A power load curve fluctuation degree evaluation method, device and equipment
By calculating the average load, total fluctuation energy, and vortex coefficient, the problem of insufficient detail in the assessment of power load curve fluctuations is solved, achieving a refined and comprehensive assessment that is applicable to power grid planning and operation scheduling.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SANXIA JINSHAJIANG YUNCHUAN HYDROPOWER DEV CO LTD
- Filing Date
- 2026-05-09
- Publication Date
- 2026-06-05
AI Technical Summary
Existing power load curve fluctuation assessment techniques lack the ability to reflect curve details, resulting in assessment results that are not precise or comprehensive enough to accurately depict the true fluctuation of the power load curve.
By calculating the average load, total fluctuation energy, and Wenbo coefficient, and combining this with a flexibly set assessment period, the fluctuation details at different times are captured. Furthermore, the dimensionless Wenbo coefficient index is introduced to enhance the comparability and accuracy of the assessment results.
It enables detailed characterization of power load curves, improving the comprehensiveness, precision, and accuracy of assessments. It can reveal details of short-term drastic fluctuations and long-term overall trends, ensuring the comparability of assessment results across different time scales.
Smart Images
Figure CN122153210A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of power system assessment technology, specifically to a method, apparatus, and equipment for assessing the degree of fluctuation in power load curves. Background Technology
[0002] With the advancement of new power system construction, a large number of intermittent and fluctuating power sources such as wind power and photovoltaics have been connected to the grid, and the diversified loads on the user side are growing rapidly, making the volatility of power load increasingly significant. The degree of fluctuation in the load curve directly affects the peak-shaving pressure of the power grid, reserve capacity demand, safe and stable operation of the power grid, and power quality. Therefore, a scientific and accurate quantitative assessment of the volatility of the load curve is an important foundation for power grid planning, operation scheduling, demand-side management, and market transactions.
[0003] Currently, commonly used indicators for assessing load volatility include range, variance, standard deviation, load factor, and peak-valley ratio. However, these traditional indicators have certain limitations: the range only considers two extreme points, losing a large amount of process information; while variance and standard deviation consider all points, they are greatly affected by the average value, and their dimension is the square of power, making their physical meaning less intuitive; load factor and peak-valley ratio reflect the overall shape of the load rather than the details of fluctuations. Furthermore, these assessment methods provide a holistic description of the daily load, lacking a response to the details of the curve, resulting in assessment results that are not refined or comprehensive enough to accurately depict the true degree of fluctuation in the power load curve. Summary of the Invention
[0004] This application provides a method, apparatus, and equipment for assessing the degree of fluctuation of power load curves, which can solve the technical problem that existing power load curve fluctuation assessment technologies lack the ability to reflect curve details, resulting in assessment results that are not refined and comprehensive enough and have poor assessment accuracy.
[0005] To achieve the above objectives, in a first aspect, this application provides a method for assessing the fluctuation degree of power load curves, the method comprising: The average load is calculated based on the power load values of each sampling point in the power load curve within the preset evaluation period.
[0006] The total fluctuating energy is calculated based on the power load value and the average load.
[0007] Based on the average load, the total fluctuating energy, and the total number of sampling points, a variability coefficient is calculated to assess the degree of fluctuation in the power load curve.
[0008] Furthermore, in one embodiment, the evaluation period is set according to the evaluation purpose, the required accuracy of the evaluation, and the need for detailed curve reflection, and its value is less than or equal to 24 hours.
[0009] Furthermore, in one embodiment, calculating the total fluctuating energy based on the power load value and the average load includes: The difference between the power load value and the average load at each sampling point is calculated and squared to obtain the square of the fluctuation component at each sampling point.
[0010] The total wave energy is obtained by summing the squares of the wave components at all sampling points.
[0011] Furthermore, in one embodiment, the formula for calculating the Wenbo coefficient is: W=S / (N*P_avg²) ,in, W Represents the Wenbo coefficient, S Represents the total fluctuation energy. N Indicates the total number of sampling points. P_avg This indicates the average load.
[0012] Furthermore, in one embodiment, the method further includes: The arithmetic square root of the von Neumann coefficient is calculated to obtain the standard von Neumann coefficient, which is used to assess the degree of fluctuation of the power load curve.
[0013] Furthermore, in one embodiment, the method further includes: The 24-hour period is divided into one or more time periods according to the assessment cycle. The fluctuation coefficient of each time period is calculated and summed to obtain the daily fluctuation coefficient, which is used to characterize the cumulative fluctuation of the power load curve throughout the day.
[0014] Furthermore, in one embodiment, a daily average fluctuation coefficient is calculated based on the daily fluctuation coefficient and the number of time periods to assess the overall fluctuation of the power load curve throughout the day.
[0015] Furthermore, in one embodiment, before calculating the average load, the power load value is cleaned, and abnormal data is removed or corrected.
[0016] Secondly, this application provides a device for assessing the fluctuation degree of power load curves, the device comprising: The load calculation module is used to calculate the average load based on the power load values of each sampling point in the power load curve within a preset evaluation period.
[0017] An energy calculation module is used to calculate the total fluctuating energy based on the power load value and the average load.
[0018] The coefficient calculation module is used to calculate the fluctuation coefficient based on the average load, the total fluctuation energy, and the total number of sampling points to assess the degree of fluctuation of the power load curve.
[0019] Thirdly, embodiments of this application provide a power load curve fluctuation assessment device, which includes a processor, a memory, and a power load curve fluctuation assessment program stored in the memory and executed by the processor, wherein when the power load curve fluctuation assessment program is executed by the processor, it implements the steps of the power load curve fluctuation assessment method.
[0020] The beneficial effects of the technical solutions provided in this application include: This application calculates the average load based on the power load values at each sampling point in the power load curve within a preset evaluation period; calculates the total fluctuation energy based on the power load values and the average load; and calculates the Wenbo coefficient based on the average load, the total fluctuation energy, and the total number of sampling points to assess the degree of fluctuation in the power load curve. By introducing a flexibly configurable evaluation period, it can capture fluctuation details at different time scales. Simultaneously, the Wenbo coefficient is a dimensionless index, and its value directly corresponds to the strength of the fluctuation, enhancing the comparability of the evaluation results and effectively improving the comprehensiveness, precision, and accuracy of the power load curve fluctuation assessment. Attached Figure Description
[0021] Figure 1 This is a flowchart of a method for assessing the degree of fluctuation in power load curves according to an embodiment of this application.
[0022] Figure 2 This is a comparative schematic diagram showing the calculation of the Wenbo coefficient for two different daily load curves using the method of this application in an embodiment of this application.
[0023] Figure 3 This is a comparative diagram showing the calculation of the daily ripple coefficient using the method described in this application for two different daily load curves, as an embodiment of this application.
[0024] Figure 4 This is a block diagram of the power load curve fluctuation assessment device according to an embodiment of this application.
[0025] Figure 5 This is a schematic diagram of the hardware structure of the power load curve fluctuation assessment device according to an embodiment of this application. Detailed Implementation
[0026] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.
[0027] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.
[0028] Firstly, embodiments of this application provide a method for assessing the degree of fluctuation in power load curves.
[0029] In one embodiment, see Figure 1 As shown, the above-mentioned methods for assessing the fluctuation of the power load curve include: S1. Calculate the average load based on the power load values of each sampling point in the power load curve within the preset evaluation period.
[0030] S2. Calculate the total fluctuating energy based on the above power load values and the above average load.
[0031] S3. Based on the above average load, the above total fluctuation energy, and the total number of sampling points, calculate the fluctuation coefficient to assess the degree of fluctuation of the above power load curve.
[0032] In this embodiment, a flexible evaluation cycle mechanism is introduced to perform multi-scale refined decomposition and quantitative analysis of the power load curve.
[0033] By calculating the average load based on the power load values of each sampling point in the power load curve within a preset assessment period, a benchmark reference line for the load level within that period is established, providing a unified comparison benchmark for subsequent fluctuation quantification. By calculating the total fluctuation energy based on the aforementioned power load values and average load, the deviation of each sampling point from the average load is squared and accumulated. This retains the fluctuation information of all sampling points while effectively amplifying the influence weight of significant fluctuations through squaring, thus comprehensively capturing the subtle fluctuation characteristics of the load curve throughout the entire time period. By calculating the Wenbo coefficient based on the aforementioned average load, total fluctuation energy, and total number of sampling points, the total fluctuation energy is normalized using the product of the square of the average load and the total number of sampling points. This eliminates the interference of the absolute load magnitude, assessment period length, and sampling frequency differences on the assessment results, making the Wenbo coefficient a dimensionless pure numerical indicator.
[0034] This embodiment achieves a detailed characterization of the fluctuation degree of the power load curve. By adjusting the evaluation period, it can not only observe the details of short-term drastic fluctuations, but also grasp the overall fluctuation trend over a long period of time. At the same time, it ensures the direct comparability of evaluation results under different power users and different time scales. It effectively overcomes the limitations of traditional indicators that only focus on extreme points or are constrained by dimensions, and significantly improves the comprehensiveness, precision and accuracy of the evaluation of the fluctuation degree of the power load curve.
[0035] Furthermore, in one embodiment, in step S1 above, the average load is calculated based on the power load values of each sampling point in the power load curve within a preset evaluation period. The specific steps are as follows: Obtain the power load curve of the target assessment object within a preset assessment period. The power load curve is obtained by sampling at fixed time intervals. Δt The power load curve is composed of continuous power load points collected (usually every 5 minutes, 15 minutes, or 1 hour), and the total number of sampling points is denoted as . N The total sampling time T 0 =N*Δt Set up power load sampling points. P = {P_1, P_...} 2, ..., P_N} , P_1 Indicates the first 1 One sampling point, P_2 Indicates the first 2 One sampling point, P_N Indicates the first N Each sampling point is used. The data from these sampling points is then cleaned to remove or correct outlier data.
[0036] The evaluation period is set according to the evaluation purpose, the required accuracy of the evaluation, and the needs of the curve details. Its value is less than or equal to 24 hours. In this embodiment, it can be 1 hour, 2 hours, or 24 hours.
[0037] Based on the above power load sampling points P The average load during the aforementioned assessment period is calculated using the following formula: (1), in, P_avg Indicates average load. P_i Indicates the first i One sampling point.
[0038] In this embodiment, by establishing a standardized data acquisition and preprocessing mechanism, a reliable data foundation and a unified spatiotemporal benchmark are laid for subsequent fluctuation quantification calculations.
[0039] By limiting the assessment period to 24 hours or less, and prioritizing typical configurations such as 1 hour, 2 hours, or 24 hours, the system satisfies both the need to capture short-term intraday fluctuations (short period) and the need to grasp the overall macroscopic stability of the daily load (long period), while ensuring seamless integration with the subsequent calculation logic of the daily load fluctuation coefficient. Furthermore, the average load within the assessment period is calculated using an arithmetic mean method, providing a simple and robust benchmark for the load level during that period, offering a unified reference for the quantitative comparison of fluctuation deviations at subsequent sampling points. Through front-end data governance and parameter optimization, the data reliability, computational stability, and scenario adaptability of the power load curve fluctuation assessment are significantly improved.
[0040] Furthermore, in one embodiment, in step S2 above, the total fluctuating energy is calculated based on the above-mentioned power load value and the above-mentioned average load. The specific steps are as follows: Calculate the difference between the power load value at each sampling point and the average load, and square the difference to obtain the square of the fluctuation component at each sampling point.
[0041] The total wave energy is obtained by summing the squares of the wave components at all sampling points.
[0042] In this embodiment, each power load sampling point is traversed, the difference between each power load sampling point and the above average load is calculated, and the square is taken to obtain the square of the fluctuation component corresponding to each power load sampling point. The calculation formula is as follows: (2), in, i = 1, 2, ..., N , D_i This represents the square of the fluctuation component.
[0043] This step converts fluctuations in power load (the degree to which they deviate from the average value) into non-negative quantified values and amplifies the impact of larger fluctuations through squaring.
[0044] Furthermore, the total fluctuation energy within the assessment period is obtained by summing the squares of the fluctuation components at all power load sampling points, using the following formula: (3), in, S This represents the total fluctuation energy.
[0045] In this embodiment, by constructing a fluctuation energy quantification mechanism based on the sum of squared deviations, the fine-grained capture of fluctuation information of the load curve throughout the entire time period and the enhanced characterization of significant fluctuations are achieved.
[0046] In the process of calculating the square of the fluctuation component at each sampling point, by traversing each power load sampling point and calculating the square of its difference from the average load, the originally positive and negative deviation values are uniformly transformed into non-negative quantitative indicators. This not only preserves the directional independence of each sampling point's deviation from the benchmark (focusing only on the magnitude of the deviation), but also achieves non-linear amplification of larger fluctuations through the mathematical properties of squaring operations. The contribution of sampling points with greater deviations to the total fluctuation energy increases exponentially, thereby effectively highlighting the violent fluctuation segments in the load curve and avoiding the drawback of treating small, frequent fluctuations and large, occasional fluctuations equally in the assessment.
[0047] In the process of calculating the total fluctuation energy, the discrete time series data is integrated into a single energy accumulation index by strictly summing the squares of the fluctuation components of all sampling points. This comprehensively aggregates the fluctuation information at every moment within the evaluation period, overcoming the limitation of traditional range indicators that only focus on two extreme points and lose a large amount of process information. It also makes up for the shortcomings of indicators such as peak-valley difference rate in reflecting the frequency and distribution of fluctuations.
[0048] By employing a technical approach of point-by-point quantization, squared enhancement, and global accumulation, a fluctuation energy measurement system that is comprehensive, sensitive, and comparable has been constructed. This provides a solid physical quantity foundation for the subsequent normalization calculation of the fluctuation coefficient and the comparison of fluctuation levels under different scenarios, significantly improving the precision and accuracy of the characterization of power load curve fluctuations.
[0049] Furthermore, in one embodiment, the formula for calculating the Wenbo coefficient in step S3 above is: W=S / (N*P_avg²) (4), in, W Represents the Wenbo coefficient, S Represents the total fluctuation energy. N Indicates the total number of sampling points. P_avg This indicates the average load.
[0050] In this embodiment, the total fluctuation energy is normalized to obtain the final evaluation index, the Wenbo coefficient. Normalization aims to eliminate the influence of the evaluation period duration and sampling frequency on the calculation results, enhancing its comparability both horizontally (different users, different regions) and vertically (different dates). The denominator of the above formula (4) (N*P_avg²) This can be considered a reference benchmark related to the total electricity consumption and average load level during the aforementioned assessment period. (Vinbo coefficient) W It is a dimensionless pure numerical value. W The larger the value, the more drastic the fluctuation of the load curve relative to its average level during the assessment period; W The smaller the value, the smoother the load curve.
[0051] Furthermore, in one embodiment, the above-mentioned method for assessing the fluctuation of the power load curve further includes: Calculate the arithmetic square root of the above Venn coefficient to obtain the standard Venn coefficient, which is used to assess the degree of fluctuation of the above power load curve. The calculation formula is as follows: (5), in, W_s This represents the standard von Neumann coefficient.
[0052] In this embodiment, as a further optimization, the arithmetic square root form of the Wenbo coefficient can also be used. W_s"It is closer to a relative standard of fluctuation amplitude in a physical sense" and may be more explanatory in certain application scenarios.
[0053] Furthermore, in one embodiment, the above-mentioned method for assessing the fluctuation of the power load curve further includes: The 24-hour assessment period is divided into one or more time periods. The fluctuation coefficients for each time period are calculated and summed to obtain the daily fluctuation coefficient, which characterizes the cumulative fluctuation of the power load curve throughout the day.
[0054] In this embodiment, the 24 hours are divided into M time periods according to the aforementioned evaluation cycle, where M is a positive integer with a maximum value of 24 / T. The Wenbo coefficient for each time period is calculated and summed to obtain the daily Wenbo coefficient. The calculation formula is as follows: (6), in, W_d Indicates the daily waveform coefficient. W_si This represents the waveform coefficient for the i-th time period.
[0055] In this embodiment, by constructing a multi-period accumulation mechanism, the hierarchical aggregation of intraday fluctuation characteristics and the quantitative characterization of the cumulative effect throughout the day are realized.
[0056] By dividing the 24-hour period into M consecutive time periods according to the assessment cycle, this approach retains the ability of short-cycle assessments to capture the details of local fluctuations while establishing a bridge from micro-fluctuations to macro-cumulative data through time period division and segmented calculation. Calculating the fluctuation coefficient for each time period separately ensures independent quantification and standardization of the fluctuation level within each period, avoiding assessment biases caused by differences in time period length or load level variations. By rigorously summing the fluctuation coefficients of each time period to obtain the daily fluctuation coefficient, the scattered fluctuation energy across different time periods is organically integrated, forming a comprehensive indicator that fully reflects the total cumulative fluctuation throughout the day. This indicator is particularly suitable for scenarios requiring assessment of the cumulative effect of peak-shaving pressure, the daily demand for reserve capacity, or the risk of continuous deterioration in power quality, providing an intuitive quantitative basis for grid operation and dispatch.
[0057] Meanwhile, the dynamic adaptation mechanism of the value of M and the evaluation period T (M≤24 / T) ensures the rationality of the division and the feasibility of the calculation. When T is 24 hours, M automatically degenerates to 1, and the daily Wenbo coefficient is the single-period Wenbo coefficient, which maintains the consistency and coherence of the evaluation system under different time granularities.
[0058] This embodiment effectively solves the contradiction that a single overall indicator cannot distinguish time-period differences and a single sub-indicator is difficult to grasp the trend throughout the day, significantly improving the spatiotemporal resolution and application relevance of the assessment of the fluctuation degree of power load curves.
[0059] Furthermore, in one embodiment, based on the aforementioned daily fluctuation coefficient and the number of time periods, a daily average fluctuation coefficient is calculated to assess the overall daily fluctuation of the aforementioned power load curve. The calculation formula is as follows: (7), in, W_p This represents the daily average wave coefficient.
[0060] In this embodiment, the daily fluctuation coefficient can reflect the cumulative fluctuation of the power load curve throughout the day, while the daily average fluctuation coefficient can reflect the overall load fluctuation of the day.
[0061] Here is a specific embodiment of the above-mentioned method for assessing the fluctuation of power load curves, with the assessment period T being 24 hours and the sampling time interval being... Δt The time limit is 15 minutes, and the specific steps are as follows: Collect power load data of a power plant on a certain day and construct a sequence. P 1. The formula for calculating the total number of sampling points for power load data is as follows: N=T / Δt (8).
[0062] The total number of sampling points for the power load data calculated using the above formula (8) is 96.
[0063] Calculate the average load for that day: P 1 _avg=800MW , MW It is a megawatt.
[0064] Calculate each sampling point (P 1 _i-P 1 _avg)² For example, the power load at the 30th sampling point is 1400. MW Then the square of its fluctuation component D 1 _30=(1400-800)²=360000 .
[0065] For 96 D 1 _i Summing them up yields the total wave energy. S 1 ≈3.55×10 6 MW² .
[0066] Calculate the Wenbo coefficient: W 1 =S 1 / (96*800²)≈3.55×106 / (6.14×10 7 )≈0.058 .
[0067] Calculate the standard Wenbo coefficient: W 1 _s=sqrt(0.0578)≈0.240 .
[0068] For comparison, load data from another day with relatively stable fluctuations at the station were obtained, and its variability coefficient was calculated. W 1 ≈ 0.018 , W 1 _s≈0.135 See also Figure 2 As shown, Figure 2 This diagram illustrates the comparison of calculating the fluctuation coefficient using the method described in this application for two different daily load curves. Curve A represents dates with larger fluctuations, and curve B represents dates with smaller fluctuations; both curves have their fluctuation coefficients calculated using the method described in this application.
[0069] This comparison aims to verify the discriminative power and monotonicity of the Wenbo coefficient. The more drastic the load curve fluctuations, the larger the Wenbo coefficient; conversely, the more gradual the fluctuations, the smaller the Wenbo coefficient. Therefore, the Wenbo coefficient can accurately reflect the severity of load curve fluctuations and is consistent with intuitive human judgment.
[0070] Here is a specific embodiment two of the above-mentioned method for assessing the fluctuation of the power load curve, with the assessment period T set to 1 hour and the sampling time interval... Δt The time limit is 15 minutes, and the specific steps are as follows: Collect power load data of a power plant on a certain day and construct a sequence. P 2.
[0071] The total number of sampling points for the power load data calculated using the above formula (8) is 4.
[0072] Calculate the average load for the first hour P 2 _avg=1350MW .
[0073] Calculate each sampling point (P 2 _i-P 2 _avg)² If the load at the first point is 1200MW, then the square of its fluctuation component is... D 2 _1=(1200-1350)²=22500 .
[0074] For 4 D 2 _i Summing them up yields the total wave energy. S=90000MW² .
[0075] Calculate the Wenbo coefficient: W 2 =S 2 / (4*1350²)=90000 / (7.29×10 6 )≈0.0123 .
[0076] Calculate the standard Wenbo coefficient W 2 _s=sqrt(0.0123)≈0.1109 .
[0077] For comparison, load data from another day with relatively stable fluctuations at the station were obtained, and its variability coefficient was calculated. W 2 =0 , W 2 _s=0 See also Figure 3 As shown, Figure 3 This is a comparative diagram showing the calculation of the daily ripple coefficient for two different daily load curves using the method described in this application.
[0078] Calculate the daily waveform coefficient of curve 1. W_d=2.959 Daily average Wenbo coefficient W_p=0.515 Calculate the daily oscillation coefficient of curve 2 and... W_d=0.4083 Daily average Wenbo coefficient W_p=0.017 .
[0079] Figure 3 In the data, curve 1 shows significantly greater fluctuations than curve 2. Since curves 1 and 2 have the same peak-to-valley load difference, using this difference to describe the curve fluctuations would result in distortion. Similarly, calculating the von Neumann coefficient using a 24-hour sampling period reveals that curve 2 has a larger von Neumann coefficient, also indicating distortion. Adjusting the sampling period appropriately can avoid these problems and more accurately reflect the details of load fluctuations.
[0080] Therefore, it can be concluded that the peak-valley load difference method leads to distorted fluctuation assessment results because it only focuses on two extreme points, completely ignoring the frequency and amplitude of changes in the intermediate process. When the peak-valley differences of two curves are the same but the actual fluctuations differ greatly, traditional assessment methods and long-cycle assessments will both produce assessment errors. However, this application can accurately identify the true degree of fluctuation severity by shortening the assessment cycle.
[0081] Secondly, embodiments of this application also provide a device for assessing the degree of fluctuation of power load curves.
[0082] In one embodiment, see Figure 4 As shown, the above-mentioned power load curve fluctuation assessment device includes a load calculation module, an energy calculation module, and a coefficient calculation module, specifically: The load calculation module is used to calculate the average load based on the power load values of each sampling point in the power load curve within a preset evaluation period.
[0083] The energy calculation module is used to calculate the total fluctuating energy based on the above-mentioned power load value and the above-mentioned average load.
[0084] The coefficient calculation module is used to calculate the fluctuation coefficient based on the average load, the total fluctuation energy, and the total number of sampling points to assess the degree of fluctuation of the power load curve.
[0085] Thirdly, this application provides an embodiment of an apparatus for assessing the degree of fluctuation of a power load curve. The apparatus includes a processor, a memory, and a power load curve fluctuation assessment program stored in the memory and executed by the processor, wherein when the power load curve fluctuation assessment program is executed by the processor, it implements the steps of the aforementioned power load curve fluctuation assessment method.
[0086] The aforementioned equipment for assessing the fluctuation of the power load curve can be a personal computer (PC), laptop, workstation, server, industrial control computer, or other device with data processing capabilities.
[0087] Reference Figure 5 , Figure 5 This is a schematic diagram of the hardware structure of the power load curve fluctuation assessment device involved in the embodiments of this application. In this embodiment, the power load curve fluctuation assessment device may include a processor, a memory, a communication interface, and a communication bus.
[0088] The communication bus can be of any type and is used to interconnect the processor, memory, and communication interface.
[0089] The communication interface includes input / output (I / O) interfaces, physical interfaces, and logical interfaces used for interconnecting internal components of the power load curve fluctuation assessment device, as well as interfaces used for interconnecting the power load curve fluctuation assessment device with other devices (such as other computing devices or user equipment). Physical interfaces can be Ethernet interfaces, fiber optic interfaces, ATM interfaces, etc.; user equipment can be displays, keyboards, etc.
[0090] Memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.
[0091] The processor can be a general-purpose processor, which can call the power load curve fluctuation assessment program stored in the memory and execute the power load curve fluctuation assessment method provided in the embodiments of this application. For example, the general-purpose processor can be a central processing unit (CPU). The method executed when the power load curve fluctuation assessment program is called can refer to the various embodiments of the power load curve fluctuation assessment method of this application, and will not be repeated here.
[0092] Those skilled in the art will understand that Figure 5 The hardware structure shown does not constitute a limitation of this application and may include more or fewer components than shown, or combine certain components, or have different component arrangements.
[0093] It should be noted that the sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.
[0094] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus. The terms "first," "second," and "third," etc., are used to distinguish different objects, etc., and do not indicate a sequence, nor do they limit "first," "second," and "third" to different types.
[0095] In the description of the embodiments of this application, terms such as "exemplary," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a concrete manner.
[0096] In some processes described in the embodiments of this application, multiple operations or steps are included in a specific order. However, it should be understood that these operations or steps may not be executed in the order they appear in the embodiments of this application, or they may be executed in parallel. The sequence number of the operation is only used to distinguish different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed sequentially or in parallel, and these operations or steps may be combined.
[0097] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device to execute the methods described in the various embodiments of this application.
[0098] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.
Claims
1. A method for assessing the degree of fluctuation in power load curves, characterized in that, The method includes: Calculate the average load based on the power load values of each sampling point in the power load curve within the preset evaluation period; Calculate the total fluctuating energy based on the power load value and the average load; Based on the average load, the total fluctuating energy, and the total number of sampling points, a variability coefficient is calculated to assess the degree of fluctuation in the power load curve.
2. The method for assessing the degree of fluctuation in power load curves as described in claim 1, characterized in that, The evaluation period is set according to the evaluation purpose, the required accuracy of the evaluation, and the needs for the curve details to reflect the requirements, and its value is less than or equal to 24 hours.
3. The method for assessing the degree of fluctuation in the power load curve as described in claim 1, characterized in that, The total fluctuating energy is calculated based on the power load value and the average load, including: The difference between the power load value and the average load at each sampling point is calculated and squared to obtain the square of the fluctuation component at each sampling point. The total wave energy is obtained by summing the squares of the wave components at all sampling points.
4. The method for assessing the degree of fluctuation of power load curves as described in claim 1, characterized in that, The formula for calculating the wave coefficient is: W=S / (N*P_avg²), where W represents the wave coefficient, S represents the total wave energy, N represents the total number of sampling points, and P_avg represents the average load.
5. The method for assessing the degree of fluctuation in power load curves as described in claim 1, characterized in that, The method further includes: The arithmetic square root of the von Neumann coefficient is calculated to obtain the standard von Neumann coefficient, which is used to assess the degree of fluctuation of the power load curve.
6. The method for assessing the degree of fluctuation in power load curves as described in claim 1, characterized in that, The method further includes: The 24-hour period is divided into one or more time periods according to the assessment cycle. The fluctuation coefficient of each time period is calculated and summed to obtain the daily fluctuation coefficient, which is used to characterize the cumulative fluctuation of the power load curve throughout the day.
7. The method for assessing the degree of fluctuation of power load curves as described in claim 6, characterized in that, Based on the daily fluctuation coefficient and the number of time periods, the daily average fluctuation coefficient is calculated to assess the overall daily fluctuation of the power load curve.
8. The method for assessing the degree of fluctuation of power load curve as described in claim 1, characterized in that, Before calculating the average load, the power load value is also cleaned, and abnormal data is removed or corrected.
9. A device for assessing the degree of fluctuation in power load curves, characterized in that, The device includes: The load calculation module is used to calculate the average load based on the power load values of each sampling point in the power load curve within a preset evaluation period. An energy calculation module is used to calculate the total fluctuating energy based on the power load value and the average load. The coefficient calculation module is used to calculate the fluctuation coefficient based on the average load, the total fluctuation energy, and the total number of sampling points to assess the degree of fluctuation of the power load curve.
10. A device for assessing the degree of fluctuation in power load curves, characterized in that, The power load curve fluctuation assessment device includes a processor, a memory, and a power load curve fluctuation assessment program stored in the memory and executed by the processor, wherein when the power load curve fluctuation assessment program is executed by the processor, it implements the steps of the power load curve fluctuation assessment method as described in any one of claims 1 to 8.