Power load control method, device, equipment and medium

Abstract

The invention discloses a power load control method and device, equipment and a medium, and relates to the field of load control. The method comprises the following steps: according to a control instruction currently issued by a power grid, determining values of a plurality of reliability indexes when each type of power load receives the control instruction; according to the determined values of the plurality of reliability indexes, obtaining a comprehensive response reliability coefficient of each type of power load when the control instruction is received; sequentially selecting corresponding power loads to participate in load control according to the comprehensive response reliability coefficients from large to small until the sum of the maximum power of the selected power loads is greater than or equal to the power regulation requirement of the control instruction for the first time; and according to the daily curve of the power load participating in load control, solving the load control model by taking the lowest carbon emission of the power system as an optimization target, and obtaining a load control strategy. According to the invention, the accuracy and reliability of power load control can be improved.

Description

Technical Field

[0001] The present application relates to the field of load control, and in particular to a method, device, equipment and medium for controlling power load. Background Art

[0002] Renewable energy sources such as wind and photovoltaics will gradually replace traditional fossil fuels and become the primary energy source in power systems. However, the inherent uncertainty and volatility of wind and photovoltaic power pose significant challenges to balancing supply and demand in power grids. Demand response (DR), as a key means of load-side regulation, fully leverages the flexibility of various adjustable resources. Its rapid response and low cost can effectively promote supply and demand balance and stable operation of new power systems.

[0003] Currently, there are two main methods for load control: price-driven regulation, through adjusting electricity prices or providing incentive compensation, to regulate and control loads; and direct load control through dispatch control signals. If users have signed direct control contracts, power dispatch agencies can prioritize their participation in load control and rationally allocate their response capacity through measures such as establishing a user ranking table.

[0004] The existing technology of directly controlling the load through regulating signals has the following disadvantages:

[0005] Disadvantage 1: Failure to achieve precise load control. Different adjustable resources differ in response speed, response time, load characteristics, etc. The existing load control technology incorporates all adjustable loads into the optimization model during optimization scheduling, without arranging them in order according to the characteristics and regulation characteristics of different types of loads, and reasonably allocating response capacity.

[0006] Disadvantage 2: The reliability of load response is not taken into consideration. When facing the control instructions of the power grid, the response enthusiasm and reliability of different types of loads are also different. Some controlled loads may not be able to fully respond to the direct control instructions of DR. The actual adjustment of the load is less than expected, which affects the actual effect of DR. Summary of the Invention

[0007] The purpose of this application is to provide a power load control method, device, equipment and medium, which can improve the accuracy and reliability of power load control.

[0008] To achieve the above objectives, this application provides the following solutions:

[0009] In the first aspect, the present application provides a method for controlling electric loads, comprising: determining the values ​​of multiple reliability indicators of each type of electric load when receiving the control instruction based on the control instruction currently issued by the power grid; obtaining the comprehensive response reliability coefficient of each type of electric load when receiving the control instruction based on the values ​​of the multiple reliability indicators; selecting the corresponding electric loads in order from large to small according to the comprehensive response reliability coefficients to participate in load control until the maximum power sum of the selected electric loads is greater than or equal to the power regulation requirement of the control instruction for the first time; solving the load control model and obtaining the load control strategy based on the daily curve of the electric loads participating in the load control, with the lowest carbon emissions from the operation of the power system as the optimization goal; the load control strategy includes the response power of the electric loads participating in the load control.

[0010] In a second aspect, the present application provides an electric load control device, comprising: a reliability index determination module, a reliability coefficient acquisition module, a load control module and a solution module.

[0011] The reliability index determination module is used to determine the values ​​of multiple reliability indicators of each type of power load when receiving the control instruction based on the control instruction currently issued by the power grid.

[0012] The reliability coefficient obtaining module is used to obtain the comprehensive response reliability coefficient of each type of power load when receiving the control instruction based on the values ​​of the multiple reliability indicators.

[0013] The load control module is used to select corresponding power loads to participate in load control in descending order of comprehensive response reliability coefficients until the maximum power sum of the selected power loads is greater than or equal to the power regulation requirement of the control instruction for the first time.

[0014] The solution module is used to solve the load control model and obtain the load control strategy based on the daily curve of the power load participating in the load control, with the lowest carbon emissions from the power system operation as the optimization goal; the load control strategy includes the response power of the power load participating in the load control.

[0015] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned power load control method.

[0016] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which implements the above-mentioned power load control method when executed by a processor.

[0017] According to the specific embodiments provided in this application, this application has the following technical effects:

[0018] The present application provides a method, apparatus, equipment and medium for controlling electric loads, and proposes the concept of a comprehensive response reliability coefficient, which is used to measure the response reliability of different types of electric loads, so as to screen out the electric load with the highest reliability for control based on the comprehensive response reliability coefficients of different types of electric loads, effectively avoiding power system operation problems caused by unreliable load response, and improving the reliability of electric load control; based on the high and low comprehensive response reliability coefficients of various types of electric loads, the priority sorting and capacity allocation of different types of electric loads during load control are realized, the resource allocation of adjustable loads is optimized, the regulation potential of various types of electric loads is maximized, and the accuracy of electric load control is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 A flow chart of a power load control method provided in one embodiment of the present application;

[0021] Figure 2 A schematic diagram of a load control optimization process provided in an embodiment of the present application;

[0022] Figure 3 A functional architecture diagram of a power load control method provided in one embodiment of the present application;

[0023] Figure 4 A schematic diagram of functional modules of a power load control device provided in another embodiment of the present application;

[0024] Figure 5 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0026] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0027] In an exemplary embodiment, Figure 1 As shown, a method for controlling electric load is provided, comprising the following steps 101 to 104. In which:

[0028] Step 101: According to the control instruction currently issued by the power grid, the values ​​of multiple reliability indicators of each type of power load when receiving the control instruction are determined.

[0029] Step 102: Obtaining a comprehensive response reliability coefficient of each type of power load when receiving a control instruction based on the values ​​of the plurality of reliability indicators.

[0030] Step 103: Select corresponding power loads in descending order of comprehensive response reliability coefficients to participate in load control until the maximum sum of the selected power loads is greater than or equal to the power regulation requirement of the control instruction for the first time.

[0031] Step 104: Based on the daily curve of the electric loads involved in load control, with the optimization goal of minimizing carbon emissions from power system operation, solve the load control model and obtain a load control strategy; the load control strategy includes the response power of the electric loads involved in load control.

[0032] In the process of directly dispatching and controlling loads with the help of control signals, in order to effectively avoid load rebound problems and enhance the reliability and stability of load response, the above steps 101 to 104 are implemented to achieve the ordering and capacity allocation of different types of loads during load control based on the response coefficients of various types of adjustable loads. This application can effectively improve the efficiency of load control and enhance the reliability of load control in regional power grids.

[0033] In another exemplary embodiment of the present application, power loads are broadly categorized into temperature-controlled loads, energy storage loads, and industrial loads based on their characteristics. Temperature-controlled loads include loads such as thermal storage boilers and air conditioners, energy storage loads include loads such as batteries and electric vehicles, and industrial loads include loads on electrolytic aluminum and steel production lines. Leveraging existing digital systems such as Marketing 2.0, electricity consumption collection systems, and the SG186 system, underlying load data for different load types is collected, including historical load usage data over time periods.

[0034] In another exemplary embodiment of the present application, when different types of power loads face control instructions, the matching degree of their response time, the size of the load adjustment rate, and the fatigue degree of the load response will affect the load response enthusiasm and reliability, resulting in the controlled load being unable to fully respond to the scheduling control instruction, the actual load adjustment amount being less than expected or causing load rebound, which will affect the actual control effect. Multiple reliability indicators include: start time fit, operating period fit, load response fatigue, and load adjustment reliability. The following calculates each reliability indicator separately to measure the reliability of the load when it is controlled.

[0035] The start time fit λ1 represents the fit between the actual start time of DR and the actual usage period of the load. It is an indicator to measure the adaptability of demand response and load in the time dimension. The calculation formula for the start time fit is:

[0036]

[0037] Where, T1 is the load start time, T2 is the load end time, T DR is the actual time when the load control command is issued, T m is the maximum allowable time deviation. [T1, T2] is the load operation time period. When the load control instruction is issued within [T1, T2], the load response reliability is high. When the load control instruction is issued beyond the load operation time period [T1, T2], the load response reliability gradually decreases.

[0038] The operating period fit λ2 represents the degree of matching between the DR operating period and the load operating period. The calculation formula for the operating period fit is:

[0039]

[0040] T OL =max[0,min(T b ,T2)-max(T a ,T1)];

[0041] Where, T OL T is the overlap between the load control period and the load operation period. a is the load control start time, T b The higher the overlap between the DR control period and the load operation period, the more reliable the load response.

[0042] Load response fatigue λ3 represents the degree to which the load's response reliability gradually declines due to the recent frequent response of the load to direct control instructions. The calculation formula for load response fatigue is:

[0043]

[0044] Where, T last is the time when the load control instruction was last received, T rc is the time required for load recovery. DR If the load recovery time exceeds the time required, the reliability is 1. DR If it is less than or equal to the time required for load recovery, the reliability of load response is reduced.

[0045] Load adjustment reliability, λ4, is an important parameter used to quantify the impact of the load's power adjustment degree on reliability during the process of receiving control commands. Excessive load adjustment affects the load's actual production operation. In this case, the load is less likely to respond normally, and the response reliability is poor. The calculation formula for load adjustment reliability is:

[0046]

[0047] Where η is the load adjustment rate, P a is the power before load adjustment, P b is the power after load adjustment, η max is the maximum load regulation rate. As the load regulation rate increases, the reliability of the load response decreases.

[0048] In another exemplary embodiment of the present application, the comprehensive response reliability coefficient λ of different types of loads is obtained by obtaining the geometric mean. The calculation formula of the comprehensive response reliability coefficient is:

[0049]

[0050] In another exemplary embodiment of the present application, the above method for determining the daily curve of the power load participating in load control can be replaced by the following steps 201 to 207:

[0051] Step 201: Obtain the power of the electric load participating in load control during the historical power consumption period.

[0052] Step 202: any power of the electric load participating in load control within the historical power consumption period is used as a data point to be measured, and the power other than the data point to be measured within the historical power consumption period is used as a reference data point.

[0053] Step 203: Calculate the Euclidean distance between the data point to be measured and the reference data point using the Euclidean distance method.

[0054] Step 204: Determine the average Euclidean distance between all benchmark data points in the historical electricity usage period as the benchmark distance.

[0055] Step 205: If the average Euclidean distance between the data point to be measured and the reference data point is greater than the reference distance, the data point to be measured is determined to be an abnormal point, and the abnormal point is removed to obtain the power of the electric load participating in load control after removal during the historical power consumption period.

[0056] Step 206: Using the K-nearest neighbor algorithm, fill in the missing values ​​in the power of the electric loads participating in load control after being eliminated during the historical power consumption period.

[0057] The K-Nearest Neighbor (KNN) algorithm uses the known values ​​of adjacent data points where the data is missing to estimate missing or outliers.

[0058] Step 207: Using the K-medoids algorithm, cluster the power of the electric loads participating in load control after filling in the historical power consumption period to obtain a daily curve for each type of electric load.

[0059] The horizontal axis of the daily curve is the different time points in a typical day, and the vertical axis is the historical power.

[0060] With reference to steps 201 to 207 , a daily curve of each type of power load may also be obtained before step 101 .

[0061] In another exemplary embodiment of the present application, the load control model includes an objective function and constraints, wherein the constraints include gas turbine constraints, energy storage system constraints, adjustable load constraints, and renewable energy constraints.

[0062] The objective function is:

[0063]

[0064] Where f is the carbon emission of power system operation, β huo is the carbon footprint factor generated by the thermal power unit during its life cycle, β pv is the carbon footprint factor generated by photovoltaics during its life cycle, β wt is the carbon footprint factor generated by the wind turbine during its life cycle, β ess is the carbon footprint factor generated by energy storage during its life cycle, β load is the carbon footprint factor generated by the power load during its life cycle, is the power of the thermal power unit at time t, is the photovoltaic power at time t, is the power of the wind turbine at time t, is the charging power of the energy storage at time t, is the discharge power of the energy storage at time t, is the power of the i-th power load at time t.

[0065] The gas turbine constraints are:

[0066]

[0067] Where, is the power of the thermal power unit at time t, P G,max 、P G,min are the upper and lower limits of gas turbine output, R up 、R down are the maximum ramp-up power and maximum ramp-down power of the gas turbine respectively, and T is the scheduling period.

[0068] The energy storage system constraints are:

[0069]

[0070] Where, is the charging power of the energy storage at time t, is the maximum charging power allowed by the energy storage at time t; is the discharge power of the energy storage at time t, S is the maximum discharge power allowed by the energy storage at time t; min and S max are the minimum remaining capacity and maximum remaining capacity allowed by energy storage, S 0 is the initial state of charge of the energy storage, η′ is the charge and discharge efficiency of the energy storage, and Δt is the charge and discharge time of the energy storage.

[0071] The adjustable load constraint is:

[0072]

[0073] Where, is the power of the i-th electric load at time t, is the maximum interruption power of the i-th power load at time t; M L,i is the maximum allowable interruption power of the i-th power load during the scheduling period.

[0074] The renewable energy constraint is:

[0075]

[0076] Where, is the photovoltaic power at time t, is the maximum output of photovoltaic power at time t; is the power of the wind turbine at time t, is the maximum output of the wind turbine at time t.

[0077] Figure 2 The load control optimization process is shown. Figure 2The general process of steps 103 to 104 is as follows:

[0078] 1) After data processing and data aggregation, the daily curves of various types of power loads and the comprehensive response reliability coefficients of various types of power loads are obtained, and these two data are input into the load control model as the initial data for optimization.

[0079] 2) After obtaining the comprehensive reliability coefficients for each type of power load, the loads with the highest reliability are sorted from highest to lowest, and the load with the highest reliability is selected for load control. If the selected load cannot meet the power regulation requirements of the control command, the next load is selected for load control until the power requirements of the control command are met. The maximum power of a power load is equal to the power obtained by increasing the rated power by 20%.

[0080] 3) Using minimizing system carbon emissions as the objective function, and using generator output, renewable energy output, and adjustable load call limits as constraints, the optimization process considers only the load with the highest reliability coefficient, resulting in a load control strategy that considers load response reliability. This application ultimately determines which power loads participate in regulation and the power levels to be achieved by these participating loads.

[0081] Figure 3 The functional architecture of a power load control method provided by this application is shown. Based on the level of various adjustable load response coefficients, this application accurately implements the prioritization of different types of power loads in the load control process and the rational allocation of response capacity. The application of this solution can significantly improve load control efficiency, effectively enhance the reliability of regional power grid load control, and provide a solid guarantee for the stable operation of the power system.

[0082] The advantages of this application are as follows:

[0083] 1. In order to address the problem that the reliability of load response is not considered during load control, resulting in the load not being able to fully respond to control instructions or rebounding, this application proposes the concept of a comprehensive response reliability coefficient to measure the response reliability of different types of loads, thereby screening out the most reliable load for control, effectively avoiding power system operation problems caused by unreliable load response, reducing control costs and potential risks, and improving overall operation efficiency and stability.

[0084] 2. In response to the problem of failing to achieve precise control of loads, this application proposes sorting by the size of the comprehensive response reliability coefficient, screening out the loads with the best reliability for control, realizing the order and capacity allocation of different types of loads during load control, optimizing the resource allocation of adjustable loads, and maximizing the regulation potential of various types of loads.

[0085] Based on the same inventive concept, embodiments of the present application also provide an electric load control device for implementing the above-mentioned electric load control method. The implementation solution provided by this device is similar to the implementation solution described in the above-mentioned method. Therefore, the specific limitations of one or more electric load control device embodiments provided below can be found in the above-mentioned limitations of the electric load control method and will not be repeated here.

[0086] In an exemplary embodiment, Figure 4 As shown, a power load control device is provided, which includes: a reliability index determination module, a reliability coefficient acquisition module, a load control module and a solution module.

[0087] The reliability index determination module is used to determine the values ​​of multiple reliability indicators of each type of power load when receiving the control instruction based on the control instruction currently issued by the power grid.

[0088] The reliability coefficient obtaining module is used to obtain the comprehensive response reliability coefficient of each type of power load when receiving the control instruction based on the values ​​of the multiple reliability indicators.

[0089] The load control module is used to select corresponding power loads to participate in load control in descending order of comprehensive response reliability coefficients until the maximum power sum of the selected power loads is greater than or equal to the power regulation requirement of the control instruction for the first time.

[0090] The solution module is used to solve the load control model and obtain the load control strategy based on the daily curve of the power load participating in the load control, with the lowest carbon emissions from the power system operation as the optimization goal; the load control strategy includes the response power of the power load participating in the load control.

[0091] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 5As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store load control strategies. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for controlling power load is implemented.

[0092] Those skilled in the art will understand that Figure 5 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present application and does not constitute a limitation on the computer device to which the solution of the present application is applied. A specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps of the above-mentioned method embodiments when executing the computer program.

[0093] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0094] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0095] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0096] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0097] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0098] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for controlling power load, characterized in that: include: According to the control instructions currently issued by the power grid, the values ​​of multiple reliability indicators of each type of power load when receiving the control instructions are determined; Obtaining a comprehensive response reliability coefficient of each type of power load when receiving a control instruction based on the values ​​of the plurality of reliability indicators; Select corresponding power loads to participate in load control in descending order of comprehensive response reliability coefficients until the maximum power sum of the selected power loads is greater than or equal to the power regulation requirement of the control instruction for the first time; According to the daily curve of the electric load participating in load control, with the minimum carbon emissions of the power system operation as the optimization goal, the load control model is solved to obtain the load control strategy; the load control strategy includes the response power of the electric load participating in load control.

2. The power load control method according to claim 1, characterized in that: The plurality of reliability indicators include: start time consistency, operation period consistency, load response fatigue and load adjustment reliability; The calculation formula for start time compatibility is: In the formula, λ1 is the start time fit, T1 is the load start time, T2 is the load end time, T DR is the actual time when the load control command is issued, T m is the maximum allowed time deviation; The calculation formula for runtime fit is: T OL =max[0,min(T b ,T2)-max(T a ,T1)]; Where λ2 is the running time fit, T OL T is the overlap between the load control period and the load operation period. a is the load control start time, T b is the end time of load control; The calculation formula of load response fatigue is: Where λ3 is the load response fatigue, T last is the time when the load control instruction was last received, T rc The time required for load recovery; The calculation formula for load adjustment reliability is: Where λ4 is the load adjustment reliability, η is the load adjustment rate, P a is the power before load adjustment, P b is the power after load adjustment, η max is the maximum load regulation rate.

3. The power load control method according to claim 1, characterized in that: The calculation formula of the comprehensive response reliability coefficient is: Where λ is the comprehensive response reliability coefficient, λ1 is the start time consistency, λ2 is the operating period consistency, λ3 is the load response fatigue, and λ4 is the load adjustment reliability.

4. The power load control method according to claim 1, characterized in that: The method for determining the daily curve of the power load involved in load control specifically includes: Obtain the power of the electric loads participating in load control during the historical power consumption period; Any power of the electric load participating in load control within the historical power consumption period is used as a data point to be measured, and the power other than the data point to be measured within the historical power consumption period is used as a reference data point; The Euclidean distance method is used to calculate the Euclidean distance between the data point to be measured and the reference data point; Determine the average Euclidean distance between all benchmark data points in the historical electricity consumption period as the benchmark distance; If the average Euclidean distance between the data point to be measured and the reference data point is greater than the reference distance, the data point to be measured is determined to be an abnormal point, and the abnormal point is removed to obtain the power of the electric load participating in load control after the abnormal point is removed during the historical power consumption period; Use the K-nearest neighbor algorithm to fill in the missing values ​​in the power of the electric loads participating in load control after being eliminated during the historical power consumption period; The K-medoids algorithm is used to cluster the power of the electric loads participating in load control after filling in the historical power consumption period, and the daily curve of the electric loads participating in load control is obtained.

5. The power load control method according to claim 1, characterized in that: The load control model includes: an objective function and constraints; The constraints include gas turbine constraints, energy storage system constraints, adjustable load constraints and renewable energy constraints.

6. The power load control method according to claim 5, characterized in that: The objective function is: Where f is the carbon emission of power system operation, β huo is the carbon footprint factor generated by the thermal power unit during its life cycle, β pv is the carbon footprint factor generated by photovoltaics during its life cycle, β wt is the carbon footprint factor generated by the wind turbine during its life cycle, β ess is the carbon footprint factor generated by energy storage during its life cycle, β load is the carbon footprint factor generated by the power load during its life cycle, P t G is the power of the thermal power unit at time t, P t PV is the photovoltaic power at time t, P t WT is the power of the wind turbine at time t, P t ch is the charging power of the energy storage at time t, P t dis is the discharge power of the energy storage at time t, is the power of the i-th power load at time t.

7. The power load control method according to claim 5, characterized in that: The gas turbine constraints are: Where, P t G is the power of the thermal power unit at time t, P G,max 、P G,min are the upper and lower limits of gas turbine output, R up 、R down are the maximum ramp-up power and maximum ramp-down power of the gas turbine, respectively, and T is the scheduling period; The energy storage system constraints are: Where, P t ch is the charging power of the energy storage at time t, P is the maximum charging power allowed by the energy storage at time t; t dis is the discharge power of the energy storage at time t, S is the maximum discharge power allowed by the energy storage at time t; min and S max are the minimum remaining capacity and maximum remaining capacity allowed by energy storage, S 0 is the initial charge capacity of the energy storage, η′ is the energy storage charge and discharge efficiency, and Δt is the energy storage charge and discharge time; The adjustable load constraint is: Where, is the power of the i-th electric load at time t, is the maximum interruption power of the i-th power load at time t; M L,i is the maximum allowable interruption power of the i-th power load during the dispatch period; The renewable energy constraint is: Where, P t PV is the photovoltaic power at time t, P t PV,max is the maximum output of photovoltaic power at time t; P t WT is the power of the wind turbine at time t, P t WT,max is the maximum output of the wind turbine at time t.

8. An electric load control device, characterized in that: include: A reliability index determination module is used to determine the values ​​of multiple reliability indicators of each type of power load when receiving the control instruction based on the control instruction currently issued by the power grid; A reliability coefficient obtaining module, configured to obtain a comprehensive response reliability coefficient of each type of power load when receiving a control instruction based on the values ​​of the plurality of reliability indicators; A load control module is used to select corresponding power loads to participate in load control in descending order of comprehensive response reliability coefficients until the maximum sum of the selected power loads is greater than or equal to the power regulation requirement of the control instruction for the first time; The solution module is used to solve the load control model and obtain the load control strategy based on the daily curve of the power load participating in the load control, with the lowest carbon emissions from the power system operation as the optimization goal; the load control strategy includes the response power of the power load participating in the load control.

9. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the power load control method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the power load control method according to any one of claims 1 to 7 is implemented.

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