Load aggregation method and device, nonvolatile storage medium and computer equipment

Abstract

The invention discloses a load aggregation method and device, a nonvolatile storage medium and computer equipment. The method comprises the steps that reference loads corresponding to a plurality of load sides in a power system are obtained, and the reference loads represent the power consumption level under the normal operation condition; based on the reference loads corresponding to the multiple load sides and preset adaptability indexes corresponding to multiple scenes, adaptability indexes corresponding to the multiple load sides in the multiple scenes are determined, and the multiple scenes comprise a climbing scene, a valley filling scene and a peak clipping scene; and aggregating the plurality of load sides based on the adaptability indexes corresponding to the plurality of load sides in the plurality of scenes to obtain load side aggregation groups corresponding to the plurality of scenes. According to the invention, the technical problem that the aggregation result is not accurate enough because the aggregation method at the current load side generally focuses on the load resource itself is solved.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a load aggregation method, device, non-volatile storage medium and computer equipment. Background Art

[0002] Load-side resources are numerous and have widely varying performance. Current methods for aggregating these resources typically focus on the load resources themselves, often based on their physical characteristics. This analysis lacks comprehensiveness. Existing methods often employ analogies with generation-side energy sources, simply analyzing output complementarity. This approach fails to fully consider the impact of the diverse nature of various load-side resource types on grid balance, resulting in a limited utilization of the overall load-side resource regulation capabilities. Currently, no effective solutions have been proposed to address these issues. Summary of the Invention

[0003] Embodiments of the present invention provide a load aggregation method, apparatus, non-volatile storage medium, and computer device to at least solve the technical problem that current load-side aggregation methods generally focus on the load resources themselves, resulting in inaccurate aggregation results.

[0004] According to one aspect of an embodiment of the present invention, a load aggregation method is provided, comprising: obtaining a benchmark load corresponding to each of a plurality of load sides in a power system, wherein the benchmark load represents the power consumption level under normal operating conditions; determining an adaptability index corresponding to each of the plurality of load sides in a plurality of scenarios based on the benchmark loads corresponding to each of the plurality of load sides and adaptability indicators corresponding to each of a plurality of preset scenarios, wherein the plurality of scenarios include a climbing scenario, a valley filling scenario, and a peak shaving scenario; aggregating the plurality of load sides based on the adaptability index corresponding to each of the plurality of load sides in the plurality of scenarios to obtain a load side aggregation group corresponding to each of the plurality of scenarios.

[0005] Optionally, obtaining a benchmark load corresponding to each of the multiple load sides in the power system includes: obtaining electricity consumption data corresponding to each of the multiple load sides, wherein the multiple load sides include a temperature control load side, an industrial load side, and an electric vehicle load side; and calculating a benchmark load corresponding to each of the multiple load sides based on the electricity consumption data corresponding to each of the multiple load sides.

[0006] Optionally, based on the adaptability index corresponding to each of the multiple load sides in multiple scenarios, the multiple load sides are aggregated to obtain load side aggregation groups corresponding to each of the multiple scenarios, wherein the method for obtaining the load side aggregation group corresponding to the target scenario is as follows, where the target scenario is any one of the multiple scenarios: obtaining the index threshold corresponding to the target scenario; determining the load side aggregation group corresponding to the target scenario based on the load side whose adaptability index exceeds the index threshold corresponding to the target scenario.

[0007] Optionally, the load side whose adaptability index does not exceed the index threshold corresponding to the target scenario is determined as the non-compliant load side; the non-compliant load sides are combined in pairs to obtain a non-compliant load side group; based on the adaptability index corresponding to the non-compliant load side, the adaptability index corresponding to the non-compliant load side group is determined; the non-compliant load sides in the non-compliant load side group whose adaptability index exceeds the index threshold corresponding to the target scenario are aggregated into the load side aggregation group corresponding to the target scenario.

[0008] Optionally, the adaptability indicators corresponding to the ramp-up scenario include response speed, adjustment frequency and maximum adjustable capacity; the adaptability indicators corresponding to the peak-shaving scenario include response speed, maximum adjustable capacity and adjustable duration; and the adaptability indicators corresponding to the valley-filling scenario include response speed, maximum adjustable capacity, adjustable duration and calling frequency.

[0009] Optionally, a control demand of the power system is obtained; based on the control demand, a load side aggregation group to be controlled is determined; based on a preset objective function, a control instruction is generated; based on the control instruction, the load side aggregation group to be controlled is controlled.

[0010] According to another aspect of an embodiment of the present invention, a load aggregation device is also provided, including: an acquisition module for acquiring a benchmark load corresponding to each of multiple load sides in a power system, wherein the benchmark load represents the power consumption level under normal operating conditions; a determination module for determining the adaptability index corresponding to each of the multiple load sides in multiple scenarios based on the benchmark loads corresponding to each of the multiple load sides and the adaptability indicators corresponding to each of the preset multiple scenarios, wherein the multiple scenarios include climbing scenarios, valley filling scenarios and peak shaving scenarios; an aggregation module for aggregating the multiple load sides based on the adaptability index corresponding to each of the multiple load sides in the multiple scenarios to obtain a load side aggregation group corresponding to each of the multiple scenarios.

[0011] According to another aspect of an embodiment of the present invention, a non-volatile storage medium is provided. The non-volatile storage medium includes a stored program, wherein when the program is running, the device where the non-volatile storage medium is located is controlled to execute any one of the above load aggregation methods.

[0012] According to yet another aspect of an embodiment of the present invention, a computer device is provided. The computer device includes a processor, and the processor is configured to run a program. When the program is run, any one of the above load aggregation methods is executed.

[0013] According to yet another aspect of an embodiment of the present invention, a computer program product is provided, including a computer program. When the computer program is executed by a processor, any one of the above load aggregation methods is implemented.

[0014] In an embodiment of the present invention, a load aggregation method is adopted to obtain a benchmark load corresponding to each of multiple load sides in the power system, wherein the benchmark load represents the power consumption level under normal operating conditions; based on the benchmark load corresponding to each of the multiple load sides and the adaptability indicators corresponding to each of the preset multiple scenarios, the adaptability indexes corresponding to each of the multiple load sides in multiple scenarios are determined, wherein the multiple scenarios include climbing scenarios, valley filling scenarios and peak shaving scenarios; based on the adaptability indexes corresponding to each of the multiple load sides in multiple scenarios, the multiple load sides are aggregated to obtain load side aggregation groups corresponding to each of the multiple scenarios, thereby achieving the purpose of load side aggregation based on the adaptability indicators in different scenarios, thereby realizing the technical effect of improving the accuracy of aggregation, and further solving the technical problem that the current load side aggregation method generally focuses on the load resource itself, resulting in inaccurate aggregation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0016] Figure 1 A hardware structure block diagram of a computer terminal for implementing a load aggregation method is shown;

[0017] Figure 2 is a flow chart of a load aggregation method according to an embodiment of the present invention;

[0018] Figure 3 is a flow chart of a load aggregation method according to an optional embodiment of the present invention;

[0019] Figure 4 4 is a structural block diagram of a load aggregation device provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0021] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0022] According to an embodiment of the present invention, a method embodiment of a load aggregation method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0023] The method embodiment provided in the first embodiment of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 FIG1 shows a hardware structure block diagram of a computer terminal for implementing a load aggregation method. Figure 1 As shown, the computer terminal 10 may include one or more (illustrated as 102a, 102b, ..., 102n in the figure) processors (the processor may include but is not limited to a microprocessor MCU or a programmable logic device FPGA and other processing devices), a memory 104 for storing data. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. It will be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components than shown, or with Figure 1 Different configurations shown.

[0024] It should be noted that the one or more processors and / or other data processing circuits described above may generally be referred to herein as "data processing circuitry." The data processing circuitry may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuitry may be a single, independent processing module, or may be incorporated in whole or in part into any of the other components of the computer terminal 10. As described in the embodiments of the present application, the data processing circuitry serves as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0025] Memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the load aggregation method in the embodiments of the present invention. The processor executes the software programs and modules stored in memory 104 to execute various functional applications and data processing, thereby implementing the load aggregation method for the application described above. Memory 104 can include high-speed random access memory and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, memory 104 can further include memory remotely located relative to the processor, and such remote memory can be connected to computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0026] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 10 .

[0027] Figure 2 FIG. 1 is a flow chart of a load aggregation method according to an embodiment of the present invention. Figure 2 As shown, the method includes the following steps:

[0028] Step S202: obtaining a reference load corresponding to each of a plurality of load sides in the power system, wherein the reference load represents a power consumption level under normal operating conditions.

[0029] In this step, the power system typically includes multiple loads, such as temperature-controlled loads, industrial loads, and electric vehicles. Different loads can impact different aspects of the power system. Therefore, these loads need to be aggregated. By aggregating these loads, the regulation capabilities of these small-capacity resources can be pooled to create a larger, more flexible regulation capability. This helps to utilize energy more efficiently and reduce waste. For example, by intelligently scheduling electric vehicle charging, charging can be performed during off-peak hours, thereby balancing the grid load.

[0030] Benchmark load generally refers to the average or typical power consumption level of load-side resources under normal operating conditions. It provides the power system with a forecast of load resource consumption in an unregulated state, helping dispatch centers or virtual power plants (VPPs) understand the underlying patterns of power demand and better plan and dispatch resources. The corresponding benchmark load can be determined from real-time power data from the load side. Power consumption data can be collected from various load-side resources, such as from the power company's metering system, smart meters, or load management devices. The consumption patterns of load-side resources are then analyzed to identify consumption characteristics under normal operating conditions. This may involve statistical analysis, time series analysis, and machine learning techniques such as cluster analysis, principal component analysis (PCA), and neural networks to identify typical load behavior and patterns. Based on the analysis results, a mathematical model of the load-side resource is developed. These models can be statistical, physics-based, or machine learning-based. The purpose of the model is to predict the power consumption level of the load-side resource under normal operating conditions. Based on the established model, the benchmark load of the load-side resource is calculated.

[0031] Step S204, based on the benchmark loads corresponding to the multiple load sides and the adaptability indicators corresponding to the preset multiple scenarios, determine the adaptability indexes corresponding to the multiple load sides in multiple scenarios, where the multiple scenarios include climbing scenarios, valley filling scenarios and peak shaving scenarios.

[0032] In this step, adaptability indices corresponding to multiple load-side scenarios can be determined based on the adaptability indicators corresponding to each of the multiple scenarios. In the scheduling and optimization of power systems and virtual power plants (VPPs), scenario adaptability indicators are a set of quantitative parameters used to measure the ability and suitability of load-side resources (such as adjustable electrical equipment, energy storage systems, and electric vehicles) to respond to grid demand under specific operating scenarios. Different operating scenarios, such as peak shaving, valley filling, and ramping, have different requirements for the response characteristics of load-side resources, and adaptability indicators are designed to reflect these requirements. The peak shaving scenario aims to reduce power demand during peak load periods to avoid grid overload. The valley filling scenario aims to increase power demand during low load periods to fully utilize excess generation capacity. The ramping scenario focuses on rapid changes in grid load demand and requires load-side resources to quickly respond to increases or decreases in power demand. Therefore, the adaptability indicator for the ramping scenario can be used to measure the responsiveness and adaptability of load resources to rapid increases in demand. The adaptability indicator for the valley filling scenario can be used to assess the ability of load resources to increase power consumption during low load periods to balance the grid. The peak shaving scenario adaptability index can be used to measure the potential of load resources to reduce power consumption during peak hours to alleviate grid pressure. The adaptability index of each load-side resource is then calculated for each preset scenario. The adaptability index is a quantitative indicator used to represent the performance and applicability of load resources in a specific scenario. Once the adaptability index of all load-side resources in all preset scenarios is calculated, this information can be used for load-side aggregation and then used in the scheduling strategy of the virtual power plant. Resources with higher adaptability indexes will be dispatched preferentially in specific scenarios to maximize the balancing effect of the grid and the overall responsiveness of load-side resources. Load-side aggregation based on scenarios can aggregate load sides with similar regulation characteristics, facilitating the regulation of the power system.

[0033] Through scenario aggregation, dispersed, small-capacity load resources can be transformed into a unified, large-capacity, adjustable resource. This enables power system dispatchers to more effectively handle imbalances in power supply and demand, especially in critical scenarios such as peak shaving, valley filling, and ramping, allowing them to quickly adjust the power consumption of load-side resources to achieve supply and demand balance and improve grid stability and responsiveness.

[0034] Step S206 : Aggregating the multiple load sides based on the adaptability indexes corresponding to the multiple load sides in the multiple scenarios to obtain load side aggregation groups corresponding to the multiple scenarios.

[0035] In this step, the adaptability indexes corresponding to multiple load sides in multiple scenarios are aggregated to form load side aggregation groups for different scenarios. The threshold value of the adaptability index can be set according to the specific requirements of each scenario. Those resources with adaptability index higher than the threshold value are screened out from all load side resources. The screened resources are classified and classified into corresponding scenario sets based on their adaptability indexes in different scenarios. For each scenario set, the classified load side resources are aggregated. The goal of aggregation is to form a holistic and efficient regulation capability that can quickly respond to grid needs in specific scenarios. Optimization algorithms (such as linear programming, dynamic programming or genetic algorithms) can be used to determine the best resource combination to maximize the matching degree between the adaptability index and scenario requirements.

[0036] Aggregated load-side resources can be utilized more efficiently to replace or supplement traditional power generation resources. For example, in peak shaving scenarios, intelligently dispatching load-side resources can reduce peak power generation costs, avoid excessive investment in reserve capacity, and thus save on the overall cost of power system operation and maintenance. Through this process, the power system can more intelligently and efficiently manage the power supply and demand balance of the grid, improving its flexibility and responsiveness.

[0037] Through the above steps, the purpose of load-side aggregation based on adaptability indicators in different scenarios can be achieved, thereby realizing the technical effect of improving the accuracy of aggregation, and further solving the technical problem that the current load-side aggregation method generally focuses on the load resources themselves, resulting in inaccurate aggregation results.

[0038] As an optional embodiment, obtaining a benchmark load corresponding to each of multiple load sides in a power system includes: obtaining electricity consumption data corresponding to each of the multiple load sides, wherein the multiple load sides include a temperature control load side, an industrial load side, and an electric vehicle load side; and calculating a benchmark load corresponding to each of the multiple load sides based on the electricity consumption data corresponding to each of the multiple load sides.

[0039] Optionally, a benchmark load corresponding to each of multiple load sides in the power system can be obtained. These multiple load sides can include temperature-controlled loads, industrial loads, and electric vehicle loads. The benchmark load side can be determined based on the power data corresponding to each load side. For example, electricity usage data corresponding to each of the multiple load sides can be collected first. For temperature-controlled loads, electricity usage data for temperature-controlled equipment (such as air conditioners and water heaters) can be collected, including operating status, power demand, indoor temperature, and set temperature. The data can then be cleaned to remove outliers and fill in missing values ​​to ensure data integrity and accuracy. The data can also be analyzed by time series to identify usage patterns and seasonal variations of temperature-controlled equipment. For industrial loads, detailed electricity usage data can be obtained, including production cycles, equipment efficiency, and production demand. This industrial load data can then be cleaned and processed to identify load patterns under normal operating conditions and eliminate data anomalies caused by production failures or abnormal events. For electric vehicle loads, charging demand data can be collected, including charging time, charging location, charging power, and battery status. Then, electric vehicle charging data is analyzed to identify user charging behavior patterns, taking into account differences in charging demand across different time periods and locations. Based on this acquired power usage data, a benchmark load can be calculated. For temperature-controlled loads, this calculation can be performed by analyzing the average power usage or typical power usage patterns of temperature-controlled equipment under normal operating conditions to determine the benchmark load at a specific ambient temperature and a set temperature. For industrial loads, the benchmark load can be calculated based on the average power usage of industrial load equipment during a normal production cycle, taking into account equipment efficiency, production schedules, and seasonal factors. For electric vehicle loads, the average daily charging demand of electric vehicles can be analyzed, taking into account user behavior patterns (such as commuting time and holiday charging demand variations), battery capacity, and charging efficiency to determine the benchmark load for the electric vehicle load. The calculated benchmark load can be used as a reference point to assess the regulation potential and responsiveness of load-side resources in different scenarios. In peak shaving, valley filling, or ramp-up scenarios, these resources can be intelligently dispatched based on their deviation from the benchmark load to achieve supply and demand balance and system stability.

[0040] Through these steps, the power system can accurately understand the power consumption levels of temperature-controlled loads, industrial loads, and electric vehicle loads, which is crucial for intelligent scheduling, optimizing resource allocation, and improving grid efficiency. Accurate calculation and dynamic adjustment of baseline loads will help the power system better adapt to various operating scenarios, enhance demand response capabilities, reduce operating costs, and provide users with more reliable and efficient power services.

[0041] As an optional embodiment, based on the adaptability index of each of the multiple load sides in multiple scenarios, the multiple load sides are aggregated to obtain load side aggregation groups corresponding to the multiple scenarios, wherein the method for obtaining the load side aggregation group corresponding to the target scenario is as follows, where the target scenario is any one of the multiple scenarios: obtaining the index threshold corresponding to the target scenario; determining the load side aggregation group corresponding to the target scenario based on the load side whose adaptability index exceeds the index threshold corresponding to the target scenario.

[0042] Optionally, an index threshold corresponding to a scenario can be set to determine the load side whose adaptability index in that scenario exceeds the index threshold as the load side in the load side aggregation group corresponding to that scenario. Specifically, the index threshold can be set based on actual conditions; the higher the threshold, the stricter the screening of load side resources.

[0043] Specifically, the load aggregation method for ramping scenarios is as follows:

[0044] 1. Calculate the ramp scenario fitness index Y of all load-side resources participating in the aggregation C,i .

[0045] 2. Set the load-side resource screening threshold value L for the ramping scenario slope , that is, the index threshold corresponding to the climbing scene, where the threshold value L slope The value range is: L slope ≥1. The higher the threshold value, the stricter the screening of adjustable resource conditions.

[0046] 3. Filter the load side resources according to the scene threshold value. If Y C,i ≥L slope , then it meets the scenario requirements and belongs to the load-side aggregation group of the scenario.

[0047] 4. Calculate the adjustable capacity continuity curve of the load aggregation group composed of all load-side resources that meet the scenario requirements.

[0048] The load aggregation method for peak shaving scenarios is as follows:

[0049] 1. Calculate the ramp scenario fitness index Y of all load-side resources participating in the aggregation P,i .

[0050] 2. Set the load-side resource screening threshold value L for peak shaving scenarios peak , that is, the index threshold corresponding to the peak shaving scenario, where the threshold value L peak The value range is: L peak ≥0. The higher the threshold value, the stricter the screening of adjustable resource conditions.

[0051] 3. Filter the load side resources according to the scene threshold value. If YP,i ≥L peak , then it meets the scenario requirements and belongs to the load-side aggregation group of the scenario.

[0052] 4. Calculate the adjustable capacity continuity curve of the load aggregation group composed of all load-side resources that meet the scenario requirements.

[0053] The method for load aggregation in valley filling scenarios is as follows:

[0054] 1. Calculate the ramp scenario fitness index Y of all load-side resources participating in the aggregation V,i .

[0055] 2. Set the load-side resource screening threshold value L for the valley filling scenario valley . (Threshold value L valley The value range is: L valley ≥0, the higher the threshold value, the stricter the screening of adjustable resource conditions)

[0056] 3. Filter the load side resources according to the scene threshold value. If Y V,i ≥L valley , then it meets the scenario requirements and belongs to the load-side aggregation group of the scenario.

[0057] 4. Calculate the adjustable capacity continuity curve of the load aggregation group composed of all load-side resources that meet the scenario requirements.

[0058] The adjustable capacity continuity curve is calculated for a load aggregation group consisting of all load-side resources that meet the scenario requirements. This curve can intuitively display the range of power capacity that the aggregated load resources can provide or absorb within a specific time period, providing decision support for grid scheduling and demand response. Based on the adjustable capacity continuity curve, the dispatch system can develop more accurate and efficient dispatch plans. For example, in a peak shaving scenario, the system can use the curve to determine the total amount of load that can be reduced during peak hours and the duration of this reduction, thereby avoiding grid overload and reducing peak power demand.

[0059] As an optional embodiment, the load side whose adaptability index does not exceed the index threshold corresponding to the target scenario is determined to be a non-compliant load side; the non-compliant load sides are combined in pairs to obtain a non-compliant load side group; based on the adaptability index corresponding to the non-compliant load side, the adaptability index corresponding to the non-compliant load side group is determined; the non-compliant load sides in the non-compliant load side group whose adaptability index exceeds the index threshold corresponding to the target scenario are aggregated into the load side aggregation group corresponding to the target scenario.

[0060] Optionally, for load sides whose adaptability index does not exceed the index threshold, they can be combined in pairs. If the adaptability index of the combined group exceeds the index threshold, the load side group can be included in the load side aggregation group corresponding to the corresponding scenario.

[0061] For example, the filtered "non-compliant load sides" are combined in pairs to form "non-compliant load side groups." This is because in some cases, two individual load side resources may each have low adaptability, but when combined, they may exhibit higher scenario adaptability due to the complementary nature of their resource characteristics. Each non-compliant load side group is analyzed and its adaptability index for the target scenario is recalculated. "Non-compliant load side groups" whose combined adaptability index exceeds the threshold corresponding to the target scenario are filtered out. These load side groups are transformed into "compliant load sides" due to their complementarity, meaning they can effectively respond to grid demand in specific scenarios. The "non-compliant load sides" in the "compliant load side group" filtered out in the previous step are aggregated into the load side aggregation group corresponding to the target scenario. This means that even if these resources do not fully meet the scenario requirements initially, through combination and optimization, they can become resources that effectively respond to grid demand.

[0062] By combining load-side resources in pairs, resources that might not have met the scenario requirements individually can be reused, improving resource utilization and economic efficiency across the entire system. Furthermore, the combined load-side groups can exhibit improved scenario adaptability, enabling the power system to respond more flexibly and efficiently to various operating scenarios, such as peak shaving, valley filling, and ramping. Through this series of steps, the power system can more comprehensively assess and utilize load-side resources, achieving more refined and efficient energy management while promoting user participation and improving the quality of power services.

[0063] As an optional embodiment, the adaptability indicators corresponding to the climbing scenario include response speed, adjustment frequency and maximum adjustable capacity; the adaptability indicators corresponding to the peak shaving scenario include response speed, maximum adjustable capacity and adjustable duration; and the adaptability indicators corresponding to the valley filling scenario include response speed, maximum adjustable capacity, adjustable duration and calling frequency.

[0064] Alternatively, ramping scenarios typically occur when electricity demand rises rapidly, such as in the morning or evening. These scenarios require the grid to quickly increase supply to match the increase in demand. Key adaptability metrics for ramping scenarios include: Response speed: The speed with which load-side resources receive dispatch instructions and begin increasing load. Resources with fast response times can quickly "ramp," helping the grid quickly increase power supply capacity. Adjustment rate: The amount of load that a load-side resource can increase per unit time, typically measured in kW / min or kW / h. Maximum adjustable capacity: The maximum amount of load that a load-side resource can increase in a ramping scenario. This measures the maximum "ramp" capability a resource can provide. Peak shaving scenarios occur when electricity demand reaches a peak and the grid faces the risk of overload, requiring rapid load reduction to prevent system overload. Key adaptability metrics for peak shaving scenarios include: Response speed: The speed with which load-side resources receive dispatch instructions and begin reducing load. Fast response is crucial in peak shaving scenarios to prevent peak loads from impacting the grid. Maximum adjustable capacity: The maximum amount of load that a load-side resource can reduce in a peak shaving scenario. Resources with high peak-shaving capabilities can help the grid quickly reduce peak demand and alleviate grid pressure. Adjustable duration: The maximum length of time a load-side resource can remain in a peak-shaving state, typically ranging from 15 minutes to several hours. A longer duration helps ensure more stable load control and prevent rapid load rebounds. Valley-filling scenarios occur when electricity demand is low and supply is relatively abundant, such as in the middle of the night or early morning. During these times, the grid needs to increase load to fully utilize excess power and avoid waste. Valley-filling adaptability metrics include: Response speed: While valley-filling scenarios may require less stringent response speed than peak-shaving or ramping scenarios, fast response still helps quickly increase load and fully utilize excess power. Maximum adjustable capacity: The maximum amount of load that a load-side resource can increase in a valley-filling scenario. Resources with high valley-filling capabilities can help the grid increase electricity demand during off-peak periods, improving energy efficiency. Adjustable duration: The maximum length of time a load-side resource can remain in a valley-filling state. A longer duration helps steadily increase load and fully utilize electricity resources, especially when renewable energy generation (such as wind and solar) is unstable. Call frequency: Valley-filling scenarios may require a lower call frequency to avoid the impact of frequent changes on users and devices.

[0065] Specifically, according to the characteristic that the climbing scene generally requires a higher response speed, the (upper) climbing scene adaptability index Y of the adjustable load i is defined as C,i for;

[0066]

[0067] Where sign(x) is the sign function, P tis the adjustable power of load i at time t. When load i has the potential to adjust upward at time t, P t > 0, when load i has the potential to adjust downward at time t, P t <0;v i is the regulation rate of load i; v slope The load adjustment rate required for ramping scenarios.

[0068] When P t >0, that is, sign(P t )=1, Y C,i The value of v i is directly proportional to When P t <0, that is When Y C,i The value of v i Inversely proportional relationship, Y C,i The value range is v imin is the minimum regulation rate of load i, v imax is the maximum regulation rate of load i.

[0069] The threshold value L corresponding to the climbing scene slope The value range is: L slope ≥1. The higher the threshold value, the stricter the screening of adjustable resource conditions.

[0070] The peak shaving scenario has a general requirement for response speed and a relatively long requirement for duration, generally at the level of 15 minutes. However, the peak shaving call frequency is generally low, and the controllable load can usually meet the conditions. Based on this, the peak shaving scenario adaptation performance index Y of the controllable load i is defined. P,i as follows:

[0071]

[0072] Where, P t is the adjustable power of load i at time t. When load i has the potential to adjust upward at time t, P t > 0, when load i has the potential to adjust downward at time t, P t <0;v i is the regulation rate of load i; v peak Load regulation rate required for peak shaving scenarios; The load adjustable power duration required for peak shaving scenario, T i c is the adjustable power duration of load i.

[0073] The threshold value L corresponding to the peak shaving scenario peak The value range is: L peak≥0. The higher the threshold value, the stricter the screening of adjustable resource conditions.

[0074] The valley filling scenario is similar to the peak shaving scenario. It has a general requirement for response speed, a relatively long duration, and a low call frequency. The controllable load can basically meet the conditions. The adaptability index Y of the valley filling scenario of the controllable load i is V,i as follows:

[0075]

[0076] Where, P t is the adjustable power of load i at time t. When load i has the potential to adjust upward at time t, P t > 0, when load i has the potential to adjust downward at time t, P t <0;v i is the regulation rate of load i; v valley Load regulation rate required for valley filling scenarios; The load adjustable power duration required for valley filling scenario, T i c is the adjustable power duration of load i.

[0077] The threshold value L corresponding to the valley filling scene valley The value range is: L valley ≥0. The higher the threshold value, the stricter the screening of adjustable resource conditions.

[0078] The adjustable resource adjustment characteristics used in the scenario fitness index calculation are defined as follows: maximum adjustable capacity, response time, adjustment rate, and adjustable duration.

[0079] 1) Maximum adjustable capacity (kW):

[0080]

[0081] Where, Indicates the average baseline load power of the adjustable load group before adjustment during the adjustment period (kW); The average aggregate power (kW) of the adjustable load group after maximum regulation during the regulation period. FL refers to the adjustable load group, which can be a temperature-controlled load group, an electric vehicle load group, an energy storage load group, or other combinations.

[0082] 2) Response time (min):

[0083] t FL =t first -t strat

[0084] Where, t firstIndicates the time (min) when the load group power first reaches the regulation target value; t strat Indicates the adjustment start time (min).

[0085] 3) Adjustment rate (kW / min):

[0086] θ FL =ΔP FL / t FL

[0087] Where ΔP FL Indicates the adjustable load group regulation capacity (kW); t FL Indicates the response time (min).

[0088] 4) Adjustable duration (h): The duration of power regulation for the flexible load group (h),

[0089] Combining these adaptability metrics allows us to assess the applicability and regulation potential of different types of load-side resources in specific scenarios, leading to more efficient resource scheduling and power system balancing. For example, electric vehicle charging stations may be more suitable for ramp-up scenarios because they can quickly respond and increase charging loads; whereas industrial loads may be more suitable for peak-shaving scenarios because they typically have higher adjustable capacity and longer durations. Temperature-controlled loads, such as air conditioners, may play an important role in valley-filling scenarios because they can increase energy consumption at night or early in the morning, helping to balance supply and demand.

[0090] As an optional embodiment, the control demand of the power system is obtained; based on the control demand, the load side aggregation group to be controlled is determined; based on the preset objective function, a control instruction is generated; based on the control instruction, the load side aggregation group to be controlled is controlled.

[0091] Alternatively, power system regulation requirements are typically determined by system operating conditions, including power supply and demand balance, frequency and voltage stability, and peak load shifting and valley shifting. Demand information may come from the grid dispatch center, power market signals, weather forecast data (for renewable energy generation), and other real-time monitoring systems. Specific demand types may include: Peak shaving: Reducing load during peak demand periods to avoid overloading power supply facilities. Valley shifting: Increasing load during low demand periods to balance supply and demand and fully utilize generation resources. Ramp-up: Rapidly adjusting load to accommodate fluctuations in renewable energy generation. Once regulation requirements are determined, the next step is to analyze which load-side aggregation groups can effectively respond to these requirements. This typically involves classifying and evaluating load-side resources to determine their adaptability index and regulation potential. Based on the demand type, the dispatch center may select: Temperature-controlled load aggregation groups: These include air conditioners and water heaters, suitable for valley shifting and peak shaving. Electric vehicle charging load aggregation groups: These are suitable for peak shaving, valley shifting, and ramping due to their high response speed and flexibility. Industrial load aggregation groups: These include interruptible industrial production loads, suitable for peak shaving. After determining the load-side aggregation group to be regulated, the dispatch center will use the preset objective function to generate specific regulation instructions. The objective function may include minimizing user impact, maximizing economic benefits, minimizing regulation costs, or maximizing renewable energy consumption, among other factors. By solving the optimization algorithm, the optimal regulation strategy for each load-side aggregation group can be obtained, including: Regulation time: determining when to start and end regulation. Regulation amount: defining the amount of load that needs to be increased or decreased. Regulation method: such as gradual regulation or rapid regulation, which depends on the response characteristics of each aggregation group and the urgency of the regulation demand. Finally, the dispatch center sends the regulation instructions to the control units of the load-side aggregation group, which are responsible for performing specific regulation operations.

[0092] During the execution of control commands, the control unit provides real-time feedback on the execution status, including the effectiveness of the control commands, load changes, and any abnormalities. The dispatch center monitors and adjusts based on this feedback to ensure that control targets are achieved. It also considers possible non-response or delayed response and fine-tunes the control strategy accordingly.

[0093] This process reflects the dynamic and flexible nature of the demand response mechanism in smart grids. By fine-tuning the management of load-side resources, the power system can more effectively respond to various operational challenges, improve the matching of power supply and demand, reduce operating costs, and support a high proportion of renewable energy access and utilization.

[0094] Figure 3 is a flow chart of a load aggregation method according to an optional embodiment of the present invention. Figure 3 As shown, the following is a specific example to show the specific calculation process of load side aggregation:

[0095] (1) Input the regulation characteristic matrix of all load-side resources participating in the aggregation:

[0096]

[0097] Among them, the adjustment characteristics of various flexible resources under time section k constitute the matrix A k , a row of the matrix represents the various regulation characteristic indicators of this type of load-side resources, and a column represents the value of a regulation characteristic indicator of different load-side resources.

[0098] Specifically, the regulation characteristics include the maximum adjustable capacity ΔP max , response time t, adjustment rate θ, adjustable duration T c When the matrix A k is represented as follows:

[0099]

[0100] (2) Calculate the scenario adaptability index of each load-side resource participating in the aggregation.

[0101] (3) According to the scenario adaptability index and threshold value, various load-side resources suitable for different balancing scenarios of the power grid are screened out to form a load aggregation group suitable for a certain balancing scenario.

[0102] (4) The load resources whose scenario fitness index does not reach the threshold value are complemented in pairs. If the scenario fitness index of the load aggregate after complementation reaches the threshold value, the load aggregate is classified into the corresponding scenario set.

[0103] (5) The single type of load resources selected in step (3) and the load aggregate formed by the two types of load resources in step (4) together constitute a load side aggregation group suitable for a certain balancing scenario.

[0104] It should be noted that for the aforementioned method embodiments, for simplicity of description, they are all expressed as a series of action combinations. However, those skilled in the art should be aware that the present invention is not limited by the order of the actions described, because according to the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0105] Through the description of the above implementation methods, those skilled in the art can clearly understand that the load aggregation method according to the above embodiment can be implemented by means of software plus the necessary general hardware platform. Of course, it 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 the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0106] According to an embodiment of the present invention, a load aggregation device for implementing the above load aggregation method is also provided. Figure 4 is a structural block diagram of a load aggregation device provided according to an embodiment of the present invention, such as Figure 4 As shown, the load aggregation device includes: an acquisition module 402, a determination module 404 and an aggregation module 406. The load aggregation device is described below.

[0107] The acquisition module 402 is configured to acquire a reference load corresponding to each of multiple load sides in the power system, wherein the reference load represents a power consumption level under normal operating conditions.

[0108] The determination module 404 is connected to the acquisition module 402 and is used to determine the adaptability index corresponding to each of the multiple load sides in multiple scenarios based on the benchmark loads corresponding to each of the multiple load sides and the adaptability indicators corresponding to each of the preset multiple scenarios, wherein the multiple scenarios include climbing scenarios, valley filling scenarios and peak shaving scenarios.

[0109] The aggregation module 406 is connected to the determination module 404 and is used to aggregate the multiple load sides based on the adaptability indexes corresponding to the multiple load sides in the multiple scenarios to obtain load side aggregation groups corresponding to the multiple scenarios.

[0110] It should be noted that the acquisition module 402, determination module 404, and aggregation module 406 described above correspond to steps S202 to S206 in the embodiment. The examples and application scenarios implemented by the various modules and corresponding steps are the same, but are not limited to the contents disclosed in the above embodiment. It should be noted that the above modules, as part of the apparatus, can be run in the computer terminal 10 provided in the embodiment.

[0111] An embodiment of the present invention may provide a computer device. Optionally, in this embodiment, the computer device may be located in at least one of a plurality of network devices in a computer network. The computer device includes a memory and a processor.

[0112] Among them, the memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the load aggregation method and device in the embodiments of the present invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, realizing the above-mentioned load aggregation method. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include a memory remotely located relative to the processor, and these remote memories can be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0113] The processor can call the information and application programs stored in the memory through the transmission device to perform the following steps: obtaining the benchmark load corresponding to each of the multiple load sides in the power system, wherein the benchmark load represents the power consumption level under normal operating conditions; determining the adaptability index corresponding to each of the multiple load sides in multiple scenarios based on the benchmark load corresponding to each of the multiple load sides and the adaptability indicators corresponding to each of the preset multiple scenarios, wherein the multiple scenarios include climbing scenarios, valley filling scenarios and peak shaving scenarios; based on the adaptability index corresponding to each of the multiple load sides in multiple scenarios, aggregating the multiple load sides to obtain load side aggregation groups corresponding to each of the multiple scenarios.

[0114] Optionally, the processor may also execute the program code of the following steps: obtaining a benchmark load corresponding to each of the multiple load sides in the power system, including: obtaining electricity consumption data corresponding to each of the multiple load sides, wherein the multiple load sides include a temperature-controlled load side, an industrial load side, and an electric vehicle load side; and calculating a benchmark load corresponding to each of the multiple load sides based on the electricity consumption data corresponding to each of the multiple load sides.

[0115] Optionally, the processor may also execute the program code of the following steps: based on the adaptability index corresponding to each of the multiple load sides in multiple scenarios, aggregating the multiple load sides to obtain load side aggregation groups corresponding to each of the multiple scenarios, wherein the steps for obtaining the load side aggregation group corresponding to the target scenario are as follows, where the target scenario is any one of the multiple scenarios: obtaining the index threshold corresponding to the target scenario; determining the load side aggregation group corresponding to the target scenario based on the load side whose adaptability index exceeds the index threshold corresponding to the target scenario.

[0116] Optionally, the processor may also execute the program code of the following steps: determining the load side whose adaptability index does not exceed the index threshold corresponding to the target scenario as a non-compliant load side; combining the non-compliant load sides in pairs to obtain a non-compliant load side group; determining the adaptability index corresponding to the non-compliant load side group based on the adaptability index corresponding to the non-compliant load side; and aggregating the non-compliant load sides in the non-compliant load side group whose adaptability index exceeds the index threshold corresponding to the target scenario into the load side aggregation group corresponding to the target scenario.

[0117] Optionally, the above-mentioned processor can also execute the program code of the following steps: the adaptability indicators corresponding to the climbing scenario include response speed, adjustment frequency and maximum adjustable capacity, the adaptability indicators corresponding to the peak shaving scenario include response speed, maximum adjustable capacity and adjustable duration, and the adaptability indicators corresponding to the valley filling scenario include response speed, maximum adjustable capacity, adjustable duration and calling frequency.

[0118] Optionally, the above-mentioned processor can also execute the program code of the following steps: obtaining the control requirements of the power system; determining the load side aggregation group to be controlled based on the control requirements; generating control instructions based on a preset objective function; and controlling the load side aggregation group to be controlled based on the control instructions.

[0119] An embodiment of the present invention provides a load aggregation method, which obtains a benchmark load corresponding to each of multiple load sides in a power system, wherein the benchmark load represents the power consumption level under normal operating conditions; based on the benchmark load corresponding to each of the multiple load sides and the adaptability indicators corresponding to each of the preset multiple scenarios, determines the adaptability index corresponding to each of the multiple load sides in multiple scenarios, wherein the multiple scenarios include climbing scenarios, valley filling scenarios and peak shaving scenarios; based on the adaptability index corresponding to each of the multiple load sides in multiple scenarios, aggregates the multiple load sides to obtain load side aggregation groups corresponding to the multiple scenarios, thereby achieving the purpose of load side aggregation based on the adaptability indicators in different scenarios, thereby realizing the technical effect of improving the accuracy of aggregation, and further solving the technical problem that the current load side aggregation method generally focuses on the load resources themselves, resulting in inaccurate aggregation results.

[0120] A person skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device through a program, and the program can be stored in a non-volatile storage medium, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0121] The embodiment of the present invention further provides a non-volatile storage medium. Optionally, in this embodiment, the non-volatile storage medium can be used to store the program code executed by the load aggregation method provided in the embodiment.

[0122] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0123] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: obtaining a benchmark load corresponding to each of the multiple load sides in the power system, wherein the benchmark load represents the power consumption level under normal operating conditions; determining the adaptability index corresponding to each of the multiple load sides in multiple scenarios based on the benchmark load corresponding to each of the multiple load sides and the adaptability indicators corresponding to each of the preset multiple scenarios, wherein the multiple scenarios include climbing scenarios, valley filling scenarios and peak shaving scenarios; aggregating the multiple load sides based on the adaptability index corresponding to each of the multiple load sides in the multiple scenarios to obtain a load side aggregation group corresponding to each of the multiple scenarios.

[0124] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: obtaining a benchmark load corresponding to each of multiple load sides in the power system, including: obtaining electricity consumption data corresponding to each of the multiple load sides, wherein the multiple load sides include a temperature-controlled load side, an industrial load side, and an electric vehicle load side; and calculating the benchmark load corresponding to each of the multiple load sides based on the electricity consumption data corresponding to each of the multiple load sides.

[0125] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: based on the adaptability index corresponding to each of the multiple load sides in multiple scenarios, aggregating the multiple load sides to obtain load side aggregation groups corresponding to each of the multiple scenarios, wherein the steps for obtaining the load side aggregation group corresponding to the target scenario are as follows, where the target scenario is any one of the multiple scenarios: obtaining the index threshold corresponding to the target scenario; determining the load side aggregation group corresponding to the target scenario based on the load side whose adaptability index exceeds the index threshold corresponding to the target scenario.

[0126] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: determining that the load side whose adaptability index does not exceed the index threshold corresponding to the target scenario is a non-compliant load side; combining the non-compliant load sides in pairs to obtain a non-compliant load side group; determining the adaptability index corresponding to the non-compliant load side group based on the adaptability index corresponding to the non-compliant load side; and aggregating the non-compliant load sides in the non-compliant load side group whose adaptability index exceeds the index threshold corresponding to the target scenario into the load side aggregation group corresponding to the target scenario.

[0127] Optionally, in this embodiment, the non-volatile storage medium is configured to store program codes for executing the following steps: the adaptability indicators corresponding to the climbing scenario include response speed, adjustment frequency and maximum adjustable capacity; the adaptability indicators corresponding to the peak shaving scenario include response speed, maximum adjustable capacity and adjustable duration; the adaptability indicators corresponding to the valley filling scenario include response speed, maximum adjustable capacity, adjustable duration and calling frequency.

[0128] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: obtaining the control requirements of the power system; determining the load side aggregation group to be controlled based on the control requirements; generating a control instruction based on a preset objective function; and controlling the load side aggregation group to be controlled based on the control instruction.

[0129] An embodiment of the present invention also provides a computer program product, including a computer program. Optionally, in this embodiment, when the computer program is executed by a processor, it can achieve: obtaining a benchmark load corresponding to each of multiple load sides in the power system, wherein the benchmark load represents the power consumption level under normal operating conditions; determining the adaptability index corresponding to each of the multiple load sides in multiple scenarios based on the benchmark loads corresponding to each of the multiple load sides and the adaptability indicators corresponding to each of the preset multiple scenarios, wherein the multiple scenarios include climbing scenarios, valley filling scenarios and peak shaving scenarios; based on the adaptability index corresponding to each of the multiple load sides in multiple scenarios, aggregating the multiple load sides to obtain a load side aggregation group corresponding to each of the multiple scenarios.

[0130] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0131] In the above embodiments of the present invention, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0132] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0133] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0134] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0135] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a non-volatile storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, and other media that can store program code.

[0136] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A load aggregation method, characterized in that: include: Obtaining a reference load corresponding to each of a plurality of load sides in the power system, wherein the reference load represents a power consumption level under normal operating conditions; Determining, based on the respective benchmark loads corresponding to the plurality of load sides and the respective adaptability indicators corresponding to the plurality of preset scenarios, the adaptability indexes corresponding to the respective plurality of load sides in the plurality of scenarios, wherein the plurality of scenarios include a hill climbing scenario, a valley filling scenario, and a peak shaving scenario; Based on the adaptability indexes corresponding to the multiple load sides in the multiple scenarios, the multiple load sides are aggregated to obtain load side aggregation groups corresponding to the multiple scenarios.

2. The method according to claim 1, characterized in that The obtaining of the respective reference loads corresponding to the plurality of load sides in the power system includes: Acquire power consumption data corresponding to each of the multiple load sides, wherein the multiple load sides include a temperature control load side, an industrial load side, and an electric vehicle load side; Based on the power consumption data corresponding to each of the plurality of load sides, a reference load corresponding to each of the plurality of load sides is calculated.

3. The method according to claim 1, characterized in that Based on the adaptability indexes of the multiple load sides corresponding to the multiple scenarios, the multiple load sides are aggregated to obtain load side aggregation groups corresponding to the multiple scenarios. The method for obtaining the load side aggregation group corresponding to the target scenario is as follows, where the target scenario is any one of the multiple scenarios: Obtaining an index threshold corresponding to the target scene; Based on the load side whose adaptability index exceeds the index threshold corresponding to the target scenario, a load side aggregation group corresponding to the target scenario is determined.

4. The method according to claim 3, characterized in that Also includes: Determining a load side whose adaptability index does not exceed an index threshold corresponding to the target scenario as a non-compliant load side; Combining the non-standard load sides in pairs to obtain a non-standard load side group; Determining the adaptability index corresponding to the non-standard load side group based on the adaptability index corresponding to the non-standard load side; Aggregate the non-compliant load sides in the non-compliant load side group whose adaptability index exceeds the index threshold corresponding to the target scenario into the load side aggregation group corresponding to the target scenario.

5. The method according to any one of claims 1 to 4, characterized in that The adaptability indicators corresponding to the ramp-up scenario include response speed, adjustment frequency and maximum adjustable capacity; the adaptability indicators corresponding to the peak-shaving scenario include response speed, maximum adjustable capacity and adjustable duration; and the adaptability indicators corresponding to the valley-filling scenario include response speed, maximum adjustable capacity, adjustable duration and calling frequency.

6. The method according to any one of claims 1 to 4, characterized in that Also includes: Obtaining control requirements of the power system; Based on the regulation demand, determining the load side aggregation group to be regulated; Generate control instructions based on the preset objective function; Based on the control instruction, the load side aggregation group to be regulated is regulated.

7. A load aggregation device, characterized in that: include: An acquisition module is configured to acquire a reference load corresponding to each of a plurality of load sides in the power system, wherein the reference load represents a power consumption level under normal operating conditions; a determination module, configured to determine, based on the respective benchmark loads corresponding to the respective load sides and the respective adaptability indicators corresponding to the respective preset scenarios, an adaptability index corresponding to each of the plurality of load sides in the plurality of scenarios, wherein the plurality of scenarios include a hill climbing scenario, a valley filling scenario, and a peak shaving scenario; An aggregation module is used to aggregate the multiple load sides based on the adaptability indexes corresponding to the multiple load sides in the multiple scenarios to obtain load side aggregation groups corresponding to the multiple scenarios.

8. A non-volatile storage medium, characterized in that: The non-volatile storage medium includes a stored program, wherein when the program is executed, the device where the non-volatile storage medium is located is controlled to execute the load aggregation method according to any one of claims 1 to 6.

9. A computer device, characterized in that: include: memory and processor, The memory stores a computer program; The processor is configured to execute a computer program stored in the memory, and when the computer program is executed, the processor is enabled to execute the load aggregation method according to any one of claims 1 to 6.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the load aggregation method according to any one of claims 1 to 6 is implemented.

Images