Power quality optimization method, electronic device, storage medium and program product

Abstract

The embodiment of the invention provides an electric energy quality optimization method, electronic equipment, a storage medium and a program product. The invention relates to the field of power quality optimization, and the method comprises the steps: obtaining the sensitivity of the user equipment to a total index value of the power quality of a target park for any user equipment in the target park; according to the sensitivity of the user equipment to the total index value, calculating the total economic loss of the user equipment corresponding to different power quality levels; calculating a user demand evaluation value of the user equipment changing from the first specified electric energy quality grade to the second specified electric energy quality grade; and determining a power quality optimization scheme of the target park based on the user demand evaluation values of all the user equipment in the target park. The method is used for achieving the technical effect of improving the electric energy quality level of the target park.

Description

Technical Field

[0001] The present application relates to the field of power quality optimization, and in particular to a power quality optimization method, electronic equipment, storage medium, and program product. Background Art

[0002] With the rapid development of industrial parks, electricity demand from users within them is rapidly increasing. As comprehensive areas housing diverse users, industrial parks present significant variations in power quality requirements due to differences in production processes, equipment characteristics, and operating modes. Therefore, with the promotion of smart grids and green energy concepts, optimizing power quality is becoming increasingly important.

[0003] Existing technologies for optimizing power quality in target industrial parks often use fixed-threshold power quality optimization methods or economic cost-based optimization methods. Fixed-threshold power quality optimization methods set the same power quality threshold for all user devices based on national or industry standards. By monitoring grid parameters in real time, if a metric exceeds the threshold, universal remediation measures are implemented to ensure power quality meets standards. Economic cost-based optimization methods, on the other hand, quantify the economic losses caused by power quality fluctuations by analyzing data such as user device failure rates and repair costs. These methods optimize power quality management solutions with the goal of minimizing total economic losses.

[0004] However, existing technologies vary significantly in the tolerance of various types of equipment to power quality indicators. Different devices exhibit varying sensitivities to power quality issues such as voltage fluctuations, frequency fluctuations, harmonics, and flicker during operation, directly impacting operational stability and production efficiency. Furthermore, power quality issues can lead to additional energy consumption and carbon emissions, making environmental considerations particularly important for power quality optimization. Consequently, existing technologies for optimizing power quality suffer from poor results. Summary of the Invention

[0005] The embodiments of the present application provide a method for optimizing power quality, electronic equipment, storage medium, and program product to achieve the technical effect of improving the power quality level of a target park.

[0006] In a first aspect, an embodiment of the present application provides a method for optimizing power quality, including:

[0007] For any user equipment in a target park, obtaining the sensitivity of the user equipment to a total index value of the power quality of the target park; wherein the target park includes multiple user equipment;

[0008] Based on the sensitivity of user equipment to the total index value, the total economic losses of user equipment corresponding to different power quality levels are calculated respectively; the power quality level is determined according to the total index value of the power quality of the target park;

[0009] Calculating a user demand assessment value of a user device when power quality is changed from a first specified power quality level to a second specified power quality level; wherein the first specified power quality level and the second specified power quality level are two different power quality levels among different power quality levels;

[0010] Based on the user demand assessment values ​​of all user devices in the target park, the power quality optimization plan of the target park is determined.

[0011] In one possible implementation, before obtaining the sensitivity of the user equipment to the total indicator value of the power quality of the target campus, the method further includes:

[0012] Calculate the total power quality index value of the target park based on the actual values ​​of the power quality sub-indicators of the target park and the corresponding weights;

[0013] Determine the power quality level based on the total index value of power quality.

[0014] In one possible implementation, obtaining the sensitivity of the user equipment to the total indicator value of the power quality of the target campus includes:

[0015] The sensitivity of the user equipment to each power quality sub-indicator is calculated based on the actual value of the power quality sub-indicator, the acceptable threshold of the user equipment to the power quality sub-indicator, and the corresponding sensitivity coefficient;

[0016] The sensitivity of the user equipment to the total power quality index value is calculated based on the sensitivity of the user equipment to each power quality sub-index and a preset interaction coefficient; wherein the interaction coefficient is calculated based on an interaction coefficient regression model.

[0017] In one possible implementation, based on the sensitivity of the user equipment to the total power quality index value, the total economic losses of the user equipment corresponding to different power quality levels are calculated, including:

[0018] For a target power quality level, calculating energy consumption loss and defect loss of the user equipment corresponding to the target power quality level based on the sensitivity of the user equipment to the total index value of power quality; the target power quality level is one of multiple power quality levels;

[0019] The sum of energy consumption loss and defect loss is determined as the economic loss of the user equipment corresponding to the target power quality level;

[0020] The carbon emissions of the user equipment are calculated based on the carbon emission factor of the target park's power grid and the additional energy consumption of the user equipment corresponding to the target power quality level;

[0021] The economic loss and carbon emissions are determined as the total economic loss of the user equipment corresponding to the target power quality level.

[0022] In one possible implementation, calculating the energy consumption loss and defect loss of the user equipment corresponding to the target power quality level based on the user equipment's sensitivity to the total power quality index value includes:

[0023] The energy loss of the user equipment corresponding to the target power quality level is calculated based on the user equipment's sensitivity to the total power quality index value, the user equipment's additional energy consumption corresponding to the target power quality level, the real-time electricity price, the user equipment's ideal energy consumption, and the efficiency factor related to the target power quality level.

[0024] The defect loss of the user equipment corresponding to the target power quality level is calculated based on the defective rate corresponding to the target power quality level, the unit defective loss of the user equipment, and the relationship coefficient between the sensitivity of the user equipment and the defective rate.

[0025] In one possible implementation, calculating a user demand assessment value of a user equipment when the power quality level of the user equipment is changed from a first specified power quality level to a second specified power quality level includes:

[0026] Obtaining a first total economic loss corresponding to a first specified power quality level and a second total economic loss corresponding to a second specified power quality level;

[0027] Obtaining a willingness to pay for changing from a first specified power quality level to a second specified power quality level according to a difference between the first total economic loss and the second total economic loss;

[0028] Calculate the net benefit corresponding to the first specified power quality level based on the willingness to pay of all user devices in the target park and the service cost corresponding to the first specified power quality level;

[0029] Determine the net benefit as the user demand assessment value.

[0030] In one possible implementation, based on the user demand evaluation values ​​of all user devices in the target park, a power quality optimization solution for the target park is determined, including:

[0031] determining a maximum net benefit value from a plurality of net benefit values ​​corresponding to different power quality levels;

[0032] The power quality level corresponding to the maximum net profit value is determined as the power quality optimization plan for the target park.

[0033] In a second aspect, an embodiment of the present application provides a device for optimizing power quality, including:

[0034] An acquisition module, configured to acquire, for any user equipment in a target park, the sensitivity of the user equipment to the total index value of the power quality of the target park; wherein the target park includes multiple user equipment;

[0035] A first processing module is configured to calculate the total economic losses of the user equipment corresponding to different power quality levels according to the sensitivity of the user equipment to the total index value; the power quality level is determined according to the total index value of the power quality of the target park;

[0036] a second processing module, configured to calculate a user demand assessment value of a user equipment when the power quality level of the user equipment is changed from the first specified power quality level to the second specified power quality level; wherein the first specified power quality level and the second specified power quality level are two different power quality levels among the different power quality levels;

[0037] The second processing module is used to determine a power quality optimization solution for the target park based on the user demand evaluation values ​​of all user devices in the target park.

[0038] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a memory, a processor;

[0039] The memory stores computer-executable instructions;

[0040] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the above first aspect and / or various possible implementations of the first aspect.

[0041] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the first aspect above and / or various possible implementation methods of the first aspect.

[0042] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above first aspect and / or various possible implementation methods of the first aspect.

[0043] The power quality optimization method, electronic device, storage medium, and program product provided in the embodiments of the present application obtain the sensitivity of any user device in a target park to the total index value of the target park's power quality; calculate the total economic loss of the user device corresponding to different power quality levels based on the user device's sensitivity to the total index value; calculate the user demand assessment value of the user device when changing from a first specified power quality level to a second specified power quality level; and determine the power quality optimization plan for the target park based on the user demand assessment values ​​of all user devices in the target park. This method can effectively improve the optimization effect of the power quality of the target park. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0045] Figure 1 Schematic diagram of the process of optimizing the power quality provided in this application Figure 1 ;

[0046] Figure 2 Schematic diagram of the process of optimizing the power quality provided in this application Figure 2 ;

[0047] Figure 3 A schematic diagram of the structure of the power quality optimization device provided in this application;

[0048] Figure 4 This is a hardware diagram of the power quality optimization device provided in this application.

[0049] The above drawings illustrate specific embodiments of the present application, which will be described in more detail below. These drawings and the textual description are not intended to limit the scope of the present application in any way, but rather to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0050] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, like numbers in different figures represent like or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible embodiments consistent with the present application. Rather, they are merely examples of methods and approaches consistent with certain aspects of the present application, as detailed in the appended claims.

[0051] In existing technology, there are two main approaches to optimizing power quality in target industrial parks. One is a fixed-threshold optimization method. This method sets the same power quality threshold for all user devices according to national or industry standards. By monitoring grid parameters in real time, if a certain indicator exceeds the threshold, universal control measures are implemented to ensure that power quality meets the standard. The other is an economic cost optimization method. This method uses statistical data such as the failure rate and maintenance cost of user devices to quantify the economic losses caused by power quality fluctuations. The method then optimizes the power quality control solution with the goal of minimizing the total economic loss.

[0052] However, the existing technology has several significant technical problems. First, there is a lack of user differentiation analysis, that is, the differences in the sensitivity of different user devices to power quality are not fully considered, which leads to irrational allocation of governance resources, which may result in insufficient protection for highly sensitive users or excessive investment in low-sensitivity users. Secondly, the existing methods do not fully correlate power quality fluctuations with user economic losses or environmental costs, ignore the comprehensive consideration of economic and environmental costs, and are difficult to support the optimization decision of comprehensive benefits. In addition, the existing technology ignores the synergistic effects between different power quality problems and does not deeply analyze the synergistic impact when multiple indicators exceed the standards at the same time, resulting in a lack of targeted governance measures and difficulty in effectively controlling complex power quality problems. Finally, the existing methods often ignore the additional energy consumption and carbon emissions caused by power quality problems, fail to meet the needs of green energy management and sustainable development, and do not take into account the user's willingness to pay or equipment sensitivity differences, resulting in the optimization results may deviate from the actual needs of users. Therefore, the existing technology does not give sufficient consideration to the integrity of the system and is only guided by a single economic goal. It does not balance the cost of improving the overall power quality of the park with the comprehensive benefits of multiple users, which may lead to inefficient resource allocation. Therefore, there is a technical problem that the optimization effect of power quality in the existing technology is poor.

[0053] The power quality optimization method, electronic device, storage medium, and program product provided in the embodiments of the present application obtain the sensitivity of any user device in a target park to the total index value of the target park's power quality; calculate the total economic loss of the user device corresponding to different power quality levels based on the user device's sensitivity to the total index value; calculate the user demand assessment value of the user device when changing from a first specified power quality level to a second specified power quality level; and determine the power quality optimization plan for the target park based on the user demand assessment values ​​of all user devices in the target park. This method can effectively improve the optimization effect of the power quality of the target park.

[0054] The following specific embodiments describe in detail the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0055] Figure 1 Schematic diagram of the process of optimizing the power quality provided in this application Figure 1 ,like Figure 1 As shown, the method includes:

[0056] S101: For any user equipment in a target park, obtain the sensitivity of the user equipment to a total indicator value of power quality in the target park.

[0057] In this embodiment, the target park includes multiple user devices. By obtaining the sensitivity of each user device in the target park to the total power quality index value of the target park, the differences between different user devices in the target park are emphasized, thereby improving the rationality and comprehensiveness of subsequent power quality optimization and avoiding insufficient protection for highly sensitive users or excessive investment in less sensitive users during power quality optimization.

[0058] S102: Calculate the total economic losses of the user equipment corresponding to different power quality levels according to the sensitivity of the user equipment to the total index value.

[0059] In this embodiment, the power quality level is determined based on the target park's total power quality index value. Based on this total power quality index value, power quality can be divided into five levels, descending from high to low quality. Each level represents a different degree of power quality, covering various conditions from optimal to worst, and can help analyze and evaluate the stability and reliability of electricity. Grading power quality not only accurately identifies weak links in the power grid and differences in equipment sensitivity, but also provides a decision-making basis for optimizing resource allocation. As shown in the table below, power quality can be graded based on the total power quality index value.

[0060] Table 1 Power quality level classification table

[0061]

[0062] S103: Calculate a user demand assessment value of the user equipment when the power quality level of the user equipment is changed from the first specified power quality level to the second specified power quality level.

[0063] In this embodiment, the first designated power quality level and the second designated power quality level are two different power quality levels among different power quality levels. For example, the first designated power quality level can be determined as Q0; the user demand assessment value for each user device in the target park is calculated separately for the power quality level from Q0 to Q1, the user demand assessment value for Q0 to Q2, the user demand assessment value for Q0 to Q3, and the user demand assessment value for Q0 to Q4. The problem to be solved in this application is how to find an optimal method for power quality optimization. Therefore, the sensitivity of the user device to power quality, the economic and environmental impacts of power quality changes on the user device, and the benefits involved in power quality regulation in the target park are taken into account. The user demand assessment value for the user device changing from the first designated power quality level to the second designated power quality level actually refers to the total economic loss corresponding to the user device under different power quality levels, thereby further determining the net benefit of the target park under the second designated power quality level. This is a necessary technical means for determining a power quality optimization method.

[0064] S104: Determine a power quality optimization solution for the target park based on the user demand evaluation values ​​of all user devices in the target park.

[0065] In this embodiment, according to step S103, multiple user demand evaluation values ​​corresponding to each user device can be obtained, and then multiple user demand evaluation values ​​of all user devices in the target park can be obtained. According to the multiple user demand evaluation values, the power quality optimization plan of the target park can be determined. This method can optimize the power quality throughout the entire park and select the most appropriate power quality level, thereby maximizing the economic benefits of users and promoting the sustainable development of the park, taking into account the dual goals of economic benefits and environmental protection.

[0066] The power quality optimization method, electronic device, storage medium, and program product provided in the embodiments of the present application obtain the sensitivity of any user device in a target park to the total index value of the target park's power quality; calculate the total economic loss of the user device corresponding to different power quality levels based on the user device's sensitivity to the total index value; calculate the user demand assessment value of the user device when changing from a first specified power quality level to a second specified power quality level; and determine the power quality optimization plan for the target park based on the user demand assessment values ​​of all user devices in the target park. This method can effectively improve the optimization effect of the power quality of the target park.

[0067] Figure 2 Schematic diagram of the process of optimizing the power quality provided in this application Figure 2 ,like Figure 2 As shown, this embodiment Figure 1Based on the embodiment, the power quality optimization method is described in detail, wherein determining the power quality level of the target park can be implemented through step S201; obtaining the sensitivity of the user equipment to the total index value of the power quality of the target park can be implemented through step S202; determining the energy consumption loss and defect loss of the user equipment corresponding to the target power quality level can be implemented through steps S203 to S204; calculating the total economic loss of the user equipment corresponding to different power quality levels can be implemented through step S205; determining the user demand assessment value can be implemented through steps S206 to S207; determining the power quality optimization plan of the target park can be implemented according to step S208; the method includes:

[0068] S201. Calculate the total index value of the power quality of the target park based on the actual values ​​of the power quality sub-indicators of the target park and the corresponding weights; and determine the power quality level based on the total index value of the power quality.

[0069] In this embodiment, by obtaining the actual value and weight of each power quality sub-indicator, the total index value of the power quality of the target park is obtained, and then the power quality level corresponding to the target park can be obtained through the total index value of the power quality and Table 1 in Example 1.

[0070] In one possible implementation, the power quality in the target park includes multiple power quality sub-indicators, including but not limited to voltage deviation, voltage fluctuation, three-phase voltage imbalance, frequency deviation, voltage sag, voltage swell, voltage interruption and harmonics.

[0071] In one possible implementation, the total power quality index value of the target park can be determined based on the weight coefficient of each power quality sub-index and the actual value of the actually measured power quality index. The total power quality index value is obtained as objective data through the following formula, and the total power quality index value can be divided:

[0072]

[0073] Among them, q tot is the total power quality index value of the target park; w j is the weight of the jth power quality sub-index; x j is the actual value of the jth power quality sub-indicator; p is the total number of power quality sub-indicators.

[0074] Furthermore, the weight of the j-th power quality sub-index can be calculated by the following formula:

[0075]

[0076] Among them, w j is the weight of the jth power quality sub-index; xj is the actual value of the jth power quality sub-index; It is the sum of the actual values ​​of all power quality sub-indicators.

[0077] S202. For any user equipment in the target campus, calculate the user equipment's sensitivity to each power quality sub-indicator based on the actual value of the power quality sub-indicator, the user equipment's acceptable threshold for the power quality sub-indicator, and the corresponding sensitivity coefficient; and calculate the user equipment's sensitivity to the total power quality index value based on the user equipment's sensitivity to each power quality sub-indicator and a preset interaction coefficient.

[0078] In this embodiment, the sensitivity coefficient of each user equipment to each power quality sub-indicator may be determined according to the following formula:

[0079]

[0080] Among them, S i,j (x j ) is the sensitivity coefficient of user equipment i to the j-th power quality sub-indicator; x j is the actual value of the jth power quality sub-index; T i,j k is the acceptable threshold value of user equipment i for the jth power quality sub-indicator; i,j is the sensitivity coefficient, which indicates the growth rate of the loss caused to user equipment i after the jth power quality sub-index exceeds the acceptable threshold; p i,j is the nonlinear index of the impact of the j-th power quality sub-index on user equipment i, which is used to control the growth rate of sensitivity.

[0081] Furthermore, if the sensitivity coefficients of user device i to the power quality sub-indicators in the target campus have two or more non-zero values, this indicates that when multiple power quality sub-indicators coexist in the target campus, the different power quality sub-indicators will have a synergistic effect, and the impact on each user device is not simply the sum of the effects of the various power quality sub-indicators acting alone. Therefore, the summed impact of the user device's sensitivity coefficients to multiple power quality sub-indicators must be calculated according to the following formula. This is actually the user device's sensitivity to the total power quality indicator value:

[0082]

[0083] Among them, S i (x) is the sensitivity of user equipment i to the total index value of power quality; is the sum of all non-zero sensitivity coefficients of user equipment i; k i,j,k is the interaction coefficient, which represents the synergistic effect of at least two power quality sub-indicators on user equipment i; T i,jis the acceptable threshold value of user equipment i for the jth power quality sub-indicator; T i,k is the acceptable threshold of user equipment i for the kth power quality sub-indicator; p i,j is the nonlinear index of the impact of the j-th power quality sub-index on user equipment i; p i,k is the nonlinear index of the impact of the kth power quality sub-index on user equipment i; x j is the actual value of the jth power quality sub-index; x k is the actual value of the kth power quality sub-index.

[0084] Optionally, the interaction coefficient is calculated based on an interaction coefficient regression model. i,j,k Relying on historical data of user device i, the impact of user data is estimated through regression analysis. The following are the specific calculation steps:

[0085] By analyzing the production data of user equipment under different power quality levels, the economic loss and index x are fitted. j Assuming that the loss is linearly related to the part of the indicator that exceeds the standard, the economic loss of user equipment i under different power quality levels is L i,m , which is constructed as a function containing a single indicator and a synergistic effect, as shown in the following formula:

[0086]

[0087] Among them, L i,m is the economic impact of historical data point m on user device i; x j,m with x k,m are the actual values ​​of power quality sub-index j and power quality sub-index k at historical data point m; β0 is the benchmark coefficient, which refers to the economic loss caused by other problems of user equipment i when there is no power quality problem; β j is the single effect coefficient of power quality sub-index j, which refers to the economic loss caused when only power quality sub-index j exists; β j,k is the single effect coefficient of power quality sub-index j, which refers to the economic loss caused when only power quality sub-index j exists; ε m is the random error; the nonlinear index p i,j With p i,k Determined by the characteristics of the user's device.

[0088] In order to accurately calculate the impact of multiple power quality sub-indicators on user equipment i, the amount of historical data collected by user equipment i should be sufficient and cover a combination of multiple power quality indicators. However, since power quality problems do not exist in all data points, the historical data must first be pre-processed to eliminate redundant data and achieve accurate analysis. j,mAs shown in the following formula:

[0089]

[0090] Where I(·) is the indicator function. If x j,m >T i,j , I(·) is 1, otherwise it is 0.

[0091] The calculation formulas for nonlinear terms and synergistic terms are as follows:

[0092]

[0093] Through data preprocessing, redundant data in the collected data is eliminated to avoid the problem of long calculation time and difficulty caused by large amounts of data. The amount of data after preprocessing is recorded as M.

[0094] In order to accurately fit the interaction coefficient k i,j,k , using Multiple Regression Analysis (MRA) to establish a relationship model between multiple power quality sub-indicators and the economic losses of user equipment i, to predict the changes in the economic losses of user equipment i. MRA can simultaneously consider the impact of multiple independent variables on the dependent variable and is suitable for constructing complex practical problems. First, construct the power quality independent variable matrix X and the economic loss dependent variable matrix Y, as shown in the following formula:

[0095]

[0096] Among them, the economic loss dependent variable matrix Y is an M×1 matrix, L i,n It refers to the economic loss value of user equipment i at data point n. The power quality independent variable matrix X is an M×N matrix (N is related to the number of specified indicators, here N=4). In the matrix X, the elements in the first column are all 1, representing the benchmark coefficient β0. The other columns are the calculation results of the nonlinear term and the synergistic term of the indicator, representing the influence of the individual and synergistic effects of the power quality indicators.

[0097] Based on this, the regression formula is defined:

[0098]

[0099] Among them, β is an N×1 matrix (N is related to the number of specified indicators, here N=4); ε is an M×1 error matrix.

[0100] By using the least squares method, the β value can be obtained as follows: i,j,k =β j,k , if k i,j,k >0, indicating that the synergistic effect makes the sensitivity S i (x) becomes larger; if ki,j,k <0, indicating that the synergistic effect makes the sensitivity S i (x) becomes smaller.

[0101]

[0102] Based on this, the sensitivity of each user equipment to the overall power quality index can be obtained. Since power quality has different levels, the sensitivity of the user equipment to each power quality level can be obtained.

[0103] S203. Calculate the energy consumption loss of the user equipment corresponding to the target power quality level based on the user equipment's sensitivity to the total index value of power quality, the additional energy consumption of the user equipment corresponding to the target power quality level, the real-time electricity price, the ideal energy consumption of the user equipment, and the efficiency factor related to the target power quality level.

[0104] In this embodiment, energy loss refers to the additional energy consumption and electricity bills caused by the reduction in equipment efficiency due to the deterioration of power quality. This value is an objective fact based on the change in power quality and can be calculated according to the following formula:

[0105]

[0106] in, For user equipment i, the power quality level is Q m Energy loss; P m,i Refers to the user equipment i in the power quality level of Q m The additional energy consumption under m,i It increases as the power quality decreases and is calculated through equipment efficiency, reflecting the loss of equipment operating efficiency; e Refers to the real-time electricity price, in yuan / MWh; q i represents the normal energy consumption of user equipment i under ideal power quality conditions, in MWh; η j Indicates that the power quality level is Q m The relevant efficiency factor, η, decreases as the power quality level decreases j It will decrease, reflecting the degree of equipment efficiency decline, which is related to the user's specific equipment and is a device-specific parameter. i When (x)>0, it means that the deterioration of power quality affects the user's device i, the device efficiency decreases, the device energy consumption increases, and more power is consumed, resulting in additional energy loss for the user.

[0107] By calculating energy losses based on the sensitivity of user equipment to the total power quality index value, combined with the additional energy consumption, real-time electricity price, ideal energy consumption, and efficiency factor corresponding to the corresponding power quality level, the weight of the sensitivity's impact on the equipment's economic efficiency can be quantified, allowing for accurate identification of the responsiveness of highly sensitive equipment to changes in power quality levels during the optimization process. By dynamically linking sensitivity with energy losses, power quality levels can be adjusted to meet the operational needs of highly sensitive equipment, while also weighing the impact of real-time electricity prices and efficiency factors on overall costs. This allows the optimization solution to achieve a dynamic balance between reducing users' comprehensive economic losses, improving power resource utilization efficiency, and ensuring system stability, ultimately achieving the coordinated economic and technical optimization of campus-level power quality management.

[0108] S204. Calculate the defect loss of the user equipment corresponding to the target power quality level based on the defective rate corresponding to the target power quality level, the unit defective loss of the user equipment, and the relationship coefficient between the sensitivity of the user equipment and the defective rate.

[0109] In this embodiment, defect loss refers to the economic loss caused by quality problems of user-produced products due to power quality. This value is based on the objective fact of changes in power quality. Its calculation formula is as follows:

[0110]

[0111] Among them, u m The power quality level is Q m The defective rate under this condition reflects the impact of sensitivity on product quality; c i,defect is the unit defective loss of user equipment i, in RMB / %; α m Refers to the coefficient of relationship between the sensitivity of user equipment i and the defective rate; x m The power quality level is Q m The typical values ​​of each indicator under the level are taken as the median value of the classification range, such as the voltage imbalance of Q1 is 1.25%; when the sensitivity of user equipment i is S i When (x)>0, it means that the power quality index exceeds the standard, causing the defective rate of user i to increase, resulting in an increase in defect losses.

[0112] Based on the sensitivity of user equipment to the total index value of power quality, combined with the defective rate corresponding to the target power quality level, the unit defective loss of user equipment, and the relationship coefficient between sensitivity and defective rate, when calculating defective losses, sensitivity can dynamically reflect the degree to which equipment performance is directly affected by power quality fluctuations. Through the quantitative correlation between sensitivity and defective rate, the potential risk of increased defective rate due to power quality problems in highly sensitive equipment can be accurately identified, and then the contribution weight to economic costs can be assessed based on unit defective loss. This process uses sensitivity as the core driving factor of defective loss, enabling the optimization method to specifically weigh the quality risks and repair costs of different equipment. While reducing the defective rate and reducing the overall losses of users, it also takes into account the technical feasibility and economic efficiency of adjusting the power quality level, ultimately achieving the dual goals of product quality assurance and efficient allocation of system resources.

[0113] S205. Determine the sum of energy consumption loss and defect loss as the economic loss of the user equipment corresponding to the target power quality level; calculate the carbon emissions of the user equipment based on the grid carbon emission factor of the target park and the additional energy consumption of the user equipment corresponding to the target power quality level; determine the economic loss and carbon emissions as the total economic loss of the user equipment corresponding to the target power quality level.

[0114] In this embodiment, the economic loss is the sum of the energy consumption loss and the defect loss. Therefore, the economic loss of each user equipment for each power quality level in the target park can be obtained.

[0115] Furthermore, the electricity in the power grid comes from a variety of power generation methods, such as thermal power generation, hydropower generation, wind power generation, solar power generation, etc. The carbon emission intensity of each power generation method is different, so different types of power generation methods have different impacts on the environment. The additional energy consumption caused by power quality problems leads to increased carbon emissions, which in turn has a certain impact on the environment. Therefore, the total economic loss corresponding to each user device for each power quality level can be determined based on the economic loss and carbon emissions. Specifically, according to the proportion of each power generation method f k and its carbon emission intensity ef k , calculate the grid carbon emission factor as shown below:

[0116]

[0117] Where EF is the carbon emission factor of the power grid, the unit is t·CO2 / MWh; f k is the proportion of electricity generated by the kth power generation method; ef k is the carbon emission intensity of the kth power generation mode, in t·CO2 / MWh.

[0118] The calculation formula for the carbon emissions caused by user device i is as follows:

[0119]

[0120] Where, E i,m is the carbon emission of user equipment i, in t·CO2; P m,i Refers to the user equipment i in the power quality level of Q m The additional energy consumption under the above conditions is expressed in MWh.

[0121] Based on this, the carbon emissions corresponding to each power quality level of each user device can be obtained. The method provided in this application jointly determines the economic loss and carbon emissions as the total economic loss of the user device corresponding to the target power quality level, which can break through the single economic cost dimension and build a comprehensive evaluation system for the coordination of environmental and economic benefits. By quantifying the response intensity of the equipment to power quality fluctuations through sensitivity parameters, and synchronously correlating economic losses with implicit environmental costs, the combined impact of power quality level adjustments on the user's comprehensive costs and social environmental responsibilities can be more completely mapped. This fusion mechanism enables the optimization method to not only focus on the economy of equipment operation, but also incorporate sustainable development goals under carbon emission constraints. By dynamically adjusting the power quality threshold of highly pollution-sensitive equipment through sensitivity, while reducing the direct economic losses of users, it promotes the minimization of carbon emissions, thereby forming a multi-objective balance between technical feasibility, economic rationality and environmental compliance, which not only improves the operating efficiency of the park energy system, but also strengthens the fulfillment of corporate social responsibility and green transformation capabilities, providing a scientific basis for low-carbon power quality management.

[0122] S206. Obtain a first total economic loss corresponding to the first specified power quality level and a second total economic loss corresponding to the second specified power quality level; and obtain a willingness to pay for changing from the first specified power quality level to the second specified power quality level based on a difference between the first total economic loss and the second total economic loss.

[0123] In this example, willingness to pay WTP i,m The amount that the user equipment is willing to pay to improve the power quality level from the first specified power quality level to the second specified power quality level. The calculation formula is as follows:

[0124]

[0125] Among them, C i,0 represents the economic loss of user equipment i under the power quality level Q0; C i,m Indicates that user equipment i is in power quality level Q m economic losses.

[0126] Based on this, multiple willingness to pay for the user equipment to be upgraded from a first specified power quality level to a second specified power quality level can be obtained.

[0127] S207. Calculate the net benefit corresponding to the first specified power quality level based on the willingness to pay corresponding to all user devices in the target park and the service cost corresponding to the first specified power quality level; and determine the net benefit as the user demand assessment value.

[0128] In this embodiment, the net income NB m Indicates that the power quality level is selected as Q m The overall economic benefit at that time is the total user willingness to pay minus the service cost, as shown in the following formula:

[0129]

[0130] Where N is the total number of user devices in the campus; Cost m The power supplier provides power quality level Q m The service cost of power quality.

[0131] Based on this, the net benefit corresponding to each power quality level of the target park can be obtained, and the net benefit of each power quality level is the corresponding user demand assessment value of the target park.

[0132] S208. Determine a maximum net benefit value from a plurality of net benefit values ​​corresponding to different power quality levels; and determine the power quality level corresponding to the maximum net benefit value as the power quality optimization solution for the target park.

[0133] In this embodiment, the target park can select the best power quality level as Q m To optimize the overall power quality and maximize the user's net benefit, the net benefit is used as the objective function for calculation, as shown in the following formula:

[0134]

[0135] in, The power quality level that maximizes net benefits is the best choice for the park.

[0136] Therefore, the power quality optimization method provided in the embodiment of the present application, by grading the power quality level of the target park, further calculates the sensitivity of each user device in the target park to the power quality level, thereby accurately evaluating the tolerance of each user device to power quality problems, quantifying the specific impact of power quality problems on equipment operation, and fully considering the individual differences in power quality of different user devices in the target park. Based on this, the economic losses and carbon emissions of each target user in the target park under each power quality level are determined to obtain the total economic loss, and then the willingness to pay of each user device is obtained. The user demand assessment value under each power quality level of the target park is further determined based on the willingness to pay of all user devices, thereby determining the power quality optimization plan of the target park. This method comprehensively quantifies the dual impact of power quality problems on the user's economy and environment by calculating the additional energy consumption, economic losses and carbon emissions of user devices at different power quality levels, helping users understand the specific impact of power quality fluctuations on their operating costs, production efficiency and environmental burden, providing strong data support for power quality management, and providing a scientific basis for the overall power quality optimization of the park. A comprehensive evaluation and optimization method was proposed to select the optimal power quality level, combining user willingness to pay and net benefits. This method optimizes power quality across the entire park and selects the most appropriate power quality level, thereby maximizing user economic benefits and promoting sustainable development of the park, achieving both economic benefits and environmental protection.

[0137] Figure 3 The schematic diagram of the structure of the power quality optimization device provided in this application is as follows: Figure 3 As shown, the power quality optimization device 30 provided in this embodiment includes:

[0138] An acquisition module 301 is configured to acquire, for any user equipment in a target park, a sensitivity of the user equipment to a total index value of power quality in the target park; wherein the target park includes multiple user equipment;

[0139] A first processing module 302 is configured to calculate the total economic losses of the user equipment corresponding to different power quality levels based on the user equipment's sensitivity to the total index value; the power quality level is determined based on the total index value of the power quality of the target park;

[0140] The second processing module 303 is configured to calculate a user demand assessment value of the user equipment when the user equipment changes from the first specified power quality level to the second specified power quality level; wherein the first specified power quality level and the second specified power quality level are two different power quality levels among different power quality levels;

[0141] The second processing module 303 is configured to determine a power quality optimization solution for the target park based on the user demand evaluation values ​​of all user devices in the target park.

[0142] In a possible implementation, the acquisition module 301 is further configured to:

[0143] Calculate the total power quality index value of the target park based on the actual values ​​of the power quality sub-indicators of the target park and the corresponding weights;

[0144] Determine the power quality level based on the total index value of power quality.

[0145] In a possible implementation, the acquisition module 301 is further configured to:

[0146] The sensitivity of the user equipment to each power quality sub-indicator is calculated based on the actual value of the power quality sub-indicator, the acceptable threshold of the user equipment to the power quality sub-indicator, and the corresponding sensitivity coefficient;

[0147] The sensitivity of the user equipment to the total power quality index value is calculated based on the sensitivity of the user equipment to each power quality sub-index and a preset interaction coefficient; wherein the interaction coefficient is calculated based on an interaction coefficient regression model.

[0148] In a possible implementation, the first processing module 302 is further configured to:

[0149] For a target power quality level, calculating energy consumption loss and defect loss of the user equipment corresponding to the target power quality level based on the sensitivity of the user equipment to the total index value of power quality; the target power quality level is one of multiple power quality levels;

[0150] The sum of energy consumption loss and defect loss is determined as the economic loss of the user equipment corresponding to the target power quality level;

[0151] The carbon emissions of the user equipment are calculated based on the carbon emission factor of the target park's power grid and the additional energy consumption of the user equipment corresponding to the target power quality level;

[0152] The economic loss and carbon emissions are determined as the total economic loss of the user equipment corresponding to the target power quality level.

[0153] In a possible implementation, the first processing module 302 is further configured to:

[0154] The energy loss of the user equipment corresponding to the target power quality level is calculated based on the user equipment's sensitivity to the total power quality index value, the user equipment's additional energy consumption corresponding to the target power quality level, the real-time electricity price, the user equipment's ideal energy consumption, and the efficiency factor related to the target power quality level.

[0155] The defect loss of the user equipment corresponding to the target power quality level is calculated based on the defective rate corresponding to the target power quality level, the unit defective loss of the user equipment, and the relationship coefficient between the sensitivity of the user equipment and the defective rate.

[0156] In a possible implementation, the second processing module 303 is further configured to:

[0157] Obtaining a first total economic loss corresponding to a first specified power quality level and a second total economic loss corresponding to a second specified power quality level;

[0158] Obtaining a willingness to pay for changing from a first specified power quality level to a second specified power quality level according to a difference between the first total economic loss and the second total economic loss;

[0159] Calculate the net benefit corresponding to the first specified power quality level based on the willingness to pay of all user devices in the target park and the service cost corresponding to the first specified power quality level;

[0160] Determine the net benefit as the user demand assessment value.

[0161] In a possible implementation, the second processing module 303 is further configured to:

[0162] determining a maximum net benefit value from a plurality of net benefit values ​​corresponding to different power quality levels;

[0163] The power quality level corresponding to the maximum net profit value is determined as the power quality optimization plan for the target park.

[0164] The power quality optimization device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effects are similar and will not be described in detail in this embodiment.

[0165] Figure 4 This is a hardware diagram of the power quality optimization device provided in this application. Figure 4 As shown, the electronic device 40 provided in this embodiment includes: at least one processor 401 and a memory 402. Optionally, the device 40 further includes a communication component 403. The processor 401, the memory 402 and the communication component 403 are connected via a bus 404.

[0166] In a specific implementation process, at least one processor 401 executes the computer-executable instructions stored in the memory 402, so that the at least one processor 401 performs the above method.

[0167] The specific implementation process of the processor 401 can be found in the above method embodiment. Its implementation principle and technical effects are similar and will not be repeated here in this embodiment.

[0168] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASICs), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the present invention may be directly executed by a hardware processor or by a combination of hardware and software modules within the processor.

[0169] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage.

[0170] A bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Buses can be categorized as address buses, data buses, and control buses. For ease of illustration, the buses in the drawings of this application are not limited to just one bus or just one type of bus.

[0171] The present application also provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0172] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above method is implemented.

[0173] The readable storage medium may be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium may be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0174] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be an integral part of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0175] The division of units is merely a logical functional division; actual implementations may employ alternative divisions, such as combining or integrating multiple units or components into another system, or omitting or disabling certain features. Furthermore, any direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via an interface, method, or unit, and may be electrical, mechanical, or otherwise.

[0176] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0177] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0178] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of the present invention. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0179] Those skilled in the art will appreciate that all or part of the steps in the above-described method embodiments can be implemented using hardware associated with program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0180] Finally, it should be noted that those skilled in the art will readily identify other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art not disclosed herein. The present invention is not limited to the precise structure described above and illustrated in the accompanying drawings, and various modifications and variations may be made without departing from the scope thereof. The scope of the present invention is limited solely by the appended claims.

Claims

1. A method for optimizing power quality, characterized in that: The method comprises: For any user equipment in a target park, obtaining the sensitivity of the user equipment to the total index value of the power quality of the target park; wherein the target park includes multiple user equipment; calculating, based on the sensitivity of the user equipment to the total index value, the total economic losses of the user equipment corresponding to different power quality levels; the power quality level is determined based on the total index value of the power quality of the target park; Calculating a user demand assessment value of the user equipment when the power quality level of the user equipment is changed from a first specified power quality level to a second specified power quality level; wherein the first specified power quality level and the second specified power quality level are two different power quality levels among the different power quality levels; Based on the user demand evaluation values ​​of all user devices in the target park, a power quality optimization solution for the target park is determined.

2. The method according to claim 1, characterized in that Before obtaining the sensitivity of the user equipment to the total indicator value of the power quality of the target park, the method further includes: Calculate the total power quality index value of the target park according to the actual values ​​of the power quality sub-indicators of the target park and the corresponding weights; The power quality level is determined according to the total index value of the power quality.

3. The method according to claim 2, characterized in that The obtaining of the sensitivity of the user equipment to the total index value of the power quality of the target park includes: Calculating the sensitivity of the user equipment to each of the power quality sub-indicators according to the actual value of the power quality sub-indicator, the acceptable threshold of the user equipment to the power quality sub-indicator, and the corresponding sensitivity coefficient; The sensitivity of the user equipment to the total index value of the power quality is calculated based on the sensitivity of the user equipment to each of the power quality sub-indicators and a preset interaction coefficient; wherein the interaction coefficient is calculated based on an interaction coefficient regression model.

4. The method according to claim 1, wherein The calculating, according to the sensitivity of the user equipment to the total index value of the power quality, the total economic losses of the user equipment corresponding to different power quality levels includes: For a target power quality level, calculating, based on the sensitivity of the user equipment to the total index value of the power quality, the energy consumption loss and defect loss of the user equipment corresponding to the target power quality level; the target power quality level is one of the multiple power quality levels; determining the sum of the energy consumption loss and the defect loss as the economic loss of the user equipment corresponding to the target power quality level; Calculating the carbon emissions of the user equipment based on the carbon emission factor of the power grid of the target park and the additional energy consumption of the user equipment corresponding to the target power quality level; The economic loss and the carbon emission are determined as a total economic loss of the user equipment corresponding to the target power quality level.

5. The method according to claim 4, characterized in that The calculating, based on the sensitivity of the user equipment to the total index value of the power quality, the energy consumption loss and defect loss of the user equipment corresponding to the target power quality level includes: Calculating the energy consumption loss of the user equipment corresponding to the target power quality level based on the sensitivity of the user equipment to the total power quality index value, the additional energy consumption of the user equipment corresponding to the target power quality level, the real-time electricity price, the ideal energy consumption of the user equipment, and the efficiency factor related to the target power quality level; The defect loss of the user equipment corresponding to the target power quality level is calculated based on the defective rate corresponding to the target power quality level, the unit defective loss of the user equipment, and the relationship coefficient between the sensitivity of the user equipment and the defective rate.

6. The method according to claim 4, characterized in that The calculating the user demand assessment value of the user equipment changing from the first specified power quality level to the second specified power quality level includes: Obtaining a first total economic loss corresponding to the first specified power quality level and a second total economic loss corresponding to the second specified power quality level; obtaining a willingness to pay for changing from the first specified power quality level to the second specified power quality level according to a difference between the first total economic loss and the second total economic loss; Calculating a net benefit corresponding to the first specified power quality level based on the willingness to pay corresponding to all the user devices in the target park and the service cost corresponding to the first specified power quality level; The net benefit is determined as the user demand evaluation value.

7. The method according to claim 6, characterized in that The determining of a power quality optimization solution for the target park based on the user demand evaluation values ​​of all user devices in the target park includes: determining a maximum net benefit value from a plurality of net benefit values ​​corresponding to the different power quality levels; The power quality level corresponding to the maximum net profit value is determined as the power quality optimization plan for the target park.

8. An electronic device, characterized in that: include: Memory, processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor performs the method according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 7 when executed by a processor.

10. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 7 when executed by a processor.

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