Method, device, equipment and medium for assessing summer peak load reduction potential

Abstract

The present invention discloses a method, device, equipment and medium for evaluating the potential for reduction of summer peak load. The method comprises the following steps: obtaining an average typical daily load curve of spring and autumn and a typical daily load curve of summer, and correcting the average typical daily load curve of spring and autumn based on the typical daily load curve of summer to obtain a target average typical daily load curve of spring and autumn, thereby reducing the influence of the characteristics of spring and autumn on the evaluation of temperature control load; then determining the peak load in the typical daily load curve of summer based on the target average typical daily load curve of spring and autumn, wherein the peak load is the load during the period of extreme high temperature, thereby paying more attention to the period of summer load that is most significantly affected by high temperature, and highlighting the correlation between the temperature control load and extreme high temperature; finally, utilizing an equivalent thermal parameter model of the temperature control load to evaluate the potential for reduction of the peak load, thereby evaluating the reducible temperature control load for the period of extreme high temperature, thereby facilitating the optimization of the planning and operation of the power system.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a method, device, equipment and medium for evaluating the reduction potential of summer peak load. Background Art

[0002] As the peak load of the power grid in summer continues to be refreshed and the peak-to-valley difference continues to increase, many regions have implemented management measures such as peak-to-valley time-of-use electricity prices and two-part electricity prices. In order to better improve the demand response management measures for high temperatures in summer and further optimize the planning and operation of the power system, it is necessary to evaluate the peak load in summer.

[0003] At present, the traditional summer peak load assessment takes the typical daily load curves of spring and autumn as the load baseline, and evaluates the air conditioning load in the typical daily load in summer based on the load baseline. Although this method can reduce the impact of the natural load growth rate to a certain extent, it ignores the seasonal characteristics of production plans in different industries, making the peak load assessment results too ideal. At the same time, the load characteristic baseline based on spring and autumn cannot reflect the peak load during the extreme high temperature period in summer, and the peak load during the extreme high temperature period is an important factor causing the imbalance of power supply and demand and increasing the peak-to-valley difference of system operation. It can be seen that there is an urgent need for a method to effectively evaluate the peak load during the extreme high temperature period in summer and its reduction potential. Summary of the invention

[0004] The present invention provides a method, device, equipment and medium for evaluating the reducible potential of summer peak load, so as to solve the technical problem that the current summer peak load ignores the seasonal characteristics of production plans of different industries.

[0005] In order to solve the above technical problems, in a first aspect, the present invention provides a method for assessing the potential for reducing summer peak loads, comprising:

[0006] Obtain load samples, which include average typical daily load curves in spring and autumn and typical daily load curves in summer;

[0007] Based on the typical daily load curve in summer, the average typical daily load curve in spring and autumn is corrected to obtain the target average typical daily load curve in spring and autumn;

[0008] Based on the target average typical daily load curve in spring and autumn, determine the peak load in the typical daily load curve in summer. The peak load is the load during the extremely high temperature period.

[0009] The equivalent thermal parameter model of temperature-controlled load is used to evaluate the potential for reducing peak load.

[0010] As a preferred method, based on the summer typical daily load curve, the spring and autumn average typical daily load curve is corrected to obtain the target spring and autumn average typical daily load curve, including:

[0011] Using the preset normalization formula, the average typical daily load curve in spring and autumn is corrected according to the typical daily load curve in summer to obtain the target average typical daily load curve in spring and autumn. The preset normalization formula is:

[0012]

[0013] represents the target spring and autumn average typical daily load curve, L su, represents the typical daily load curve in summer within the time period t, L spr,t Represents the average daily load curve of spring and autumn seasons within the time period t.

[0014] Preferably, based on the target average typical daily load curve in spring and autumn, the peak load in the typical daily load curve in summer is determined, including:

[0015] Using the preset peak load determination strategy, the peak load in the typical daily load curve in summer is determined according to the target average typical daily load curve in spring and autumn. The expression of the preset peak load determination strategy is:

[0016]

[0017] L ru,t Indicates the peak load in the time period t, L su,t represents the typical daily load curve in summer within the time period t, It represents the average typical daily load curve in the target spring and autumn, and τ represents the period of extreme high temperature.

[0018] As a preference, an equivalent thermal parameter model of the temperature control load is used to evaluate the potential for reduction of the peak load, including:

[0019] Using the equivalent thermal parameter model, the air conditioning temperature is determined according to the equivalent thermal parameters of the air conditioner;

[0020] Based on the peak load and air conditioning temperature, calculate the temperature control load that can be reduced in the target area.

[0021] Preferably, the equivalent thermal parameter model includes a cooling state sub-model and a controlled state sub-model, and the cooling state sub-model expression is:

[0022]

[0023] The expression of the controlled state submodel is:

[0024]

[0025] Among them, θ i,t represents the temperature of the i-th air conditioner at time t, θ α is the ambient temperature of the target area, P i is the rated power of the i-th air conditioner, η i is the air conditioning efficiency coefficient of the i-th air conditioner, Δt is the time interval; R i and C i are the equivalent thermal parameters of the i-th air conditioner, R i Indicates thermal resistance parameters, C i Represents thermal capacitance parameters.

[0026] Preferably, based on the peak load and the air conditioning temperature, the temperature control load that can be reduced in the target area is calculated, including:

[0027] The temperature control load that can be reduced is calculated based on the peak load and air conditioning temperature using the calculation formula for the temperature control load that can be reduced in the target area. The calculation formula for the temperature control load that can be reduced is:

[0028]

[0029]

[0030] δ=θ max -θ min ;

[0031] Among them, P cut Indicates the maximum temperature control load that can be reduced, L ru represents the peak load, θ α is the ambient temperature of the target area, δ represents the spatial temperature range, θ max Indicates the highest air-conditioning temperature accepted by users in the target area, θ min It represents the lowest air-conditioning temperature accepted by users in the target area, R represents the average air-conditioning thermal resistance parameter in the target area, P represents the average air-conditioning rated power in the target area, and η is the average air-conditioning efficiency coefficient in the target area.

[0032] In a second aspect, the present invention further provides a device for assessing the potential for reducing summer peak loads, comprising:

[0033] An acquisition module is used to acquire load samples, the load samples include an average typical daily load curve in spring and autumn and a typical daily load curve in summer;

[0034] A correction module is used to correct the average typical daily load curve in spring and autumn based on the typical daily load curve in summer to obtain the target average typical daily load curve in spring and autumn;

[0035] A determination module is used to determine the peak load in the typical daily load curve in summer based on the target average typical daily load curve in spring and autumn, where the peak load is the load during the extremely high temperature period;

[0036] An evaluation module is used to evaluate the potential for peak load reduction using an equivalent thermal parameter model of the temperature-controlled load.

[0037] Preferably, the evaluation module includes:

[0038] A determination unit, used for determining the air conditioning temperature according to the equivalent thermal parameters of the air conditioner using an equivalent thermal parameter model;

[0039] The calculation unit is used to calculate the reducible temperature control load in the target area based on the peak load and the air-conditioning temperature.

[0040] In a third aspect, the present invention further provides a computer device, comprising a processor and a memory, wherein the memory is used to store a computer program, and when the computer program is executed by the processor, the method for assessing the potential for reducing summer peak load as described in the first aspect is implemented.

[0041] In a fourth aspect, the present invention further provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method for assessing the curtailment potential of summer peak load as described in the first aspect.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] The present invention obtains the average typical daily load curve of spring and autumn and the typical daily load curve of summer, and based on the typical daily load curve of summer, corrects the average typical daily load curve of spring and autumn to obtain the target average typical daily load curve of spring and autumn, thereby reducing the influence of spring and autumn season characteristics on temperature control load evaluation to highlight the summer load characteristics; then based on the target average typical daily load curve of spring and autumn, the peak load in the typical daily load curve of summer is determined, and the peak load is the load during the extremely high temperature period, so as to pay more attention to the period in summer that is most significantly affected by high temperature, and highlight the correlation between temperature control load and extreme high temperature; finally, the equivalent thermal parameter model of temperature control load is used to evaluate the potential for reduction of peak load, so as to evaluate the reducible temperature control load for the extremely high temperature period, which is helpful to formulate a more reasonable user demand response strategy and optimize the planning and operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 A schematic flow chart of a method for assessing the potential for reducing summer peak loads according to an embodiment of the present invention;

[0045] Figure 2 A schematic diagram of a typical daily load curve before correction shown in an embodiment of the present invention;

[0046] Figure 3 A schematic diagram of a modified typical daily load curve shown in an embodiment of the present invention;

[0047] Figure 4 A schematic diagram of the structure of a device for assessing the potential for reducing summer peak loads according to an embodiment of the present invention;

[0048] Figure 5 The figure is a schematic diagram of the structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] Please refer to Figure 1 , Figure 1 The flowchart of a method for assessing the potential reduction of summer peak load provided by an embodiment of the present invention is shown in FIG. The method for assessing the potential reduction of summer peak load provided by an embodiment of the present invention can be applied to computer devices, including but not limited to smart phones, laptops, tablet computers, desktop computers, physical servers, and cloud servers. Figure 1 As shown, the summer peak load reduction potential assessment method of this embodiment includes steps S101 to S104, which are described in detail as follows:

[0051] Step S101, obtaining load samples, wherein the load samples include an average typical daily load curve in spring and autumn and a typical daily load curve in summer.

[0052] In this step, according to meteorological conditions such as temperature and humidity, reasonable load data is selected to determine the average typical daily load curve in spring and autumn (including spring and autumn) and the typical daily load curve in summer. Optionally, load data for a period of time in the target area is obtained and the load data is preliminarily processed: according to the preset evaluation requirements, meteorological information such as temperature and humidity and social factors such as working days and holidays are comprehensively considered to select the required load data. For example, April and October with clear weather and normal working days can be selected as the typical daily load samples L in spring and autumn. spr , select sunny and normal working days in July and August as the typical summer day load sample L su Optionally, samples that are obviously abnormal due to force majeure or the like are eliminated.

[0053] Step S102: Based on the summer typical daily load curve, the spring and autumn average typical daily load curve is corrected to obtain a target spring and autumn average typical daily load curve.

[0054] In this step, the average typical daily load in spring and autumn as in the background technology can reduce the calculation result of the temperature control load due to the natural load growth with economic development, but this method does not take into account the impact of the seasonal characteristics of different industries on the load demand. To this end, the present invention abstracts the seasonal characteristics into the shape of a typical daily load curve, and converts the average typical daily load curve in spring and autumn to the typical daily load curve in summer through a correction method, which highlights the range of occurrence of the peak load of the typical daily load, and correspondingly weakens the impact of its absolute value, thereby reducing the impact of the seasonal characteristics of the user's production plan on the temperature control load assessment.

[0055] Optionally, in order to reduce the seasonal characteristics of the user's production plan itself, a normalization method is used to convert the average typical daily load curve in spring and autumn into a typical daily load curve in summer.

[0056] In some embodiments, the spring and autumn average typical daily load curve is corrected according to the summer typical daily load curve using a preset normalization formula to obtain a target spring and autumn average typical daily load curve. The preset normalization formula is:

[0057]

[0058] represents the target spring and autumn average typical daily load curve, L su,t represents the typical daily load curve in summer within the time period t, L spr,t Represents the average daily load curve of spring and autumn seasons within the time period t.

[0059] In this embodiment, after normalization and conversion, the average total load demand of a typical day in spring and autumn will be the same as the total load demand of a typical day in summer, so a part of the temperature control load caused by the increase in average temperature will be lost. However, this embodiment emphasizes that the user's electricity demand has different characteristics in different seasons, that is, the shape of the load curve is different. In other words, after the normalization correction, due to the same total load, the difference in absolute values ​​of the typical daily load curves of the two seasons will be weakened, but normalization will not change the shape and other statistical characteristics of the daily load curve (such as the location of the peak load and the peak-to-valley difference of the load curve), so the normalized typical daily load curve will further highlight the characteristics of the typical daily load curves in different seasons.

[0060] Step S103, based on the target spring and autumn average typical daily load curve, determine the peak load in the summer typical daily load curve, where the peak load is the load during the extremely high temperature period.

[0061] In this step, the modified spring and autumn average daily load curve is used as the load baseline, which can identify the period in the typical summer daily load curve that is significantly affected by extreme high temperatures as the peak load period.

[0062] In some embodiments, a preset peak load determination strategy is used to determine the peak load in the summer typical daily load curve according to the target spring and autumn average typical daily load curve. The expression of the preset peak load determination strategy is:

[0063]

[0064] L ru,t represents the peak load in the time period t, L su,t represents the typical daily load curve in summer within the time period t, represents the target average typical daily load curve in spring and autumn, and τ represents the extreme high temperature period.

[0065] In this embodiment, the peak load during the extremely high temperature period in summer is the summer load minus the corrected spring load. The present invention focuses on the period most significantly affected by high temperature weather as the peak load period, and focuses on the temperature control load during this period. In fact, power imbalance is also most likely to occur during this period, which has a greater impact on the power system. Therefore, by evaluating the temperature control load during this period, a more targeted user-side demand response solution can be proposed.

[0066] Step S104: using an equivalent thermal parameter model of the temperature control load, evaluating the reduction potential of the peak load.

[0067] In this step, the temperature control load includes a cooling state and a controlled state, and the corresponding equivalent thermal parameter models are a cooling state sub-model and a controlled state sub-model. Optionally, the cooling state sub-model expression is:

[0068]

[0069] The expression of the controlled state submodel is:

[0070]

[0071] Among them, θ i,t represents the temperature of the i-th air conditioner at time t, θ α is the ambient temperature of the target area. It can be assumed that the ambient temperature in the target area remains unchanged within a certain period of time. i is the rated power of the i-th air conditioner, η i is the air conditioning efficiency coefficient of the i-th air conditioner, Δt is the time interval; R i and Ci are the equivalent thermal parameters of the i-th air conditioner, R i Indicates thermal resistance parameters (℃ / kW), C i Indicates thermal capacitance parameter (kWh / ℃).

[0072] In some embodiments, the use of an equivalent thermal parameter model of a temperature-controlled load to assess the potential for reduction of the peak load includes:

[0073] Determining the air conditioning temperature according to the equivalent thermal parameter model of the air conditioning;

[0074] Based on the peak load and the air-conditioning temperature, a reducible temperature-controlled load in a target area is calculated.

[0075] In this embodiment, under the incentive demand response project, if the temperature is within the acceptable range for users, the grid company or user aggregator can bring the temperature control load under control, so the air-conditioning load that can be reduced in the target area is determined based on the air-conditioning temperature acceptable to users in the target area.

[0076] Optionally, a calculation formula for reducible temperature control load is used to calculate the reducible temperature control load in the target area according to the peak load and the air-conditioning temperature. The calculation formula for reducible temperature control load is:

[0077]

[0078]

[0079] δ=θ max -θ min ;

[0080] Among them, P cut Indicates the maximum temperature control load that can be reduced, L ru represents the peak load, θ α is the ambient temperature of the target area, δ represents the spatial temperature range, θ max Indicates the highest air-conditioning temperature accepted by users in the target area, θ min It represents the lowest air-conditioning temperature accepted by users in the target area, R represents the average air-conditioning thermal resistance parameter in the target area, P represents the average air-conditioning rated power in the target area, and η is the average air-conditioning efficiency coefficient in the target area.

[0081] It should be noted that the reducible air-conditioning load is related to factors such as ambient temperature, air-conditioning set temperature, and user-acceptable temperature range. In fact, the number of users is huge, and the air-conditioning load composition is also very complex, with different parameters, which manifests as a heterogeneous load composition. By selecting a limited number of representative users and their corresponding parameters as the homogeneous load composition, the temperature control load can be aggregated according to the number of representative parameters, load demand, etc., and finally the evaluation results of the reducible temperature control load of the entire user can be obtained.

[0082] Among them, η i represents the air conditioning efficiency coefficient of the i-th user, P cut,i represents the reducible temperature control load of the i-th user.

[0083] As an example but not a limitation, taking the 24-hour load data as an example, the sunny days in April and October with the highest temperature not higher than 28 degrees Celsius are selected as samples, and their average daily load is calculated as the average typical daily load curve in spring and autumn. The sunny days in July and August with the highest temperature higher than 35 degrees Celsius are selected as samples, and their average daily load is calculated as the typical daily load curve in summer.

[0084] like Figure 2 As shown in the figure, the typical daily load curve B in spring and autumn has different seasonal characteristics from the typical daily load curve A in summer, that is, in addition to the absolute value, the shape is also different. Specifically, the double peaks of the typical daily load in spring and autumn appear at 11:00-12:00 in the afternoon and 19:00 in the evening, while the peak of the typical daily load in summer appears at 12:00 in the afternoon, around 15:00 in the afternoon and 22:00-23:00 at night. Due to the increase in temperature in summer, users' demand for load increases, resulting in differences in the absolute values ​​of typical daily loads in different seasons. However, the seasonal characteristics of the curve are also related to the seasonal characteristics of the user's own production and life plans and electricity demand. Therefore, it is necessary to conduct research based on the seasonal characteristics of the typical daily load to find the peak load period that is most obviously affected by high temperature.

[0085] The typical daily load in spring and autumn is converted to summer by normalization. Through the corrected typical daily load, the period in summer most affected by extreme high temperature and the corresponding peak load are obtained, such as Figure 3 After determining the peak period and peak load, the corresponding evaluation results can be obtained according to the commonly used evaluation method of temperature control load that can be reduced, which serves as an important reference for the formulation of demand response projects. Take the ambient temperature of 35 degrees Celsius, the air conditioning set temperature of 22 degrees Celsius, and the temperature range of 19-25 degrees Celsius as an example. In actual implementation, the corresponding results can be obtained by considering the actual different user parameter compositions, as shown in the following table.

[0086]

[0087] It should be noted that, based on the original average typical daily load in spring and autumn as the load baseline, the present invention takes into account that in addition to the temperature control load, the production and life of users themselves have certain seasonal characteristics, which leads to different load demands in spring and autumn and summer. In order to reduce the impact of this factor on the evaluation of temperature control load, a normalized mathematical method is adopted to highlight the peak load moments in summer. Specifically, the average typical daily load in spring and autumn is converted to summer, and the corrected average typical daily load in spring and autumn is used as the load benchmark to obtain the time period in summer that is most significantly affected by the temperature, and the temperature control load that can be reduced is evaluated for this time period. Compared with existing methods, the present invention pays more attention to the time period in summer that is most significantly affected by high temperature, highlights the correlation between temperature control load and extreme high temperature, and conducts an evaluation of temperature control load that can be reduced, which helps to formulate a more reasonable user demand response strategy, with significant advantages.

[0088] In order to implement the summer peak load reduction potential assessment method corresponding to the above method embodiment, so as to achieve the corresponding functions and technical effects. Figure 4 , Figure 4 The structure block diagram of a summer peak load reduction potential assessment device provided by an embodiment of the present invention is shown. For the convenience of explanation, only the parts related to this embodiment are shown. The summer peak load reduction potential assessment device provided by an embodiment of the present invention includes:

[0089] An acquisition module 401 is used to acquire load samples, wherein the load samples include an average typical daily load curve in spring and autumn and a typical daily load curve in summer;

[0090] A correction module 402 is used to correct the spring and autumn average typical daily load curve based on the summer typical daily load curve to obtain a target spring and autumn average typical daily load curve;

[0091] A determination module 403 is used to determine the peak load in the summer typical daily load curve based on the target spring and autumn average typical daily load curve, wherein the peak load is the load during the extremely high temperature period;

[0092] The evaluation module 404 is used to evaluate the reduction potential of the peak load by using an equivalent thermal parameter model of the temperature control load.

[0093] In some embodiments, the correction module 402 is specifically used to:

[0094] The spring and autumn average typical daily load curve is corrected according to the summer typical daily load curve by using a preset normalization formula to obtain a target spring and autumn average typical daily load curve. The preset normalization formula is:

[0095]

[0096] represents the target spring and autumn average typical daily load curve, L su,t represents the typical daily load curve in summer within the time period t, L spr,t Represents the average daily load curve of spring and autumn seasons within the time period t.

[0097] In some embodiments, the determining module 403 is specifically configured to:

[0098] The peak load in the summer typical daily load curve is determined by using a preset peak load determination strategy according to the target spring and autumn average typical daily load curve. The expression of the preset peak load determination strategy is:

[0099]

[0100] L ru,t represents the peak load in the time period t, L su,t represents the typical daily load curve in summer within the time period t, represents the target average typical daily load curve in spring and autumn, and τ represents the extreme high temperature period.

[0101] In some embodiments, the evaluation module 404 includes:

[0102] A determination unit, configured to determine the air-conditioning temperature according to the equivalent thermal parameters of the air-conditioning by using the equivalent thermal parameter model;

[0103] A calculation unit is used to calculate the reducible temperature control load in the target area based on the peak load and the air-conditioning temperature.

[0104] In some embodiments, the equivalent thermal parameter model includes a cooling state sub-model and a controlled state sub-model, and the cooling state sub-model expression is:

[0105]

[0106] The expression of the controlled state submodel is:

[0107]

[0108] Among them, θ i,t represents the temperature of the i-th air conditioner at time t, θ α is the ambient temperature of the target area, P i is the rated power of the i-th air conditioner, η i is the air conditioning efficiency coefficient of the i-th air conditioner, Δt is the time interval; R i and C iare the equivalent thermal parameters of the i-th air conditioner, R i Indicates thermal resistance parameters, C i Represents thermal capacitance parameters.

[0109] In some embodiments, the computing unit is specifically configured to:

[0110] The reducible temperature control load calculation formula is used to calculate the reducible temperature control load in the target area according to the peak load and the air conditioning temperature. The reducible temperature control load calculation formula is:

[0111]

[0112]

[0113] δ=θ max -θ min ;

[0114] Among them, P cut Indicates the maximum temperature control load that can be reduced, L ru represents the peak load, θ α is the ambient temperature of the target area, δ represents the spatial temperature range, θ max Indicates the highest air-conditioning temperature accepted by users in the target area, θ min It represents the lowest air-conditioning temperature accepted by users in the target area, R represents the average air-conditioning thermal resistance parameter in the target area, P represents the average air-conditioning rated power in the target area, and η is the average air-conditioning efficiency coefficient in the target area.

[0115] The above-mentioned summer peak load reduction potential assessment device can implement the summer peak load reduction potential assessment method of the above-mentioned method embodiment. The optional items in the above-mentioned method embodiment are also applicable to this embodiment and will not be described in detail here. The rest of the contents of the embodiment of the present invention can refer to the contents of the above-mentioned method embodiment, and will not be repeated in this embodiment.

[0116] Figure 5 A schematic diagram of the structure of a computer device provided in one embodiment of the present invention. Figure 5 As shown, the computer device 5 of this embodiment includes: at least one processor 50 ( Figure 5 Only one is shown in the figure) a processor, a memory 51, and a computer program 52 stored in the memory 51 and executable on the at least one processor 50, and when the processor 50 executes the computer program 52, the steps in any of the above method embodiments are implemented.

[0117] The computer device 5 may be a computing device such as a smart phone, a tablet computer, a desktop computer, a cloud server, etc. The computer device may include but is not limited to a processor 50 and a memory 51. Those skilled in the art will appreciate that Figure 5 It is only an example of the computer device 5 and does not constitute a limitation on the computer device 5. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include input and output devices, network access devices, etc.

[0118] The processor 50 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0119] In some embodiments, the memory 51 may be an internal storage unit of the computer device 5, such as a hard disk or memory of the computer device 5. In other embodiments, the memory 51 may also be an external storage device of the computer device 5, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the computer device 5. Further, the memory 51 may also include both an internal storage unit of the computer device 5 and an external storage device. The memory 51 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program, etc. The memory 51 may also be used to temporarily store data that has been output or is to be output.

[0120] In addition, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0121] An embodiment of the present invention provides a computer program product. When the computer program product is run on a computer device, the computer device implements the steps in the above-mentioned method embodiments when executing the computer device.

[0122] In several embodiments provided by the present invention, it is understood that each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved.

[0123] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program codes.

[0124] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for assessing the potential for reducing summer peak load, characterized in that: include: Obtaining load samples, the load samples including an average typical daily load curve in spring and autumn and a typical daily load curve in summer; Based on the summer typical daily load curve, the spring and autumn average typical daily load curve is corrected to obtain a target spring and autumn average typical daily load curve; Based on the target spring and autumn average typical daily load curve, determine the peak load in the summer typical daily load curve, the peak load being the load during the extremely high temperature period, including: using a preset peak load determination strategy, according to the target spring and autumn average typical daily load curve, determine the peak load in the summer typical daily load curve, the expression of the preset peak load determination strategy is: ； Indicated in The peak load during the time period, Indicated in Typical daily load curve in summer within the time period, represents the target spring and autumn average typical daily load curve, Indicates periods of extreme high temperatures; Using an equivalent thermal parameter model of the temperature control load, evaluating the potential for reduction of the peak load, including: using the equivalent thermal parameter model to determine the air conditioning temperature according to the equivalent thermal parameters of the air conditioner; calculating the reducible temperature control load in the target area based on the peak load and the air conditioning temperature; The equivalent thermal parameter model includes a cooling state sub-model and a controlled state sub-model. The cooling state sub-model expression is: ； The expression of the controlled state submodel is: ； in, represents the temperature of the i-th air conditioner at time t, is the ambient temperature of the target area, is the rated power of the i-th air conditioner, is the air conditioning efficiency coefficient of the i-th air conditioner, is the time interval; and are the equivalent thermal parameters of the i-th air conditioner, Represents thermal resistance parameters, Represents thermal capacitance parameters; The reducible temperature control load calculation formula is used to calculate the reducible temperature control load in the target area according to the peak load and the air conditioning temperature. The reducible temperature control load calculation formula is: ； ； ； in, Indicates the maximum temperature control load that can be reduced. represents the peak load, is the ambient temperature of the target area, Represents the spatial temperature range, Indicates the highest air-conditioning temperature accepted by users in the target area. Indicates the lowest air-conditioning temperature accepted by users in the target area. Represents the average air conditioning thermal resistance parameter in the target area, represents the average air conditioner rated power in the target area, is the average air conditioning efficiency coefficient in the target area.

2. The method for assessing the potential for reducing summer peak load according to claim 1, characterized in that: The method of correcting the spring and autumn average typical daily load curve based on the summer typical daily load curve to obtain a target spring and autumn average typical daily load curve includes: The spring and autumn average typical daily load curve is corrected according to the summer typical daily load curve by using a preset normalization formula to obtain a target spring and autumn average typical daily load curve. The preset normalization formula is: ； represents the target spring and autumn average typical daily load curve, Indicated in Typical daily load curve in summer within the time period, Indicated in Average daily load curves for spring and autumn seasons within the time period.

3. A device for assessing the potential reduction of summer peak load, characterized in that: include: An acquisition module for acquiring load samples, wherein the load samples include an average typical daily load curve in spring and autumn and a typical daily load curve in summer; A correction module, used for correcting the spring and autumn average typical daily load curve based on the summer typical daily load curve to obtain a target spring and autumn average typical daily load curve; A determination module is used to determine the peak load in the summer typical daily load curve based on the target spring and autumn average typical daily load curve, wherein the peak load is the load during the extremely high temperature period, including: using a preset peak load determination strategy, according to the target spring and autumn average typical daily load curve, to determine the peak load in the summer typical daily load curve, wherein the expression of the preset peak load determination strategy is: ； Indicated in The peak load during the time period, Indicated in Typical daily load curve in summer within the time period, represents the target spring and autumn average typical daily load curve, Indicates periods of extreme high temperatures; An evaluation module is used to evaluate the potential for reduction of the peak load by using an equivalent thermal parameter model of the temperature control load, including: using the equivalent thermal parameter model to determine the air conditioning temperature according to the equivalent thermal parameters of the air conditioner; and calculating the reducible temperature control load in the target area based on the peak load and the air conditioning temperature; The equivalent thermal parameter model includes a cooling state sub-model and a controlled state sub-model. The cooling state sub-model expression is: ； The expression of the controlled state submodel is: ； in, represents the temperature of the i-th air conditioner at time t, is the ambient temperature of the target area, is the rated power of the i-th air conditioner, is the air conditioning efficiency coefficient of the i-th air conditioner, is the time interval; and are the equivalent thermal parameters of the i-th air conditioner, Represents thermal resistance parameters, Represents thermal capacitance parameters; The reducible temperature control load calculation formula is used to calculate the reducible temperature control load in the target area according to the peak load and the air conditioning temperature. The reducible temperature control load calculation formula is: ； ； ； in, Indicates the maximum temperature control load that can be reduced. represents the peak load, is the ambient temperature of the target area, Represents the spatial temperature range, Indicates the highest air-conditioning temperature accepted by users in the target area. Indicates the lowest air-conditioning temperature accepted by users in the target area. Represents the average air conditioning thermal resistance parameter in the target area, represents the average air conditioner rated power in the target area, is the average air conditioning efficiency coefficient in the target area.

4. The summer peak load reduction potential assessment device according to claim 3, characterized in that: The evaluation module comprises: a determination unit, configured to determine the air-conditioning temperature according to the equivalent thermal parameters of the air-conditioning by using the equivalent thermal parameter model; A calculation unit is used to calculate the reducible temperature control load in the target area based on the peak load and the air-conditioning temperature.

5. A computer device, characterized in that: The method comprises a processor and a memory, wherein the memory is used to store a computer program, and when the computer program is executed by the processor, the method for assessing the curtailment potential of the summer peak load according to any one of claims 1 to 2 is implemented.

6. A computer-readable storage medium, characterized in that: The device stores a computer program, which, when executed by a processor, implements the method for assessing the reduction potential of summer peak load as claimed in any one of claims 1 to 2.

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