Demand side load space-time dynamic characteristic analysis method and system considering air temperature influence

Abstract

The invention provides a demand side load spatio-temporal dynamic characteristic analysis method and system considering temperature influence, and the method comprises the steps: carrying out the clustering analysis of the power loads in different industries and different seasons, determining the typical daily load curves in different industries and different seasons, carrying out the classification based on the load characteristics corresponding to the typical days in different industries, and obtaining a typical daily load curve; according to the method, different load categories are divided, and the daily load rate, the daily minimum load rate, the peak-valley difference rate and the day and night power utilization characteristics are combined as the description of the power load characteristics of different industries, so that the analysis accuracy of the power load characteristics of different industries is effectively improved, and a foundation is laid for peak regulation or valley filling of a power system; meanwhile, according to the scheme, the air temperature is brought into load characteristic analysis, and the proportion of the cooling load in the summer load is calculated to serve as characteristic representation of different industries, so that the precision of industry load characteristic analysis can be effectively improved, and meanwhile, the power utilization requirements of users in different industries under the extreme weather condition can be better met.

Description

Technical Field

[0001] The present invention belongs to the field of electrical automation technology, and in particular relates to a method and system for analyzing the spatiotemporal dynamic characteristics of demand-side load considering the influence of temperature. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] The electricity consumption behaviors of different industries are diverse and complex. At the same time, with the popularization of various intelligent measurement terminals on the user side, a massive amount of user-side data has been brought to the power system. With the continuous acceleration of urbanization, especially in the context of increasingly frequent extreme weather events such as high temperature heat waves in summer, the maximum load of the power grid is continuing to grow, and the peak-to-valley difference is also increasing. This change has led to a sharp increase in the demand for cooling loads in buildings, especially during peak periods, when the power supply is extremely tight and the security situation of the power grid has become more severe. Therefore, it is particularly important to conduct dynamic analysis of load characteristics.

[0004] Load characteristic research, or load profiling, aims to deeply explore and locate the potential of various industries in terms of flexibility regulation. The study of power user behavior profiling technology can help power companies understand user behavior and improve marketing capabilities, and can also serve as a basis for future real-time electricity price formulation. Through this study, power operators can more accurately select appropriate industries for demand response to meet the challenges of peak power consumption; at the same time, through in-depth analysis of power load data, they can more clearly understand the changing laws of power load on different time scales, as well as the distribution characteristics of load in different regions and industries. This analysis not only provides valuable information for operators such as power companies, but also can more effectively predict and manage power demand, thereby optimizing the allocation of power resources, helping to strengthen load management in different industries, promote efficient use of power resources, reduce the pressure on power grid operation, and thus ensure the safety and stability of the power system.

[0005] The inventors have found that the current analysis of load characteristics on the power demand side mainly has the following problems:

[0006] Current load characteristics analysis usually focuses on static data processing, but pays less attention to the dynamic characteristics of load in time and space. Since the power load is not constant, it is affected by many factors, such as different time periods (such as the difference in electricity consumption between daytime and nighttime), seasonal changes (such as the difference in air conditioning electricity consumption between winter and summer), and regional development levels (such as the difference in electricity consumption patterns between urban and rural areas). Failure to comprehensively consider the dynamic characteristics of time and space may lead to a biased understanding of actual electricity consumption behavior, which in turn affects the dispatch and resource allocation of the power system.

[0007] In existing power load characteristic analysis models, temperature factors are often ignored or not given sufficient attention. Temperature is an important environmental factor affecting power load, especially in summer and winter, when changes in temperature directly affect the demand for air conditioning and heating. Lack of consideration of temperature may reduce the accuracy of analysis results, thus failing to truly reflect the actual power demand of different industries and users under different climatic conditions. In addition, this neglect may also affect decision makers' power supply planning and load scheduling under high or low temperature conditions, increasing the risk of power system operation.

[0008] Existing load characteristics analysis often focuses only on historical data and behavior patterns of loads, ignoring the evaluation of adjustable loads. Adjustable loads refer to loads that can be adjusted through demand-side management strategies during peak periods of electricity demand, covering adjustable industrial, commercial and residential electricity. The lack of evaluation of adjustable potential may prevent power companies from fully utilizing existing regulation resources to improve the flexibility and adaptability of the power grid. Summary of the invention

[0009] The embodiments of the present invention provide a method and system for analyzing the spatiotemporal dynamic characteristics of demand-side loads taking into account the influence of temperature, so as to solve the problems that the traditional solutions fail to comprehensively consider the spatiotemporal dynamic characteristics, which may lead to a biased understanding of the actual electricity consumption behavior, thereby affecting the scheduling and resource allocation of the power system; and the temperature factor is ignored in the existing power load characteristic analysis model, resulting in reduced accuracy of the analysis results and increased risks of power system operation; and the existing load characteristic analysis often only focuses on the historical data and behavior patterns of the load, ignoring the evaluation of adjustable loads, so that power companies fail to make full use of existing regulation resources to improve the flexibility and adaptability of the power grid.

[0010] According to a first aspect of an embodiment of the present invention, a method for analyzing spatiotemporal dynamic characteristics of demand-side load considering the influence of temperature is provided, comprising:

[0011] Based on the historical power load data of various industries in different seasons in the preset area, the typical days of power load in different industries in different seasons are obtained through cluster analysis;

[0012] Based on the load characteristics corresponding to the typical days of power loads in different industries and seasons, the power loads of different industries are classified;

[0013] Based on the classification results of power loads in different industries, combined with the daily load rate, daily minimum load rate, peak-to-valley difference rate, and day-and-night power consumption characteristics of the corresponding industries, the power load characteristics of different industries are described;

[0014] Based on the fitting function types of the maximum load and average temperature in the low and medium temperature ranges of different industries, combined with historical power load data, the benchmark load curves and benchmark temperature ranges of different industries are determined;

[0015] Based on the obtained benchmark temperature range and combined with the temperature in the region, the summer load curves corresponding to different industries are extracted from the historical power load data;

[0016] Based on the difference between the summer average load curve and the benchmark load curve, determine the cooling load of different industries;

[0017] Based on the description of power load characteristics in different industries and the proportion of cooling load in summer load of different industries, the spatiotemporal dynamic characteristics analysis of demand-side load is realized.

[0018] Furthermore, based on the load characteristics corresponding to typical days of power load in different industries and seasons, the power loads of different industries are classified into categories, specifically into peak-facing loads, valley-filling loads, continuous loads and periodic loads, wherein the peak-facing load has a peak period near the peak period of the benchmark load, and a valley period in the non-peak period of the benchmark load, and the peak-to-valley difference rate is higher than the peak-to-valley difference rate of the benchmark load; the valley-filling load has a peak period in the non-peak period of the benchmark load, and a valley period in the peak period of the benchmark load; the continuous load has a peak period in the peak period of the benchmark load, and a valley period in the non-peak period of the benchmark load, and the peak-to-valley difference rate is lower than the peak-to-valley difference rate of the benchmark load; the periodic load includes at least a preset number of peak-to-valley fluctuations and a peak-to-valley difference rate greater than the benchmark load.

[0019] Furthermore, the day and night electricity consumption characteristics are determined by calculating the ratio of daytime electricity consumption to nighttime electricity consumption of a typical daily load curve.

[0020] Furthermore, the fitting function type based on the maximum load and average temperature in the low and medium temperature sections of different industries is combined with historical power load data to determine the benchmark load curves and benchmark temperature ranges for different industries, specifically: for the monotonically increasing fitting function type, the preset interval before its first temperature sensitive point is used as the benchmark temperature range; for the quasi-convex fitting function type, the preset interval near its first sensitive point is used as the benchmark temperature range; for the quasi-concave fitting function type, the preset interval before its first temperature sensitive point is used as the benchmark temperature range; and, based on the determined benchmark temperature range, combined with historical power load data, the benchmark load curves for different industries are determined.

[0021] Furthermore, based on the obtained reference temperature range and in combination with the temperature in the area, summer load curves corresponding to different industries are extracted from the historical power load data. Specifically, when the temperature in the area is higher than the reference temperature range, a cooling load is generated; and summer load curves for different industries are constructed using the data in the historical power load data where the temperature is higher than the reference temperature range.

[0022] Furthermore, the analysis of the spatiotemporal dynamic characteristics of the demand-side load is specifically as follows: based on the description of the power load characteristics of different industries combined with the proportion of cooling loads of different industries in the summer load, the potential of different industries in different regions to participate in peak-shaving or valley-filling in different seasons is analyzed to determine the adjustable load; based on the adjustable load, peak-shaving or valley-filling of the power system is achieved.

[0023] Furthermore, the load characteristics corresponding to the typical days of power load in different industries and seasons are specifically load sequences formed by the power load sizes corresponding to the typical days of power load in different industries and seasons at intervals of preset time periods.

[0024] According to a second aspect of an embodiment of the present invention, a demand side load spatiotemporal dynamic characteristic analysis system considering the influence of temperature is provided, comprising:

[0025] A power load characteristic description determination unit is used to obtain typical days of power loads in different seasons for different industries through cluster analysis based on historical power load data of different industries in different seasons in a preset area; classify the power loads of different industries based on the load characteristics corresponding to the typical days of power loads in different seasons for different industries; based on the power load classification results of different industries, combined with the load daily load rate, daily minimum load rate, peak-to-valley difference rate and day and night power consumption characteristics of the corresponding industries, as power load characteristic descriptions for different industries;

[0026] A cooling load determination unit is used to determine the benchmark load curve and benchmark temperature range of different industries based on the fitting function type of the maximum load and the average temperature in the low and medium temperature sections of different industries, combined with historical power load data; based on the obtained benchmark temperature range, combined with the temperature in the area, the summer load curve corresponding to different industries is extracted from the historical power load data; based on the difference between the summer load average curve and the benchmark load curve, the cooling load of different industries is determined;

[0027] The analysis unit is used to analyze the spatiotemporal dynamic characteristics of the demand-side load based on the description of the power load characteristics of different industries and the proportion of cooling loads of different industries in the summer load.

[0028] According to a third aspect of an embodiment of the present invention, there is provided an electronic device comprising a memory, a processor and a computer program stored and running on the memory, wherein when the processor executes the program, a method for analyzing the spatiotemporal dynamic characteristics of demand side load considering the influence of temperature is implemented.

[0029] According to a fourth aspect of an embodiment of the present invention, a non-transitory computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, a method for analyzing the spatiotemporal dynamic characteristics of demand-side load considering the influence of temperature is implemented.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] (1) The present invention provides a method and system for analyzing the spatiotemporal dynamic characteristics of demand-side loads taking into account the influence of temperature. The scheme performs cluster analysis on the electricity loads of different industries in different seasons, determines the typical daily load curves of different industries in different seasons, and classifies the loads based on the load characteristics corresponding to the typical days of different industries. The loads are divided into different categories. At the same time, the daily load rate, daily minimum load rate, peak-to-valley difference rate, and day and night electricity consumption characteristics are combined as descriptions of the power load characteristics of different industries, which facilitates load characteristic analysis and adjustable potential evaluation, effectively improves the accuracy of power load characteristic analysis of different industries, and lays a foundation for peak load regulation or valley filling of the power system.

[0032] (2) The solution described in the present invention incorporates air temperature into load characteristic analysis. By calculating the proportion of cooling load in summer load as a characteristic representation of different industries, it can effectively improve the accuracy of industry load characteristic analysis. At the same time, it can also better meet the electricity demand of users in different industries under extreme weather conditions.

[0033] (3) Based on the demand-side spatiotemporal dynamic characteristics analysis solution provided by the present invention, it is possible to analyze the dynamic characteristics of the load from multiple dimensions such as temperature, region, season and industry, evaluate the adjustable potential according to the regional and industrial development characteristics and the seasonal characteristics of the power load, and effectively improve the feasibility and effectiveness of the peak-shaving or valley-filling scheduling strategy of the power system.

[0034] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0036] Figure 1 This is a schematic diagram of a reference load peak period described in an embodiment of the present invention;

[0037] Figure 2 This is a schematic diagram of the peak load described in an embodiment of the present invention;

[0038] Figure 3 This is a schematic diagram of a valley-filling load described in an embodiment of the present invention;

[0039] Figure 4 This is a schematic diagram of a continuous load described in an embodiment of the present invention;

[0040] Figure 5 It is a schematic diagram of the periodic load described in the embodiment of the present invention;

[0041] Figure 6 Schematic diagram of a monotonically increasing fitting function described in an embodiment of the present invention;

[0042] Figure 7 It is a schematic diagram of a quasi-convex fitting function described in an embodiment of the present invention;

[0043] Figure 8 It is a schematic diagram of a concave-like fitting function described in an embodiment of the present invention;

[0044] Fig. 9 This is a schematic diagram of summer load described in an embodiment of the present invention;

[0045] Fig.10 A schematic diagram of the average summer load curve for working days and non-working days described in an embodiment of the present invention;

[0046] Fig.11 It is a schematic diagram of a summer load curve and a typical reference load curve described in an embodiment of the present invention;

[0047] Fig.12 It is a schematic diagram of the difference between the summer load curve and the typical reference load curve described in the embodiment of the present invention;

[0048] FIG. 13( a ) is a schematic diagram of a typical daily curve of electricity load in the wholesale and retail industries in different seasons in City A according to an embodiment of the present invention;

[0049] FIG13( b ) is a schematic diagram of a typical daily curve of electricity load in different seasons for the metal products industry in City A according to an embodiment of the present invention;

[0050] FIG13( c ) is a schematic diagram of a typical daily curve of electricity load in the postal industry of City A in different seasons according to an embodiment of the present invention;

[0051] FIG13( d ) is a schematic diagram of a typical daily curve of electricity load in the real estate industry of City A in different seasons according to an embodiment of the present invention;

[0052] FIG13( e ) is a schematic diagram of a typical daily curve of electricity load in different seasons for the ceramic industry in City A according to an embodiment of the present invention;

[0053] FIG13( f ) is a schematic diagram of a typical daily curve of electricity load in the accommodation and catering industry in different seasons in City A according to an embodiment of the present invention;

[0054] FIG. 13( g ) is a schematic diagram of a typical daily curve of electricity load in different seasons for the public service and organization management industries of City A according to an embodiment of the present invention;

[0055] FIG13(h) is a schematic diagram of a typical daily curve of electricity load in the transportation industry of City A in different seasons according to an embodiment of the present invention;

[0056] FIG. 13( i ) is a schematic diagram of a typical daily curve of electricity load in different seasons for the storage industry in City A according to an embodiment of the present invention;

[0057] FIG. 14( a ) is a schematic diagram of a typical daily curve of electricity load in different seasons for the nonferrous metal smelting and rolling processing industry in City A described in an embodiment of the present invention;

[0058] FIG14( b ) is a schematic diagram of a typical daily curve of power load in the computer industry of City A in different seasons according to an embodiment of the present invention;

[0059] FIG. 14( c ) is a schematic diagram of a typical daily curve of electricity load in different seasons for the rubber and plastic products industry in City A according to an embodiment of the present invention;

[0060] FIG14( d ) is a schematic diagram of a typical daily curve of electricity load in different seasons for the information transmission industry in City A according to an embodiment of the present invention;

[0061] FIG. 14( e ) is a schematic diagram of a typical daily curve of electricity load in different seasons for the chemical raw materials and chemical products industry in City A according to an embodiment of the present invention;

[0062] FIG. 15( a ) is a schematic diagram of a typical daily curve of electricity load in different seasons for the urban and rural residential electricity consumption industry in City A according to an embodiment of the present invention;

[0063] FIG15( b ) is a schematic diagram of a typical daily curve of electricity load in different seasons for the cement industry in City A according to an embodiment of the present invention;

[0064] Fig.16 This is a schematic diagram of a typical daily curve of electricity load in different seasons for the glass industry in City A described in an embodiment of the present invention;

[0065] FIG. 17( a ) is a schematic diagram of load curves for leather, rough, feather and their products in the peak-type industries in City B described in an embodiment of the present invention;

[0066] FIG. 17( b ) is a schematic diagram of load curves of the paper and paper products industry and the shoemaking industry in the peak-type industries of City B described in an embodiment of the present invention;

[0067] FIG. 17( c ) is a schematic diagram of load curves of the peak-type industries of Chinese education, arts and crafts, sports and entertainment in City B according to an embodiment of the present invention;

[0068] FIG. 17( d ) is a schematic diagram of a load curve of the food manufacturing industry in the peak-type industry of City B described in an embodiment of the present invention;

[0069] FIG. 17( e ) is a schematic diagram of a load curve of the electrical machinery and equipment manufacturing industry in the peak-type industry of City B described in an embodiment of the present invention;

[0070] Fig.18 The load curves of summer working days and non-working days and the load curves of reference working days and non-working days of different industries in city A described in the embodiment of the present invention;

[0071] Fig.19 A bar chart showing the proportion of cooling loads of various industries on working days in City A described in the embodiment of the present invention;

[0072] Fig. 20 This is a bar chart of the proportion of cooling loads in various industries on non-working days in City A described in the embodiment of the present invention.

[0073] Fig.21 This is a flow chart of a method for analyzing the spatiotemporal dynamic characteristics of demand-side load considering the impact of temperature described in an embodiment of the present invention. DETAILED DESCRIPTION

[0074] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0075] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0076] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0077] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0078] In one or more embodiments, Fig.21 As shown, this embodiment provides a method for analyzing the spatiotemporal dynamic characteristics of demand-side load considering the influence of temperature, which specifically includes the following processing steps:

[0079] Step 1: Based on the historical power load data of various industries in different seasons in the preset area, obtain the typical days of power load in different industries in different seasons through cluster analysis; classify the power load of different industries based on the load characteristics corresponding to the typical days of power load in different seasons of different industries; based on the power load classification results of different industries, combine the load daily load rate, daily minimum load rate, peak-to-valley difference rate and day and night power consumption characteristics of the corresponding industry as the power load characteristic description of different industries;

[0080] In a specific implementation, the load characteristics corresponding to the typical days of power load in different industries and seasons are specifically load sequences formed by the power load sizes corresponding to the typical days of power load in different industries and seasons at intervals of preset time periods.

[0081] In a specific implementation, the power loads of different industries are classified based on the load characteristics corresponding to typical days of power loads in different industries and seasons, and are specifically divided into peak-facing loads, valley-filling loads, continuous loads and periodic loads, wherein the peak-facing load has a peak period near the peak period of the benchmark load, a valley period in the non-peak period of the benchmark load, and a peak-to-valley difference rate is higher than the peak-to-valley difference rate of the benchmark load; the valley-filling load has a peak period in the non-peak period of the benchmark load, and a valley period in the peak period of the benchmark load; the continuous load has a peak period in the peak period of the benchmark load, a valley period in the non-peak period of the benchmark load, and a peak-to-valley difference rate is lower than the peak-to-valley difference rate of the benchmark load; the periodic load includes at least a preset number of peak-to-valley fluctuations and a peak-to-valley difference rate greater than the benchmark load.

[0082] In a specific implementation, the day and night electricity consumption characteristics are determined by calculating the ratio of daytime electricity consumption to nighttime electricity consumption of a typical daily load curve.

[0083] In a specific implementation, the construction of the power load characteristic description specifically includes:

[0084] The main technical concept is: perform k-means clustering on the power load data of different industries in different periods and four seasons to obtain the typical days of power load in each industry and season; divide the load of each industry into four categories: peak-facing, valley-filling, continuous and periodic loads according to the 96-point load portrait characteristics of the typical day. Calculate the four load indicators of daily load rate, daily minimum load rate, peak-to-valley difference rate, and day and night power consumption characteristics of different industries in different regions; comprehensively describe the dynamic characteristics of the demand-side load in the spatiotemporal domain based on the classification results and indicator results, and analyze its adjustable potential. Among them, the 96 points in the 96-point load portrait characteristics usually refer to the division of the time dimension, that is, there are 24 hours in a day, one interval every 15 minutes, and there will be 96 time points in a day.

[0085] Specifically, by clustering the four-season data of different industries, we get the typical days of each season. The four seasons are divided as follows: spring from March to April, summer from May to September, autumn from October to November, and winter from December and January to February.

[0086] Select 4 cluster centers and divide all samples into clusters according to the nearest principle. The basic idea is to compare the distance between each case and the centroid one by one after selecting the cluster center (i.e., the centroid), first classify the case with the closest distance into the same category as the centroid, and then repeatedly calculate and iterate until the set requirements are met.

[0087] The total electricity load of each city is defined as the benchmark load of the city for comparison and classification of industries, such as Figure 1 As shown in the figure, the red dashed box period is the peak period of the benchmark load. According to the characteristics of typical daily load, quantitative comparison with the benchmark load and its peak period can be divided into four categories: peak load, valley load, continuous load, and periodic load. The four typical loads in a certain area are as follows Figure 2-Figure 5 shown.

[0088] (1) Peak load

[0089] like Figure 2 As shown in the figure, the peak period of the peak load occurs near the peak period of the benchmark load, and the valley period occurs during the non-peak period of the benchmark load, and the peak-to-valley difference rate is higher than the peak-to-valley difference rate of the benchmark load. This type of load is affected by the industry's work system and working hours. When the system load level is high, there will be a significant increase in electricity consumption, and when the system load level is low, the electricity consumption will stop or be greatly reduced.

[0090] (2) Valley-filling load

[0091] like Figure 3As shown in the figure, the peak period of valley-filling load occurs during the non-peak period of the benchmark load, and the valley period occurs during the peak period of the benchmark load. The characteristics of valley-filling load are opposite to those of peak-facing load. Its power consumption is mainly concentrated in the period when the system load level is low, and it will reduce or even stop power consumption during the period when the system load level is high.

[0092] (3) Continuous load

[0093] like Figure 4 As shown in the figure, the peak period of continuous load occurs during the peak period of benchmark load, and the valley period occurs during the non-peak period of benchmark load, and the peak-valley difference rate is lower than the peak-valley difference rate of benchmark load. Continuous load is often highly coherent load of various equipment in the production process.

[0094] (4) Cyclic load

[0095] like Figure 5 As shown in the figure, the periodic load has three or more obvious peak-valley fluctuations and the peak-valley difference rate is greater than the benchmark load. The equipment of the periodic load operates at different times, and the load curve will fluctuate periodically, and the duration is relatively long.

[0096] Daily load rate, daily minimum load rate, peak-to-valley difference rate and day-and-night electricity consumption characteristics are used as quantitative indicators for load classification and load characteristic analysis.

[0097] The daily load rate (γ) refers to the ratio of the daily average load to the daily maximum load, which is used to indicate the degree of load change within a day. The higher the value, the smaller the load change within a day. The daily load rate calculation formula is as follows:

[0098] γ=P d,av / P d,max

[0099] Among them, P d,av is the daily average load, P d,max The maximum daily load.

[0100] The daily minimum load rate (β) refers to the ratio of the daily minimum load to the daily maximum load, which is used to indicate the degree of change of the minimum load within a day. The higher the value, the smaller the change of the minimum load within a day. The calculation formula of the daily minimum load rate is as follows:

[0101] β=P d,min / P d,max

[0102] Where P d,min is the daily minimum load, P d,max The maximum daily load.

[0103] The peak-to-valley difference rate refers to the percentage of the daily peak-to-valley difference to the daily maximum load. The calculation formula for the peak-to-valley difference rate is as follows:

[0104] Peak-to-valley ratio = P d,max -P d,min / P d,max

[0105] Where P d,min is the daily minimum load, P d,max The maximum daily load.

[0106] Day and night power consumption characteristics. Set the daytime working hours to 06:00-18:00 and the night working hours to 18:00-06:00 the next day. By calculating the typical daily load curve, the daytime power consumption Q d and night electricity consumption Q n The ratio A of day and night is used to determine the characteristics of day and night electricity consumption. The formula is as follows:

[0107]

[0108] Where P t is the load value on the load curve at time t.

[0109] If A>1.10, it is considered that the load's day and night power consumption characteristics are large daily power consumption;

[0110] If A<0.99, it is considered that the load consumes a lot of electricity at night;

[0111] If 0.99≤A≤1.10, it is considered that the power consumption of the load is similar during the day and at night.

[0112] Taking the power load data of 17 industries in City A as an example, the indicator calculation results are shown in Table 1 and Table 2.

[0113] Table 1 Index calculation results

[0114]

[0115] Table 2 Index calculation results

[0116]

[0117] Step 2: Based on the fitting function type of the maximum load and average temperature in the low and medium temperature sections of different industries, combined with historical power load data, determine the benchmark load curves and benchmark temperature ranges of different industries; based on the obtained benchmark temperature range, combined with the temperature in the region, extract the corresponding summer load curves of different industries from the historical power load data; based on the difference between the summer load average curve and the benchmark load curve, determine the cooling load of different industries;

[0118] In a specific implementation, the fitting function type based on the maximum load and the average temperature in the low and medium temperature sections of different industries is combined with historical power load data to determine the benchmark load curves and benchmark temperature ranges for different industries, specifically: for a monotonically increasing fitting function type, a preset interval before its first temperature sensitive point is used as the benchmark temperature range; for a quasi-convex fitting function type, a preset interval near its first sensitive point is used as the benchmark temperature range; for a quasi-concave fitting function type, a preset interval before its first temperature sensitive point is used as the benchmark temperature range; and, based on the determined benchmark temperature range, combined with historical power load data, the benchmark load curves for different industries are determined.

[0119] In the specific implementation, based on the obtained reference temperature range and combined with the temperature in the area, the summer load curves corresponding to different industries are extracted from the historical power load data. Specifically, when the temperature in the area is higher than the reference temperature range, a cooling load is generated; and the summer load curves of different industries are constructed based on the data in the historical power load data where the temperature is higher than the reference temperature range.

[0120] In the specific implementation, the cooling load is determined by the following technical concept: first determine the benchmark load, and finally calculate the cooling load according to the definition. It should be noted that it is generally believed that a day in April or October with a temperature below 20°C does not contain air conditioning load. It is regarded as a typical day in spring or autumn, and the average temperature is used as the temperature index to quantitatively study its relationship with the daily maximum load and cooling load, thereby providing a basis for the selection of typical days. It specifically includes the following processing procedures:

[0121] Step 201: Determine the baseline load

[0122] Through a large amount of data analysis, it is found that there are two temperature sensitive points in the fitting relationship between the maximum load and the average temperature of each industry. Since the benchmark load does not contain the cooling load, the benchmark load is taken in the medium and low temperature section (that is, the temperature section before the high temperature sensitive point). The medium and low temperature section fitting functions of different industries are divided into three types: monotonically increasing, convex and concave. Monotonically increasing fitting function (such as Figure 6 As shown in the figure, when the daily average temperature is greater than the first temperature sensitive point, the load growth begins to accelerate; when the daily average temperature is greater than the second temperature sensitive point, the load growth rate accelerates significantly. At this time, the load is sensitive to temperature changes and is defined as temperature sensitive load. At this time, a certain temperature interval before the first temperature sensitive point of the industry is selected as the reference temperature interval. When the maximum temperature on a certain day is higher than the reference temperature interval, it is considered that a cooling load occurs on that day; otherwise, it does not occur. Quasi-convex fitting function (such as Figure 7As shown in the figure, when the daily average temperature is lower than the first temperature sensitive point, the load increases as the temperature decreases; when the daily average temperature is higher than the first temperature sensitive point, the load growth begins to accelerate as the temperature increases, and when the second temperature sensitive point is passed, the load growth rate is significantly accelerated; at this time, combined with the actual situation of the industry, the junction of the first temperature range and the second temperature range of the industry is selected as the reference temperature range (that is, the temperature range near the first temperature sensitive point). Quasi-concave fitting function (such as Figure 8 As shown in the figure, when the daily average temperature is lower than the first temperature sensitive point, the load increases with the increase of temperature; when the daily average temperature is higher than the first temperature sensitive point, the load begins to decrease with the increase of temperature, and the load growth begins to accelerate when the second temperature sensitive point is passed; at this time, based on the actual situation of the industry, a temperature interval before the first temperature sensitive point of the industry is selected as the benchmark temperature interval.

[0123] Step 202: Summer load determination

[0124] According to the climatic conditions of various cities, taking City A as an example, in 2020, the monthly average daily average temperature of City A from May to September was higher than 25 degrees Celsius, and the monthly average daily maximum temperature from June to August was higher than 30 degrees Celsius, and in September it was 29.696 degrees Celsius, close to 30 degrees Celsius; in 2021, the monthly average daily average temperature from June to September was higher than 28 degrees Celsius, while in May it was lower than 28 degrees Celsius. Fig. 9 To reduce randomness, the selected summer load set is averaged every hour to obtain the summer load average curve. To avoid errors caused by changes in people's production and lifestyle on working days and weekends, this study separates working days and non-working days for calculation, as shown in Figure 2. Fig.10 shown.

[0125] Step 203: Determine the cooling load

[0126] Calculate the difference between the summer load curve and the typical benchmark load curve to obtain the 24-hour cooling load (such as Fig.11 , Fig.12 The ratio of cooling load to summer load is the proportion of cooling load in summer load. The average proportion of cooling load in 24 hours is taken to get the average proportion of the industry.

[0127] Step 3: Based on the description of power load characteristics of different industries and the proportion of cooling loads in summer loads of different industries, the spatiotemporal dynamic characteristics of demand-side loads are analyzed.

[0128] In the specific implementation, the analysis of the spatiotemporal dynamic characteristics of the demand-side load is specifically as follows: based on the description of the power load characteristics of different industries and the proportion of cooling loads of different industries in the summer load, the potential of different industries in different regions to participate in peak-shaving or valley-filling in different seasons is analyzed to determine the adjustable load; based on the adjustable load, peak-shaving or valley-filling of the power system is achieved.

[0129] In the specific implementation, the spatiotemporal dynamic characteristics analysis mainly adopts the following technical concepts: starting from the four dimensions of temperature, industry, season and region, according to the summer power load and its peak-to-valley difference and industry differences, based on a large amount of hourly measured power load data by industry, in-depth analysis of the spatiotemporal dynamic characteristics of power load and the potential of various industries to participate in demand response is carried out, aiming to provide data support and theoretical basis for the formulation of power dispatch and demand response strategies, which specifically includes the following processing procedures:

[0130] (1) Analysis based on the description of power load characteristics of different industries:

[0131] Starting from the four dimensions of temperature, industry, season and region, according to the summer electricity load and its peak-to-valley difference and industry differences, based on a large amount of hourly measured data on electricity load by industry, we deeply analyze the spatiotemporal dynamic characteristics of electricity load and the potential of various industries to participate in demand response, aiming to provide data support and theoretical basis for the formulation of power dispatch and demand response strategies.

[0132] In the analysis process, seasons are considered as time scales, and cities are considered as spatial scales. The dynamic characteristics of loads in different seasons in the same industry in the same region, the same seasons in the same industry in different regions, and the same seasons in different industries in the same region are compared. Specifically, industries whose average summer electricity load accounts for more than 5% of the average summer total electricity load of the whole society are defined as industries with large electricity loads. This judgment standard can identify industries with strong demand for electricity supply during the high temperatures in summer. In addition, on this basis, the peak-to-valley difference of the industry is further evaluated to determine its potential for participating in peak load regulation or valley filling.

[0133] Specifically, if the peak-to-valley difference of an industry is above 200MW, it can be judged that the industry is suitable for participating in power peak load regulation or valley filling, which means that the industry can effectively reduce the load during peak power consumption periods, or increase the load during low power consumption periods, helping the power system to balance supply and demand. If the peak-to-valley difference of an industry is below 200MW and above 50MW, it is judged that the industry has certain potential for peak load regulation or valley filling. Although its participation capacity is relatively weak, it can still provide certain support for the dispatch of the power system. On the contrary, if the peak-to-valley difference is below 50MW, or the average summer power load of the industry accounts for less than 5% of the average total summer power load of the whole society, it is believed that the peak load regulation or valley filling potential of the industry needs to be further explored, and more incentives or technical means may be needed to enhance its participation capacity.

[0134] In this embodiment, the typical daily curves of electricity load in various industries and seasons in City A in 2021 are shown in Figure 13- Fig.16 As shown in Figure 17, the peak-type industry load curve of City B is shown in Figure 17. Among the industries counted in City A, there are 9 peak-type industries, which is more than the 5 peak-type industries in City B, accounting for about 40% of the total electricity load of the whole society. Generally speaking, typical service industries have large daily electricity consumption and are more likely to belong to peak-type industries. Except for the ceramic industry, the above 9 industries all belong to the service industry. It can be inferred that City A is vigorously developing the service industry. There are 5 continuous industries in City A, accounting for about 25% of the total electricity load of the whole society, of which the computer industry alone accounts for more than 15%. The power load of the computer industry in summer is all above 2000MW. The computer power load of City B in the same season is relatively small and is not included in the statistics. From this, it can be inferred that City A is vigorously developing high-tech industries.

[0135] It is found that the daily peak-to-valley difference rate of continuous industries is the lowest in summer, while the daily peak-to-valley difference rate of cyclical industries is relatively high in summer. It is worth noting that the peak-to-valley difference rate of some peak-facing and valley-filling industries also shows a high level. There are 10 industries in City A that belong to peak-facing and cyclical industries, and the overall peak-to-valley difference in electricity consumption is large, indicating that there are significant differences in the volatility of electricity demand among different types of industries. Further analysis shows that the daily load rate of continuous industries is generally higher in summer, and its daily minimum load rate in summer is also at the highest level, while the daily minimum load rate of cyclical industries in summer is relatively low. There are 5 continuous industries in the region, so the overall load variation is large, reflecting the stability and continuity of these industries in electricity demand.

[0136] Finally, it is observed that the daily electricity consumption of peak-type industries is generally large, indicating that these industries have a strong demand for electricity during peak hours. At the same time, some valley-filling industries also show large electricity consumption at night, which means that they can effectively use electricity resources during the low electricity demand period. However, in this area, only the cement industry has similar day and night electricity consumption, and the overall situation shows a trend that the daily electricity consumption is much greater than the night electricity consumption. This phenomenon provides an important reference for power dispatching, suggesting that when formulating power demand response strategies, we should fully consider the power consumption characteristics and seasonal differences of the industry to achieve more efficient power resource allocation.

[0137] (2) Analysis based on the proportion of cooling load in summer load of different industries:

[0138] Conduct quantitative analysis of cooling loads for each industry, draw load curves for summer working days and non-working days, as well as load curves for benchmark working days and non-working days for each industry, calculate the average, minimum and maximum proportions of cooling loads for each industry, and rank the average proportions of each industry. Fig.18 , Fig.19 , Fig. 20 shown.

[0139] The average cooling load of the tertiary industry in City A, including real estate, accommodation and catering, postal service, wholesale and retail, warehousing, public service and management organization, transportation, information transmission, software and information technology service industry, etc., accounts for 40.58% on working days and 41.41% on non-working days. Among them, the cooling load of real estate, accommodation and catering, and wholesale and retail industries is relatively high, which is greater than 40% on working days and greater than 45% on non-working days, with great load regulation potential; the cooling load of the tertiary industry on working days and non-working days is not much different, with a difference of less than 5%. It can be seen from the bar chart that the cooling load of urban and rural residents' living electricity ranks first on both working days and non-working days, with great load regulation potential. The top 7 cooling loads on working days and non-working days are all tertiary industries, and their cooling loads account for more than or close to 40%, namely: accommodation and catering, real estate, postal service, warehousing, wholesale and retail, public service and management organization, and transportation, all of which have great load regulation potential. The cooling load of the secondary industry is significantly lower, below 20% on working days, and below 20% on non-working days, except for the chemical raw materials and chemical products manufacturing industry, which has an abnormal cooling load of 36.51%. Further, considering that the cooling load of the selected secondary industry accounts for an average of 12.29% on working days and 17.25% on non-working days, both below 20%, it can be considered that its production is less correlated with temperature and has a small load regulation potential.

[0140] In one or more embodiments, corresponding to the above method, this embodiment provides a demand side load spatiotemporal dynamic characteristic analysis system considering the influence of temperature, including:

[0141] A power load characteristic description determination unit is used to obtain typical days of power loads in different seasons for different industries through cluster analysis based on historical power load data of different industries in different seasons in a preset area; classify the power loads of different industries based on the load characteristics corresponding to the typical days of power loads in different seasons for different industries; based on the power load classification results of different industries, combined with the load daily load rate, daily minimum load rate, peak-to-valley difference rate and day and night power consumption characteristics of the corresponding industries, as power load characteristic descriptions for different industries;

[0142] A cooling load determination unit is used to determine the benchmark load curve and benchmark temperature range of different industries based on the fitting function type of the maximum load and the average temperature in the low and medium temperature sections of different industries, combined with historical power load data; based on the obtained benchmark temperature range, combined with the temperature in the area, the summer load curve corresponding to different industries is extracted from the historical power load data; based on the difference between the summer load average curve and the benchmark load curve, the cooling load of different industries is determined;

[0143] The analysis unit is used to analyze the spatiotemporal dynamic characteristics of the demand-side load based on the description of the power load characteristics of different industries and the proportion of cooling loads of different industries in the summer load.

[0144] Furthermore, the relevant technical details of the system described in this embodiment are described in detail in Embodiment 1, and thus will not be repeated here.

[0145] In further embodiments, there is also provided:

[0146] An electronic device includes a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method described in Embodiment 1 is performed. For the sake of brevity, no further description is given here.

[0147] It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general-purpose processors, digital signal processors DSP, application-specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0148] The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0149] A computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the method described in embodiment 1 is completed.

[0150] The method in the first embodiment can be directly embodied as a hardware processor, or a combination of hardware and software modules in the processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware. To avoid repetition, it will not be described in detail here.

[0151] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for analyzing the spatiotemporal dynamic characteristics of demand-side load considering the influence of temperature, characterized in that: include: Based on the historical power load data of various industries in different seasons in the preset area, the typical days of power load in different industries in different seasons are obtained through cluster analysis; Based on the load characteristics corresponding to the typical days of power loads in different industries and seasons, the power loads of different industries are classified; Based on the classification results of power loads in different industries, combined with the daily load rate, daily minimum load rate, peak-to-valley difference rate, and day-and-night power consumption characteristics of the corresponding industries, the power load characteristics of different industries are described; Based on the fitting function types of the maximum load and average temperature in the low and medium temperature ranges of different industries, combined with historical power load data, the benchmark load curves and benchmark temperature ranges of different industries are determined; Based on the obtained benchmark temperature range and combined with the temperature in the region, the summer load curves corresponding to different industries are extracted from the historical power load data; Based on the difference between the summer average load curve and the benchmark load curve, determine the cooling load of different industries; Based on the description of power load characteristics in different industries and the proportion of cooling load in summer load of different industries, the spatiotemporal dynamic characteristics analysis of demand-side load is realized.

2. A method for analyzing the spatiotemporal dynamic characteristics of demand-side load considering the influence of temperature as claimed in claim 1, characterized in that: Based on the load characteristics corresponding to typical days of power load in different industries and seasons, the power loads of different industries are classified into categories, specifically into peak-facing loads, valley-filling loads, continuous loads and periodic loads, wherein the peak-facing load has a peak period near the peak period of the benchmark load, a valley period in the non-peak period of the benchmark load, and a peak-to-valley difference rate is higher than the peak-to-valley difference rate of the benchmark load; the valley-filling load has a peak period in the non-peak period of the benchmark load, and a valley period in the peak period of the benchmark load; the continuous load has a peak period in the peak period of the benchmark load, a valley period in the non-peak period of the benchmark load, and a peak-to-valley difference rate is lower than the peak-to-valley difference rate of the benchmark load; the periodic load includes at least a preset number of peak-to-valley fluctuations and a peak-to-valley difference rate greater than the benchmark load.

3. A method for analyzing the spatiotemporal dynamic characteristics of demand-side load considering the influence of temperature as claimed in claim 1, characterized in that: The day and night electricity consumption characteristics are determined by calculating the ratio of daytime electricity consumption to nighttime electricity consumption of a typical daily load curve.

4. A method for analyzing the spatiotemporal dynamic characteristics of demand-side load considering the influence of temperature as claimed in claim 1, characterized in that: The fitting function type based on the maximum load and average temperature in the low and medium temperature sections of different industries is combined with historical power load data to determine the benchmark load curves and benchmark temperature ranges of different industries, specifically: for the monotonically increasing fitting function type, the preset interval before its first temperature sensitive point is used as the benchmark temperature range; for the quasi-convex fitting function type, the preset interval near its first sensitive point is used as the benchmark temperature range; for the quasi-concave fitting function type, the preset interval before its first temperature sensitive point is used as the benchmark temperature range; And, based on the determined benchmark temperature range and combined with historical power load data, the benchmark load curves for different industries are determined.

5. The method for analyzing the spatiotemporal dynamic characteristics of demand-side load considering the influence of temperature as claimed in claim 1, characterized in that: Based on the obtained reference temperature range and in combination with the temperature in the area, summer load curves corresponding to different industries are extracted from the historical power load data. Specifically, when the temperature in the area is higher than the reference temperature range, a cooling load is generated; and summer load curves for different industries are constructed based on the data in the historical power load data where the temperature is higher than the reference temperature range.

6. A method for analyzing the spatiotemporal dynamic characteristics of demand-side load considering the influence of temperature as claimed in claim 1, characterized in that: The analysis of the spatiotemporal dynamic characteristics of the demand-side load is specifically as follows: based on the description of the power load characteristics of different industries and the proportion of cooling loads of different industries in the summer load, the potential of different industries in different regions to participate in peak load regulation or valley filling in different seasons is analyzed to determine the adjustable load; based on the adjustable load, peak load regulation or valley filling of the power system is achieved.

7. A method for analyzing the spatiotemporal dynamic characteristics of demand-side load considering the influence of temperature as claimed in claim 1, characterized in that: The load characteristics corresponding to the typical days of power load in different industries and seasons are specifically a load sequence composed of the power load sizes corresponding to the typical days of power load in different industries and seasons at intervals of preset time periods.

8. A demand side load spatiotemporal dynamic characteristics analysis system considering the influence of temperature, characterized in that: include: A power load characteristic description determination unit is used to obtain typical days of power loads in different seasons for different industries through cluster analysis based on historical power load data of different industries in different seasons in a preset area; classify the power loads of different industries based on the load characteristics corresponding to the typical days of power loads in different seasons for different industries; based on the power load classification results of different industries, combined with the load daily load rate, daily minimum load rate, peak-to-valley difference rate and day and night power consumption characteristics of the corresponding industries, as power load characteristic descriptions for different industries; A cooling load determination unit is used to determine the benchmark load curve and benchmark temperature range of different industries based on the fitting function type of the maximum load and the average temperature in the low and medium temperature sections of different industries, combined with historical power load data; Based on the obtained benchmark temperature range and combined with the temperature in the region, the summer load curves corresponding to different industries are extracted from the historical power load data; Based on the difference between the summer average load curve and the benchmark load curve, determine the cooling load of different industries; The analysis unit is used to analyze the spatiotemporal dynamic characteristics of the demand-side load based on the description of the power load characteristics of different industries and the proportion of cooling loads of different industries in the summer load.

9. An electronic device comprising a memory, a processor and a computer program stored and running on the memory, characterized in that: When the processor executes the program, a method for analyzing the spatiotemporal dynamic characteristics of demand-side load considering the influence of temperature is implemented as described in any one of claims 1-7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, a method for analyzing the spatiotemporal dynamic characteristics of demand-side load considering the influence of temperature is implemented as described in any one of claims 1 to 7.

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