new power systems is mostly limited to the top-level design level of new power systems, mainly focusing on the qualitative analysis of the construction form and evolution path. Although these studies provide theoretical guidance for the planning and development of new power systems, they lack quantitative evaluation methods for new power systems. Existing technologies usually use qualitative analysis methods. Since this method mainly relies on the experience, subjective judgment and non-numerical description of experts to evaluate the performance of systems or projects, it is difficult to conduct a comprehensive and quantitative evaluation of the actual performance of new power systems. On the other hand, the existing evaluation methods cannot fully cover the widely coupled influencing factors in new power systems in different regions, resulting in the evaluation results provided being not comprehensive and accurate, and it is difficult to achieve synchronous quantitative comparison of the construction process of new power systems in different regions and provide cross-regional comprehensive evaluation. Summary of the invention

[0004] In order to achieve synchronous quantitative comparison of the construction progress of new power systems in different regions and provide systematic and accurate evaluation results, the present invention provides a hierarchical multi-dimensional comprehensive evaluation system and method based on new power systems. The technical solutions adopted are as follows:

[0005] The technical solution of the first aspect of the present invention provides a hierarchical multi-dimensional comprehensive evaluation system based on a new power system, the system comprising:

[0006] a classification module configured to perform feature level classification on the power system and extract feature level classification results;

[0007] The power system evaluation module is configured to calculate power system related indicator data using different perspective dimensions for different feature level units;

[0008] A normalization module, configured to perform normalization processing on power system related indicator data under different feature level units;

[0009] The result output module is configured to output the power system evaluation result according to the power system related indicator data after normalization processing of different feature level units.

[0010] Furthermore, the characteristic level classification of the power system includes:

[0011] A first feature level unit configured to obtain feature parameters related to clean energy in the power system;

[0012] a second feature level unit configured to obtain feature parameters related to power supply of the power system;

[0013] a third characteristic level unit configured to obtain characteristic parameters related to economic benefits of the power system;

[0014] a fourth characteristic level unit configured to obtain characteristic parameters related to power system supply and demand coordination;

[0015] The fifth characteristic level unit is configured to obtain characteristic parameters related to the regulation capability of the power system.

[0016] Furthermore, for different feature level units, different perspective dimensions are used to calculate the power system related indicator data, including:

[0017] For the first feature level unit, the first indicator data set is extracted from the power source perspective dimension, load perspective dimension, and low-carbon perspective dimension respectively; the first indicator data set includes: the proportion of renewable energy utilization efficiency, the proportion of non-fossil energy installed capacity, the proportion of cross-regional green electricity procurement, the proportion of renewable energy consumption, the proportion of electrical energy consumption, the proportion of industrial green energy consumption, the carbon intensity of thermal power generation, and the carbon emission growth suppression rate.

[0018] Furthermore, for different feature level units, the power system related indicator data are calculated using different perspective dimensions, including:

[0019] For the second feature level, the second indicator data set is extracted from the power supply perspective dimension, the grid perspective dimension, the load perspective dimension, and the energy storage perspective dimension respectively; the second indicator data set includes: system reserve rate, unplanned outage rate of supporting power supply, new energy system balance contribution, system primary leveling margin, new energy grid technology penetration rate, distributed power supply support ratio, new energy day-ahead prediction accuracy, distributed energy day-ahead prediction accuracy, new energy station short-circuit capacity ratio, multi-feed DC system short-circuit capacity ratio, grid-side grid control penetration rate, whole network voltage qualification rate, user power outage time index, load day-ahead prediction accuracy, industrial user power outage time index, energy storage side grid control penetration rate.

[0020] Furthermore, for different feature level units, the power system related indicator data are calculated using different perspective dimensions, including:

[0021] For the third feature level, the third indicator data set is extracted from the power source perspective dimension, grid perspective dimension, load perspective dimension, and energy storage perspective dimension respectively; the third indicator data set includes: power source market entity participation rate, distributed power source market entity participation rate, power market system maturity index, spot market development stage index, market-oriented electricity proportion, new entity participation rate, load-side market entity participation rate, green power trading proportion, and energy storage market entity participation rate.

[0022] Furthermore, for different feature level units, the power system related indicator data are calculated using different perspective dimensions, including:

[0023] For the fourth characteristic level, the fourth indicator data set is extracted from the power supply perspective dimension and the load perspective dimension respectively; the fourth indicator data set includes: distributed renewable energy penetration rate, local consumption rate of distributed power sources, load side response capability index, virtual power plant aggregation scale index, proportion of load responding in seconds, air conditioning and electric heating load regulation capability, electric vehicle charging infrastructure coverage rate, and electric vehicle V2G participation rate.

[0024] Furthermore, for different feature level units, the power system related indicator data are calculated using different perspective dimensions, including:

[0025] For the fifth characteristic level, the fifth indicator data set is extracted from the power source perspective dimension, power grid perspective dimension, load perspective dimension, energy storage perspective dimension, and digital perspective dimension respectively; the fifth indicator data set includes: the proportion of adjustable capacity of power sources, the digitalization and intelligence rate of power plants, the comprehensive index of power grid digitalization, the intelligent rate of power grid dispatching, the utilization efficiency of cross-regional transmission channels, the proportion of distributed resource regulation, the penetration rate of smart microgrids, the coverage rate of fast charging piles, the proportion of adjustable capacity on the energy storage side, the utilization rate of new energy storage power stations, the comprehensive index of power system digitalization, and the comprehensive index of power system automation.

[0026] Furthermore, normalizing the power system related indicator data under different characteristic level units includes:

[0027] Preprocessing the power system related indicator data at different feature levels, the preprocessing at least includes data cleaning, missing value processing, abnormal value processing and indicator direction unification;

[0028] Normalize the pre-processed power system related indicator data at different feature levels;

[0029] For each feature level unit, the relative importance matrix between different indicators is constructed based on the hierarchical analysis method and the weights are calculated;

[0030] For each feature level unit, the normalized related index data is used for weighted summation to obtain the evaluation index of each feature level unit.

[0031] Furthermore, the power system evaluation results output according to the power system related indicator data after normalization processing of different feature level units include:

[0032] The evaluation indexes of different feature level units are converted into fuzzy numbers using membership functions, and a fuzzy evaluation matrix is ​​constructed.

[0033] According to the preset weights of different feature level units and the fuzzy evaluation matrix, the power system evaluation results are output.

[0034] The technical solution of the second aspect of the present invention provides a hierarchical multi-dimensional comprehensive evaluation method based on a new power system, which is used in the hierarchical multi-dimensional comprehensive evaluation system based on a new power system provided by the technical solution of the first aspect of the present invention, and the method includes:

[0035] Perform feature hierarchical classification on the power system and extract feature hierarchical classification results;

[0036] For different characteristic level units, different perspective dimensions are used to calculate the power system related indicator data;

[0037] Normalize the power system related indicator data under different characteristic level units;

[0038] The power system assessment results are output based on the power system related indicator data after normalization processing of different feature level units.

[0039] The present invention has the following beneficial effects:

[0040] The hierarchical multi-dimensional comprehensive evaluation system and method based on the new power system provided by the present invention can comprehensively cover all aspects of the power system by classifying the new power system into feature levels, and using different perspective dimensions to calculate the power system-related indicator data for different feature level units, thereby ensuring the comprehensiveness and systematicness of the evaluation results; normalizing the power system-related indicator data under different feature level units, and providing quantitative evaluation results. The system can not only comprehensively cover all aspects of the power system, ensuring the comprehensiveness and systematicness of the evaluation results, but also avoid the subjectivity of qualitative analysis through quantitative analysis, realize the synchronous quantitative comparison of the construction progress of new power systems in different regions, and provide a cross-regional comprehensive evaluation method. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0042] Figure 1 A schematic diagram of the system structure of a hierarchical multi-dimensional comprehensive evaluation system based on a novel power system provided by an embodiment of the present invention;

[0043] Figure 2 A method flow chart of a hierarchical multi-dimensional comprehensive evaluation method based on a new power system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0044] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the hierarchical multi-dimensional comprehensive evaluation system and method based on a new power system proposed by the present invention, its specific implementation method, structure, characteristics and effects are described as follows in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0045] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0046] The following is a detailed description of a specific scheme of a hierarchical multi-dimensional comprehensive evaluation system and method based on a new type of power system provided by the present invention in conjunction with the accompanying drawings.

[0047] See also Figure 1 , which shows a schematic diagram of the system structure of a hierarchical multi-dimensional comprehensive evaluation system based on a new power system provided by an embodiment of the present invention, the system comprising:

[0048] a classification module configured to perform feature level classification on the power system and extract feature level classification results;

[0049] The power system evaluation module is configured to calculate power system related indicator data using different perspective dimensions for different feature level units;

[0050] A normalization module, configured to perform normalization processing on power system related indicator data under different feature level units;

[0051] The result output module is configured to output the power system evaluation result according to the power system related indicator data after normalization processing of different feature level units.

[0052] Preferably, the characteristic level classification of the power system includes:

[0053] A first feature level unit configured to obtain feature parameters related to clean energy in the power system;

[0054] a second feature level unit configured to obtain feature parameters related to power supply of the power system;

[0055] a third characteristic level unit configured to obtain characteristic parameters related to economic benefits of the power system;

[0056] a fourth characteristic level unit configured to obtain characteristic parameters related to power system supply and demand coordination;

[0057] The fifth characteristic level unit is configured to obtain characteristic parameters related to the regulation capability of the power system.

[0058] In this embodiment, through feature hierarchical classification, the system can comprehensively cover all aspects of the power system, including clean energy, power supply, economic benefits, supply and demand coordination and regulation capabilities, ensuring the comprehensiveness and systematicness of the evaluation results.

[0059] Preferably, for different feature level units, using different perspective dimensions to calculate power system related indicator data includes:

[0060] For the first feature level unit, the first indicator data set is extracted from the power perspective dimension, the load perspective dimension, and the low-carbon perspective dimension respectively; the first indicator data set includes:

[0061] Dimension from the power supply perspective: the proportion of renewable energy utilization efficiency, specifically the ratio of the actual renewable energy power generation used in the target area to the set target value, which is used to reflect the effectiveness of the target area or project in promoting the use of renewable energy; the proportion of non-fossil energy installed capacity, specifically the proportion of the total installed capacity or power generation of non-fossil energy (such as wind power and solar energy) in the target area to the installed capacity or total power generation of the entire power system; the proportion of cross-regional green power procurement, specifically the proportion of green power purchased from abroad by the target area to all external power procurement in the region, which is used to indicate the degree of dependence of the target area on imported green power;

[0062] Load perspective: Renewable energy consumption ratio, specifically the ratio of green energy consumption to total electricity consumption in the target area, reflecting the user's preference for clean electricity; electrical energy consumption ratio, specifically the ratio of electrical energy consumption in the target area to total energy consumption; industrial green energy consumption ratio, specifically the ratio of green energy consumption in the target area's industry to the total industrial energy consumption;

[0063] Low-carbon perspective dimensions: Carbon intensity of thermal power generation, specifically the carbon dioxide emissions generated by thermal power generating units per unit of power generation in the target area; Carbon emission growth suppression rate, specifically the ratio of carbon dioxide emissions from newly added local fossil fuel power stations in the target area in the past two years to the carbon dioxide emissions from the overall fossil fuel power stations in the two years, used to track the growth trend of fossil energy-related carbon emissions in the target area;

[0064] This embodiment can fully cover all aspects related to clean energy by extracting the first indicator data set from the three perspectives of power supply, load, and low carbon, ensuring the comprehensiveness and systematicness of the evaluation results for the first feature level unit.

[0065] Preferably, for different feature level units, using different perspective dimensions to calculate power system related indicator data also includes:

[0066] For the second feature level, the second indicator data set is extracted from the power source perspective dimension, the grid perspective dimension, the load perspective dimension, and the energy storage perspective dimension respectively; the second indicator data set includes:

[0067] Dimensions from the power supply perspective: System reserve rate, specifically the ratio of the reserve capacity of the power system in the target area to the total installed capacity, reflecting the system's ability to respond to emergencies; Unplanned outage rate of supporting power sources, specifically the ratio of the number of unplanned outage hours of supporting power sources (such as coal-fired power, gas-fired power, hydropower, pumped storage, and nuclear power) in a year to the total number of supporting power sources multiplied by the number of hours in the whole year (8760 hours), reflecting the reliability of supporting power sources; New energy system balance contribution, specifically the average value of the scale of new energy participation in the balance in the two most tense hours before the target area's control month dispatch, used to measure the contribution of new energy to system balance; System primary leveling margin, specifically the ratio of the comprehensive primary frequency regulation capacity of all units in the power system in the target area to the maximum load of the system, used to reflect the system's frequency regulation capability; New energy grid-building technology penetration rate, specifically the ratio of the number of new energy units with grid-building technology in the target area to the total number of new energy units; Distributed power support ratio, specifically the proportion of distributed power capacity with support capacity in the target area to the total distributed power capacity;

[0068] Grid perspective dimension: New energy station short-circuit capacity ratio, specifically the ratio of the short-circuit capacity of the low-voltage side of the equivalent power generation unit of each new energy station in the target area to the equivalent apparent power of new energy after considering the influence of other new energy stations, which is used to reflect the voltage stability of the new energy access point; multi-infeed DC system short-circuit capacity ratio, specifically the ratio of the short-circuit capacity of each DC feed-in commutation busbar in the target area to the equivalent DC power after considering the influence of other DC loops, which is used to evaluate the support capacity of the multi-infeed DC system for the access point voltage; grid-side network control penetration rate, specifically the proportion of the number of grid-side devices with network-type capabilities in the target area to the total number of grid-side devices; the whole network voltage qualification rate, specifically the weighted average of the voltage qualification rates of Class A, Class B, Class C and Class D nodes in the power system in the target area, which is used to reflect the voltage quality of the entire power network;

[0069] Load perspective dimension: the accuracy of new energy day-ahead prediction, which is 1 minus the root mean square error of the new energy day-ahead prediction in the target area; the accuracy of distributed energy day-ahead prediction, which is 1 minus the root mean square error of the distributed energy day-ahead prediction in the target area; the user power outage time index, which is the ratio of each power outage time in the target area statistical time multiplied by the number of power outage households to the total number of power supply and power users, used to reflect the power supply interruption of users; the accuracy of load day-ahead prediction, which is 1 minus the root mean square error of the load day-ahead prediction in the target area; the power outage time index of industrial users, which is the ratio of each power outage time of industrial users in the target area statistical time multiplied by the number of industrial power outage households to the total number of industrial power supply and power users, used to reflect the power supply reliability for industrial users;

[0070] Energy storage perspective dimension: energy storage side grid-type control penetration rate, specifically the ratio of the number of energy storage devices with grid-type capabilities to the total number of energy storage devices in the target area;

[0071] This embodiment extracts the second indicator data set from four perspectives: power source, power grid, load, and energy storage, comprehensively covering all aspects related to power supply in the power system, and ensuring the comprehensiveness and systematicness of the evaluation results.

[0072] Preferably, for different feature level units, using different perspective dimensions to calculate power system related indicator data also includes:

[0073] For the third feature level, the third indicator data set is extracted from the power source perspective dimension, the grid perspective dimension, the load perspective dimension, and the energy storage perspective dimension respectively; the third indicator data set includes:

[0074] Power supply perspective: the participation rate of power supply market players, specifically the proportion of the number of power supply market players (such as power generation companies) actually participating in the market in the target area to the total number of all power supply players; the participation rate of distributed power supply market players, specifically the proportion of the number of distributed power supply market players (such as small wind power, photovoltaic power, etc.) actually participating in the market in the target area to the total number of all distributed power supply players;

[0075] Grid perspective: The maturity index of the power market system, which specifically evaluates the market perfection of the target area through the existence or non-existence of a series of market mechanisms; for example, the medium- and long-term market, spot market, frequency regulation ancillary service market, standby ancillary service market, peak load ancillary service market, capacity market, green power trading market, green certificate trading and retail package mechanism, etc. Different scores are assigned to different market mechanisms;

[0076] The spot market development stage index is specifically for scoring the target area according to the different development stages of the spot market. It is divided into different stages from undeveloped to formal operation. Each stage corresponds to a score, which reflects the maturity and development progress of the spot market and can be expressed as:

[0077]

[0078] Load perspective dimensions: market-based electricity ratio, specifically the ratio of electricity participating in market transactions in the target area to the total power generation; new subject participation rate, specifically the ratio of the number of new subjects (such as virtual power plants and demand response aggregators) participating in the market in the target area to the total number of subjects on the load side, which is used to reflect the level of activity of emerging market subjects in the market; load-side market subject participation rate, specifically the ratio of the number of load-side market subjects (such as industrial users and commercial users) actually participating in the market in the target area to the total number of subjects on the load side, which is used to reflect the level of activity of the load-side market; green power trading ratio, specifically the ratio of green power (such as renewable energy power) traded in the target area to the total traded power;

[0079] Energy storage perspective: energy storage market player participation rate, specifically the ratio of the number of energy storage players participating in the target area to the total number of energy storage players;

[0080] In this embodiment, by extracting the third indicator data set from four perspectives, namely, power source, power grid, load and energy storage, it comprehensively covers multiple key indicators, including the participation rate of power source market players, the participation rate of distributed power source market players, the maturity index of power market system, the spot market development stage index, the market-oriented electricity ratio, the participation rate of new players, the participation rate of load-side market players, the green power trading ratio and the participation rate of energy storage market players. These indicators can comprehensively reflect the participation of the power market, the degree of perfection of the market mechanism, the maturity and development progress of the spot market, and the activeness of various market players in the market, which will help to improve the openness and competitiveness of the power market, promote the active participation of distributed power sources and emerging market players, increase the trading ratio of green power, enhance the flexibility and responsiveness of the market, and thus promote the healthy development of the power market and the efficient operation of the power system.

[0081] Preferably, for different feature level units, using different perspective dimensions to calculate power system related indicator data also includes:

[0082] For the fourth feature level, the fourth indicator data set is extracted from the power perspective dimension and the load perspective dimension respectively; the fourth indicator data set includes:

[0083] Dimension from the power supply perspective: Distributed renewable energy penetration rate, specifically the proportion of distributed renewable energy (such as wind power, photovoltaic power, etc.) installed capacity in the target area to the total installed capacity in the area; Distributed power local consumption rate, specifically the ratio of 1 minus the reverse power supply of 35 kV and below distributed power sources connected to the target area to the total power generation of distributed power sources. The higher the value, the more distributed power generation power is consumed locally, reducing dependence on the power grid;

[0084] Load perspective dimension: Load side response capacity index, specifically the ratio of the maximum adjustable capacity of the load-side adjustable resources in the target area to the annual maximum load, reflecting the maximum response capacity that the load-side resources can provide when the system needs it; Virtual power plant aggregation scale index, specifically the ratio of the total scale of the aggregated resources of the virtual power plant in the target area to the annual maximum load, used to measure the ability of the virtual power plant to provide services similar to traditional power plants by aggregating multiple dispersed small resources; Second-level response load ratio, specifically the ratio of the adjustable load with a response time of seconds or less in the target area to the annual maximum load, reflecting the proportion of loads that can respond quickly to dispatch instructions in a short time; Air conditioning and electric heating load regulation capacity, specifically the ratio of the maximum regulation capacity of the air conditioning load in the target area plus the maximum regulation capacity of electric heating to the annual maximum load, reflecting the regulation capacity provided by the air conditioning and electric heating loads when the system needs it; Electric vehicle charging infrastructure coverage rate, specifically 1 divided by the target area vehicle-to-pile ratio (number of electric vehicles / number of charging piles); Electric vehicle V2G participation, specifically the ratio of the charging power of electric vehicles in the target area during off-peak hours plus the discharge power during peak hours to the annual charging power;

[0085] In this embodiment, by extracting the fourth indicator data set from the two perspectives of power supply and load, it comprehensively covers multiple key indicators such as distributed renewable energy penetration rate, local absorption rate of distributed power sources, load side response capability index, virtual power plant aggregation scale index, second-level response load ratio, air conditioning and electric heating load regulation capability, electric vehicle charging infrastructure coverage rate, and electric vehicle V2G participation rate. These indicators can comprehensively reflect the utilization efficiency of distributed new energy in the target area, the local absorption capacity of the power system, and the flexibility and responsiveness of the load side, which will help to improve the penetration rate and local absorption rate of distributed renewable energy, enhance the responsiveness of load-side resources, promote the development of virtual power plants, enhance the flexibility and reliability of the power system, and at the same time promote the construction of electric vehicle charging infrastructure and the application of V2G technology, thereby achieving efficient operation and sustainable development of the power system.

[0086] Preferably, for different feature level units, using different perspective dimensions to calculate power system related indicator data also includes:

[0087] For the fifth feature level, the fifth indicator data set is extracted from the power source perspective dimension, the grid perspective dimension, the load perspective dimension, the energy storage perspective dimension, and the digital perspective dimension respectively; the fifth indicator data set includes:

[0088] Dimension from the power supply perspective: the proportion of adjustable power capacity, which is the ratio of the adjustable power capacity to the total power capacity in the target area; the digitalization and intelligence rate of power plants, which is the ratio of the number of power sources with observable, measurable, adjustable and controllable functions to the total number of power sources in the target area;

[0089] Grid perspective: Grid digitalization comprehensive index, which is the sum of the target area grid digital mapping rate, equipment digital penetration rate, artificial intelligence scale application effect, and production equipment and facility digitalization rate divided by 4; grid dispatching intelligence rate, which is the ratio of the number of grid equipment with intelligent dispatching capabilities to the total number of grid equipment in the target area; inter-regional transmission channel utilization efficiency, which is the ratio of the available capacity of the target area's inter-regional transmission channel to the regional maximum load plus the available capacity of the inter-regional transmission channel, plus the ratio of the annual power generation of various power sources to the annual utilization hours, and then taking the average value to measure the utilization efficiency and flexibility of the inter-regional transmission channel;

[0090] Load perspective dimensions: Distributed resource control ratio, specifically the ratio of the scale of controllable distributed resources in the target area to the total scale of distributed resources; Smart microgrid penetration rate, specifically the ratio of the installed capacity of smart microgrid power sources in the target area to the total installed capacity of power sources; Fast charging pile coverage rate, specifically the ratio of the number of fast charging piles in the target area to the total number of charging piles;

[0091] Energy storage perspective: the proportion of adjustable capacity on the energy storage side, specifically the ratio of the adjustable capacity of energy storage in the target area to the total energy storage capacity; the utilization rate of new energy storage power stations, specifically the ratio of the annual centralized call times of each new energy storage power station in the target area to the amount of new energy storage;

[0092] Digital perspective dimension: Power system digitalization comprehensive index, specifically the weighted average of the digital construction effectiveness index, data governance and application effectiveness, and information system safe operation rate of the target area, is used to evaluate the digital development level of the power system; power system automation comprehensive index, specifically the arithmetic average of the system perception index, system controllability index, and intelligent decision-making level of the target area, is used to evaluate the automation degree and development level of the power system;

[0093] In this embodiment, by extracting the fifth indicator data set from the five perspectives of power supply, power grid, load, energy storage and digitalization, it comprehensively covers multiple key indicators such as the proportion of adjustable capacity of power supply, digitalization and intelligence rate of power plants, comprehensive index of digitalization of power grid, intelligent rate of power grid dispatching, utilization efficiency of cross-regional transmission channels, proportion of distributed resource regulation, penetration rate of smart microgrids, coverage rate of fast charging piles, proportion of adjustable capacity on the energy storage side, utilization rate of new energy storage power stations, comprehensive index of digitalization of power system and comprehensive index of automation of power system. These indicators can comprehensively reflect the flexibility, intelligence level, degree of digitalization and efficient utilization of resources of the power system, which is helpful to improve the regulation capacity of power supply and energy storage, enhance the digitalization and intelligence level of power grid, improve the flexibility and responsiveness of load side, promote the construction of electric vehicle charging infrastructure, and promote the application of new energy storage technology, so as to realize efficient operation, reliability and sustainable development of power system.

[0094] Preferably, normalizing the power system related indicator data at different feature level units includes:

[0095] Preprocess the power system-related indicator data at different feature levels. The preprocessing includes at least data cleaning, missing value processing, outlier processing, and indicator direction unification. Specifically, check and correct or delete outliers and missing values, and convert all indicators into positive indicators, that is, the larger the value, the better the performance. For negative indicators (such as carbon emission intensity), the reciprocal can be taken or the maximum value minus the indicator value can be used.

[0096] The pre-processed power system related indicator data at different feature levels are normalized; specifically, each indicator data can be scaled to the interval [0,1] through minimum-maximum normalization processing;

[0097] For each feature level unit, a relative importance matrix between different indicators is constructed based on the hierarchical analysis method and the weights are calculated; specifically, a judgment matrix is ​​constructed based on the importance of each indicator. For example, if a certain indicator is more important than another indicator, it is given a higher score;

[0098] For each feature level unit, the normalized related index data is used for weighted summation to obtain the evaluation index of each feature level unit, which can be expressed as:

[0099]

[0100] In the formula, S i represents the evaluation index of the i-th feature level unit; w ij represents the weight of the jth indicator of the i-th feature level unit; x i ′ j represents the normalized value of the jth indicator of the i-th feature level unit; n represents the total number of indicators of the i-th feature level unit.

[0101] Preferably, outputting the power system evaluation result according to the power system related indicator data after normalization processing of different feature level units includes:

[0102] The evaluation indexes of different feature level units are converted into fuzzy numbers using membership functions, and a fuzzy evaluation matrix is ​​constructed. Specifically, the commonly used membership functions are triangular membership functions and trapezoidal membership functions. For the evaluation indexes of different feature level units, their membership values ​​under different membership functions are calculated, and a fuzzy evaluation matrix is ​​constructed, in which each row represents the membership value of a feature level unit under different membership levels. For example, if there are 5 membership levels (e.g., low, lower, medium, higher, and high), the fuzzy evaluation matrix can be expressed as:

[0103]

[0104] In the formula, R represents the fuzzy evaluation matrix; u represents the membership level;

[0105] According to the preset weights of different feature level units and the fuzzy evaluation matrix, the power system evaluation results are output; specifically, the preset weight vector (for example, the weight determined by the analytic hierarchy process AHP) is used to perform weighted synthesis operation on the fuzzy evaluation matrix; the final fuzzy comprehensive evaluation result is obtained by calculating the weighted average of each column, and the grade corresponding to the maximum membership value is selected as the final power system evaluation result; this method not only provides quantitative and objective evaluation results, but also can deal with uncertainty and ambiguity, enhances the scientificity and accuracy of the evaluation, and through fuzzy comprehensive evaluation, the technical solution can more accurately reflect the various performance characteristics of the power system, promote the healthy development of the power market and the efficient operation of the power system.

[0106] In summary, the present invention constructs an evaluation system for the construction of new power systems based on the three-level framework idea of ​​"feature hierarchy classification-multi-perspective dimension-index calculation"; wherein, the feature hierarchy classification corresponds to the five major characteristics of the new power system, namely, clean energy, power supply, economic benefits, supply and demand coordination, and regulation capability; the multi-perspective dimension covers six dimensions, namely, power supply perspective dimension, load perspective dimension, low-carbon perspective dimension, grid perspective dimension, energy storage perspective dimension, and digital perspective dimension, based on the top-level design strategy of the new power system, and comprehensively covers all aspects of the new power system; finally, for the five feature hierarchy units, the relevant dimensions in the six dimensions are considered to calculate the index data, so that the system is suitable for the calculation of evaluation indicators for the development of new power systems of different scales, different energy endowments and grid structures; the new power systems in different regions can be evaluated, and the normalization technology and weighted analysis technology can be used to calculate the evaluation scores of the new power systems in different regions, and finally the synchronous quantitative comparison of the construction progress of the new power systems in different regions is realized, and the evaluation work of the new power system with new energy as the main body is completed.

[0107] See also Figure 2 , which shows a method flow chart of a hierarchical multi-dimensional comprehensive evaluation method based on a new power system provided by an embodiment of the present invention, the method comprising:

[0108] Step S100: performing feature level classification on the power system and extracting feature level classification results;

[0109] Step S200: Calculating power system related indicator data using different perspective dimensions for different feature level units;

[0110] Step S300: normalizing the power system related indicator data under different feature level units;

[0111] Step S400: Outputting the power system evaluation result according to the power system related indicator data after normalization processing of different feature level units.

[0112] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0113] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A hierarchical multi-dimensional comprehensive evaluation system based on a new power system, characterized by: The system comprises: a classification module configured to perform feature level classification on the power system and extract feature level classification results; The power system evaluation module is configured to calculate power system related indicator data using different perspective dimensions for different feature level units; A normalization module, configured to perform normalization processing on power system related indicator data under different feature level units; The result output module is configured to output the power system evaluation result according to the power system related indicator data after normalization processing of different feature level units.

2. The hierarchical multi-dimensional comprehensive evaluation system based on the new power system according to claim 1 is characterized in that: The characteristic level classification of the power system includes: A first feature level unit configured to obtain feature parameters related to clean energy in the power system; a second feature level unit configured to obtain feature parameters related to power supply of the power system; a third characteristic level unit configured to obtain characteristic parameters related to economic benefits of the power system; a fourth characteristic level unit configured to obtain characteristic parameters related to power system supply and demand coordination; The fifth characteristic level unit is configured to obtain characteristic parameters related to the regulation capability of the power system.

3. The hierarchical multi-dimensional comprehensive evaluation system based on the new power system according to claim 2 is characterized in that: For different feature level units, different perspective dimensions are used to calculate the power system related indicator data, including: For the first feature level unit, the first indicator data set is extracted from the power source perspective dimension, load perspective dimension, and low-carbon perspective dimension respectively; the first indicator data set includes: the proportion of renewable energy utilization efficiency, the proportion of non-fossil energy installed capacity, the proportion of cross-regional green electricity procurement, the proportion of renewable energy consumption, the proportion of electrical energy consumption, the proportion of industrial green energy consumption, the carbon intensity of thermal power generation, and the carbon emission growth suppression rate.

4. The hierarchical multi-dimensional comprehensive evaluation system based on the new power system according to claim 2, characterized in that: For different feature level units, the power system related indicator data calculated using different perspective dimensions also include: For the second feature level, the second indicator data set is extracted from the power supply perspective dimension, the grid perspective dimension, the load perspective dimension, and the energy storage perspective dimension respectively; the second indicator data set includes: system reserve rate, unplanned outage rate of supporting power supply, new energy system balance contribution, system primary leveling margin, new energy grid technology penetration rate, distributed power supply support ratio, new energy day-ahead prediction accuracy, distributed energy day-ahead prediction accuracy, new energy station short-circuit capacity ratio, multi-feed DC system short-circuit capacity ratio, grid-side grid control penetration rate, whole network voltage qualification rate, user power outage time index, load day-ahead prediction accuracy, industrial user power outage time index, energy storage side grid control penetration rate.

5. The hierarchical multi-dimensional comprehensive evaluation system based on the new power system according to claim 2, characterized in that: For different feature level units, the power system related indicator data calculated using different perspective dimensions also include: For the third feature level, the third indicator data set is extracted from the power source perspective dimension, grid perspective dimension, load perspective dimension, and energy storage perspective dimension respectively; the third indicator data set includes: power source market entity participation rate, distributed power source market entity participation rate, power market system maturity index, spot market development stage index, market-oriented electricity proportion, new entity participation rate, load-side market entity participation rate, green power trading proportion, and energy storage market entity participation rate.

6. The hierarchical multi-dimensional comprehensive evaluation system based on the new power system according to claim 2, characterized in that: For different feature level units, the power system related indicator data calculated using different perspective dimensions also include: For the fourth characteristic level, the fourth indicator data set is extracted from the power supply perspective dimension and the load perspective dimension respectively; the fourth indicator data set includes: distributed renewable energy penetration rate, local consumption rate of distributed power sources, load side response capability index, virtual power plant aggregation scale index, proportion of load responding in seconds, air conditioning and electric heating load regulation capability, electric vehicle charging infrastructure coverage rate, and electric vehicle V2G participation rate.

7. The hierarchical multi-dimensional comprehensive evaluation system based on the new power system according to claim 2, characterized in that: For different feature level units, the power system related indicator data calculated using different perspective dimensions also include: For the fifth characteristic level, the fifth indicator data set is extracted from the power source perspective dimension, power grid perspective dimension, load perspective dimension, energy storage perspective dimension, and digital perspective dimension respectively; the fifth indicator data set includes: the proportion of adjustable capacity of power sources, the digitalization and intelligence rate of power plants, the comprehensive index of power grid digitalization, the intelligent rate of power grid dispatching, the utilization efficiency of cross-regional transmission channels, the proportion of distributed resource regulation, the penetration rate of smart microgrids, the coverage rate of fast charging piles, the proportion of adjustable capacity on the energy storage side, the utilization rate of new energy storage power stations, the comprehensive index of power system digitalization, and the comprehensive index of power system automation.

8. The hierarchical multi-dimensional comprehensive evaluation system based on a new power system according to any one of claims 1 to 7, characterized in that: Normalization of power system related indicator data under different characteristic level units includes: Preprocessing the power system related indicator data at different feature levels, the preprocessing at least includes data cleaning, missing value processing, abnormal value processing and indicator direction unification; Normalize the pre-processed power system related indicator data at different feature levels; For each feature level unit, the relative importance matrix between different indicators is constructed based on the hierarchical analysis method and the weights are calculated; For each feature level unit, the normalized related index data is used for weighted summation to obtain the evaluation index of each feature level unit.

9. The hierarchical multi-dimensional comprehensive evaluation system based on the new power system according to claim 8, characterized in that: The power system evaluation results are output based on the power system related indicator data after normalization of different feature level units, including: The evaluation indexes of different feature level units are converted into fuzzy numbers using membership functions, and a fuzzy evaluation matrix is ​​constructed. According to the preset weights of different feature level units and the fuzzy evaluation matrix, the power system evaluation results are output.

10. A hierarchical multi-dimensional comprehensive evaluation method based on a new power system is characterized by: A hierarchical multi-dimensional comprehensive evaluation system based on a new power system according to any one of claims 1 to 9, the method comprising: Perform feature hierarchical classification on the power system and extract feature hierarchical classification results; For different characteristic level units, different perspective dimensions are used to calculate the power system related indicator data; Normalize the power system related indicator data under different characteristic level units; The power system assessment results are output based on the power system related indicator data after normalization processing of different feature level units.

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