Aggregate Dynamic Interaction Method Based on Ancillary Services and Related Devices

Abstract

The present application provides an aggregate dynamic interaction method based on ancillary services and related devices. The method includes: obtaining energy efficiency data and ancillary service data corresponding to a target aggregate; the target aggregate includes a target sets; constructing a model based on the energy efficiency data and the ancillary service data to obtain a dynamic interaction model; obtaining the grid demand power of the target grid corresponding to the target aggregate within a preset first control period; calculating a reference power adjustment amount through the dynamic interaction model according to the grid demand power; adjusting the power of the target aggregate according to the reference power adjustment amount to ensure that the total power of the target aggregate and the grid demand power are in a power balance state. By interactively adjusting the power of the target aggregate, the grid load change can be responded to in real time, and the power balance between users and the grid can be coordinated.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and in particular, to an aggregate dynamic interaction method based on ancillary services and related devices. Background Art

[0002] With the large-scale access of distributed power sources in the power system and the diversification of the electricity consumption characteristics of different types of users such as industrial, commercial, and residential users, the structure and operation of the power system have become more complex. The power change characteristics of different types of users and power sources are different, making it more difficult to control the power balance of the power grid.

[0003] In the existing power system, the interaction mode between users and the power grid is relatively single, lacking effective information communication and collaborative control means. Users often passively accept the power supply arrangements of the power grid and cannot dynamically adjust according to their own electricity consumption characteristics and the real-time needs of the power grid. This not only limits the enthusiasm of users to participate in the optimal operation of the power system, but also affects the overall operation efficiency and stability of the power system.

[0004] Therefore, how to coordinate the power balance between users and the power grid urgently needs to be solved. Summary of the Invention

[0005] The embodiments of the present application provide an aggregate dynamic interaction method based on ancillary services and related devices. By constructing a dynamic interaction model, calculating a reference power adjustment amount according to the power demand of the power grid and adjusting the power of the target aggregate, it can respond to the load change of the power grid in real time, avoid power shortage or surplus, and coordinate the power balance between users and the power grid.

[0006] In a first aspect, the embodiments of the present application provide an aggregate dynamic interaction method based on ancillary services, and the method includes:

[0007] Obtain the energy efficiency data and ancillary service data corresponding to the target aggregate; the target aggregate includes a target sets; the target set includes any one of the following: industrial user set, commercial user set, residential user set, distributed power source set; a is an integer greater than 1;

[0008] Construct a model according to the energy efficiency data and the ancillary service data to obtain a dynamic interaction model;

[0009] Obtain the power demand of the target power grid corresponding to the target aggregate in a preset first control period;

[0010] Calculate a reference power adjustment amount through the dynamic interaction model according to the power demand of the power grid;

[0011] Adjust the power of the target aggregator according to the reference power adjustment amount to ensure that the total power of the target aggregator is in a power balance state with the grid demand power.

[0012] In a second aspect, an embodiment of the present application provides an aggregator dynamic interaction device based on ancillary services. The device includes a first acquisition module, a construction module, a second acquisition module, a calculation module, and an adjustment module, where:

[0013] The first acquisition module is configured to acquire energy efficiency data and ancillary service data corresponding to a target aggregator; the target aggregator includes a target sets; the target set includes any one of the following: an industrial user set, a commercial user set, a residential user set, and a distributed power source set; a is an integer greater than 1;

[0014] The construction module is configured to construct a model based on the energy efficiency data and the ancillary service data to obtain a dynamic interaction model;

[0015] The second acquisition module is configured to acquire the grid demand power of the target grid corresponding to the target aggregator within a preset first control period;

[0016] The calculation module is configured to calculate, through the dynamic interaction model, a reference power adjustment amount according to the grid demand power;

[0017] The adjustment module is configured to adjust the power of the target aggregator according to the reference power adjustment amount to ensure that the total power of the target aggregator is in a power balance state with the grid demand power.

[0018] In a third aspect, an embodiment of the present application provides an electronic device, including a processor, a memory, a communication interface, and one or more programs, where the one or more programs are stored in the memory and configured to be executed by the processor, and the programs include instructions for executing the steps in any method of the first aspect of the embodiments of the present application.

[0019] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute some or all of the steps described in any method of the first aspect of the embodiments of the present application.

[0020] In a fifth aspect, an embodiment of the present application provides a computer program product, where the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute some or all of the steps described in any method of the first aspect of the embodiments of the present application. The computer program product can be a software installation package.

[0021] By implementing the embodiments of the present application, a dynamic interaction model can be constructed, the reference power adjustment amount is calculated according to the power demand of the power grid, and the power of the target aggregator is adjusted, which can respond to the change of the power grid load in real time, avoid power shortage or surplus, and coordinate the power balance between users and the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0023] Figure 1 FIG. is an application scenario diagram for power adjustment of a target aggregator provided by an embodiment of the present application;

[0024] Figure 2 FIG. is a composition structure diagram of a target aggregator provided by an embodiment of the present application;

[0025] Figure 3 FIG. is a system architecture diagram of a power adjustment system provided by an embodiment of the present application;

[0026] Figure 4 FIG. is a schematic structural diagram of an electronic device provided by an embodiment of the present application;

[0027] Figure 5 FIG. is a schematic flowchart of a method for dynamic interaction of an aggregator based on ancillary services provided by an embodiment of the present application;

[0028] Figure 6 FIG. is a schematic flowchart of calculating a power adjustment amount provided by an embodiment of the present application;

[0029] Figure 7 FIG. is a schematic flowchart of adjusting a power adjustment amount provided by an embodiment of the present application;

[0030] Figure 8 FIG. is a functional module composition block diagram of a device for dynamic interaction of an aggregator based on ancillary services provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0031] To enable those skilled in the art to better understand the solution of this application, the following will clearly and completely describe the technical solution in the embodiments of this application with reference to the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part rather than all of the embodiments of this application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in this application without creative efforts belong to the scope of protection of this application.

[0032] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned accompanying drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally further include steps or units not listed, or may optionally further include other steps or units inherent to these processes, methods, products or devices.

[0033] Referring to "embodiments" herein means that the specific features, structures or characteristics described in connection with the embodiments can be included in at least one embodiment of this application. The phrase appears in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0034] With the large-scale access of distributed power sources in the power system and the diversification of the electricity consumption characteristics of different types of users such as industrial, commercial and residential users, the structure and operation of the power system become more complex. The power change characteristics of different types of users and power sources vary greatly, making it more difficult to control the power balance of the power grid. In the existing power system, the interaction mode between users and the power grid is relatively single, lacking effective information communication and coordinated control means. Users often passively accept the power supply arrangements of the power grid and cannot make dynamic adjustments according to their own electricity consumption characteristics and the real-time needs of the power grid. This not only limits the enthusiasm of users to participate in the optimal operation of the power system, but also affects the overall operation efficiency and stability of the power system. Therefore, how to coordinate the power balance between users and the power grid urgently needs to be solved.

[0035] To solve the above problems, an embodiment of the present application provides an aggregate dynamic interaction method and related device based on ancillary services, which obtain energy efficiency data and ancillary service data corresponding to a target aggregate; the target aggregate includes a target sets; the target set includes any one of the following: industrial user set, commercial user set, residential user set, distributed power source set; a is an integer greater than 1; a model is constructed according to the energy efficiency data and the ancillary service data to obtain a dynamic interaction model; the grid demand power of the target grid corresponding to the target aggregate within a preset first control period is obtained; the reference power adjustment amount is calculated through the dynamic interaction model according to the grid demand power; the power of the target aggregate is adjusted according to the reference power adjustment amount to ensure that the total power of the target aggregate is in a power balance state with the grid demand power. By constructing a dynamic interaction model, calculating the reference power adjustment amount based on the grid demand power and adjusting the power of the target aggregate, it can respond to the grid load change in real time, avoid power shortage or surplus, and coordinate the power balance between users and the grid.

[0036] For ease of understanding, please refer to Figure 1 , Figure 1 FIG. is an application scenario diagram of power adjustment for a target aggregate provided by an embodiment of the present application. Among them, the target aggregate is an aggregate composed of industrial users, commercial users, residential users, distributed power sources, etc. The target aggregate can provide information such as energy efficiency data, ancillary service data, and power adjustment capabilities to the power adjustment system, and can also receive adjustment instructions given by the power adjustment system to adjust its own power; the target grid is used for power supply and distribution. The target grid can provide information such as grid demand power to the power adjustment system, facilitating the target aggregate to adjust its power to match the power demand fluctuation of the target grid; the power adjustment system can receive relevant data information from the target aggregate and the target grid, analyze and calculate based on this information, and then issue a power adjustment instruction to the target aggregate to achieve a power balance state where the total power of the target aggregate is equal to the grid demand power.

[0037] For ease of understanding, please refer to Figure 2 , Figure 2 FIG. is a composition structure diagram of a target aggregate provided by an embodiment of the present application. Among them, the target aggregate includes an industrial user set, a commercial user set, a residential user set, and a distributed power source set. The industrial user set can be set as I, the commercial user set can be set as C, the residential user set can be set as R, and the distributed power source set can be set as D.

[0038] Among them, the industrial user set includes various industrial enterprises. Their electricity consumption characteristics usually have a large power demand, and the electricity consumption period is relatively fixed or has a certain production cycle, with relatively high requirements for power supply reliability. For the industrial user set, considering the continuity of the production process and the stability of the electricity load, the basic electricity load can be determined by analyzing the historical electricity consumption data of the industrial user set. , as well as the adjustable electricity load range . In actual production, through the intelligent transformation of production equipment, such as installing smart meters, controllable switches, etc., the accurate monitoring and control of the electricity load can be realized.

[0039] Among them, the commercial user set covers commercial places such as shopping malls, office buildings, and hotels. Their electricity consumption peaks are generally concentrated during business hours. Equipment such as lighting, air conditioning, and elevators consume a large amount of electricity, with obvious daily load fluctuation characteristics, and there are also certain requirements for power quality and power supply stability. Since the electricity load of the commercial user set is greatly affected by business hours and business activities, the electricity consumption characteristics at different times can be analyzed to determine the electricity load during peak hours and the electricity load during off-peak hours , as well as the load regulation ability that can participate in interaction . For example, shopping malls can reduce the lighting brightness during non-business hours by optimizing the control strategy of the lighting system to reduce the electricity load.

[0040] Among them, the residential user set represents residential household users. The electricity consumption behavior of this residential user set is random. The electricity consumption pattern of the residential user set can be analyzed by using a preset probability statistical method, so as to determine the average power (r ∈ R, j represents the type of electrical equipment) and the usage probability of different electrical equipment corresponding to the residential user set, so as to estimate the total electricity load of the residential user set. Among them, historical electricity consumption data of the residential user set can also be collected through the smart home appliance platform to establish a user electricity consumption behavior model, so as to more accurately predict the electricity load of the residential user set.

[0041] Among them, the distributed power source set includes distributed power generation units such as small solar power generation devices, wind turbines, and biomass energy power generation equipment. Among them, taking photovoltaic power generation as an example, according to its installed capacity , light intensity G, and conversion efficiency , its power generation power is calculated. To improve the efficiency of photovoltaic power generation, a smart tracking system can be adopted to keep the photovoltaic panels at the best lighting angle at all times.

[0042] For easy understanding, please refer to Figure 3 , Figure 3It is a system architecture diagram of a power regulation system provided by an embodiment of the present application. The power regulation system includes a data acquisition module, a dynamic interaction module, and an instruction output module.

[0043] Among them, the data acquisition module is responsible for collecting energy efficiency data and auxiliary service data of target aggregations (such as industrial, commercial, residential users, and distributed power sources), as well as data such as the grid demand power of the target power grid within a preset control period. For example, it collects information such as the real-time power consumption of industrial users and the power generation of distributed power sources.

[0044] Among them, the dynamic interaction module can build a dynamic interaction model based on the data obtained by the data acquisition module. Through this dynamic interaction model, it analyzes the power interaction relationship between the target aggregation and the target power grid, and calculates the reference power adjustment amount according to the grid demand power. For example, based on the grid load change and the characteristics of each user and power source, it determines how each part should adjust the power.

[0045] Among them, the instruction output module can convert the reference power adjustment amount calculated by the dynamic interaction module into specific adjustment instructions and send them to each part in the target aggregation (such as industrial user equipment, distributed power source control devices, etc.) to guide them to perform power adjustment operations, so as to achieve the balance between the total power of the target aggregation and the grid demand power. During the communication process, to ensure the accuracy and timeliness of data transmission, a reliable communication protocol can be adopted, such as a dedicated power communication protocol, and the communication data is encrypted and verified to prevent data transmission errors and malicious tampering. To improve the reliability and anti-interference ability of communication, means such as redundant communication links and signal enhancement technologies can be adopted to ensure that the adjustment instructions or control instructions can be accurately transmitted to each user and device. At the same time, establish a communication failure emergency plan so that when a communication failure occurs, measures can be taken in a timely manner to ensure the basic operation of the power system.

[0046] It can be seen that by collecting data of the target aggregation and the target power grid, covering multi-dimensional information such as the electricity consumption characteristics of various users, the power generation of distributed power sources, and grid demand, it provides an accurate and comprehensive data basis for subsequent analysis and decision-making, avoiding adjustment deviations caused by missing or inaccurate information; by building a dynamic interaction model, fully considering the dynamic change characteristics of each subject in the power system (such as the intermittency of distributed power sources and the load fluctuation of users), it analyzes the power interaction relationship between the power grid and the aggregation in real time, better adapts to the complex and changeable power operation scenarios, and improves the scientificity and effectiveness of the power adjustment strategy; by calculating the reference power adjustment amount through the dynamic interaction model, it can tap the adjustment potential of each user and power source, reasonably allocate adjustment tasks, realize the optimal allocation of power resources among different subjects, improve the overall energy utilization efficiency, and reduce the operation cost.

[0047] The following is combined with Figure 4The electronic device in the embodiments of the present application will be described. Figure 4 It is a schematic structural diagram of an electronic device provided by an embodiment of the present application. As Figure 4 shown, the electronic device includes one or more processors, a memory, a communication interface, and one or more programs. The processor is communicatively connected to the memory and the communication interface through an internal communication bus.

[0048] Among them, the processor is mainly used for: obtaining energy efficiency data and auxiliary service data corresponding to the target aggregate; the target aggregate includes a target sets; the target set includes any one of the following: industrial user set, commercial user set, residential user set, distributed power source set; a is an integer greater than 1; constructing a model based on the energy efficiency data and the auxiliary service data to obtain a dynamic interaction model; obtaining the grid demand power of the target power grid corresponding to the target aggregate within a preset first control period; calculating a reference power adjustment amount through the dynamic interaction model according to the grid demand power; adjusting the power of the target aggregate according to the reference power adjustment amount to ensure that the total power of the target aggregate is in a power balance state with the grid demand power.

[0049] Among them, the one or more programs are stored in the above-mentioned memory and are configured to be executed by the above-mentioned processor. The one or more programs include instructions for executing any step in the above-mentioned method embodiments.

[0050] Among them, the processor can be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field programmable gate array, or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof, which will not be specifically limited here. The processor can also be a combination for implementing computing functions, such as including a combination of one or more microprocessors. The communication unit can be a communication interface, a transceiver, a transceiver circuit, etc., and the storage unit can be a memory. The memory can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories.

[0051] It can be understood that the electronic device may include more or fewer structural elements than those in the above structural block diagram. For example, it includes a power module, physical buttons, a Wi-Fi module, a speaker, a Bluetooth module, sensors, a display module, etc., which will not be limited here. It can be understood that the electronic device can carry the Figure 3 system architecture as described above.

[0052] After understanding the software and hardware architecture of the present application, the following will be combined with Figure 5 to describe a method for dynamic interaction of an aggregate based on auxiliary services in the embodiments of the present application. Figure 5It is a schematic flowchart of a method for dynamic interaction of an aggregate based on ancillary services provided by an embodiment of the present application, specifically including the following steps:

[0053] Step S501, obtain the energy efficiency data and ancillary service data corresponding to the target aggregate.

[0054] Among them, the target aggregate includes a target sets; the target set includes any one of the following: industrial user set, commercial user set, residential user set, distributed power source set; a is an integer greater than 1.

[0055] Among them, the specific steps for obtaining the energy efficiency data and ancillary service data corresponding to the target aggregate include:

[0056] A1. Obtain the historical power consumption data of the target aggregate within a preset time period;

[0057] A2. Analyze and calculate the historical power consumption data to obtain the energy efficiency data; the energy efficiency data includes energy efficiency index data, load prediction data, and energy saving potential data;

[0058] A3. Obtain the adjustable load corresponding to the target aggregate;

[0059] A4. Calculate the adjustable load according to a preset peak shaving calculation formula to obtain peak shaving data;

[0060] A5. Obtain the actual grid frequency and rated frequency of the target power grid;

[0061] A6. Determine the frequency deviation according to the actual grid frequency and the rated frequency;

[0062] A7. Calculate the frequency deviation according to a preset frequency modulation calculation formula to obtain frequency modulation data;

[0063] A8. Obtain the historical load data of the target power grid within the preset time period;

[0064] A9. Calculate the historical load data to obtain the load fluctuation standard deviation;

[0065] A10. Calculate the load fluctuation standard deviation according to a preset reserve capacity calculation formula to obtain reserve capacity data;

[0066] A11. Determine the ancillary service data according to the peak shaving data, the frequency modulation data, and the reserve capacity data.

[0067] In a specific embodiment, first, historical power consumption data of a target aggregator within a preset time period is obtained. The historical power consumption data includes, but is not limited to, power consumption information of industrial user sets, commercial user sets, residential user sets, and distributed power source sets. The preset time period includes, but is not limited to, one day, one week, or one month, and no specific limitation is made here. Then, the historical power consumption data is analyzed and calculated to obtain corresponding energy efficiency data, which includes energy efficiency index data, load prediction data, and energy-saving potential data. Among them, the energy efficiency index data includes, but is not limited to, the energy efficiency ratio and power factor, and no specific limitation is made here. The calculation formula of the energy efficiency index data is as follows:

[0068]

[0069]

[0070] Among them, EER represents the energy efficiency ratio; represents the power factor; P represents the active power; S represents the apparent power, which refers to the product of the effective values of voltage and current in an AC circuit and is used to represent the total capacity of the circuit. Its unit is volt-ampere (VA).

[0071] Among them, the load prediction data of the target aggregator can be determined by a preset time series analysis method. For example, the autoregressive moving average model can be used to model the historical power consumption data P(t) to predict the future power consumption load P(t + k), that is, the load prediction data. Among them, t represents time, and k represents the prediction time step. The accuracy of the load prediction data can be evaluated by calculating the mean absolute error and root mean square error. The calculation formulas of the mean absolute error and root mean square error are as follows:

[0072]

[0073]

[0074] Among them, MAE represents the mean absolute error; n represents the number of prediction samples; i represents the i-th prediction sample; represents the actual power consumption load; represents the predicted power consumption load.

[0075] It should be noted that by comparing the values of the mean absolute error and root mean square error, the accuracy of the model can be intuitively understood. The smaller the value, the more accurate the prediction. Then, through a preset big data analysis technology, combined with the values of the mean absolute error and root mean square error, a large amount of historical power consumption data is processed and analyzed to continuously optimize the parameters of the model, thereby improving the accuracy of the load prediction data.

[0076] Among them, the energy-saving potential data includes but is not limited to energy-saving potential and energy-saving investment return rate, and no specific limitation is made here. The calculation formula of the energy-saving potential is as follows:

[0077]

[0078] Among them, represents the energy-saving potential of the reference user set; represents the current energy consumption of the reference user set, and the reference user set includes but is not limited to industrial user sets, commercial user sets, and residential user sets, and no specific limitation is made here; represents the theoretical minimum energy consumption after the reference user set adopts the preset energy-saving measures. The energy-saving measures include but are not limited to energy-saving lighting renovation, replacement of high-efficiency motors, off-peak power consumption, and on-demand power consumption, and no specific limitation is made here.

[0079] Among them, the calculation formula of the energy-saving investment return rate is as follows:

[0080]

[0081] Among them, represents the energy-saving investment return rate of the reference user set; represents the preset energy-saving cost coefficient; represents the energy-saving investment.

[0082] It should be noted that by calculating the energy-saving investment return rate, the economic benefits of the energy-saving measures can be evaluated. When the energy-saving investment return rate is greater than 0, it means that the benefits brought by the energy-saving measures are greater than the costs; when the energy-saving investment return rate is less than or equal to 0, it means that the benefits brought by the energy-saving measures are less than or equal to the costs. For example, for industrial user sets, before replacing high-efficiency energy-saving equipment, this energy-saving investment return rate can be used to judge whether the energy-saving measures are feasible and provide a quantitative basis for energy-saving decisions.

[0083] Next, obtain the adjustable load corresponding to the target aggregate, and calculate the adjustable load according to the preset peak shaving calculation formula to obtain peak shaving data. Among them, the adjustable load includes , that is, the adjustable loads of industrial user sets, commercial user sets, and residential user sets. The peak shaving data includes but is not limited to the total peak shaving capacity, effective peak shaving energy, and peak shaving effect evaluation indicators. The peak shaving effect evaluation indicators include peak shaving response time, peak shaving accuracy, and peak shaving capacity utilization rate, and no specific limitation is made here. The peak shaving response time refers to the time from when the target aggregate receives the peak shaving instruction to when it reaches the adjustment power corresponding to the peak shaving instruction. The peak shaving accuracy is used to represent the degree of closeness between the actual adjustment power of the target aggregate and the desired adjustment power. Among them, the peak shaving calculation formula is as follows:

[0084]

[0085] Among them, represents the total peak shaving capacity; represents the adjustable load of the industrial user set; represents the adjustable load of the commercial user set; represents the adjustable load of the residential user set.

[0086]

[0087] Among them, represents the peak shaving capacity utilization rate; represents the actually participating peak shaving capacity.

[0088]

[0089] Among them, represents the effective peak shaving energy, that is, the effective energy actually used for peak shaving; represents the theoretical peak shaving energy, , represents the peak shaving response time in the peak shaving effect evaluation index. In practical applications, through a preset optimal scheduling algorithm, the peak shaving tasks of the target aggregator can be reasonably allocated to improve the peak shaving capacity utilization rate and reduce energy loss.

[0090] Then, obtain the actual grid frequency and the rated frequency of the target power grid, calculate the frequency deviation based on the actual grid frequency and the rated frequency, and then calculate the frequency deviation according to the preset frequency modulation calculation formula to obtain the frequency modulation data. The frequency modulation data includes, but is not limited to, the frequency deviation, the cost of the frequency modulation service, and the frequency modulation effect evaluation index. The frequency modulation effect evaluation index includes the frequency recovery time and the frequency stability, which are not specifically limited here. The frequency recovery time refers to the time required for the frequency of the target power grid to return to the specified range under the action of frequency modulation after the frequency deviates from the rated value due to a disturbance; the frequency stability is used to represent the degree of stability of the frequency of the target power grid during the frequency modulation process. The higher the stability, the smaller the frequency fluctuation and the more stable the operation of the target power grid. Among them, the frequency modulation calculation formula is as follows:

[0091]

[0092] Among them, represents the regulating power; represents the preset frequency modulation coefficient; represents the frequency deviation. It should be noted that considering the moment of inertia and the frequency change rate of the target power grid, the calculation formula of the regulating power can also be as follows:

[0093]

[0094] Among them, Represents the regulation power; Represents the moment of inertia of the target power grid; Represents the rate of change of frequency of the target power grid; t represents time.

[0095]

[0096] Among them, Represents the cost of frequency regulation service; Represents a preset frequency regulation cost coefficient; Represents the frequency recovery time in the frequency regulation effect evaluation index. It should be noted that during the frequency regulation process, by real-time monitoring the frequency change of the target power grid and according to the frequency regulation calculation formula, quickly adjust the power output of the target aggregate, and at the same time consider the cost of frequency regulation service, so as to optimize the frequency regulation strategy.

[0097] Next, obtain the historical load data of the target power grid within a preset time period, calculate the historical load data to obtain the standard deviation of load fluctuation, and calculate the standard deviation of load fluctuation according to the preset reserve capacity calculation formula to obtain the reserve capacity data. The reserve capacity data includes but is not limited to reserve capacity and the maintenance cost of reserve service, and no specific limitation is made here. Among them, the reserve capacity calculation formula is as follows:

[0098]

[0099] Among them, Represents the reserve capacity; Represents the coefficient related to the confidence level of the reserve capacity, which can be obtained by looking up the preset standard normal distribution table; Represents the standard deviation of load fluctuation.

[0100] Among them, the calculation formula corresponding to the maintenance cost of reserve service is as follows:

[0101]

[0102] Among them, Represents the maintenance cost of reserve service; Represents the maintenance cost per unit of reserve capacity. It should be noted that according to the actual demand and reliability requirements of the target power grid, reasonably determine the reserve capacity, which can reduce the maintenance cost of reserve service and improve the cost performance of reserve service.

[0103] Finally, integrate the peak shaving data, frequency regulation data and reserve capacity data to obtain the ancillary service data.

[0104] It can be seen that by analyzing historical power consumption data to obtain energy efficiency data, energy efficiency indicators can be clarified, load can be predicted, energy-saving potential can be mined, energy-saving measures can be taken specifically, energy utilization efficiency can be improved, and power consumption costs and energy losses can be reduced. By calculating peak shaving data and frequency modulation data, and calculating reserve capacity data based on the standard deviation of load fluctuations, the ability of the power grid to respond to peak-valley load changes, frequency fluctuations, and load mutations can be effectively improved, the safe and stable operation of the power grid can be guaranteed, and accidents such as power outages can be reduced.

[0105] In a possible embodiment, first, determine the energy-saving contribution amount according to the energy-saving potential data; determine the first compensation amount according to the preset energy-saving compensation unit price and the energy-saving contribution amount; then, distribute the first compensation amount to the first target set corresponding to the energy-saving contribution amount in the a target sets; then, determine the peak shaving effect evaluation index and the frequency modulation effect evaluation index according to the peak shaving data and the frequency modulation data respectively; calculate the peak shaving data and the frequency modulation data respectively according to the preset reward coefficient to obtain the first reward amount and the second reward amount; determine the second target set corresponding to the peak shaving effect evaluation index in the a target sets; distribute the first reward amount to the second target set; determine the third target set corresponding to the frequency modulation effect evaluation index in the a target sets; distribute the second reward amount to the third target set.

[0106] Specifically, first, analyze the energy-saving potential data to determine the contribution degree of each target set in the target aggregate in terms of energy saving, that is, the energy-saving contribution amount. Then, multiply the preset energy-saving compensation unit price by the energy-saving contribution amount to obtain the first compensation amount. Then, determine the target set corresponding to the energy-saving contribution amount in the a target sets as the first target set, and distribute the first compensation amount to the first target set to encourage users to continuously take energy-saving measures.

[0107] Next, analyze the peak shaving data and the frequency modulation data to obtain the peak shaving effect evaluation index and the frequency modulation effect evaluation index. Then, calculate the peak shaving data and the frequency modulation data respectively according to the preset frequency modulation reward coefficient to obtain the first reward amount and the second reward amount. Among them, the preset frequency modulation reward coefficient includes the peak shaving reward coefficient and the frequency modulation reward coefficient, and the calculation formula is as follows:

[0108]

[0109] Among them, represents the first reward amount; 、 、 all represent the peak shaving reward coefficient; represents the total peak shaving capacity; represents the peak shaving response time; Indicates the peak shaving accuracy.

[0110]

[0111] Among them, Indicates the second reward amount; 、 、 All indicate the frequency modulation reward coefficient; Indicates the regulation power; Indicates the frequency recovery time; Indicates the frequency stability.

[0112] Finally, determine the target set corresponding to the peak shaving effect evaluation index in the a target sets as the second target set, and distribute the first reward amount to the second target set to encourage users to actively participate in the peak shaving auxiliary service of the target power grid. Determine the target set corresponding to the frequency modulation effect evaluation index in the a target sets as the third target set, and distribute the second reward amount to the third target set to encourage users to actively participate in the frequency modulation auxiliary service of the target power grid.

[0113] It can be seen that through the economic compensation and reward mechanism, each target set in the target aggregator is encouraged to actively explore energy-saving potential and actively participate in the peak shaving and frequency modulation auxiliary services of the target power grid, thereby promoting the efficient utilization of energy and the stable operation of the power system.

[0114] Step S502, construct a model based on the energy efficiency data and the auxiliary service data to obtain a dynamic interaction model.

[0115] Among them, the specific steps of constructing a model based on the energy efficiency data and the auxiliary service data to obtain a dynamic interaction model include:

[0116] B1. Integrate the energy efficiency data and the auxiliary service data to obtain model input data;

[0117] B2. Determine the power exchange constraint conditions between the target power grid and the target aggregator;

[0118] B3. Construct an optimization objective function according to the preset model predictive control method to obtain the first optimization objective function;

[0119] B4. Combine the preset barrier function with the first optimization objective function to obtain an augmented objective function;

[0120] Among them, the augmented objective function is as follows:

[0121]

[0122] Among them, represents the augmented objective function; represents the first optimization objective function; represents the barrier factor, which is used for continuous iterative update; represents the barrier function; x represents the decision variable in vector form, including the a power adjustment amounts corresponding to the a objective sets;

[0123] B5. Iteratively update the barrier factor of the augmented objective function according to the model input data to obtain the target barrier factor;

[0124] B6. Adjust the augmented objective function according to the target barrier factor to obtain the second objective function;

[0125] B7. Determine the dynamic interaction model according to the second objective function and the power exchange constraint condition.

[0126] In a specific embodiment, first, integrate the energy efficiency data and the ancillary service data to obtain the model input data. Then determine the power exchange constraint condition between the target power grid and the target aggregator. Among them, the power exchange constraint condition is as follows:

[0127]

[0128] Among them, represents the target power flow, that is, the power flowing from the target aggregator to the target power grid; represents the lower limit value corresponding to the target power flow; represents the upper limit value corresponding to the target power flow. Among them, the calculation formula of the target power flow is as follows:

[0129]

[0130] Among them, represents the total power of the target aggregator; represents the real-time demand power of the power grid. It should be noted that when the target power flow is greater than 0, it means that the target aggregator delivers power to the target power grid; when the target power flow is less than 0, it means that the target aggregator absorbs power from the target power grid; when the target power flow is equal to 0, it means that there is no interactive power transmission between the target aggregator and the target power grid.

[0131]

[0132] Among them, represents the power of a single industrial user, and i represents an individual in the industrial user set I; represents the power of a single commercial user, and c represents an individual in the commercial user set C; $p_{r}$ represents the power of a single residential user, and $r$ represents an individual in the set $R$ of residential users; $p_{d}$ represents the power of a single distributed power source, and $d$ represents an individual in the set $D$ of distributed power sources.

[0133] Next, an optimization objective function is constructed according to the preset model predictive control method to obtain the first optimization objective function. Among them, the first optimization objective function is as follows:

[0134]

[0135] Among them, $J_{1}$ represents the first optimization objective function; $N$ represents the prediction horizon; $k$ represents the time interval of the prediction horizon; $, $, all represent weight coefficients; $t$ represents the current time; $p_{g}(t + k)$ represents the real-time demand power of the power grid at the $k$-th future moment; $p_{agg}(t + k)$ represents the total power of the target aggregator at the $k$-th future moment; $\Delta p_{i}^{I}(t + k)$ represents the power adjustment amount of a single industrial user at the $k$-th future moment; $\Delta p_{i}^{C}(t + k)$ represents the power adjustment amount of a single commercial user at the $k$-th future moment; $\Delta p_{r}(t + k)$ represents the power adjustment amount of a single residential user at the $k$-th future moment. It should be noted that power adjustment can be performed only on each user set, or on each user set and the distributed power source set, and no specific limitation is made here.

[0136] Then, the preset barrier function is combined with the first optimization objective function to obtain the augmented objective function. Among them, the augmented objective function is as follows:

[0137]

[0138] Among them, $J$ represents the augmented objective function; $J_{1}$ represents the first optimization objective function; $\mu$ represents the barrier factor, which is used for continuous iterative update; $B(x)$ represents the barrier function; $x$ represents the decision variable in vector form, including $a$ power adjustment amounts corresponding to $a$ target sets. Among them, the decision variable $x$ in vector form can be expressed as , and no specific limitation is made here.

[0139] It should be noted that when optimizing the first optimization objective function through the preset optimization algorithm, for the inequality , the corresponding barrier function is introduced, then during the optimization process, near the constraint boundary (such that Solutions close to 0) are penalized to prevent the iteration points of the optimization algorithm from crossing the constraint boundary and ensure that the solutions always remain within the feasible region. Among them, represents a function of the decision variable x, m represents the number of constraint conditions, and this barrier function is as follows:

[0140]

[0141] Finally, the barrier factor is iteratively updated according to a preset iterative algorithm. During the iteration process, the barrier factor is continuously adjusted according to the model input data, so that the constraint effect of the barrier function on the decision variable becomes gradually reasonable, avoiding the decision variable from exceeding constraints such as power exchange, and approaching the optimal solution of the optimization problem, thereby obtaining the target barrier factor. Among them, the iterative algorithm can be an iterative rule related to the interior point method, which is not specifically limited here. Then, the target barrier factor is substituted into the augmented objective function, and the augmented objective function is adjusted to obtain the second optimization objective function. Then, taking the second optimization objective function as the optimization goal and combining the power exchange constraint conditions, a dynamic interaction model is constructed.

[0142] It should be noted that the target power grid can send control commands to the target aggregator according to the real-time total power of the aggregator and the power grid demand power, and the target aggregator can adjust the power of each target set, that is, the power of each user and device, according to the control commands, so as to achieve real-time power balance.

[0143] It can be seen that by integrating multi-source data and considering the power exchange constraint conditions, the model can comprehensively reflect the actual operation conditions of the power grid and the aggregator, improve the accuracy and reliability of the model, and provide a more accurate basis for decision-making. By constructing and optimizing the objective function and using the barrier function to handle the constraint conditions, the optimal solution can be found under different operation scenarios, enabling the model to adapt to the complex and changeable operation environment of the power system and improving the operation efficiency and stability of the power grid.

[0144] Step S503, obtain the power grid demand power of the target power grid corresponding to the target aggregator within a preset first control period.

[0145] Specifically, a preset fixed time interval is determined as a control period, and this fixed time interval can be set according to factors such as the dynamic characteristics of the target power grid, the response speed of user equipment, and the transmission delay of the communication system. For example, for a power system with rapid changes, such as a system containing a large amount of distributed energy, the fixed time interval can be set to the second level or the millisecond level; for a system with relatively slow changes, the fixed time interval can be appropriately extended. Among them, the preset first control period represents this fixed time interval, and the grid demand power of the target power grid in the preset first control period can be obtained, providing a data basis for subsequent operations such as power regulation and resource allocation. It should be noted that within each control period, the grid demand power and the total power of the target aggregate of the target power grid are monitored and analyzed in real time, facilitating the realization of power supply-demand balance and ensuring the stable operation of the target power grid.

[0146] Step S504, calculate according to the grid demand power through the dynamic interaction model to obtain a reference power regulation amount.

[0147] For easy understanding, please refer to Figure 6 , Figure 6 which is a schematic flowchart of a process for calculating a power regulation amount provided by an embodiment of the present application. The calculation according to the grid demand power through the dynamic interaction model to obtain a reference power regulation amount specifically includes the following steps:

[0148] C1. Obtain the actual power of each of the a target sets in a preset second control period to obtain a first power; the preset second control period is the previous control period of the preset first control period;

[0149] C2. Determine the sum of the a first powers as the first total power;

[0150] C3. Obtain the power regulation constraint conditions of each of the a target sets to obtain a power regulation constraint conditions;

[0151] C4. Determine a first power regulation amount according to the first total power and the grid demand power;

[0152] C5. Determine a first power regulation amount corresponding to the a power regulation constraint conditions according to the first power regulation amount through the dynamic interaction model;

[0153] C6. Determine the reference power regulation amount according to the a first power regulation amounts.

[0154] In a specific embodiment, first, the actual power of each target set in a target set of a is obtained within a preset second control period, obtaining a first powers, where the preset second control period is the previous control period of the preset first control period. Then, the a first powers are added together to obtain a first total power. Next, the power adjustment constraint conditions of each target set in the a target sets are obtained, obtaining a power adjustment constraint conditions. A first power adjustment amount is determined based on the first total power and the grid demand power, that is, by comparing the difference between the first total power and the grid demand power, the first power adjustment amount that the target aggregate needs to adjust to meet the grid demand power is calculated. Then, using the dynamic interaction model, based on the first power adjustment amount and the a power adjustment constraint conditions, a first power adjustment amounts are determined. Among them, each first power adjustment amount in the a first power adjustment amounts corresponds to a target set. Finally, the a first power adjustment amounts are added together to obtain a reference power adjustment amount.

[0155] It can be seen that by comprehensively considering historical power, current demand, constraint conditions, etc., and using the dynamic interaction model to accurately determine the power adjustment amounts of each target set, refined control of the power system is achieved, ensuring the balance between power supply and demand of the power grid.

[0156] In a possible embodiment, within the t-th control period, the power update formula for industrial user i is as follows:

[0157]

[0158] Among them, represents the actual power consumption of industrial user i within the t-th control period; represents the actual power consumption of industrial user i in the previous control period of the t-th control period; represents the power adjustment amount of industrial user i within the t-th control period.

[0159] Among them, the power adjustment constraint conditions of industrial user i are as follows:

[0160]

[0161] Among them, represents the lower limit value of the power adjustment amount of industrial user i; Represents the upper limit value of the power adjustment amount of industrial user i. It should be noted that the power adjustment of industrial users should take into account the technological requirements and operation stability of production equipment to ensure that the power adjustment is within a reasonable and safe range, and to avoid the impact on equipment operation, production processes, and grid stability due to excessive or too small adjustment amplitude. For example, in continuous production processes such as steel smelting and chemical production, high requirements are placed on power stability, and power adjustment should not interfere with production continuity and product quality. Therefore, on the premise of meeting production demands, the power adjustment amount is reasonably determined according to power adjustment constraint conditions to achieve the balance between power system regulation and industrial production demands.

[0162] In a possible embodiment, within the t-th control period, the power update formula for commercial user c is as follows:

[0163]

[0164] Wherein, Represents the actual power consumption of commercial user c within the t-th control period; Represents the actual power consumption of commercial user c in the previous control period of the t-th control period; Represents the power adjustment amount of commercial user c within the t-th control period.

[0165] It should be noted that for the set of commercial users C, the electricity consumption characteristics and load adjustment ability constraints need to be considered to ensure that the adjusted power is within a reasonable range. Commercial users can reasonably adjust the electricity consumption time and power without affecting normal business operations. For example, a shopping mall can reduce the power consumption of non-critical equipment during peak electricity consumption periods, such as reducing the lighting brightness and adjusting the air-conditioning temperature setting value.

[0166] In a possible embodiment, within the t-th control period, the power update formula for residential user r is as follows:

[0167]

[0168] Wherein, Represents the actual power consumption of residential user r within the t-th control period; Represents the actual power consumption of residential user r in the previous control period of the t-th control period; Represents the power adjustment amount of residential user r within the t-th control period.

[0169] It should be noted that for the set R of residential users, the randomness of their electricity consumption behavior needs to be considered. At the same time, during the adjustment process, the usage probabilities and average power changes of various electrical devices need to be comprehensively considered to ensure that the normal electricity demand of residential users is not greatly affected. Among them, residential users can, through smart electrical devices and user interfaces, independently select the ways and degrees of participating in demand response. For example, residential users can set to automatically turn off some non-essential electrical devices during peak electricity consumption periods, or adjust the charging time of electric vehicles to off-peak electricity consumption periods, which are not specifically limited here.

[0170] In a possible embodiment, within the t-th control period, for distributed power source d, such as a photovoltaic power generation device, its actual power generation not only is affected by light intensity and conversion efficiency, but also the power regulation during interaction with the target power grid needs to be considered. The power update formula for this distributed power source d is as follows:

[0171]

[0172] Among them, represents the actual power generation of distributed power source d in the t-th control period; represents the relevant parameters of distributed power source d, such as the area of the photovoltaic panels of the photovoltaic power generation device; represents the conversion efficiency in the t-th control period; represents the light intensity in the t-th control period.

[0173] Among them, the power generation can be adjusted by adjusting the relevant parameters of distributed power source d, and the adjusted power update formula is as follows:

[0174]

[0175] Among them, represents the actual power generation of distributed power source d in the t-th control period after adjusting the relevant parameters of distributed power source d; represents the influence coefficient of adjusting the relevant parameters of distributed power source d on its power generation. Among them, the maximum power point tracking technology can also be used to adjust the operating point of the photovoltaic array of the photovoltaic power generation device in real time according to conditions such as light intensity and ambient temperature, so that it always operates at the maximum power output state, further improving the power generation.

[0176] In a possible embodiment, energy storage devices can also be integrated into the target aggregator to coordinate and optimize the power regulation project of the target aggregator. Among them, let the charging and discharging power of the energy storage device be , when the energy storage device is in the charging state, is negative, and when the energy storage device is in the discharging state, is a positive value. Among them, the state of charge of the energy storage device is used to reflect its remaining power, and the calculation formula is as follows:

[0177]

[0178] Among them, represents the state of charge of the energy storage device in the t-th control period; represents the state of charge of the energy storage device in the previous control period of the t-th control period; represents the charging efficiency of the energy storage device; represents the discharging efficiency of the energy storage device; represents the rated capacity of the energy storage device; represents the duration corresponding to a single control period;

[0179] Among them, the charging and discharging power of the energy storage device needs to meet its power limit, and the corresponding power constraint conditions of the energy storage device are as follows:

[0180]

[0181] Among them, represents the lower limit value of the charging and discharging power of the energy storage device; represents the upper limit value of the charging and discharging power of the energy storage device.

[0182] It should be noted that by restricting the upper and lower limits of the charging and discharging power of the energy storage device, it is possible to avoid damaging the device due to the power exceeding the device's tolerance range. At the same time, the state of charge of the energy storage device needs to be maintained within a preset reasonable range . During the power regulation process, the power change of the energy storage device works in coordination with other users and devices. For example, when the target grid power is excessive, the excess electric energy is preferentially stored in the energy storage device; when the target grid power is insufficient, the energy storage device discharges.

[0183] Among them, the calculation formula for the updated total power of the target aggregate is as follows:

[0184]

[0185] It should be noted that to optimize the charging and discharging strategy of the energy storage device, a model predictive control-based method can be adopted, combined with the grid power demand of the target grid, the state of charge of the energy storage device, and the power prediction for a period of time in the future, to formulate an optimal charging and discharging plan, improving the utilization efficiency and service life of the energy storage device.

[0186] For easy understanding, please refer to Figure 7 , Figure 7It is a schematic flow diagram for adjusting the power regulation amount provided by an embodiment of the present application. After determining the a first power regulation amounts corresponding to the a power regulation constraint conditions through the dynamic interaction model, the specific steps further include:

[0187] D1. If the frequency deviation is greater than the preset frequency deviation threshold, determine a first adjustment parameter according to the frequency deviation;

[0188] D2. Adjust the first adjustment parameter according to a preset frequency correction coefficient to obtain a second adjustment parameter;

[0189] D3. Adjust each of the a first power regulation amounts according to the second adjustment parameter to obtain a second power regulation amounts;

[0190] D4. Determine the reference power regulation amount according to the a second power regulation amounts.

[0191] In a specific embodiment, first, if the frequency deviation is greater than the preset frequency deviation threshold, determine a first adjustment parameter according to the frequency deviation. Among them, the preset frequency deviation threshold can be determined according to factors such as the target power grid structure, electrical equipment characteristics, and power load types. Then, adjust the first adjustment parameter according to the preset frequency correction coefficient to obtain a second adjustment parameter. Then, adjust each of the a first power regulation amounts according to the second adjustment parameter to obtain a second power regulation amounts. Among them, the a first power regulation amounts correspond one-to-one to the a target sets. Finally, determine the reference power regulation amount according to the a second power regulation amounts.

[0192] Among them, the a target sets include an industrial user set I, a commercial user set C, a residential user set R, and a distributed power source set D. For example, for an industrial user i in the industrial user set I, the power regulation amount correction formula corresponding to it is as follows:

[0193]

[0194] Among them, represents the second power regulation amount corresponding to the industrial user i; represents the first power regulation amount corresponding to the industrial user i; represents the frequency correction coefficient; represents the frequency deviation; represents the actual power grid frequency of the target power grid; represents the rated frequency of the target power grid.

[0195] It can be seen that adjusting the power adjustment amount according to the frequency deviation can effectively address the frequency fluctuation problem of the target power grid. When the frequency deviation exceeds the preset frequency deviation threshold, adjusting the power adjustment amounts of each target set in a timely manner helps to restore the power grid frequency to the normal level, ensure the frequency stability of the power system, and avoid affecting the normal operation of equipment or even causing system failures due to abnormal frequencies.

[0196] Step S505: Adjust the power of the target aggregate according to the reference power adjustment amount to ensure that the total power of the target aggregate is in a power balance state with the power demand of the power grid.

[0197] Among them, the specific steps of adjusting the power of the target aggregate according to the reference power adjustment amount include:

[0198] E1. Determine the second total power of the target aggregate according to the reference power adjustment amount and the first total power;

[0199] E2. Obtain the deviation between the power demand of the power grid and the second total power to obtain a first power deviation;

[0200] E3. Obtain the upper power deviation limit and the lower power deviation limit corresponding to the preset power deviation range;

[0201] E4. If the first power deviation is greater than the upper power deviation limit, determine a first adjustment factor according to the difference between the first power deviation and the upper power deviation limit;

[0202] E5. Adjust the power of the target aggregate according to the first adjustment factor;

[0203] E6. If the first power deviation is less than the lower power deviation limit, determine a second adjustment factor according to the difference between the lower power deviation limit and the first power deviation;

[0204] E7. Adjust the power of the target aggregate according to the second adjustment factor.

[0205] In a specific embodiment, first, adjust the first total power according to the reference power adjustment amount to obtain the second total power of the target aggregate. Then, calculate the deviation between the power demand of the power grid and the second total power to obtain a first power deviation. Then, obtain the upper power deviation limit and the lower power deviation limit corresponding to the preset power deviation range, and this preset power deviation range can be preset according to factors such as the safety and stability requirements of the operation of the target power grid and the bearing capacity of the equipment.

[0206] Next, if the first power deviation is greater than the upper limit of the power deviation, it indicates that the power demand of the power grid is relatively too high compared to the total power of the target aggregate, that is, the power supply is insufficient. Determine the first adjustment factor according to the difference between the first power deviation and the upper limit of the power deviation. Then adjust the power of the target aggregate according to the first adjustment factor. For example, increase the power generation of the target aggregate, or reduce the power of some users or devices in the target aggregate, so that the total power of the target aggregate approaches a reasonable range. If the first power deviation is less than the lower limit of the power deviation, it indicates that the power demand of the power grid is relatively too low compared to the total power of the target aggregate, that is, the power supply is excessive. The second adjustment factor can be determined according to the difference between the lower limit of the power deviation and the first power deviation, and the power of the target aggregate can be adjusted according to the second adjustment factor. For example, reduce the power generation of the target aggregate, or increase the power consumption load. It should be noted that within each control cycle, by adjusting the power of the target aggregate, the total power of the target aggregate is made to meet the real-time power demand of the power grid, ensuring that the power supply and demand are basically balanced in each control cycle and avoiding serious power overage or shortage situations.

[0207] It can be seen that by monitoring and adjusting the power relationship between the target aggregate and the power grid in real time, it is ensured that the total power of the target aggregate matches the power demand of the power grid, maintaining the power balance of the power system and avoiding problems such as grid instability or equipment damage caused by the power deviation exceeding the reasonable range.

[0208] Among them, the power exchange constraint conditions should be observed between the target aggregate and the target power grid, that is, the power flowing from the target aggregate to the target power grid needs to meet the upper and lower limit constraints. Among them, the target power flow represents the power flowing from the target aggregate to the target power grid. When the target power flow is greater than the upper limit value, it indicates that the power transmitted from the target aggregate to the target power grid is too large, which may affect the stability of the target power grid. Then, the power generation and power consumption within the target aggregate need to be adjusted to reduce the power transmitted outward. When the target power flow is less than the lower limit value, it indicates that the target aggregate absorbs too much power from the target power grid, and corresponding measures need to be taken to reduce the power consumption load of the target aggregate or increase the power generation. Among them, in actual operation, the upper and lower limit constraints of power exchange can be dynamically adjusted according to the real-time state and safety margin of the target power grid to ensure the stable operation of the target power grid and the target aggregate.

[0209] It should be noted that when various users and devices of the target aggregator adjust power, they need to meet their corresponding device operation constraint conditions. For example, the production equipment of industrial users cannot affect the normal production process due to power adjustment, and its power adjustment speed cannot exceed the maximum adjustment rate allowed by the equipment; the adjustment of the power generation power of distributed power sources also needs to be carried out within the technical parameters of the equipment. For example, the conversion efficiency of photovoltaic power generation equipment cannot be lower than its minimum technical guarantee value. For the device operation constraint conditions, the device operation parameters of the target aggregator can be monitored in real time. When the device operation parameters approach or exceed the corresponding constraint range, the power adjustment strategy can be adjusted in a timely manner to ensure the safe and stable operation of the device.

[0210] The above mainly introduces the solution of the embodiment of the present application from the perspective of the execution process on the method side. It can be understood that in order for the electronic device to implement the above functions, it includes the corresponding hardware structure and / or software module for executing each function. Those skilled in the art should easily realize that, combining the units and algorithm steps of each example described in the embodiments provided in this article, the present application can be implemented in the form of hardware or a combination of hardware and computer software.

[0211] The embodiment of the present application can divide the functional units of the electronic device according to the above method examples. For example, each functional unit can be divided corresponding to each function, or two or more functions can be integrated into one processing unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. It should be noted that the division of units in the embodiment of the present application is illustrative, only a logical function division, and there may be other division methods in actual implementation.

[0212] In the case of dividing each functional module corresponding to each function, Figure 8 is a block diagram of the functional modules of an aggregator dynamic interaction device based on ancillary services provided by an embodiment of the present application. The aggregator dynamic interaction device 800 based on ancillary services includes a first acquisition module 810, a construction module 820, a second acquisition module 830, a calculation module 840, and an adjustment module 850, where:

[0213] The first acquisition module 810 is configured to acquire the energy efficiency data and ancillary service data corresponding to the target aggregator; the target aggregator includes a target sets; the target set includes any one of the following: an industrial user set, a commercial user set, a residential user set, a distributed power source set; a is an integer greater than 1;

[0214] The construction module 820 is configured to construct a model according to the energy efficiency data and the ancillary service data to obtain a dynamic interaction model;

[0215] The second acquisition module 830 is configured to acquire the grid demand power of the target power grid corresponding to the target aggregate within a preset first control period;

[0216] The calculation module 840 is configured to calculate, according to the grid demand power through the dynamic interaction model, to obtain a reference power adjustment amount;

[0217] The adjustment module 850 is configured to adjust the power of the target aggregate according to the reference power adjustment amount, so as to ensure that the total power of the target aggregate and the grid demand power are in a power balance state.

[0218] Optionally, in terms of acquiring the energy efficiency data and ancillary service data corresponding to the target aggregate, the first acquisition module 810 is specifically configured to:

[0219] Acquire the historical power consumption data of the target aggregate within a preset time period;

[0220] Analyze and calculate the historical power consumption data to obtain the energy efficiency data; the energy efficiency data includes energy efficiency index data, load prediction data, and energy saving potential data;

[0221] Acquire the adjustable load corresponding to the target aggregate;

[0222] Calculate the adjustable load according to a preset peak shaving calculation formula to obtain peak shaving data;

[0223] Acquire the actual grid frequency and rated frequency of the target power grid;

[0224] Determine the frequency deviation according to the actual grid frequency and the rated frequency;

[0225] Calculate the frequency deviation according to a preset frequency modulation calculation formula to obtain frequency modulation data;

[0226] Acquire the historical load data of the target power grid within the preset time period;

[0227] Calculate the historical load data to obtain the load fluctuation standard deviation;

[0228] Calculate the load fluctuation standard deviation according to a preset reserve capacity calculation formula to obtain reserve capacity data;

[0229] Determine the ancillary service data according to the peak shaving data, the frequency modulation data, and the reserve capacity data.

[0230] Optionally, the first acquisition module 810 is further specifically configured to:

[0231] Determine the energy saving contribution according to the energy saving potential data;

[0232] Determine the first compensation amount according to the preset energy-saving compensation unit price and the energy-saving contribution amount;

[0233] Distribute the first compensation amount to the first target set corresponding to the energy-saving contribution amount in the a target sets;

[0234] Determine the peak shaving effect evaluation index and the frequency modulation effect evaluation index according to the peak shaving data and the frequency modulation data respectively;

[0235] Calculate the peak shaving effect evaluation index and the frequency modulation effect evaluation index respectively according to the preset reward coefficient to obtain the first reward amount and the second reward amount;

[0236] Determine the second target set corresponding to the peak shaving effect evaluation index in the a target sets;

[0237] Distribute the first reward amount to the second target set;

[0238] Determine the third target set corresponding to the frequency modulation effect evaluation index in the a target sets;

[0239] Distribute the second reward amount to the third target set.

[0240] Optionally, in terms of constructing a model based on the energy efficiency data and the ancillary service data to obtain a dynamic interaction model, the construction module 820 is specifically configured to:

[0241] Integrate the energy efficiency data and the ancillary service data to obtain model input data;

[0242] Determine the power exchange constraint conditions between the target power grid and the target aggregator;

[0243] Construct an optimization objective function according to the preset model predictive control method to obtain the first optimization objective function;

[0244] Combine the preset barrier function with the first optimization objective function to obtain an augmented objective function;

[0245] Among them, the augmented objective function is shown as follows:

[0246]

[0247] Among them, represents the augmented objective function; represents the first optimization objective function; represents the barrier factor, which is used for continuous iterative update; denote the barrier function; x represents the decision variable in vector form, including the a power adjustment amounts corresponding to the a target sets;

[0248] Iteratively update the barrier factor of the augmented objective function according to the model input data to obtain the target barrier factor;

[0249] Adjust the augmented objective function according to the target barrier factor to obtain the second optimization objective function;

[0250] Determine the dynamic interaction model according to the second optimization objective function and the power exchange constraint conditions.

[0251] Optionally, in the aspect of calculating the reference power adjustment amount according to the grid demand power through the dynamic interaction model, the calculation module 840 is specifically configured to:

[0252] Obtain the actual power of each target set in the a target sets within a preset second control period to obtain a first powers; the preset second control period is the previous control period of the preset first control period;

[0253] Determine the sum of the a first powers as the first total power;

[0254] Obtain the power adjustment constraint conditions of each target set in the a target sets to obtain a power adjustment constraint conditions;

[0255] Determine the first power adjustment amount according to the first total power and the grid demand power;

[0256] Determine the a first power adjustment amounts corresponding to the a power adjustment constraint conditions according to the first power adjustment amount through the dynamic interaction model;

[0257] Determine the reference power adjustment amount according to the a first power adjustment amounts.

[0258] Optionally, after determining the a first power adjustment amounts corresponding to the a power adjustment constraint conditions according to the first power adjustment amount through the dynamic interaction model, the calculation module 840 is further specifically configured to:

[0259] If the frequency deviation is greater than the preset frequency deviation threshold, determine the first adjustment parameter according to the frequency deviation;

[0260] Adjust the first adjustment parameter according to a preset frequency correction coefficient to obtain a second adjustment parameter;

[0261] Adjust each of the a first power adjustment amounts according to the second adjustment parameter to obtain a second power adjustment amounts;

[0262] Determine the reference power adjustment amount according to the a second power adjustment amounts.

[0263] Optionally, in terms of adjusting the power of the target aggregate according to the reference power adjustment amount, the adjustment module 850 is specifically configured to:

[0264] Determine the second total power of the target aggregate according to the reference power adjustment amount and the first total power;

[0265] Obtain the deviation between the grid demand power and the second total power to obtain a first power deviation;

[0266] Obtain the upper power deviation limit and the lower power deviation limit corresponding to the preset power deviation range;

[0267] If the first power deviation is greater than the upper power deviation limit, determine a first adjustment factor according to the difference between the first power deviation and the upper power deviation limit;

[0268] Adjust the power of the target aggregate according to the first adjustment factor;

[0269] If the first power deviation is less than the lower power deviation limit, determine a second adjustment factor according to the difference between the lower power deviation limit and the first power deviation;

[0270] Adjust the power of the target aggregate according to the second adjustment factor.

[0271] It can be seen that by constructing a dynamic interaction model, calculating the reference power adjustment amount based on the grid demand power and adjusting the power of the target aggregate, it can respond to the change of the grid load in real time, avoid power shortage or surplus, and coordinate the power balance between users and the grid.

[0272] It should be noted that the specific implementation of each operation can adopt the corresponding description of the method embodiment shown above. The aggregate dynamic interaction device 800 based on ancillary services can be used to execute the method embodiments of the present application above, and details are not described herein again.

[0273] An embodiment of the present application further provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute some or all of the steps of any method recorded in the method embodiment above, and the computer includes an electronic device.

[0274] An embodiment of the present application also provides a computer program product. The computer program product includes a non-transitory computer-readable storage medium storing a computer program. The computer program is operable to cause a computer to execute some or all of the steps of any one of the methods described in the above method embodiments. The computer program product may be a software installation package, and the computer includes an electronic device.

[0275] It should be noted that, for the above embodiments, for simplicity of description, they are all expressed as a series of action combinations. Those skilled in the art should know that the present application is not limited by the described action sequence, because some steps in the embodiments of the present application can be performed in other sequences or simultaneously. In addition, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions, steps, modules or units involved are not necessarily essential to the embodiments of the present application.

[0276] In the above embodiments, each embodiment of the present application is described with emphasis. For the parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0277] Those skilled in the art should be able to realize that, in one or more of the above examples, the functions described in the embodiments of the present application can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, some or all of the processes or functions described in the embodiments of the present application are generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. The computer-readable storage medium may be any available medium that a computer can access, or a data storage device such as a server or a data center that includes one or more available media integrated.

[0278] Each device and product described in the above embodiments includes various modules / units, which can be software modules / units, hardware modules / units, or can be partially software modules / units and partially hardware modules / units. For example, for each device and product applied to or integrated into a chip, each module / unit it includes can be implemented in the form of hardware such as circuits. Or, at least some of the modules / units can be implemented in the form of software programs that run on the processor integrated inside the chip, and the remaining (if any) part of the modules / units can be implemented in the form of hardware such as circuits; for each device and product applied to or integrated into a chip module, each module / unit it includes can be implemented in the form of hardware such as circuits. Different modules / units can be located in the same component (such as a chip, a circuit module, etc.) or different components of the chip module. Or, at least some of the modules / units can be implemented in the form of software programs that run on the processor integrated inside the chip module, and the remaining (if any) part of the modules / units can be implemented in the form of hardware such as circuits; for each device and product applied to or integrated into a terminal device, each module / unit it includes can be implemented in the form of hardware such as circuits. Different modules / units can be located in the same component (such as a chip, a circuit module, etc.) or different components within the terminal device. Or, at least some of the modules / units can be implemented in the form of software programs that run on the processor integrated inside the terminal device, and the remaining (if any) part of the modules / units can be implemented in the form of hardware such as circuits.

[0279] The specific embodiments described above further elaborate on the purpose, technical solutions, and beneficial effects of the embodiments of the present application. It should be understood that the above are only specific embodiments of the embodiments of the present application and are not used to limit the protection scope of the embodiments of the present application. Any modifications, equivalent replacements, improvements, etc. made on the basis of the technical solutions of the embodiments of the present application should be included in the protection scope of the embodiments of the present application.

Claims

1. A dynamic interaction method of aggregates based on auxiliary services, characterized in that: The method comprises: Obtain energy efficiency data and auxiliary service data corresponding to a target aggregate; the target aggregate includes a target set; the target set includes any one of the following: an industrial user set, a commercial user set, a residential user set, and a distributed power source set; a is an integer greater than 1; Building a model based on the energy efficiency data and the auxiliary service data to obtain a dynamic interactive model; Obtaining a grid demand power of a target grid corresponding to the target aggregate within a preset first control period; Calculating according to the power demand of the power grid through the dynamic interaction model to obtain a reference power regulation amount; Performing power regulation on the target aggregate according to the reference power regulation amount to ensure that the total power of the target aggregate is in a power balance state with the power demanded by the power grid; The step of obtaining energy efficiency data and auxiliary service data corresponding to the target aggregate includes: Acquire historical electricity consumption data of the target aggregate within a preset time period; Analyze and calculate the historical electricity consumption data to obtain the energy efficiency data; the energy efficiency data includes energy efficiency index data, load forecast data, and energy saving potential data; Obtaining an adjustable load corresponding to the target polymer; Calculate the adjustable load according to a preset peak load calculation formula to obtain peak load data; Acquiring an actual grid frequency and a rated frequency of the target grid; determining a frequency deviation according to the actual grid frequency and the rated frequency; Calculating the frequency deviation according to a preset frequency modulation calculation formula to obtain frequency modulation data; Acquire historical load data of the target power grid within the preset time period; Calculating the historical load data to obtain a load fluctuation standard deviation; Calculating the load fluctuation standard deviation according to a preset spare capacity calculation formula to obtain spare capacity data; The auxiliary service data is determined according to the peak shaving data, the frequency regulation data and the spare capacity data.

2. The method according to claim 1, characterized in that The method further comprises: Determining an energy saving contribution amount based on the energy saving potential data; Determine a first compensation amount according to a preset energy-saving compensation unit price and the energy-saving contribution amount; Distribute the first compensation amount to a first target set corresponding to the energy saving contribution amount in the a target sets; Determine a peak shaving effect evaluation index and a frequency modulation effect evaluation index according to the peak shaving data and the frequency modulation data respectively; The peak load regulation effect evaluation index and the frequency regulation effect evaluation index are calculated respectively according to a preset reward coefficient to obtain a first reward amount and a second reward amount; Determine a second target set corresponding to the peak load effect evaluation index in the a target sets; distributing the first reward amount to the second target set; Determine a third target set corresponding to the frequency modulation effect evaluation index in the a target sets; The second reward amount is distributed to the third target set.

3. The method according to claim 1, characterized in that The step of calculating the power demand of the power grid by the dynamic interaction model to obtain a reference power adjustment amount includes: Acquire the actual power of each target set in the a target sets within a preset second control period to obtain a first power; the preset second control period is a control period before the preset first control period; Determine the sum of the a first powers as a first total power; Obtaining a power regulation constraint condition of each target set in the a target sets to obtain a power regulation constraint conditions; Determine a first power adjustment amount according to the first total power and the power grid demand power; Determining a first power adjustment amounts corresponding to the a power adjustment constraint conditions according to the first power adjustment amount through the dynamic interaction model; The reference power adjustment amount is determined according to the a first power adjustment amounts.

4. The method according to claim 3, characterized in that After determining the a first power adjustment amounts corresponding to the a power adjustment constraints according to the first power adjustment amount through the dynamic interaction model, the method further includes: If the frequency deviation is greater than a preset frequency deviation threshold, determining a first adjustment parameter according to the frequency deviation; Adjust the first adjustment parameter according to a preset frequency correction coefficient to obtain a second adjustment parameter; Adjust each of the a first power adjustment amounts according to the second adjustment parameter to obtain a second power adjustment amounts; The reference power adjustment amount is determined according to the a second power adjustment amounts.

5. The method according to claim 3 or 4, characterized in that The power regulation of the target polymer according to the reference power regulation amount includes: Determine a second total power of the target aggregate according to the reference power adjustment amount and the first total power; Obtaining a deviation between the power grid demand power and the second total power to obtain a first power deviation; Obtain the power deviation upper limit and power deviation lower limit corresponding to the preset power deviation range; If the first power deviation is greater than the power deviation upper limit, determining a first adjustment factor according to a difference between the first power deviation and the power deviation upper limit; adjusting the power of the target polymer according to the first adjustment factor; If the first power deviation is less than the power deviation lower limit, determining a second adjustment factor according to a difference between the power deviation lower limit and the first power deviation; The power of the target polymer is adjusted according to the second adjustment factor.

6. A dynamic interactive device based on auxiliary services, characterized in that: The device comprises a first acquisition module, a construction module, a second acquisition module, a calculation module and an adjustment module, wherein: The first acquisition module is used to acquire energy efficiency data and auxiliary service data corresponding to a target aggregate; the target aggregate includes a target set; the target set includes any one of the following: an industrial user set, a commercial user set, a residential user set, and a distributed power source set; a is an integer greater than 1; The construction module is used to construct a model according to the energy efficiency data and the auxiliary service data to obtain a dynamic interactive model; The second acquisition module is used to acquire the grid demand power of the target grid corresponding to the target aggregate within a preset first control period; The calculation module is used to calculate according to the power demand of the power grid through the dynamic interaction model to obtain a reference power adjustment amount; The regulating module is used to perform power regulation on the target aggregate according to the reference power regulation amount to ensure that the total power of the target aggregate is in a power balance state with the power demanded by the power grid; The first acquisition module is also specifically used to obtain the historical electricity consumption data of the target aggregate within a preset time period; analyze and calculate the historical electricity consumption data to obtain the energy efficiency data; the energy efficiency data includes energy efficiency index data, load forecast data, and energy-saving potential data; obtain the adjustable load corresponding to the target aggregate; calculate the adjustable load according to a preset peak-shaving calculation formula to obtain peak-shaving data; obtain the actual grid frequency and rated frequency of the target power grid; determine the frequency deviation according to the actual grid frequency and the rated frequency; calculate the frequency deviation according to a preset frequency modulation calculation formula to obtain frequency modulation data; obtain the historical load data of the target power grid within the preset time period; calculate the historical load data to obtain the load fluctuation standard deviation; calculate the load fluctuation standard deviation according to a preset spare capacity calculation formula to obtain spare capacity data; determine the auxiliary service data based on the peak-shaving data, the frequency modulation data, and the spare capacity data.

7. An electronic device, characterized in that: include: A processor, a memory, a communication interface, and one or more programs; The one or more programs are stored in the memory and configured to be executed by the processor, the programs comprising instructions for executing the steps in the method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor is caused to perform the method according to any one of claims 1 to 5.

Images