Multi-dimensional quantitative analysis method for operation efficiency of electric power system

Abstract

The invention discloses a power system operation efficiency multi-dimensional quantitative analysis method, which comprises the following steps: preprocessing dynamic indexes based on an established index system; standardizing the data of the static indexes and the dynamic indexes; performing mixed weight distribution on the static indexes and the dynamic indexes; performing comprehensive scoring on the static indexes and the dynamic indexes according to a chaos dynamical model; penalty and incentive mechanism correction is carried out on the static indexes and the dynamic indexes; the invention provides a multi-dimensional and dynamic adaptive novel electric power system operation efficiency evaluation method, high-order dynamic quantitative analysis is utilized to quantify the operation efficiency of the novel electric power system, nonlinear modeling, chaos theory and policy sensitivity analysis are combined, a more scientific and more objective quantitative analysis system is provided, and the evaluation efficiency of the operation efficiency of the novel electric power system is improved. And a quantifiable decision-making reference is provided for planning and policy making of a city-level power system.

Description

Technical Field

[0001] This invention relates to a multi-dimensional quantitative analysis method for the operational efficiency of power systems, belonging to the field of electrical engineering technology. Background Technology

[0002] The main task of building a new power system is to establish a high-proportion renewable energy supply and consumption system, promote the low-carbon transformation of the energy structure, and meet the needs of high-quality economic and social development. However, the randomness and volatility of renewable energy reduce the stability of the power system and affect the safe operation of the power grid. At the same time, due to differences in resource endowment, technological investment, and policy implementation, significant regional imbalances exist among cities in the construction of new power systems. Against this backdrop, how to scientifically quantify and analyze the development and operational efficiency of new power systems in different cities, and identify their shortcomings and optimization paths, has become a crucial issue that urgently needs to be addressed to coordinate the promotion of "dual-carbon" goals and achieve high-quality development of the power grid.

[0003] However, traditional quantitative analysis methods often rely on experts' subjective opinions or experience to score and assign weights to various indicators. The evaluation results may be affected by personal subjective preferences, lacking objectivity and consistency. There may also be evaluation differences among different experts, resulting in problems such as an imperfect indicator system and strong evaluation subjectivity. Summary of the Invention

[0004] The technical problem to be solved by this invention is that the existing technology suffers from an imperfect indicator system and strong subjectivity in evaluation.

[0005] To solve the above-mentioned technical problems, the present invention is implemented using the following technical solution.

[0006] On the one hand, this invention provides a multi-dimensional quantitative analysis method for power system operation efficiency, including:

[0007] Based on the established indicator system, the dynamic indicator data is preprocessed.

[0008] Based on the preprocessed data, the static and dynamic indicators are standardized.

[0009] Based on standardized data, a mixed weight allocation is performed on the static and dynamic indicators;

[0010] Based on the chaotic dynamics model, a comprehensive score is given for both static and dynamic indicators;

[0011] Based on the comprehensive score of static and dynamic indicators, a penalty and incentive mechanism is implemented to modify the static and dynamic indicators.

[0012] By using high-order dynamic quantitative analysis, the operational efficiency of new power systems is quantified. Combining nonlinear modeling, chaos theory, and policy sensitivity analysis, a quantifiable decision-making benchmark is provided for urban-level power system planning and policy formulation.

[0013] The indicator system includes:

[0014] Green Transition Dimension: Measures the progress and quality of new energy replacing traditional energy, reflecting the ability to achieve dual carbon targets;

[0015] Reliability dimension: Quantitatively analyze the power grid's ability to resist disturbances and maintain continuous power supply;

[0016] Economic efficiency dimension: reflects the operational efficiency and resource utilization efficiency of the power system;

[0017] Service capability dimension: Evaluating the power grid's responsiveness to user demands and load capacity level;

[0018] Infrastructure dimension: Quantifying power grid hardware investment and coverage;

[0019] Dynamic coordination indicators: Analyze the interaction relationships of multi-dimensional indicators to reveal the complexity of the power system;

[0020] The dimensions of green transformation include:

[0021] New energy grid-connected capacity: Total installed capacity of renewable energy sources such as wind and solar power connected to the grid;

[0022] Non-fossil energy share: The proportion of clean energy power generation in total electricity consumption;

[0023] Non-fossil energy power generation: Total annual power generation from zero-carbon energy sources such as hydropower, wind power, solar power, and nuclear power;

[0024] Carbon emissions per unit of electricity sold: the amount of carbon emissions corresponding to each kilowatt-hour of electricity sold;

[0025] The green transformation dimension indicators include:

[0026] Overall voltage compliance rate: the percentage of time during which voltage fluctuations are within the national standard range;

[0027] Average power outage duration for urban users: average annual power outage duration per household;

[0028] Fault rate of medium and low voltage lines: the frequency of faults in distribution network lines;

[0029] Number of faults: Total number of power distribution system faults throughout the year;

[0030] The economic efficiency dimension indicators include:

[0031] Overall line loss rate: The percentage of electricity lost during power transmission;

[0032] Electricity sales per employee: Average annual electricity sales per employee, calculated using the following formula:

[0033]

[0034] in, For purchased electricity, Total number of employees;

[0035] Return on assets: The percentage of profit generated by the total assets of the power grid, calculated using the following formula:

[0036]

[0037] in, For overall profit, Total assets;

[0038] The service capability metrics include:

[0039] Load density: the intensity of electricity demand per unit power supply area, as shown in the following formula:

[0040]

[0041] in, At maximum load, For the area of ​​power supply;

[0042] Number of customers served by each employee: The number of users served by each employee;

[0043] Average fault repair time for users: the average time from reporting a fault to restoring power;

[0044] The infrastructure dimension indicators include:

[0045] Total line length: The total extension distance of power transmission and distribution lines;

[0046] Assets per employee: The value of power grid assets allocated to each employee;

[0047] Grid-connected capacity utilization rate: The actual utilization rate of new energy installed capacity;

[0048] The dynamic collaboration indicator dimensions include:

[0049] Coordination coefficient of reliability, economy, and greenness: a three-dimensional score after standardization of reliability, economy, and greenness variables, calculated using the following formula:

[0050]

[0051]

[0052]

[0053] Where S is the standardized score for reliability, E is the standardized score for economy, and D is the standardized score for greenness. The standardized voltage pass rate score ranges from (-1, 1). The standardized power outage time score has a value range of (-1, 1). This represents the original value for the maximum power outage time among the three cities. The standardized score for the unit employee's electricity sales volume. The score is the standardized line loss rate. The score is the standardized percentage of non-fossil energy. Scoring is given for the standardized grid-connected capacity of new energy sources;

[0054] Time series volatility of renewable energy capacity: Renewable energy grid-connected capacity The annual standard deviation is calculated using the following formula:

[0055]

[0056] The standard deviation is measured in megawatts (10,000 kilowatts). For the number of years in the statistics, for Average grid-connected capacity within the year.

[0057] By integrating core indicators such as reliability, economic efficiency, and green transformation, a new, multi-dimensional, and dynamically adaptable power system operation efficiency evaluation system is constructed.

[0058] The preprocessing of dynamic indicators, including the installed capacity of new energy sources and the power generation of non-fossil energy, includes the following steps:

[0059] The installed capacity of new energy sources and the power generation of non-fossil energy sources are arranged by year and used as time series variables;

[0060] The sliding window integral method is used to evaluate the time series volatility of the new energy capacity, and the formula is as follows:

[0061]

[0062] The non-fossil energy power generation is calculated using the sliding window integral method, as shown in the following formula:

[0063]

[0064] in, For the time series variable of new energy installed capacity, The baseline time for quantitative analysis is T, where T is the year of quantitative analysis. This is an exponential decay factor, controlling the rate at which the influence of historical data on the current evaluation decays. This is a time series variable representing non-fossil energy power generation.

[0065] By introducing time series variables, recent performance is calculated through sliding window integration, and decay weights are assigned to reflect the recency effect.

[0066] The standardization of static and dynamic indicator data includes:

[0067] Applying a hyperbolic tangent function to the continuous index compresses the index data to the interval (-1, 1), as shown in the following formula:

[0068]

[0069] in, The original indicator data, For curvature, As the baseline value, It is a positive indicator, namely the difference between the historical maximum and minimum values;

[0070] Based on the distribution pattern of the indicators, a piecewise function is selected to obtain the standardized score:

[0071]

[0072] in, The hyperbolic tangent function mapping of the index, The first 25% of the data, sorted by size. This represents the top 75% of the data, sorted by size.

[0073] The standardized voltage qualification rate score Standardized power outage time score The original value of the maximum power outage time in the three cities Standardized unit employee sales volume score Standardized line loss rate score The score represents the standardized proportion of non-fossil energy. And the standardized grid-connected capacity score of new energy sources All have standardized scores The formula is used to calculate it.

[0074] The mixed weight allocation of static and dynamic indicators includes:

[0075] The weights for the indicators that need to be weighted are assigned using a combination of entropy weights and game theory weights, as shown in the following formula:

[0076]

[0077]

[0078]

[0079] in, Let j be the information entropy of index j. As an indicator Information entropy The sample number. For indicator serial number, Let i be the proportion of the i-th sample in the j-th indicator. For the first The weight of each indicator;

[0080] Game theory incorporates subjective weights, as shown in the following formula:

[0081]

[0082] in, The weights are calculated using the entropy weight method. For objective data weighting;

[0083] A sensitivity factor is introduced into the greenness indicator, as shown in the following formula:

[0084]

[0085] in, As the basic weight for the green transformation dimension, This is an adjustment coefficient used to control the sensitivity intensity. Sensitivity factor This refers to electricity generated from non-fossil energy sources.

[0086] Objective weights are calculated using the entropy weight method, and objective equilibrium weights are generated using game theory. Policy sensitivity factors are added to dynamically adjust the weights for green transformation, thereby improving the objectivity of the quantitative analysis method.

[0087] The step of comprehensively scoring static and dynamic indicators based on the chaotic dynamics model includes:

[0088] Using the Lorentz model, standardized scores are embedded into a three-phase space to generate dynamic scoring trajectories, as shown in the following formula:

[0089]

[0090] Where S is the standardized score for reliability, E is the standardized score for economy, and D is the standardized score for greenness. For system parameter one, For system parameter two, For system parameter three;

[0091] The formula for calculating the power system operation efficiency index is as follows:

[0092]

[0093]

[0094] The standardized scores for reliability, economy, and environmental friendliness are encoded into binary sequences using three-dimensional trajectories. Newly emerging patterns are detected, and the number of unique patterns is counted. , To extract the fractal dimension of the phase space trajectory, Korotkowski complexity is the shortest program length required to describe the data. The attractor volume is the volume occupied by the trajectory in three-dimensional space.

[0095] Nonlinear standardization is performed, and the data is compressed to (-1,1) using the hyperbolic tangent function to enhance the discrimination of extreme values. Quantile segmentation is performed by taking the logarithm of the head, the square of the middle, and the square root of the tail to suppress the gap of high scores, expand the difference of the middle segment, and smooth the low scores.

[0096] The system parameter one is 10, the system parameter two is 28, and the system parameter three is... At that time, the chaotic dynamics model enters a chaotic state.

[0097] The proposed modification of static and dynamic indicators through a penalty and incentive mechanism includes:

[0098] Thresholds are set for the average power outage time and comprehensive line loss rate of urban users. An exponential penalty is applied to the portion exceeding the threshold. The formula for the abnormal time penalty function is as follows:

[0099]

[0100] in, To determine the severity of the punishment, The average power outage time for urban users. This represents the threshold for the average power outage time for urban users.

[0101] For the aforementioned proportion of non-fossil energy and the growth rate of new energy, cities that exceed expectations in the proportion of non-fossil energy will receive logarithmic rewards. The innovation incentive factor formula is as follows:

[0102]

[0103] in, The percentage of non-fossil energy, This represents the expected proportion of non-fossil energy.

[0104] Secondly, the present invention provides a multi-dimensional quantitative analysis system for power system operation efficiency, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the multi-dimensional quantitative analysis method for power system operation efficiency.

[0105] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0106] This invention provides a novel, multi-dimensional, and dynamically adaptive method for evaluating the operational efficiency of power systems. It utilizes high-order dynamic quantitative analysis to quantify the operational efficiency of new power systems. By combining nonlinear modeling, chaos theory, and policy sensitivity analysis, it provides a more scientific and objective quantitative analysis system, offering quantifiable decision-making benchmarks for city-level power system planning and policy formulation.

[0107] By integrating core indicators such as reliability, economic efficiency, and green transformation, we can more comprehensively quantify and analyze the effectiveness of urban development and operation. Attached Figure Description

[0108] Figure 1 This is a flowchart illustrating the quantitative analysis method for power system development and operation efficiency as shown in Embodiment 1 of the present invention.

[0109] Figure 2 This is a schematic diagram of the index system for the quantitative analysis method of power system development and operation efficiency as shown in Embodiment 1 of the present invention. Detailed Implementation

[0110] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0111] Example 1

[0112] like Figure 1 This embodiment introduces a multi-dimensional quantitative analysis method for power system operation efficiency, including:

[0113] Based on the established indicator system, the dynamic indicator data is preprocessed.

[0114] Based on the preprocessed data, the static and dynamic indicators are standardized.

[0115] Based on standardized data, a mixed weight allocation is performed on the static and dynamic indicators;

[0116] Based on the chaotic dynamics model, a comprehensive score is given for both static and dynamic indicators;

[0117] Based on the comprehensive score of static and dynamic indicators, a penalty and incentive mechanism is implemented to modify the static and dynamic indicators.

[0118] The indicator system includes:

[0119] Green Transition Dimension: Measures the progress and quality of new energy replacing traditional energy, reflecting the ability to achieve dual carbon targets;

[0120] Reliability dimension: Quantitatively analyze the power grid's ability to resist disturbances and maintain continuous power supply;

[0121] Economic efficiency dimension: reflects the operational efficiency and resource utilization efficiency of the power system;

[0122] Service capability dimension: Evaluating the power grid's responsiveness to user demands and load capacity level;

[0123] Infrastructure dimension: Quantifying power grid hardware investment and coverage;

[0124] Dynamic coordination indicators: Analyze the interaction relationships of multi-dimensional indicators to reveal the complexity of the power system;

[0125] The green transformation dimension includes:

[0126] New energy grid-connected capacity: Total installed capacity of renewable energy sources such as wind and solar power connected to the grid;

[0127] Non-fossil energy share: The proportion of clean energy power generation in total electricity consumption;

[0128] Non-fossil energy power generation: Total annual power generation from zero-carbon energy sources such as hydropower, wind power, solar power, and nuclear power;

[0129] Carbon emissions per unit of electricity sold: the amount of carbon emissions corresponding to each kilowatt-hour of electricity sold;

[0130] The indicators for green transformation include:

[0131] Overall voltage qualification rate: the percentage of time during which voltage fluctuations are within the national standard range.

[0132] Average power outage time for urban users: average annual power outage duration per household.

[0133] Fault rate of medium and low voltage lines: the frequency of faults in distribution network lines.

[0134] Number of faults: Total number of power distribution system faults throughout the year.

[0135] Economic efficiency indicators include:

[0136] Overall line loss rate: The percentage of electricity lost during power transmission;

[0137] Electricity sales per employee: Average annual electricity sales per employee, calculated using the following formula:

[0138]

[0139] For purchased electricity, Total number of employees;

[0140] Return on assets: The percentage of profit generated by the total assets of the power grid, calculated using the following formula:

[0141]

[0142] in, For overall profit, Total assets;

[0143] The service capability metrics include:

[0144] Load density: the intensity of electricity demand per unit power supply area, as shown in the following formula:

[0145]

[0146] in, At maximum load, For the area of ​​power supply;

[0147] Number of customers served by each employee: The number of users served by each employee;

[0148] Average fault repair time for users: the average time from reporting a fault to restoring power;

[0149] The infrastructure dimension indicators include:

[0150] Total line length: The total extension distance of power transmission and distribution lines;

[0151] Assets per employee: The value of power grid assets allocated to each employee;

[0152] Grid-connected capacity utilization rate: The actual utilization rate of new energy installed capacity;

[0153] The dynamic collaboration indicator dimensions include:

[0154] Coordination coefficient of reliability, economy, and greenness: a three-dimensional score after standardization of reliability, economy, and greenness variables, calculated using the following formula:

[0155]

[0156]

[0157]

[0158] Where S is the standardized score for reliability, E is the standardized score for economy, and D is the standardized score for greenness. The standardized voltage pass rate score ranges from (-1, 1). The standardized power outage time score has a value range of (-1, 1). This represents the original value for the maximum power outage time among the three cities. The standardized score for the unit employee's electricity sales volume. The score is the standardized line loss rate. The score is the standardized percentage of non-fossil energy. The standardized grid-connected capacity of new energy sources is scored;

[0159] Time series volatility of renewable energy capacity: Renewable energy grid-connected capacity The annual standard deviation is calculated using the following formula:

[0160]

[0161] The standard deviation is measured in megawatts (10,000 kilowatts). For the number of years in the statistics, for Average grid-connected capacity within the year.

[0162] The dynamic indicators, including the installed capacity of new energy sources and the power generation of non-fossil energy, are preprocessed. The preprocessing steps include:

[0163] The indicators of new energy installed capacity and non-fossil energy power generation are arranged by year and used as time series variables;

[0164] The sliding window integral method is used to determine the time series volatility of new energy capacity, and the formula is as follows:

[0165]

[0166] The sliding window integral method is used to calculate the non-fossil energy power generation, and the formula is as follows:

[0167]

[0168] in, For the time series variable of new energy installed capacity, The baseline time for quantitative analysis is T, where T is the year of quantitative analysis. This is an exponential decay factor, controlling the rate at which the influence of historical data on the current evaluation decays. This is a time series variable representing non-fossil energy power generation.

[0169] Specifically, the year of quantitative analysis serves as a window, sliding annually over time. A larger exponential decay factor assigns higher weight to recent data, rendering historical data obsolete quickly, making it suitable for scenarios with rapid technological iteration. Conversely, a smaller exponential decay factor ensures the lasting impact of historical data, making it suitable for quantitative analysis of long-term infrastructure. Considering the rapid and large-scale installation of new energy sources in recent years and the length of the quantitative analysis period, a value of 0.1 is recommended to increase the weight of recent data.

[0170] Standardizing data for static and dynamic indicators includes:

[0171] Applying a hyperbolic tangent function to the continuous index compresses the index data to the interval (-1, 1), as shown in the following formula:

[0172]

[0173] in, The original indicator data, For curvature, As the baseline value, It is a positive indicator, namely the difference between the historical maximum and minimum values;

[0174] Based on the distribution pattern of the indicators, a piecewise function is selected to obtain the standardized score:

[0175]

[0176] in, The hyperbolic tangent function mapping of the index, The first 25% of the data, sorted by size. This represents the top 75% of the data, sorted by size.

[0177] Specifically, the greater the curvature, the more distinct the distinction between high and low scores. To balance the sensitivity and stability of data quantification analysis, a curvature of 2 is recommended.

[0178] Specifically, the continuity indicators are shown in the table below:

[0179]

[0180] Mixed weighting of static and dynamic indicators includes:

[0181] The weights for the indicators that need to be weighted are assigned using a combination of entropy weights and game theory weights, as shown in the following formula:

[0182]

[0183]

[0184]

[0185] in, Let j be the information entropy of index j. As an indicator Information entropy The sample number. For indicator serial number, Let i be the proportion of the i-th sample in the j-th indicator. For the first The weight of each indicator;

[0186] Game theory incorporates subjective weights, as shown in the following formula:

[0187]

[0188] in, The weights are calculated using the entropy weight method. For objective data weighting;

[0189] A sensitivity factor is introduced into the greenness indicator, as shown in the following formula:

[0190]

[0191] in, As the basic weight for the green transformation dimension, This is an adjustment coefficient used to control the sensitivity intensity. Sensitivity factor This refers to electricity generated from non-fossil energy sources.

[0192] Specifically, the indicators that need to be weighted are shown in the table below:

[0193]

[0194] Specifically, time-series indicators (grid-connected capacity of new energy sources, load density, and proportion of non-fossil energy electricity) need to be integrated through a sliding window before standardization; indicators related to penalties or rewards are not included in the weight allocation.

[0195] Specifically, if the entropy value of an indicator for a city undergoing quantitative analysis is high, the indicator has low discrimination and low weight; conversely, if the entropy value is low, the discrimination is high and the weight is high.

[0196] Specifically, taking into account both the growth rate of new energy sources and policy incentives, and setting the adjustment coefficient at 0.1, a 10% annual increase in new energy power generation would result in an increase in the weighting of 0.1 × 10% = 1%. According to the "Blue Book on the Development of New Power Systems," during the accelerated transition period (from now until 2030), the proportion of non-fossil energy consumption will reach 25%. 25% is acceptable.

[0197] Based on the chaotic dynamics model, a comprehensive score is calculated for both static and dynamic indicators. The steps include:

[0198] Using the Lorentz model, standardized scores are embedded into a three-phase space to generate dynamic scoring trajectories, as shown in the following formula:

[0199]

[0200] Where S is the standardized score for reliability, E is the standardized score for economy, and D is the standardized score for greenness. For system parameter one, For system parameter two, For system parameter three;

[0201] The formula for calculating the power system operation efficiency index is as follows:

[0202]

[0203]

[0204] The standardized scores for reliability, economy, and environmental friendliness are encoded into binary sequences using three-dimensional trajectories. Newly emerging patterns are detected, and the number of unique patterns is counted. , To extract the fractal dimension of the phase space trajectory, Korotkowski complexity is the shortest program length required to describe the data. The attractor volume is the volume occupied by the trajectory in three-dimensional space.

[0205] Specifically, the smaller the fractal dimension of the extracted phase space trajectory (closer to 1), the closer the trajectory is to a straight line or plane, the simpler and more stable the system is, but the less flexible it is (over-reliance on traditional energy sources); the larger the dimension, the more complex the multi-dimensional coordination of the power system becomes, and the more prone it is to loss of control. 1.5 is the ideal value, balancing stability and flexibility.

[0206] Specifically, the higher the power system operation efficiency index, that is, the higher the score, the more mature the system and the stronger its adaptability.

[0207] System parameter one is 10, system parameter two is 28, and system parameter three is... At that time, the chaotic dynamics model enters a chaotic state.

[0208] The static and dynamic indicators are modified using a penalty and incentive mechanism, including:

[0209] Thresholds are set for the average power outage time and comprehensive line loss rate of urban users. Exceeding these thresholds results in an exponential penalty. The formula for the abnormal time penalty function is as follows:

[0210]

[0211] in, To determine the severity of the punishment, The average power outage time for urban users. This represents the threshold for the average power outage time for urban users.

[0212] Cities that exceed expectations in terms of the proportion of non-fossil energy and the growth rate of new energy will receive logarithmic rewards. The innovation incentive factor formula is as follows:

[0213]

[0214] in, The percentage of non-fossil energy, This represents the expected proportion of non-fossil energy.

[0215] Example 2

[0216] Based on the same inventive concept as Embodiment 1, this embodiment introduces a multi-dimensional quantitative analysis system for power system operation efficiency, which stores a computer program. When the computer program is executed by a processor, it implements the steps of a multi-dimensional quantitative analysis method for power system operation efficiency.

Claims

1. A method for multi-dimensionally quantifying and analyzing the operational efficiency of a power system, characterized by, The method comprises the following steps: Based on the established index system, the dynamic index data is preprocessed; Based on the preprocessed data, the static index and dynamic index data are standardized; Based on the standardized data, the static index and dynamic index are assigned a mixed weight; According to the chaotic dynamics model, the static index and dynamic index are comprehensively scored; Based on the comprehensive score of the static index and dynamic index, the static index and dynamic index are corrected by the punishment and incentive mechanism.

2. The method of claim 1, wherein the power system operation performance multi-dimension quantification analysis method is characterized by, The index system comprises: Green transformation dimension: measures the progress and quality of new energy replacing traditional energy, and reflects the ability to achieve the double carbon target; Reliability dimension: quantitative analysis of the ability of the power grid to resist disturbance and continuous power supply; Economic efficiency dimension: reflects the operation benefit and resource utilization efficiency of the power system; Service capability dimension: evaluates the response capability and load carrying level of the power grid to user demand; Infrastructure dimension: quantifies the hardware investment and coverage of the power grid; Dynamic coordination index: analyzes the interaction relationship of multi-dimensional indexes, and reveals the complexity of the power system; The green transformation dimension includes: New energy grid-connected capacity: total installed capacity of renewable energy such as wind and light connected to the power grid; Non-fossil energy proportion: the proportion of clean energy generation capacity in total electricity consumption; Non-fossil energy generation capacity: annual total generation capacity of water, wind, light and nuclear zero-carbon energy; Unit carbon emission of electricity sales: carbon emission per unit of electricity sold; The green transformation dimension index includes: Comprehensive voltage qualification rate: the proportion of time within the national standard voltage fluctuation range; Average outage time of urban users: average annual outage time per household; Medium and low voltage line fault rate: fault frequency of distribution network lines; Fault times: total number of faults in the distribution system per year; The economic efficiency dimension index includes: Comprehensive line loss rate: the proportion of power loss in the transmission process; Unit employee electricity sales: annual electricity sales performance of each employee, formula as follows: wherein, purchased electricity, total number of employees; Return on assets: the proportion of profits generated by total assets of the power grid, formula as follows: wherein, is the total assets, is the total assets; The service capability dimension index includes: Load density: power demand intensity per unit of power supply area, formula as follows: wherein, is the maximum load, is the power supply area; Unit employee service customer number: the number of users served by each employee; Average fault repair time of users: average time consumption from repair to power restoration; The infrastructure dimension index includes: Total length of lines: total extension distance of transmission and distribution lines; Per capita asset value: the value of power grid assets allocated to each employee; Grid-connected capacity utilization rate: actual utilization rate of new energy installed capacity; The dynamic coordination index dimension index includes: Reliability, economy, green synergy coefficient: three-dimensional score of reliability, economy and green variable after standardization, formula as follows: wherein S is the normalized score of reliability, E is the normalized score of economy, D is the normalized score of greenness, is the normalized voltage pass rate score, with a value range of (-1, 1), is the normalized power outage time score, with a value range of (-1, 1), is the maximum power outage time original value in the three cities, is the normalized unit employee electricity sales score, is the normalized line loss rate score, is the normalized non-fossil energy proportion score; is the normalized new energy grid-connected capacity score; New energy capacity time series volatility: new energy grid-connected capacity Annual standard deviation, formula as follows: Wherein the standard deviation is in units of 10s of MW, is the number of years, is the average annual on-grid capacity.

3. The method of claim 1, wherein the method further comprises: The preprocessing of the dynamic index includes the new energy installed capacity and the non-fossil energy generation capacity index, and the preprocessing steps include: Arrange the new energy installed capacity and the non-fossil energy generation capacity index by year to make time series variables; Use sliding window integral method for the new energy installed capacity, and the new energy capacity time series fluctuation formula is as follows: Use sliding window integral method for the non-fossil energy generation capacity, formula as follows: wherein, is a time series variable of new energy installed capacity, is the estimation base time, T is the quantitative analysis year, is an exponential decay factor, controlling the decay rate of the influence of historical data on the current evaluation, is a time series variable of non-fossil energy power generation.

4. The method of claim 1, wherein the method further comprises: The data standardization of the static indicators and the dynamic indicators comprises: The hyperbolic tangent function mapping is performed on the continuity indicators to compress the indicator data to the interval of (-1, 1), and a formula is as follows: wherein, is the original index data, is the curvature, is the reference value, is the positive index, i.e. the difference between the historical maximum and minimum values; A segmented function is selected according to the indicator distribution form to obtain the standardized score: wherein, is the hyperbolic tangent function mapping of the indicator, is the 25th percentile of the data ordered by size, is the 75th percentile of the data ordered by size.

5. The method of claim 2, wherein the power system operation performance multi-dimension quantification analysis method is characterized by, the standardized voltage pass rate score , the standardized power outage time score , the maximum power outage time raw value in three cities , the standardized unit employee electricity sales score , the standardized line loss rate score , the standardized non-fossil energy proportion score , and the standardized new energy grid-connected capacity score All have standardized scores calculated by formula.

6. The method of claim 1, wherein the method further comprises: The mixed weight distribution of the static indicators and the dynamic indicators comprises: The entropy weight and the game theory combined weight are used to distribute the weight of the indicators that need to be weighted, and a formula is as follows: in, Let j be the information entropy of index j. As an indicator Information entropy The sample number. For indicator serial number, Let i be the proportion of the i-th sample in the j-th indicator. For the first The weight of each indicator; The game theory is combined with the subjective weight, and a formula is as follows: wherein, is the weight of the entropy weight method result, is the objective data weight; A sensitivity factor is introduced for the green indicators, and a formula is as follows: wherein, is the base weight for the green transition dimension, is a tuning coefficient to control the sensitivity intensity, is a sensitivity factor, is the non-fossil energy power generation.

7. The method of claim 1, wherein the method further comprises: The comprehensive scoring of the static indicators and the dynamic indicators according to the chaotic dynamics model comprises the following steps: The standardized score is embedded into a three-phase space by using the Lorenz model to generate a dynamic scoring trajectory, and a formula is as follows: Wherein, S is the standardization score of reliability, E is the standardization score of economy, D is the standardization score of green, is the system parameter one, is the system parameter two, is the system parameter three; An operation efficiency index is calculated, and a formula of the power system operation efficiency index is as follows: Where the standardized scores of reliability, economy and greenness are encoded into binary sequences, the new patterns are detected, and the number of unique patterns is counted as , For extracting the fractal dimension of the phase space trajectory, For the Kolmogorov complexity, which is used to describe the shortest program length required for the data, For the attractor volume, which is the volume occupied by the trajectory in three-dimensional space.

8. The method of claim 7, wherein the method further comprises: The system parameter one is 10, the system parameter two is 28, and the system parameter three is When the chaotic dynamic model enters a chaotic state.

9. The method of claim 1, wherein the method further comprises: The punishment and incentive mechanism correction of the static indicators and the dynamic indicators comprises: Thresholds are set for the urban user average power outage time and the comprehensive line loss rate indicators, and an exponential penalty is applied to the part exceeding the threshold, and an abnormal time penalty function formula is as follows: wherein, is the penalty intensity, is the average outage time for urban users, is the threshold for the average outage time for urban users; A logarithmic level reward is given to the city whose non-fossil energy proportion exceeds the expectation, and an innovation incentive factor formula is as follows. wherein, is the non-fossil energy proportion, is the expected non-fossil energy proportion.

10. A power system operation efficiency multi-dimension quantitative analysis system, having a computer program stored thereon, characterized in that, The computer program is executed by the processor to realize the steps of the power system operation efficiency multi-dimensional quantitative analysis method in any one of claims 1 to 9.

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