Energy-saving loss-reduction increment energy efficiency evaluation system and method based on medium and low voltage power distribution network

Abstract

The invention discloses an energy-saving loss-reduction increment energy efficiency evaluation system and method based on a medium and low voltage power distribution network, and the system comprises an energy efficiency index collection module which is used for constructing a medium and low voltage line evaluation index, a transformer area evaluation index, a distribution transformer evaluation index and corresponding sub-indexes; the index preprocessing module is used for endowing different weights to whether the collected effective data values corresponding to the different evaluation indexes are suddenly changed or not; and the energy efficiency evaluation module is used for calculating energy efficiency evaluation values of the different power distribution networks in the to-be-processed region at the specified moment or time period according to the sub-indexes and the corresponding weights. The weight distribution mode adopted by the invention is to set the change weight of the index according to whether different sub-index data are suddenly changed or not, so that the reasonability of the weight is ensured, the difference between different indexes is amplified, the weak point of energy efficiency at each moment is more easily obtained, and the evaluation value obtained by the evaluation mode is more accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid energy conservation, and in particular to an energy conservation and loss reduction incremental energy efficiency evaluation system and a corresponding method based on a medium and low voltage distribution network. Background Art

[0002] With growing energy demand, the energy efficiency of distribution networks is gaining increasing attention. my country's power grid, especially in remote areas, is plagued by high conductor losses, insufficient reactive power compensation, and the continued operation of some aging transformers. my country still relies primarily on thermal power, and high grid losses contribute to significant carbon dioxide emissions. Addressing this issue is urgent.

[0003] Traditional energy efficiency assessment methods are often limited to rough estimates of overall energy consumption, lacking in-depth analysis and quantitative evaluation of the effectiveness of energy-saving and loss-reduction measures. For example, patent publication number CN110782153A discloses a modeling method and system for a comprehensive energy efficiency assessment system for an enterprise park. This method collects AMI measurement data, obtains energy efficiency assessment indicators based on the AMI measurement data, establishes a comprehensive energy efficiency assessment model based on the energy efficiency assessment indicators, and uses the existing entropy weight method to calculate the corresponding weights based on the energy efficiency assessment model. The accuracy of the energy efficiency assessment cannot be guaranteed. Patent publication number CN118195330A discloses an energy efficiency assessment method for a multi-energy complementary integrated energy system in a university town. This method also collects indicators, calculates the indicator values ​​using relevant formulas, uses the hierarchical analysis method to determine the first weights of each level of indicators, and uses the entropy weight method to determine the second weights of each level of indicators. Therefore, the accuracy of the final assessment cannot be guaranteed. Summary of the Invention

[0004] Purpose of the invention: In order to overcome the deficiencies of the prior art, the present invention provides an energy-saving and loss-reducing incremental energy efficiency evaluation system based on medium and low voltage distribution networks. The system solves the problems in the prior art of excessive subjectivity in the weight allocation of indicators and high dependence on samples. When the data samples change, the weights will also change, resulting in weight distortion and ultimately inaccurate evaluation. The present invention also provides an energy-saving and loss-reducing incremental energy efficiency evaluation method based on medium and low voltage distribution networks.

[0005] Technical solution: According to a first aspect of the present invention, a system for evaluating energy efficiency based on medium and low voltage distribution networks for energy saving and loss reduction is provided. The system comprises:

[0006] Energy efficiency index collection module, which is used to construct medium and low voltage line evaluation indicators, substation evaluation indicators and distribution transformer evaluation indicators and their corresponding sub-indicators, and collect relevant data values ​​of different distribution networks in the processing area within a certain period of time;

[0007] The indicator preprocessing module is used to preprocess the data values ​​corresponding to different evaluation indicators collected. The preprocessing includes screening out invalid data values ​​and eliminating them, and assigning different weights based on whether the valid data values ​​have undergone mutations;

[0008] Energy efficiency evaluation module, used to calculate the energy efficiency evaluation value of different distribution networks in the processing area at a specified time or time period based on each sub-indicator and the corresponding weight;

[0009] The evaluation value calibration module is used to calibrate abnormal evaluation data in the energy efficiency evaluation values ​​of different distribution networks in different processing areas to obtain valid evaluation data values ​​for evaluating the energy efficiency of the distribution network.

[0010] Furthermore, the system also includes:

[0011] The loss reduction decision module is used to confirm the energy efficiency issues of each distribution network based on the effective evaluation data of the distribution network energy efficiency, and to provide specific loss reduction plans based on medium and low voltage lines and substations. After implementing the loss reduction plan for a period of time, the corresponding first quality index is calculated and compared with the first quality index before the loss reduction. The first quality index includes: the cumulative time rate of voltage compliance, the three-phase load imbalance, the daily load rate, and the maximum line voltage drop percentage.

[0012] A specific loss reduction plan is given based on the distribution transformer. After implementing the loss reduction plan for a period of time, the corresponding second quality index is calculated, and the current second quality index is compared and analyzed with the second quality index before the loss reduction to confirm the effectiveness of the loss reduction plan.

[0013] Further, including:

[0014] The sub-indicators of the medium and low voltage line evaluation indicators include: power supply radius qualification rate P 11 , the rate of economic operation lines P 12 , line loss qualified rate P 13 , the proportion of severely damaged lines P 14 , line power factor qualification rate P 15 , average cross-section qualification rate of main line P 16 , the qualified rate of total capacity of line access distribution P 17 ;

[0015] The sub-indicators of the substation evaluation index include: Substation power supply radius qualification rate P 21 , the average cross-section qualification rate P of the main line in the substation area 22 , the average cross-sectional area qualification rate of the building line P 23 , the proportion of low-loss areas P 24 , comprehensive line loss rate P 25 , the qualified rate of line loss in a single substation area P26 ;

[0016] The distribution transformer evaluation index includes sub-indicators such as line loss calculation for classification, voltage division, line division and substation area, and includes the energy consumption values ​​corresponding to the transformer when different temperatures, loads and powers occur, that is, analyzing the impact of changes in external temperature on the transformer energy consumption, analyzing the impact of changes in load on the transformer energy consumption, and analyzing the impact of changes in power factor on the transformer energy consumption.

[0017] Further, including:

[0018] The specific loss reduction plan based on the distribution transformer includes a first loss reduction plan and a second loss reduction plan. The first loss reduction plan includes replacing the transformer model corresponding to the calculated high line loss rate assessment data, and obtaining a transformer replacement evaluation based on the second quality index of the replaced transformer under a fixed load. The second quality index includes: load loss, no-load loss, and the total loss reduction after comparing and analyzing the current second quality index with the second quality index before loss reduction.

[0019] The second loss reduction plan includes first analyzing the current operating status of the transformer and determining whether it belongs to the optimal operating mode, and then obtaining relevant calculations for the optimal operation, wherein the optimal operating mode is the economic operating mode; the relevant calculations for the optimal operation include: calculating the upper and lower limits corresponding to the economic operating range of the replaced transformer, and analyzing and comparing the losses with those under the most economical load conditions.

[0020] Further, including:

[0021] In the indicator preprocessing module, different weights are assigned based on whether a mutation occurs in the valid data value, including:

[0022] According to the hierarchical analysis method and entropy weight method, the corresponding weights of the current indicators at a certain moment are obtained respectively, which are recorded as the first original weight and the second original weight;

[0023] The mutation degree of the current sub-indicator at a certain moment is obtained based on the valid data value, the mutation degree is determined by a trapezoidal function, and the corresponding weight optimization model is expressed by using the mutation degree and the original comprehensive weight;

[0024] The weight of the current sub-indicator at a certain moment is determined according to the weight optimization model. That is, if the degree of mutation is high, the first weight obtained by the weight optimization model is used; if there is no mutation or the degree of mutation is low, the second weight obtained by the weight optimization model is used, and so on, to obtain the energy efficiency evaluation value at the last moment.

[0025] Further, including:

[0026] The weight optimization model is expressed as:

[0027]

[0028] Among them, x i,k is the first original weight of the i-th sub-index at the k-th moment, y i,k is the second original weight of the i-th sub-index at the k-th moment, u i,k is the value corresponding to the degree of mutation, μ1 and μ2 are the thresholds of the degree of mutation, which are fixed values, and α is the weight coefficient, which is a fixed value;

[0029] The trapezoidal function corresponding to the mutation degree is expressed as:

[0030]

[0031] Among them, β i,k is the evaluation value of the current sub-index i at the kth moment, ω1, ω2 are the left and right boundary values ​​of the normal range of sub-index i at the kth moment, so u i,k The smaller the value, the higher the mutation degree of the corresponding indicator at the kth moment.

[0032] Further, including:

[0033] In the evaluation value calibration module, abnormal evaluation data in the energy efficiency evaluation values ​​of different distribution networks in different processing areas are calibrated to obtain valid evaluation data for evaluating the energy efficiency of the distribution network, including:

[0034] Construct an energy efficiency curve graph representing the same region, with the horizontal axis being time and the vertical axis being the evaluation value of the current sub-indicator, which is recorded as the first curve graph; the region includes three types of regions: suburban, urban, and rural areas;

[0035] Compare and analyze the first curve charts within 60 consecutive days in the same quarter. If the evaluation values ​​corresponding to the same time period differ by less than 0.2, they are recorded as valid evaluation data values. Otherwise, they are recorded as abnormal evaluation data. The abnormal evaluation data are processed as follows: the sum of the valid evaluation data values ​​of other times is averaged as the new valid evaluation data value after calibration.

[0036] Based on the calibrated new valid evaluation data values, the comprehensive evaluation values ​​of the current sub-indicators for the four quarters are calculated respectively, and then the total evaluation values ​​of the indicators in different regions and quarters are obtained.

[0037] On the other hand, the present invention also provides a method for evaluating incremental energy efficiency based on medium and low voltage distribution networks for energy saving and loss reduction, the method comprising:

[0038] Construct evaluation indicators for medium and low voltage lines, substations, and distribution transformers, as well as their corresponding sub-indicators, and collect relevant data values ​​for different distribution networks in the area to be processed within a certain period of time;

[0039] Preprocess the data values ​​corresponding to different evaluation indicators collected. The preprocessing includes screening out invalid data values ​​and eliminating them, and assigning different weights based on whether the valid data values ​​have undergone mutations.

[0040] Calculate the energy efficiency evaluation values ​​of different distribution networks in the processing area at a specified time or time period based on each sub-indicator and the corresponding weight;

[0041] The abnormal evaluation data in the energy efficiency evaluation values ​​of different distribution networks in different areas to be processed are calibrated to obtain valid evaluation data values ​​for evaluating the energy efficiency of the distribution network.

[0042] Furthermore, the method further comprises:

[0043] Based on the effective evaluation data of distribution network energy efficiency, the energy efficiency issues of each distribution network are confirmed, and specific loss reduction plans are proposed based on medium and low voltage lines and substations. After the loss reduction plan is implemented for a period of time, the corresponding first quality index is calculated and compared with the first quality index before the loss reduction. The first quality index includes: the cumulative time rate of voltage compliance, the three-phase load imbalance, the daily load rate, and the maximum voltage drop percentage of the line;

[0044] A specific loss reduction plan is given based on the distribution transformer. After implementing the loss reduction plan for a period of time, the corresponding second quality index is calculated, and the current second quality index is compared and analyzed with the second quality index before the loss reduction to confirm the effectiveness of the loss reduction plan.

[0045] Further, including:

[0046] The sub-indicators of the medium and low voltage line evaluation indicators include: power supply radius qualification rate P 11 , the rate of economic operation lines P 12 , line loss qualified rate P 13 , the proportion of severely damaged lines P 14 , line power factor qualification rate P 15 , average cross-section qualification rate of main line P 16 , the qualified rate of total capacity of line access distribution P 17 ;

[0047] The sub-indicators of the substation evaluation index include: Substation power supply radius qualification rate P 21 , the average cross-section qualification rate P of the main line in the substation area 22 , the average cross-sectional area qualification rate of the building line P 23 , the proportion of low-loss areas P24 , comprehensive line loss rate P 25 , the qualified rate of line loss in a single substation area P 26 ;

[0048] The distribution transformer evaluation index includes sub-indicators such as line loss calculation for classification, voltage division, line division and substation area, and includes the energy consumption values ​​corresponding to the transformer when different temperatures, loads and powers occur, that is, analyzing the impact of changes in external temperature on the transformer energy consumption, analyzing the impact of changes in load on the transformer energy consumption, and analyzing the impact of changes in power factor on the transformer energy consumption.

[0049] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0050] (1) The present invention classifies the relevant indicators of lines, substations and transformers into different sub-indicators, which facilitates separate processing according to the different characteristics of lines, substations and transformers, such as the radius and average cross-section of medium and low voltage lines, and the calculation of line losses in transformers according to classification, voltage division, line division and substation division, as well as the energy consumption values ​​corresponding to transformers under different temperatures, loads and powers. Transformers are greatly affected by temperature, load and power, so sub-indicators collect relevant data to provide more reliable data support for the next step of energy efficiency calculation;

[0051] (2) The weight distribution method adopted by the present invention is to set the change weight of the indicator according to whether the data of different sub-indicators change suddenly, thereby ensuring the rationality of the weight, amplifying the differences between different indicators, and making it easier to obtain the weak points of energy efficiency at each moment. In addition, the evaluation value obtained by this evaluation method is more accurate.

[0052] (3) The present invention checks and calibrates the abnormal data in the evaluation value obtained at a certain moment. Since abnormal data is not conducive to finding the impact of factors such as different regions or different weather on the energy efficiency of the distribution network, calibration is performed here, so that the weak points of the energy efficiency of the distribution network in each region can be discovered more accurately. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a structural diagram of the incremental energy efficiency evaluation system for energy saving and loss reduction based on medium and low voltage distribution networks according to Example 1 of the present invention;

[0054] Figure 2 This is a structural diagram of the incremental energy efficiency evaluation system for energy saving and loss reduction based on medium and low voltage distribution networks according to Example 2 of the present invention;

[0055] Figure 3 This is a flow chart of the incremental energy efficiency evaluation method for energy saving and loss reduction based on medium and low voltage distribution networks as described in Example 3 of the present invention. DETAILED DESCRIPTION

[0056] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention and not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0057] Example 1

[0058] like Figure 1 As shown, the present invention provides an energy-saving and loss-reducing incremental energy efficiency evaluation system based on a medium and low voltage distribution network, the system comprising:

[0059] The energy efficiency index collection module is used to construct medium and low voltage line evaluation indicators, substation evaluation indicators and distribution transformer evaluation indicators and their corresponding sub-indicators, and collect relevant data values ​​of different distribution networks in the area to be processed within a certain period of time.

[0060] Specifically, the sub-indicators of the evaluation indicators of medium and low voltage lines include: power supply radius qualification rate P 11 , the rate of economic operation lines P 12 , line loss qualified rate P 13 , the proportion of severely damaged lines P 14 , line power factor qualification rate P 15 , average cross-section qualification rate of main line P 16 , the qualified rate of total capacity of line access distribution P 17 ;

[0061] The sub-indicators of the substation evaluation index include: Substation power supply radius qualification rate P 21 , the average cross-section qualification rate P of the main line in the substation area 22 , the average cross-sectional area qualification rate of the building line P 23 , the proportion of low-loss areas P 24 , comprehensive line loss rate P 25 , the qualified rate of line loss in a single substation area P 26 ;

[0062] The distribution transformer evaluation index includes sub-indicators for line loss calculation of grading, voltage division, line division and sub-station area, and includes the energy consumption values ​​corresponding to the transformer when different temperatures, loads and powers occur, that is, analyzing the impact of changes in external temperature on the transformer energy consumption, analyzing the impact of changes in load on the transformer energy consumption, and analyzing the impact of changes in power factor on the transformer energy consumption. Here, the relevant indicators of the line, sub-station area and transformer are classified into different sub-indicators, which is convenient for separate processing according to the different characteristics of the line, sub-station area and transformer, such as the radius and average cross-section in medium and low voltage lines, and the line loss calculation of grading, voltage division, line division and sub-station area in the transformer, as well as the energy consumption values ​​corresponding to the transformer when different temperatures, loads and powers occur. The transformer is greatly affected by temperature, load and power, so the sub-indicators collect relevant data to provide more reliable data support for the next step of energy efficiency calculation.

[0063] The data collected should be of at least two years, and each collection should be separated by a certain period of time, such as collecting and calculating relevant data once every 10 minutes. The present invention does not improve the calculation formula of the relevant sub-indicators. A large number of existing technologies have given relevant formulas, and some computer software can also calculate directly, thereby forming a set of relevant indicators.

[0064] The indicator preprocessing module is used to preprocess the data values ​​corresponding to different evaluation indicators collected. The preprocessing includes screening out invalid data values ​​and eliminating them, and assigning different weights according to whether the valid data values ​​have undergone mutations. In this embodiment, the above indicators are all data where the larger the indicator value, the higher the energy efficiency level of the distribution network operation.

[0065] Specifically, we first identify obvious invalid data values, such as zero, spaces, garbled characters, and obviously abnormal data values. For example, a data value that is usually 0.1 may be 1000 at a certain moment. Of course, before eliminating them, related work must also be checked first. For example, extreme weather may also cause obvious errors in related lines or equipment.

[0066] After removing the relevant data, weights are assigned to the remaining valid data values. The specific method is as follows:

[0067] S1 obtains the corresponding weights of the current indicators at a certain moment according to the hierarchical analysis method and the entropy weight method, which are recorded as the first original weight and the second original weight, and then obtains the original comprehensive weight of the current indicator according to the first original weight and the second original weight;

[0068] Although the weight assignment of the entropy weight method is relatively objective, it is highly sample-dependent. As the data sample changes, the weight will also change, which may lead to weight distortion and invalid final results. The hierarchical analysis method is more subjective, resulting in the problem that the weight assignment may be biased due to subjective consciousness. Therefore, the present invention improves the weights assigned by the two methods.

[0069] S2 obtains the mutation degree of the current sub-indicator at a certain moment based on the valid data value, the mutation degree is determined by a trapezoidal function, and the corresponding weight optimization model is expressed by using the mutation degree and the original comprehensive weight.

[0070] The mutation degree described in the embodiment of the present invention is not erroneous data, but rather data mutation caused by differences in temperature, weather, and region. Therefore, it cannot be directly eliminated, but needs to be given different weights to highlight its existence. Specifically:

[0071] The trapezoidal function corresponding to the mutation degree is expressed as:

[0072]

[0073] Among them, β i,k is the evaluation value of the current sub-index i at the kth moment, ω1, ω2 are the left and right boundary values ​​of the normal range of sub-index i at the kth moment, which are defined according to relevant standards. If there is no relevant standard, the worker's experience value is used as the limit value. Therefore, u i,k The smaller the value, the higher the mutation degree of the corresponding indicator at the kth moment. According to this function, the mutation degree of the current sub-indicator at a certain moment can be obtained, so its value range is [0, 1]. The smaller the value, the higher the mutation degree of the indicator at the kth moment.

[0074] S3 determines the weight of the current sub-indicator at a certain moment according to the weight optimization model. That is, if the degree of mutation is high, the first weight obtained by the weight optimization model is used; if there is no mutation or the degree of mutation is low, the second weight obtained by the weight optimization model is used, and so on, to obtain the energy efficiency evaluation value at the last moment.

[0075] Therefore, the weight optimization model is expressed as:

[0076]

[0077] Among them, x i,k is the first original weight of the i-th sub-index at the k-th moment, y i,k is the second original weight of the i-th sub-index at the k-th moment, u i,kis the value corresponding to the degree of mutation, μ1 and μ2 are the thresholds of the degree of mutation, which are fixed values. In the embodiment of the present invention, μ1=0.7, μ2=0.9 can be selected. α is the weight coefficient, which is a fixed value. In this embodiment, α=0.1, which is the base number reflecting the size of the weight coefficient. Therefore, when u i,k <0.7, the index i has a larger mutation at the kth moment, so a larger weight coefficient is given, thereby increasing its corresponding weight. When 0.7≤u i,k When u is less than or equal to 0.9, the mutation degree of the indicator is general, and the larger weight value of the first original weight and the second original weight is used as the weight value; when u i,k When it is >0.9, that is, there is basically no mutation and it is almost the same as the previous corresponding data, the minimum weight value is given.

[0078] This type of weight distribution sets the weights of indicators based on whether different sub-indicators change suddenly, amplifying the differences between different indicators, thereby ensuring the rationality and discrimination of the weights, making it easier to obtain the weak points of energy efficiency at each moment, and the evaluation value obtained by this evaluation method is more accurate.

[0079] The energy efficiency evaluation module is used to calculate the energy efficiency evaluation values ​​of different distribution networks in the processing area at a specified time or time period based on various sub-indicators and corresponding weights.

[0080] The energy efficiency evaluation value at a certain moment can be expressed as:

[0081]

[0082] Where N is the total number of sub-indicators, d i is the effective evaluation data value corresponding to the i-th indicator, f(u i,k ) is the change weight coefficient calculated above;

[0083] The energy efficiency evaluation value for one day can be expressed as:

[0084]

[0085] Among them, ρ k is the weight value determined according to the load ratio.

[0086] Then the energy efficiency evaluation values ​​corresponding to a period of time and different quarters are calculated.

[0087] The evaluation value calibration module is used to calibrate abnormal evaluation data in the energy efficiency evaluation values ​​of different distribution networks in different processing areas to obtain valid evaluation data values ​​for evaluating the energy efficiency of the distribution network.

[0088] In order to more accurately obtain energy efficiency evaluation values ​​corresponding to different regions and different temperature factors, the evaluation value calibration module calibrates the abnormal evaluation data in the energy efficiency evaluation values ​​of different distribution networks in different processing areas to obtain valid evaluation data for evaluating the energy efficiency of the distribution network, including:

[0089] S21 constructs an energy efficiency curve graph representing the same region, where the horizontal axis is time and the vertical axis is the evaluation value of the current sub-indicator, and is recorded as a first curve graph; the region includes three types of regions: suburban areas, urban areas, and rural areas;

[0090] S22 compares and analyzes the first curve graphs within 60 consecutive days in the same quarter. If the evaluation values ​​corresponding to the same time period differ by about 0.2, it is recorded as a valid evaluation data value. Otherwise, it is recorded as abnormal evaluation data. The abnormal evaluation data here refers to erroneous data under non-extreme weather conditions. It should be noted that the data in the present invention has eliminated relevant data under extreme environments. These erroneous data are not conducive to finding the impact of different regions or different quarters on the energy efficiency of the distribution network. Therefore, calibration is performed here to more accurately discover the weak points of the energy efficiency of the distribution network in each region.

[0091] After testing, it was found that the evaluation values ​​in the same region, the same quarter and the same time period would not differ much. If the difference significantly exceeds 0.2, it basically means that there is a serious problem. At this time, staff need to investigate rather than ignore it. In order to find the weak points in energy efficiency of the distribution network in different regions and weather conditions, this application first calibrates the abnormal value to a normal value.

[0092] The abnormal evaluation data is processed as follows: the average value of the sum of the valid evaluation data values ​​at other times is taken as the calibrated new valid evaluation data value, which replaces the previous abnormal data.

[0093] Based on the calibrated new effective evaluation data, the comprehensive evaluation values ​​of the current sub-indicators for each of the four quarters are calculated, and the total evaluation values ​​of the indicators for different regions and quarters are obtained. This allows the energy efficiency shortcomings of each active distribution network to be intuitively identified, and corresponding planning and loss reduction decisions can be made.

[0094] Example 2

[0095] like Figure 2 As shown, based on Example 1, the present invention also includes a loss reduction decision module, which is used to confirm the energy efficiency problems of each distribution network based on the effective evaluation data of the distribution network energy efficiency, and provide specific loss reduction plans based on medium and low voltage lines and substations respectively. After implementing the loss reduction plan for a period of time, the corresponding first quality index is calculated, and the current first quality index is compared and analyzed with the first quality index before the loss reduction; the first quality index includes: voltage qualified cumulative time rate, three-phase load imbalance, daily load rate and line maximum voltage drop percentage;

[0096] A specific loss reduction plan is given based on the distribution transformer. After implementing the loss reduction plan for a period of time, the corresponding second quality index is calculated, and the current second quality index is compared and analyzed with the second quality index before the loss reduction to confirm the effectiveness of the loss reduction plan.

[0097] The specific loss reduction plan based on the distribution transformer includes a first loss reduction plan and a second loss reduction plan. The first loss reduction plan includes replacing the transformer model corresponding to the calculated high line loss rate assessment data, and obtaining a transformer replacement evaluation based on the second quality index of the replaced transformer under a fixed load. The second quality index includes: load loss, no-load loss, and the total loss reduction after comparing and analyzing the current second quality index with the second quality index before loss reduction.

[0098] The second loss reduction plan includes first analyzing the current operating status of the transformer and determining whether it belongs to the optimal operating mode, and then obtaining relevant calculations for the optimal operation, wherein the optimal operating mode is the economic operating mode; the relevant calculations for the optimal operation include: calculating the upper and lower limits corresponding to the economic operating range of the replaced transformer, and analyzing and comparing the losses with those under the most economical load conditions.

[0099] Since transformers are different from lines and substations, this embodiment adopts different loss reduction decisions to improve the energy efficiency of the distribution network in a more targeted manner.

[0100] Example 3

[0101] like Figure 3 As shown, the present invention also provides an energy efficiency evaluation method for energy saving and loss reduction based on medium and low voltage distribution networks, the method comprising:

[0102] Construct evaluation indicators for medium and low voltage lines, substations, and distribution transformers, as well as their corresponding sub-indicators, and collect relevant data values ​​for different distribution networks in the area to be processed within a certain period of time;

[0103] Preprocess the data values ​​corresponding to different evaluation indicators collected. The preprocessing includes screening out invalid data values ​​and eliminating them, and assigning different weights based on whether the valid data values ​​have undergone mutations.

[0104] Calculate the energy efficiency evaluation values ​​of different distribution networks in the processing area at a specified time or time period based on each sub-indicator and the corresponding weight;

[0105] The abnormal evaluation data in the energy efficiency evaluation values ​​of different distribution networks in different areas to be processed are calibrated to obtain valid evaluation data values ​​for evaluating the energy efficiency of the distribution network.

[0106] Furthermore, the method further comprises:

[0107] Based on the effective evaluation data of distribution network energy efficiency, the energy efficiency issues of each distribution network are confirmed, and specific loss reduction plans are proposed based on medium and low voltage lines and substations. After the loss reduction plan is implemented for a period of time, the corresponding first quality index is calculated and compared with the first quality index before the loss reduction. The first quality index includes: the cumulative time rate of voltage compliance, the three-phase load imbalance, the daily load rate, and the maximum voltage drop percentage of the line;

[0108] A specific loss reduction plan is given based on the distribution transformer. After implementing the loss reduction plan for a period of time, the corresponding second quality index is calculated, and the current second quality index is compared and analyzed with the second quality index before the loss reduction to confirm the effectiveness of the loss reduction plan.

[0109] Further, including:

[0110] The sub-indicators of the medium and low voltage line evaluation indicators include: power supply radius qualification rate P 11 , the rate of economic operation lines P 12 , line loss qualified rate P 13 , the proportion of severely damaged lines P 14 , line power factor qualification rate P 15 , average cross-section qualification rate of main line P 16 , the qualified rate of total capacity of line access distribution P 17 ;

[0111] The sub-indicators of the substation evaluation index include: Substation power supply radius qualification rate P 21 , the average cross-section qualification rate P of the main line in the substation area 22 , the average cross-sectional area qualification rate of the building line P 23 , the proportion of low-loss areas P 24 , comprehensive line loss rate P 25 , the qualified rate of line loss in a single substation area P 26 ;

[0112] The distribution transformer evaluation index includes sub-indicators such as line loss calculation for classification, voltage division, line division and substation area, and includes the energy consumption values ​​corresponding to the transformer when different temperatures, loads and powers occur, that is, analyzing the impact of changes in external temperature on the transformer energy consumption, analyzing the impact of changes in load on the transformer energy consumption, and analyzing the impact of changes in power factor on the transformer energy consumption.

[0113] The energy-saving and loss-reduction incremental energy efficiency evaluation method based on medium and low voltage distribution networks provided by the present invention is similar to the corresponding system and will not be described in detail here.

[0114] Example 4

[0115] Based on the above embodiment, the present invention can also provide a method for constructing an energy efficiency analysis model:

[0116] Se=we-1Se-1+we-2Se-2+we-3Se-3+we-4Se-4

[0117] Where Se represents the energy efficiency model construction evaluation, Se-1 to Se-4 subtables represent the adaptability evaluation scores of topology structure, equipment characteristics, operating parameters and environmental factors, and we-1 to we-4 represent the weights of topology structure, equipment characteristics, operating parameters and environmental factors in the evaluation, respectively.

[0118] A multi-dimensional energy efficiency analysis model is constructed by comprehensively considering the topological structure, equipment characteristics, operating parameters and environmental factors of the distribution network.

[0119] Use machine learning algorithms and big data analysis technology to train and optimize the model to improve the accuracy and predictive ability of energy efficiency assessment.

[0120] Introduce cloud computing and edge computing technologies to achieve real-time updates and adaptive adjustments of models.

[0121] Accurate calculation of incremental energy efficiency:

[0122] Based on the energy efficiency analysis model (Se=we-1Se-1+we-2Se-2+we-3Se-3+we-4Se-4), the energy efficiency of the distribution network before and after the implementation of energy-saving and loss-reduction measures is accurately calculated.

[0123] The comparative analysis method is used to quantitatively evaluate the degree of improvement in energy efficiency brought about by energy-saving and loss-reduction measures, and to obtain the incremental energy efficiency value.

[0124] Establish an incremental energy efficiency database to store and manage calculation results to facilitate subsequent analysis and decision-making.

[0125] Diversified formulation of energy efficiency optimization strategies:

[0126] Develop targeted energy efficiency optimization strategies based on the incremental energy efficiency calculation results and the prediction results of the energy efficiency analysis model.

[0127] Based on the actual needs and conditions of the distribution network, a variety of optimization plans are proposed, including equipment upgrades, operation mode adjustments, and new technology applications.

[0128] Establish an energy efficiency optimization strategy library to classify, store and manage proposed solutions to facilitate subsequent selection and implementation.

[0129] Implementation and effectiveness evaluation of energy efficiency optimization strategies:

[0130] According to the solutions in the energy efficiency optimization strategy library, select appropriate parameters and strategies for implementation.

[0131] Monitor and record data in real time during the implementation process to ensure the effective execution of the strategy.

[0132] Regularly evaluate the energy efficiency improvement effects after implementation, conduct comparative analysis with expected targets, identify problems in a timely manner and make adjustments.

[0133] Continuous monitoring and improvement of energy efficiency:

[0134] Establish a distribution network energy efficiency monitoring system to monitor the distribution network’s operating data and energy efficiency performance in real time.

[0135] Obtain the grid efficiency parameters of the evaluation object - establish an efficiency analysis model - calculate and obtain data based on the energy efficiency analysis model - optimize the solution based on the evaluation parameters.

[0136] Regularly analyze and evaluate energy efficiency monitoring data, and take timely measures to improve any energy efficiency decline or abnormal phenomena.

[0137] In combination with the development of new technologies and new methods, we will continuously update and optimize energy efficiency analysis models and energy efficiency optimization strategies to maintain continuous improvement in the energy efficiency level of the distribution network.

[0138] For example, in bustling city centers, high-rise buildings are rising from the ground, and the demand for electricity is increasing. To meet this challenge, a method for energy conservation, loss reduction, and incremental energy efficiency evaluation was implemented for a large city's high-voltage distribution network.

[0139] First, a data acquisition network was carefully laid out, with high-precision sensors and data acquisition equipment installed at key nodes throughout the high-voltage distribution network. These devices act like "power detectives," constantly capturing critical operating data such as voltage, current, and power factor.

[0140] However, data collection is only the first step. Amidst massive amounts of data, outliers are inevitable. These may be caused by equipment failures, sensor errors, or environmental factors. To ensure data accuracy, advanced algorithms are used to meticulously identify and process outliers, ensuring that every piece of data truly reflects the operating status of the distribution network.

[0141] Next, they used mathematical methods and simulation technology to construct an energy efficiency analysis model. This model, like a "power brain," comprehensively considers multiple factors, including the distribution network's topology, equipment parameters, and operating strategies, to provide an in-depth analysis of the network's energy efficiency.

[0142] The energy efficiency analysis model identified potential energy savings and loss reduction opportunities. Based on these findings, the incremental energy efficiency before and after implementing these energy-saving and loss-reduction measures was calculated. These quantitative results provide a clearer picture of the energy-saving and loss-reduction effects and provide strong support for developing targeted energy efficiency optimization strategies.

[0143] Ultimately, based on the incremental energy efficiency calculation results, a series of energy efficiency optimization measures were formulated, such as adjusting operating methods and optimizing equipment configuration. The implementation of these measures not only effectively improved the energy efficiency of the distribution network but also injected new vitality into the sustainable development of the city.

[0144] Rural low-voltage distribution network energy efficiency improvement practice expansion

[0145] In vast rural areas, low-voltage distribution networks bear the heavy responsibility of providing farmers with stable electricity. However, due to outdated equipment and poor management, rural distribution networks generally have low energy efficiency. To improve this situation, we have implemented an energy efficiency evaluation method for low-voltage distribution networks in rural areas to reduce energy losses and increase energy efficiency.

[0146] Given the unique characteristics of rural distribution networks, a tailored strategy was adopted. First, comprehensive operational data from the rural distribution network was collected. Sensors and equipment installed at key nodes captured key indicators such as voltage and current. This data provided a solid foundation for subsequent analysis.

[0147] During the data preprocessing phase, special attention was paid to data accuracy and completeness. Multiple algorithms were used to identify and process abnormal data that might occur in rural distribution networks, ensuring data reliability.

[0148] Next, an energy efficiency analysis model was constructed based on the characteristics of rural distribution networks. This model fully considers the characteristics of rural distribution networks, such as dispersed loads and long power supply distances, and provides targeted energy efficiency optimization suggestions.

[0149] Based on the results of the energy efficiency analysis model, a series of energy efficiency optimization measures were developed. For example, reactive power compensation in rural distribution networks was strengthened, line layout was optimized, and unnecessary energy consumption and losses were reduced. These measures have significantly improved the energy efficiency of rural distribution networks, providing farmers with a more stable and efficient power supply.

[0150] Energy efficiency evaluation and transaction application expansion in power market

[0151] With the continuous development and improvement of the power market, energy efficiency evaluation and trading have become important links in the market. Using energy saving, loss reduction and incremental energy efficiency evaluation methods, we provide professional energy efficiency evaluation services for the power market.

[0152] First, we conducted energy efficiency assessments on multiple distribution networks. By collecting data, building models, and conducting analytical calculations, we determined the energy efficiency status of each distribution network. These assessment results not only provide strong data support for electricity market participants, but also help them better understand the energy efficiency characteristics and optimization potential of distribution networks.

[0153] Based on the energy efficiency assessment results, customized energy efficiency optimization recommendations are provided to power market participants. These recommendations not only address optimizations in equipment configuration and operating methods, but also include the introduction and application of emerging technologies. By implementing these recommendations, power market participants can further improve the energy efficiency of their distribution networks, reduce operating costs, and enhance their market competitiveness.

[0154] Furthermore, the incremental energy efficiency evaluation method for energy conservation and loss reduction is applied to energy efficiency transactions in the power market. This provides fair and transparent energy efficiency assessment services to both parties involved in transactions, helping them accurately understand the energy efficiency status of their trading partners and formulate more rational trading strategies. This not only promotes the healthy development of the power market but also injects new impetus into energy conservation, loss reduction, and energy efficiency improvements in the distribution network.

[0155] Comprehensive application and expansion with other distribution network management technologies

[0156] In the distribution network of a certain industrial park, the integrated application of energy-saving and loss-reduction incremental energy efficiency evaluation methods, demand-side management, and energy storage optimization technologies was implemented, achieving remarkable results.

[0157] First, a comprehensive energy efficiency assessment of the industrial park's distribution network was conducted using an incremental energy efficiency evaluation method for energy conservation and loss reduction. By collecting operational data from key nodes and constructing an energy efficiency analysis model, we gained a deep understanding of the distribution network's energy efficiency status and operational characteristics. These assessment results provide strong data support for subsequent energy efficiency optimization.

[0158] Next, incorporating demand-side management techniques, we conducted an in-depth analysis of the electricity demand and behavior patterns of businesses within the industrial park. Through collaboration with these businesses, we developed targeted energy-saving measures, such as adjusting production plans and optimizing equipment operation. These measures not only reduced the businesses' electricity costs but also improved the reliability of the distribution network.

[0159] Energy storage optimization technology was also used to configure and optimize energy storage equipment within the industrial park's distribution network. During peak load periods, energy storage equipment released electricity, balancing supply and demand on the distribution network and reducing energy consumption and losses. Furthermore, energy storage equipment can absorb excess energy during off-peak hours, achieving optimal energy utilization and conservation.

[0160] Finally, the present invention also provides a computer-readable storage medium, on which is stored a program for an incremental energy efficiency evaluation algorithm for energy saving and loss reduction based on medium and low voltage distribution networks. When the incremental energy efficiency evaluation program for energy saving and loss reduction based on medium and low voltage distribution networks is executed by a processor, the steps of the incremental energy efficiency evaluation algorithm for energy saving and loss reduction based on medium and low voltage distribution networks as described above are implemented.

[0161] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0162] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0163] Obviously, those skilled in the art may make various changes and modifications to the embodiments of the present invention without departing from the spirit and scope of the embodiments of the present invention. Thus, if such changes and modifications of the embodiments of the present invention fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. An energy-saving and loss-reducing incremental energy efficiency evaluation system based on medium and low voltage distribution networks, characterized in that: The system includes: Energy efficiency index collection module, which is used to construct medium and low voltage line evaluation index, substation evaluation index and distribution transformer evaluation index and their corresponding sub-indicators, and collect relevant data values ​​of different distribution networks in the area to be processed within a certain period of time; The indicator preprocessing module is used to preprocess the data values ​​corresponding to the different evaluation indicators collected. The preprocessing includes screening out invalid data values ​​and eliminating them, and assigning different weights according to whether the valid data values ​​have mutated. Energy efficiency evaluation module, used to calculate the energy efficiency evaluation value of the distribution network in the area to be processed at a specified time or time period according to each sub-indicator and the corresponding weight; The evaluation value calibration module is used to calibrate abnormal evaluation data in the energy efficiency evaluation values ​​of the distribution network in different areas to be processed, so as to obtain effective evaluation data values ​​for evaluating the energy efficiency of the distribution network.

2. The energy-saving and loss-reducing incremental energy efficiency evaluation system based on medium and low voltage distribution networks according to claim 1 is characterized in that: The system also includes: The loss reduction decision module is used to confirm the energy efficiency problems of each distribution network based on the effective evaluation data of the distribution network energy efficiency, and give specific loss reduction plans based on medium and low voltage lines and substations respectively. After implementing the loss reduction plan for a period of time, the corresponding first quality index is calculated, and the current first quality index is compared and analyzed with the first quality index before the loss reduction; the first quality index includes: voltage qualified cumulative time rate, three-phase load imbalance, daily load rate and line maximum voltage drop percentage; A specific loss reduction plan is given based on the distribution transformer. After the loss reduction plan is implemented for a period of time, the corresponding second quality index is calculated, and the current second quality index is compared and analyzed with the second quality index before the loss reduction to confirm the effectiveness of the loss reduction plan.

3. The energy-saving and loss-reducing incremental energy efficiency evaluation system based on medium and low voltage distribution networks according to claim 1 or 2 is characterized in that: The sub-indicators of the medium and low voltage line evaluation indicators include: power supply radius qualification rate, economic operation line number rate, line line loss qualification rate, heavy loss line ratio, line power factor qualification rate, trunk line average cross-section qualification rate, line access distribution total capacity qualification rate; The sub-indicators of the substation evaluation index are: substation power supply radius qualification rate, substation trunk line average cross-section qualification rate, building line average cross-sectional qualification rate, low-loss substation ratio, substation comprehensive line loss rate, single substation line loss qualification rate; The distribution transformer evaluation index includes sub-indicators such as line loss calculation for classification, voltage division, line division and substation area, and includes the energy consumption values ​​corresponding to the transformer when different temperatures, loads and powers occur, that is, analyzing the impact of changes in external temperature on the transformer energy consumption, analyzing the impact of changes in load on the transformer energy consumption, and analyzing the impact of changes in power factor on the transformer energy consumption.

4. The energy-saving and loss-reducing incremental energy efficiency evaluation system based on medium and low voltage distribution networks according to claim 3 is characterized in that: The specific loss reduction scheme based on the distribution transformer includes a first loss reduction scheme and a second loss reduction scheme. The first loss reduction scheme includes replacing the transformer model corresponding to the high line loss rate assessment data calculated, and obtaining a transformer replacement evaluation according to the second quality index of the replaced transformer under a fixed load, the second quality index including: load loss, no-load loss, and the loss reduction of the total loss after comparing and analyzing the current second quality index with the second quality index before the loss reduction; The second loss reduction plan includes first analyzing the current operating status of the transformer and determining whether it belongs to the optimal operating mode, and then obtaining relevant calculations for the optimal operation, wherein the optimal operating mode is an economical operating mode; the relevant calculations for the optimal operation include: calculating the upper and lower limits corresponding to the economical operating range of the replaced transformer, and analyzing and comparing the losses with those under the most economical load conditions.

5. The energy-saving and loss-reducing incremental energy efficiency evaluation system based on medium and low voltage distribution networks according to claim 3 is characterized in that: In the indicator preprocessing module, different weights are assigned according to whether the valid data value has a mutation, including: According to the hierarchical analysis method and the entropy weight method, the corresponding weights of the current indicators at a certain moment are obtained respectively, which are recorded as the first original weight and the second original weight; Based on the valid data value, the mutation degree of the current sub-indicator at a certain moment is obtained, the mutation degree is determined by a trapezoidal function, and the corresponding weight optimization model is expressed by using the mutation degree and the original comprehensive weight; The weight of the current sub-indicator at a certain moment is determined according to the weight optimization model. That is, if the degree of mutation is high, the first weight obtained by the weight optimization model is used; if there is no mutation or the degree of mutation is low, other weights obtained by the weight optimization model are used, and so on to obtain the energy efficiency evaluation value at the last moment.

6. The energy-saving and loss-reducing incremental energy efficiency evaluation system based on medium and low voltage distribution networks according to claim 5 is characterized in that: The weight optimization model is expressed as: Among them, x i,k is the first original weight of the ith sub-indicator at the kth moment, y i,k is the second original weight of the ith sub-index at the kth moment, u i,k is the value corresponding to the mutation degree, μ1 and μ2 are the thresholds of the mutation degree, which are fixed values, and α is the weight coefficient, which is a fixed value; The trapezoidal function corresponding to the mutation degree is expressed as: Among them, β i,k is the evaluation value of the current sub-indicator i at the kth moment, ω1, ω2 are the left and right boundary values ​​of the normal range of sub-indicator i at the kth moment, so u i,k The smaller the value, the higher the mutation degree of the corresponding indicator at the kth moment.

7. The energy-saving and loss-reducing incremental energy efficiency evaluation system based on medium and low voltage distribution networks according to claim 5 is characterized in that: In the evaluation value calibration module, the abnormal evaluation data in the energy efficiency evaluation values ​​of different distribution networks in different areas to be processed are calibrated to obtain effective evaluation data for evaluating the energy efficiency of the distribution network, including: Construct an energy efficiency curve graph of the same region, where the horizontal axis is time and the vertical axis is the evaluation value of the current sub-indicator, and record it as the first curve graph; the region includes three types of regions: suburbs, urban areas and rural areas; Compare and analyze the first curve charts within 60 consecutive days in the same quarter. If the evaluation values ​​corresponding to the same time differ within 0.2, they are recorded as valid evaluation data values. Otherwise, they are recorded as abnormal evaluation data. The abnormal evaluation data is processed as follows: the sum of the valid evaluation data values ​​at other times is averaged as the new valid evaluation data value after calibration; According to the calibrated new valid evaluation data values, the comprehensive evaluation values ​​of the current sub-indicators for the four quarters are calculated respectively, and then the total evaluation values ​​of the indicators in different regions and quarters are obtained.

8. A method for evaluating energy efficiency based on energy saving and loss reduction increments in medium and low voltage distribution networks, characterized in that: The method includes: Construct the evaluation indexes of medium and low voltage lines, substations and distribution transformers and their corresponding sub-indicators, and collect relevant data values ​​of different distribution networks in the area to be processed within a certain period of time; Preprocess the data values ​​corresponding to different evaluation indicators collected, including screening out invalid data values ​​and eliminating them, and assigning different weights according to whether the valid data values ​​have mutated; Calculate the energy efficiency evaluation values ​​of different distribution networks in the area to be processed at a specified time or time period according to each sub-indicator and the corresponding weight; The abnormal evaluation data in the energy efficiency evaluation values ​​of different distribution networks in different areas to be processed are calibrated to obtain the effective evaluation data values ​​for evaluating the energy efficiency of the distribution network.

9. The energy saving and loss reduction incremental energy efficiency evaluation method based on medium and low voltage distribution networks according to claim 8 is characterized in that: The method further includes: The energy efficiency problems of each distribution network are confirmed based on the effective evaluation data of the distribution network energy efficiency, and specific loss reduction plans are given based on medium and low voltage lines and substations respectively. After the loss reduction plan is implemented for a period of time, the corresponding first quality index is calculated, and the current first quality index is compared and analyzed with the first quality index before the loss reduction; the first quality index includes: voltage qualified cumulative time rate, three-phase load imbalance, daily load rate and line maximum voltage drop percentage; A specific loss reduction plan is given based on the distribution transformer. After the loss reduction plan is implemented for a period of time, the corresponding second quality index is calculated, and the current second quality index is compared and analyzed with the second quality index before the loss reduction to confirm the effectiveness of the loss reduction plan.

10. The energy efficiency evaluation method for energy saving and loss reduction based on medium and low voltage distribution networks according to claim 9 is characterized in that: The sub-indicators of the medium and low voltage line evaluation indicators include: power supply radius qualification rate, economic operation line number rate, line line loss qualification rate, heavy loss line ratio, line power factor qualification rate, trunk line average cross-section qualification rate, line access distribution total capacity qualification rate; The sub-indicators of the substation evaluation index are: substation power supply radius qualification rate, substation trunk line average cross-section qualification rate, building line average cross-sectional qualification rate, low-loss substation ratio, substation comprehensive line loss rate, single substation line loss qualification rate; The distribution transformer evaluation index includes sub-indicators such as line loss calculation for classification, voltage division, line division and substation area, and includes the energy consumption values ​​corresponding to the transformer when different temperatures, loads and powers occur, that is, analyzing the impact of changes in external temperature on the transformer energy consumption, analyzing the impact of changes in load on the transformer energy consumption, and analyzing the impact of changes in power factor on the transformer energy consumption.

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