Virtual power plant real-time adjustment capability evaluation method and system

Abstract

The invention relates to the technical field of smart power grids, in particular to a virtual power plant real-time regulation capability assessment method and system, and the method comprises the steps: obtaining real-time power consumption data and historical power consumption data of each power device in a virtual power plant, determining a power minimum interval unit, correcting data abnormity, and the like. Accurate evaluation of the real-time adjustment capability of the virtual power plant is realized; the method specifically comprises the following steps: acquiring power consumption data and historical data of power equipment, generating a load curve, determining a power minimum interval unit based on the historical data, and further performing anomaly correction on the data; on this basis, the maximum and minimum real-time adjustment capability of each power device is evaluated, and the overall adjustment capability of the virtual power plant is comprehensively evaluated; through the implementation of the method, the precision and efficiency of virtual power plant scheduling can be improved, and the stability and flexibility of a power grid under different load conditions are ensured; the scheme has high real-time performance and accuracy, and is suitable for scheduling optimization of a modern intelligent power grid.

Description

Technical Field

[0001] The present invention relates to the field of smart grid technology, and in particular to a method and system for evaluating the real-time regulation capability of a virtual power plant. Background Art

[0002] With the transformation of the global energy structure and the rapid development of smart grid technology, virtual power plants (VPPs), as an intelligent power system that integrates different types of distributed energy resources (such as photovoltaics, wind power, energy storage, load regulation, etc.), are gradually becoming an important part of the future power system. Virtual power plants optimize the balance of power supply and demand and improve the flexibility and sustainability of the system by centrally managing and dispatching different power resources. However, there are still certain challenges to achieve efficient operation of virtual power plants, especially in the dynamic evaluation and management of regulation capabilities.

[0003] In the prior art, the evaluation of the regulation capability of virtual power plants mostly relies on traditional experience-based or simple model-based methods. These methods usually rely on static power equipment parameters and historical data, and lack the dynamic regulation capability evaluation of real-time power loads. The prior art ignores the real-time regulation potential of the equipment and the complexity of actual load changes by presetting the regulation capability of the equipment and analyzing it based only on some parameters. Therefore, the existing evaluation methods often cannot accurately reflect the real-time regulation capability of virtual power plants, resulting in insufficient accuracy of their scheduling decisions, which may affect the stability and scheduling efficiency of the power grid. Summary of the invention

[0004] In view of the many problems existing in the above-mentioned prior art, the present invention provides a method and system for evaluating the real-time regulation capability of a virtual power plant. The present invention combines the dynamic adjustment of the minimum power interval unit and the data anomaly correction technology to achieve an accurate evaluation of the real-time regulation capability of a virtual power plant. By performing real-time calculation and optimization on the regulation capability of each power device, the overall regulation capability of the virtual power plant is comprehensively evaluated, thereby optimizing power dispatching decisions. The present invention effectively improves the ability of virtual power plants to cope with complex changes in power demand and ensures the stable and efficient operation of the power grid.

[0005] A method for evaluating the real-time regulation capability of a virtual power plant comprises the following steps:

[0006] Obtain the power consumption data of each power equipment in the virtual power plant;

[0007] Obtain the historical power consumption data of each power equipment in the virtual power plant and generate a load curve based on the historical data;

[0008] Determine the minimum power interval unit of each power equipment in the virtual power plant;

[0009] Correct abnormal data of each power equipment in the virtual power plant;

[0010] Determine the maximum and minimum real-time regulation capabilities of each power device in the virtual power plant;

[0011] The regulation capability of each power equipment in the virtual power plant is integrated to evaluate the overall real-time regulation capability of the virtual power plant.

[0012] Preferably, obtaining the power consumption data of each power device in the virtual power plant includes: organizing the power devices aggregated in the virtual power plant into a list, and linking the electric meters of these power devices to the system to obtain real-time power consumption data of each power device.

[0013] Preferably, obtaining the historical power consumption data of each power device in the virtual power plant includes: extracting the historical power consumption data of each power device, wherein the active power of a data point is recorded at a preset period, and generating a historical power consumption data database.

[0014] Preferably, determining the minimum power interval unit of each power equipment in the virtual power plant includes: obtaining the rated power of the equipment based on its historical power data and setting the power interval; substituting different power intervals into the historical power data to obtain the number of points in each interval, and finally determining the power interval with the largest value as the minimum power interval unit.

[0015] Preferably, data anomaly correction includes: determining missing data points and correcting them using a curve fitting method, wherein the residual vector is calculated using the least squares principle and the correction coefficient is optimized; and by comparing the changes in load values ​​before and after, using a visual scatter plot to detect abnormal data points, and correcting them according to a set threshold.

[0016] Preferably, determining the maximum and minimum real-time regulation capabilities of each power equipment in the virtual power plant includes: dividing the specified power range based on the minimum power interval unit, setting multiple regulation areas, where the upper and lower limits of each area are used to calculate the maximum regulation capability and minimum regulation capability of the equipment, and further evaluating the regulation capability of each area.

[0017] Preferably, evaluating the overall regulation capability of the virtual power plant includes: integrating the regulation capability data of each power device based on the maximum and minimum real-time regulation capabilities of each device, and calculating the overall real-time regulation capability of the virtual power plant.

[0018] Preferably, when evaluating the overall regulation capability of the virtual power plant, the power scheduling between devices is further adjusted according to the real-time regulation response capability of the devices to achieve a more accurate overall regulation capability evaluation.

[0019] A system for implementing the method for evaluating the real-time regulation capability of a virtual power plant, comprising:

[0020] The power consumption data acquisition module is used to obtain the power consumption data of each power equipment in the virtual power plant;

[0021] The historical power consumption data acquisition module is used to obtain the historical power consumption data of each power equipment in the virtual power plant and generate a load curve based on the historical data;

[0022] A power minimum interval unit determination module is used to determine the power minimum interval unit of each power equipment in the virtual power plant according to historical power data;

[0023] A data correction module is used to correct abnormal data of each power device in the virtual power plant;

[0024] The regulation capacity determination module is used to determine the maximum and minimum real-time regulation capacity of each power equipment in the virtual power plant;

[0025] The overall regulation capability evaluation module is used to comprehensively analyze the regulation capability of each power equipment in the virtual power plant and evaluate the overall real-time regulation capability of the virtual power plant.

[0026] Compared with the prior art, the advantages and beneficial effects of the present invention are:

[0027] By comprehensively adopting advanced technical means such as historical power consumption data analysis, power minimum interval unit determination, and data anomaly correction, this solution achieves an accurate assessment of the real-time regulation capability of virtual power plants. This evaluation method based on real-time data and dynamic regulation models can effectively eliminate the defects of traditional methods that ignore real-time changes and differences in equipment regulation capabilities, and further enhance the resilience of virtual power plants in dynamic load adjustment. By combining data correction with dynamic evaluation, this solution not only enhances the evaluation accuracy, but also optimizes the dispatching strategy of virtual power plants under different load conditions, thereby improving the regulation efficiency and stability of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a schematic diagram of the process of the present invention;

[0029] Figure 2 This is a schematic diagram of the number of interval units and points in the present invention;

[0030] Figure 3 A visual scatter plot of outliers in the present invention;

[0031] Figure 4 It is a schematic diagram of the extension of the spacing area in the present invention;

[0032] Figure 5 It is a structural block diagram of the system of the present invention. DETAILED DESCRIPTION

[0033] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is obvious that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0034] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0035] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification, and should not be interpreted in an idealized or overly rigid manner.

[0036] A method for evaluating the real-time regulation capability of a virtual power plant comprises the following steps:

[0037] Step 1: Obtain a list of electrical equipment used by the virtual power plant.

[0038] The electrical equipment aggregated in the virtual power plant is organized into a unified list, and the electricity meters of these devices are linked to the system to obtain the power data of the equipment in real time.

[0039] Step 2: Obtain historical electricity consumption data of power equipment.

[0040] The historical power consumption data of each power equipment is extracted, that is, the active power data of one point every 15 minutes, to form a database.

[0041] Step 3: Determine the minimum power interval unit.

[0042] like Figure 2 As shown, according to the historical power data, the rated power value of the equipment is obtained as T, and the interval is set to x (x = 1, 2, 3..., T). Substitute the interval x into the historical power in turn, and find the maximum number of points y in the interval x. x , then the upper limit of the interval is x 上 , the lower limit is x 下 When there is y x When / x takes the maximum value, x is set to the minimum interval unit.

[0043] If there are multiple y / x that are all maximum values, max(x) is taken as the minimum interval unit.

[0044] Step 4: Correction of data anomalies.

[0045] During the data collection process, there may be data missing or data that is too large or too small, and these data can be corrected in advance.

[0046] ① Determine missing points and curve fitting:

[0047] f=g(y,a 1 ,a 2 ,...,a n )=a 1 g 1 (y)+a 2 g 2 (y)+...+a n g n (y)

[0048] y – variable, which serves as input to the function.

[0049] a 1 ,a 2 ,...a n ——Coefficient, used to adjust the weight g of the corresponding function i (y).

[0050] Let g 1 (y) = 1, g 2 (y) = y:

[0051]

[0052] r 1 ,r 2 ,...r n ——The elements of the residual vector represent the difference between the predicted value and the actual value;

[0053] a i ——coefficients, used for linear combination basis functions;

[0054] g i (y j )——The i-th basis function at y=y j The value at

[0055] y 1 ,y 2 ,...,y n ——actual power value;

[0056] With the help of the least squares principle and the concept of extreme value, we can get the modified a n :

[0057]

[0058] δ k ——Residual, which represents the difference between the predicted value and the actual value;

[0059] m – the number of data points;

[0060] Δ min — minimum error or minimum deviation;

[0061] a i ——coefficients, used to construct the fitting function;

[0062] ——δ k to a i The partial derivatives of are used to find the coefficient values ​​that minimize the error;

[0063] ② Determine the abnormal value. The load should be continuous on the time scale, and the difference will not change too much. Compare whether the load values ​​of the two points before and after are in the same dimension to form a visual scatter plot, such as Figure 3 shown.

[0064] Determine the range θ 1 ,θ 2 (The two values ​​are the minimum power interval unit x of the device):

[0065]

[0066] y(d,t)——load value at date d and time t;

[0067] d – date;

[0068] t——time;

[0069] θ 1 ,θ 2 ——Threshold, used to determine whether the change in load value is abnormal;

[0070] Then locate the abnormal data points and make corrections through this formula:

[0071]

[0072] y(d,t)——load value at date d and time t;

[0073] d – date;

[0074] t——time;

[0075] Step 5: Determine the maximum and minimum real-time adjustment capabilities.

[0076] Take the interval area in step 3 as the center and extend it to both sides until it covers 0kw and rated power Tkw. Set each area to X -n ...,X -1 ,X 0 ,X 1 ,X 2 ...X m ,like Figure 4 As shown. The number of points in each area is Y -n ...,Y -1 ,Y 0 ,Y 1 ,Y 2 ...Y m。

[0077] ① Determine which region the current power t belongs to and take the upper limit x of the region up and the lower limit x down The minimum real-time adjustment capability (non-sensitive adjustment) is:

[0078] D upmin =X up -t

[0079] D downmin =tX down

[0080] t——current power value;

[0081] X up ——the upper limit of the area;

[0082] X down ——the lower limit of the area;

[0083] D upmin ——Minimum upward adjustment potential;

[0084] D downmin ——Minimum downward adjustment potential;

[0085] ②Set the current power t to belong to area X t , take the data set {Y}={Y -n ...,Y -1 ,Y 0 ,Y 1 ,Y 2 ...Y m}, then area X t The number of points is Y t . Let the data set {Y}-Y t = {Z}, arrange Z in ascending order, and get {0, Z a , Z b...}, get the values ​​of a (a<t) and b (b>t), that is, the area X a and region X b , then take area X a and region X b The maximum and minimum values ​​of X aup and X adown , X bup and X bdown .

[0086] At this time, the maximum real-time adjustment capability (benefit-based adjustment) can be calculated as:

[0087] D upmax =X bdown -t

[0088] D downmax =tX aup

[0089] X bdown ——Region X b The minimum value of

[0090] X aup ——Region X a The maximum value of

[0091] D upmax ——Maximum upward adjustment potential;

[0092] D downmax ——Maximum downward adjustment potential;

[0093] Step 6: Determine the real-time regulation capability of the virtual power plant.

[0094] The above method can be used to calculate the maximum and minimum regulation capabilities of a single device in a virtual power plant. According to the real-time adjustable mode of a single device configured in the system (whether it supports efficiency-based regulation or non-sensitive regulation), the adjustment value of each device can be added to obtain a numerical evaluation of the overall real-time regulation capability of the virtual power plant.

[0095] Virtual power plant: a power coordination management system that uses advanced information and communication technologies and software systems to achieve the aggregation and coordinated optimization of distributed energy resources such as distributed power sources (DER), energy storage systems, controllable loads, and electric vehicles, so as to participate in the power market and power grid operation as a special power plant. In this article, it is the background of this technology, which is simply understood as a power dispatching mode, including peak shaving and valley filling.

[0096] Respondent: Power users participating in the virtual power plant dispatch, specifically referring to qualified response power stations in this article

[0097] Peak shaving response: During peak hours, less electricity is used, and the power consumption curve of the responding subject is obtained. By subtracting the historical average power consumption curve, the peak shaving response amount can be obtained.

[0098] Load curve: Power statistics for evaluating the power consumption habits, capabilities, and control capabilities of power equipment or power users, generally displayed in the form of a line graph. Generally, the power value of power equipment or power users is recorded at an average point every hour or every 15 minutes for 24 hours a day, and displayed through a line graph. Among them, the "actual load curve" is the actual active power value of the day; the "reported load curve" is to report the future power curve to be executed to the power grid side before the preparation for scheduling. For example, if the power at 12 noon in the reported curve is 230kw, then the power user needs to adjust the power consumption at 12 o'clock to 230kw±20% according to the reported power. "Predicted load curve" is a load curve formed by artificial experience or by predicting the power value in the future through intelligent algorithms.

[0099] Benefit-based regulation: Electricity regulation activities that have a certain impact on daily electricity consumption, such as reducing electricity consumption by shutting down HVAC equipment and reducing production efficiency. Although this regulation mode has a great impact on daily activities, it has a large adjustment power range and correspondingly high benefits.

[0100] Non-sensitive regulation: Utilize energy inertia to carry out regulation activities without affecting the normal operation and production of power users, such as slightly increasing the HVAC temperature in summer. By predicting the interval characteristics of electricity consumption through big data, the power consumption can be reduced only when necessary. The impact on production and physical sensation is kept below the red line threshold.

[0101] Power minimum interval unit: describes the minimum power change level unit of the device. For example, for a device with a rated power of 100kw, its minimum interval unit should be set between 5-20kw. However, for a device with a rated power of 10000kw, if it is set between 5-20kw, the accuracy is too high and cannot be evaluated. Therefore, it is necessary to give different minimum interval units for different devices according to the actual power change situation.

[0102] Embodiment: This scheme is used to measure the real-time regulation capability of power equipment in a virtual power plant, and the total sum is the real-time regulation capability of the virtual power plant. According to the contradictory relationship between power regulation and the power consumption habits of power users, for example, for a production line, the production volume is proportional to the power of the user. Reducing the power of the production line will reduce production efficiency, while raising the power will consume a lot of ineffective power expenditure. Therefore, regulation is divided into benefit-based regulation and ineffective regulation.

[0103] In the actual real-time regulation process, the system needs to make an assessment based on the real-time regulation capability of the virtual power plant, evaluate the number of resources to be dispatched based on the regulation demand, and issue the required regulation threshold based on the demand to ensure that the regulation is within the controllable range, rather than regulating as much as possible. Therefore, the virtual power plant needs to provide real-time regulation capabilities in advance (including benefit-based regulation and non-inductive regulation).

[0104] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware.

[0105] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for evaluating the real-time regulation capability of a virtual power plant, characterized in that: The following steps are involved: Obtain the power consumption data of each power equipment in the virtual power plant; Obtain the historical power consumption data of each power equipment in the virtual power plant and generate a load curve based on the historical data; Determine the minimum power interval unit of each power equipment in the virtual power plant; Correct abnormal data of each power equipment in the virtual power plant; Determine the maximum and minimum real-time regulation capabilities of each power device in the virtual power plant; The regulation capability of each power equipment in the virtual power plant is integrated to evaluate the overall real-time regulation capability of the virtual power plant.

2. The method for evaluating the real-time regulation capability of a virtual power plant according to claim 1, characterized in that: Acquiring the power consumption data of each power device in the virtual power plant includes: organizing the power devices aggregated in the virtual power plant into a list, and linking the electric meters of these power devices to the system to achieve the acquisition of real-time power consumption data of each power device.

3. The method for evaluating the real-time regulation capability of a virtual power plant according to claim 1, characterized in that: Obtaining the historical power consumption data of each power device in the virtual power plant includes: extracting the historical power consumption data of each power device, wherein the active power of a data point is recorded at a preset period, and generating a historical power consumption data database.

4. The method for evaluating the real-time regulation capability of a virtual power plant according to claim 1, characterized in that: Determining the minimum power interval unit of each power equipment in the virtual power plant includes: obtaining its rated power based on the historical power data of the equipment and setting the power interval; substituting different power intervals into the historical power data to obtain the number of points in each interval, and finally determining the power interval with the largest value as the minimum power interval unit.

5. The method for evaluating the real-time regulation capability of a virtual power plant according to claim 1, characterized in that: Data anomaly correction includes: determining missing data points and correcting them using curve fitting methods, in which the residual vector is calculated using the least squares principle and the correction coefficient is optimized; and by comparing the changes in load values ​​before and after, using a visual scatter plot to detect abnormal data points and correcting them according to the set threshold.

6. The method for evaluating the real-time regulation capability of a virtual power plant according to claim 1, characterized in that: Determining the maximum and minimum real-time regulation capabilities of each power equipment in the virtual power plant includes: dividing the specified power range based on the minimum power interval unit, setting multiple regulation areas, where the upper and lower limits of each area are used to calculate the maximum and minimum regulation capabilities of the equipment, and further evaluating the regulation capabilities of each area.

7. The method for evaluating the real-time regulation capability of a virtual power plant according to claim 1, characterized in that: Evaluating the overall regulation capability of a virtual power plant includes: integrating the regulation capability data of each power device based on the maximum and minimum real-time regulation capabilities of each device, and calculating the overall real-time regulation capability of the virtual power plant.

8. The method for evaluating the real-time regulation capability of a virtual power plant according to claim 7, characterized in that: When evaluating the overall regulation capability of the virtual power plant, the power scheduling between devices is further adjusted according to the real-time regulation response capability of the equipment to achieve a more accurate overall regulation capability assessment.

9. A system for implementing the method for evaluating the real-time regulation capability of a virtual power plant according to any one of claims 1 to 8, characterized in that: include: The power consumption data acquisition module is used to obtain the power consumption data of each power equipment in the virtual power plant; The historical power consumption data acquisition module is used to obtain the historical power consumption data of each power equipment in the virtual power plant and generate a load curve based on the historical data; A power minimum interval unit determination module is used to determine the power minimum interval unit of each power equipment in the virtual power plant according to historical power data; A data correction module is used to correct abnormal data of each power device in the virtual power plant; The regulation capacity determination module is used to determine the maximum and minimum real-time regulation capacity of each power equipment in the virtual power plant; The overall regulation capability evaluation module is used to comprehensively analyze the regulation capability of each power equipment in the virtual power plant and evaluate the overall real-time regulation capability of the virtual power plant.

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