Dynamic management method, system and device for load rate of main equipment of power grid and storage medium

Abstract

The invention discloses a power grid main equipment load rate dynamic management method, system and device and a storage medium, and mainly relates to the technical field of power grid equipment load rate management. Comprising the following steps that power utilization data are obtained by dispatching an SCADA system and a middle station, the power utilization data are preprocessed, a power grid main equipment load rate database is obtained, and classification of the power grid main equipment load rate database comprises the whole substation load rate, the main transformer load rate and the line load rate; classifying the data in the load rate database of the main equipment of the power grid according to user types, and carrying out load increase trend analysis on the data; according to the analysis result, peak load prediction is further automatically carried out before a summer-ward peak and a winter peak; and after summer peak and winter peak loads appear, combining prediction results of the steps to complete load analysis output. The method has the beneficial effects that the dynamic analysis and management of the load rate of the main equipment of the power grid can be realized, and a help guide is provided for formulating a peak-facing project and a power grid peak-facing plan.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid equipment load rate management, and in particular to a method, system, device and storage medium for dynamic management of power grid main equipment load rate. Background Art

[0002] In recent years, with the continuous development of the economy and society, the scale of the power grid has been expanding, the number of new equipment commissioned each year has been increasing, the load rate of main equipment has been rising, and users' requirements for power safety and reliability have been increasing. The power grid control center conducts peak load analysis (including summer peak and winter peak) every year, analyzes the heavy overload of equipment, and works with the Development Department to complete the formulation of peak projects, and formulates response plans for the safe operation of power grid equipment during the peak summer period next year. At the same time, before the summer peak and winter peak, the power grid control center will predict the summer and winter peak loads and heavy-load equipment based on the load conditions during the previous year's peak period and the implementation of infrastructure and business expansion projects since the previous year's peak load, and arrange the power grid operation mode and accident plans in advance.

[0003] Currently, peak load analysis and equipment load rate prediction are completed by manually organizing relevant data tables and project implementation status. This is a large workload, low efficiency, and it is difficult to achieve a high level of accuracy.

[0004] Therefore, there is an urgent need for a method, system, device and storage medium for dynamic management of the load rate of main equipment in a power grid to solve the above problems. Summary of the invention

[0005] The purpose of the present invention is to provide a method, system, device and storage medium for dynamic management of the load rate of main power grid equipment, which can realize dynamic analysis and management of the load rate of main power grid equipment and provide a helpful guide for formulating peak projects and power grid peak response plans.

[0006] In order to achieve the above object, the present invention is implemented through the following technical solutions:

[0007] On the one hand, a method for dynamically managing the load rate of a main power grid device comprises the following steps:

[0008] S1: Obtain power consumption data through the dispatching SCADA system and the middle station, and pre-process the power consumption data to obtain a power grid main equipment load rate database, where the classification of the power grid main equipment load rate database includes the substation load rate, main transformer load rate and line load rate;

[0009] S2: classify the data in the load rate database of the main equipment of the power grid according to the user type and analyze the load growth trend;

[0010] S3: Based on the analysis results in step S2, the peak load forecast is further automatically performed before the summer peak and winter peak;

[0011] S4: After the summer peak and winter peak loads appear, the load analysis output is completed in combination with the prediction results of step S3.

[0012] Preferably, the step S2 is classified according to user type, specifically: the data in the power grid main equipment load rate database is divided into: residential area load analysis, industrial user load analysis, commercial user load analysis, data center load analysis and charging load analysis.

[0013] Preferably, the residential area load analysis, industrial user load analysis, commercial user load analysis, data center load analysis and charging load analysis specifically include the following steps:

[0014] Select a reference period, where the reference period is specifically: the days with the highest load or the month with the highest load in the power consumption data obtained from the SCADA system and the middle station;

[0015] Calculate the load coefficients of different time periods according to the load curve of the reference period and the actual load data, the coefficients including: daily load coefficient and monthly load coefficient;

[0016] Based on the load factor and benchmark load data, the load at a certain period in the future is predicted.

[0017] Preferably, the load prediction for a certain period of time in the future is specifically:

[0018] S21: Establish an ARIMA model and calculate the lagged value of the load factor in the model:

[0019] A t =α+ω1A t-1 +ω2A t-2 +…+ω p A t-p +ρ t

[0020] Among them, ω1, ω2, …, ω p is the model parameter, α is a constant, ρ t is white noise, p is a fixed value of 12;

[0021] S22: Calculate the lagged values ​​of the statistically incorrect load factor used in the model:

[0022] A t =b+ρ t+ ω1ρ t-1 +ω2ρ t-2 +…+ω q ρ t-q

[0023] Where b is a constant, ω1ρt-1 ,ω2ρ t-2 ,…,ω q ρ t-q , is the white noise in the past, and q is a fixed value of 12;

[0024] S23: Calculate the difference order in the model.

[0025] Preferably, the calculation of the difference order in the model specifically includes the following steps:

[0026] Convert the load factor at a certain time into the load factor at the previous moment:

[0027] By t =y t-1

[0028] Among them, y t is the time series of the load factor observation period, B is the lag operation, and the above formula is lagged by n time steps, that is:

[0029] B n y t =y t-n

[0030] Using hysteresis operation, we can express the first-order difference and n-order difference, where the first-order difference is the difference between adjacent label values, which can be expressed as:

[0031] Δy t =y t -y t-1 =y t -By t =(1-B)y t

[0032] Then, the n-order difference is to subtract n-1 label values ​​from each other:

[0033] Δ n y t =y t -y t-n =y t -B n y t =(1-B n )y t

[0034] The d-order difference can be expressed as:

[0035] d_order_y=(1-B) d y t

[0036] Preferably, the step S3 is specifically as follows: according to the residential area load, industrial user load, commercial user load, data center load and charging load prediction results obtained in step S2, the load rate prediction values ​​of the summer peak and winter peak are calculated respectively, and compared with the actual summer peak and winter peak load rate values ​​to complete the evaluation.

[0037] On the other hand, a dynamic management system for the load rate of main power grid equipment is provided, including:

[0038] The data acquisition module is used to obtain power consumption data through the dispatching SCADA system and the middle station, and pre-process the power consumption data to obtain the load rate database of the main equipment of the power grid;

[0039] The data processing module is used to classify the data in the load rate database of the main equipment of the power grid according to the user type, and analyze the load growth trend thereof. Based on the analysis results, the peak load is further automatically predicted before the summer peak and winter peak.

[0040] The data output module is used to complete the load analysis output in combination with the forecast results after the summer peak and winter peak loads appear.

[0041] On the other hand, a device for dynamically managing the load rate of a main equipment in a power grid is provided, comprising a processor and a memory for storing a computer program, wherein when the processor executes the computer program, the steps of any of the above-mentioned methods are implemented.

[0042] On the other hand, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by one or more processors, the steps of any of the above methods are implemented.

[0043] Compared with the prior art, the beneficial effects of the present invention are:

[0044] On the one hand, it can be a good way to find the heavy-duty equipment that meets the peak load, and further coordinate the development department to complete the formulation of the peak load project;

[0045] On the other hand, based on the peak load situation in the previous year and the implementation of related peak load projects, combined with the progress of infrastructure and business expansion projects over the past year, the load rate of the main equipment of the power grid is predicted for the coming year to assist dispatchers in arranging the power grid operation mode and accident plans in advance. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is a flow chart of the method of the present invention;

[0047] Figure 2 It is a schematic diagram of the natural growth cycle curve of the present invention;

[0048] Figure 3It is a schematic diagram of the system structure of the present invention;

[0049] Figure 4 It is the industrial conformity peak and valley characteristic classification diagram of the present invention;

[0050] Figure 5 It is a schematic diagram of industrial electricity consumption and monthly average temperature of the present invention;

[0051] Figure 6 This is a schematic diagram of a typical summer day load curve of a shopping mall of the present invention;

[0052] Figure 7 It is a schematic diagram of a typical daily load curve of an office building in summer according to the present invention;

[0053] Figure 8 This is a schematic diagram of a typical daily load curve of a hotel in summer according to the present invention;

[0054] Fig. 9 It is a schematic diagram of energy consumption distribution of a data center of the present invention;

[0055] Fig.10 This is a load diagram of the Hua 33 Huqingping No. 1B line of the present invention;

[0056] Fig.11 It is a multi-day load diagram of the Hua 33 Shanghai-Qingping No. 1B line of the present invention. DETAILED DESCRIPTION

[0057] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms fall within the scope limited by the application equally.

[0058] In the present invention, terms such as "upper", "lower", "left", "right", "front", "back", "vertical", "horizontal", "side", "bottom", etc. indicate directions or positional relationships based on the directions or positional relationships shown in the accompanying drawings. They are relational words determined only for the convenience of describing the structural relationships of the various parts or elements of the present invention, and do not specifically refer to any part or element in the present invention and should not be understood as limitations on the present invention.

[0059] In the present invention, terms such as "fixed connection", "connected", "connection", etc. should be understood in a broad sense, indicating that it can be fixedly connected, integrally connected or detachably connected; it can be directly connected or indirectly connected through an intermediate medium. Relevant scientific research or technical personnel in this field can determine the specific meanings of the above terms in the present invention according to specific circumstances, and they should not be understood as limiting the present invention.

[0060] Embodiment 1:

[0061] like Figure 1 As shown, this embodiment provides a method for dynamically managing the load rate of a main equipment of a power grid, comprising the following steps:

[0062] S1: Obtain power consumption data through the dispatching SCADA system and the middle station, and pre-process the power consumption data to obtain a power grid main equipment load rate database, where the classification of the power grid main equipment load rate database includes the substation load rate, main transformer load rate and line load rate;

[0063] S2: classify the data in the load rate database of the main equipment of the power grid according to the user type and analyze the load growth trend;

[0064] S3: Based on the analysis results in step S2, the peak load forecast is further automatically performed before the summer peak and winter peak;

[0065] S4: After the summer peak and winter peak loads appear, the load analysis output is completed in combination with the prediction results of step S3.

[0066] Among them, the classification according to user type in step S2 is specifically: the data in the power grid main equipment load rate database is divided into: residential area load analysis, industrial user load analysis, commercial user load analysis, data center load analysis and charging load analysis.

[0067] In this embodiment, take the Shinan area of ​​Shanghai as an example:

[0068] The load of residential users is generally reflected in the cooling load of air conditioners in summer and the heating load of air conditioners in winter, especially the heating load in winter. The summer load peak of the Shinan Power Grid generally occurs at noon, while the winter load peak occurs at night. For example, on January 7, 2021, the lowest temperature on that day reached -7°C, and the peak load of the Shinan Power Grid in winter was 4.623 million kilowatts (the highest winter peak in history), at 20:20 in the evening;

[0069] In places where residential areas are concentrated, such as Tanjia Station in Pujiang Town, Chunyang Station in Zhuanqiao, Heqing Station in Old Minhang, Jingming Station that supplies power to Jing'an New Town, a large residential area, and Xianfeng Station in Longbai area, they are significantly affected by the heating load of residents in winter;

[0070] Due to the phenomenon of moderately advanced power grid construction, while meeting the overall needs of economic and social development, it also caused the low utilization efficiency of some power grid equipment in the early stage of construction. In particular, residential communities are affected by the occupancy rate and the community load generally has a significant growth cycle. Therefore, it often leads to low utilization of residential power supply facilities during a relatively long load growth period. After a certain development period, the residential load increases year by year, which leads to changes in the load rate of the main power grid equipment. The natural growth cycle curve describes the growth cycle process of a single population space constraint, and its shape is generally like Figure 2 As shown, its mathematical expression is:

[0071]

[0072] The characteristic of this curve is that the initial growth period (0ˉt0) is relatively slow, which is called the initial period, and the starting point value is a / (1+b); with the change of certain influencing factors, it enters the growth period after t0, with a faster growth rate, growing from inflection point 1 to inflection point 2; after t2 time, it enters the mature period, the growth rate tends to slow down, and finally reaches saturation. Point ((lnb) / c, a / 2) is the inflection point and central symmetry point of the growth curve; when the b and c values ​​are determined, the larger the a value, the steeper the curve between t0 and t2; when the a and c values ​​are determined, the larger the b value, the slower the curve growth rate; the load growth cycle is an important reference to reflect the stage-by-stage law of user load growth. The growth cycle of residential load also includes three stages: initial construction period, growth period, and mature period, and the length of each stage corresponding to different communities varies;

[0073] The load growth cycle curve of residential areas is mainly affected by the type of residential demand and surrounding supporting facilities. The type of residential demand affects the length of the entire load growth cycle, which is mainly divided into two categories: rigid demand (relocation, new residence) and improvement demand. The rigid demand load growth cycle is shorter, and the improvement demand load growth cycle is longer. The surrounding supporting facilities can be further divided into many secondary variables, including but not limited to: the influence of education, transportation, living facilities, culture and entertainment, medical care, etc.; education and transportation mainly affect the initial construction period; living facilities, culture and entertainment, medical care mainly affect the growth period;

[0074] By consulting relevant literature, the project team took a large mature residential community in Shinan as an example. The community has 6,000 households and has reached maturity, with a saturated load of 10,964 kW. The residential demand in the community is rigid, and there are key school districts in the surrounding area, rail transit has been built, living facilities are 3 km away from the community, cultural and entertainment facilities are 12 km away from the community, and there are medical facilities 8 km away from the community. The residential community load growth cycle model is obtained, that is, the shape parameter results of the community load growth cycle curve are a=0.9863, b=6.3365, c=0.0505, that is:

[0075]

[0076] Select a reference period, where the reference period is specifically: the days with the highest load or the month with the highest load in the power consumption data obtained from the SCADA system and the middle station;

[0077] Calculate the load coefficients of different time periods according to the load curve of the reference period and the actual load data, the coefficients including: daily load coefficient and monthly load coefficient;

[0078] Based on the load factor and benchmark load data, the load at a certain period in the future is predicted.

[0079] The load prediction for a certain period of time in the future is specifically:

[0080] S21: Establish an ARIMA model and calculate the lagged value of the load factor in the model:

[0081] A t =α+ω1A t-1 +ω2A t-2 +…+ω p A t-p +ρ t

[0082] Among them, ω1, ω2, …, ω p is the model parameter, α is a constant, ρ t is white noise, p is a fixed value of 12;

[0083] S22: Calculate the lagged values ​​of the statistically incorrect load factor used in the model:

[0084] A t =b+ρ t+ ω1ρ t-1 +ω2ρ t-2 +…+ω q ρ t-q

[0085] Where b is a constant, ω1ρ t-1 ,ω2ρ t-2 ,…,ω q ρ t-q , is the white noise in the past, and q is a fixed value of 12;

[0086] S23: Calculate the difference order in the model.

[0087] The calculation of the difference order in the model specifically includes the following steps:

[0088] Convert the load factor at a certain time into the load factor at the previous moment:

[0089] By t =y t-1

[0090] Among them, y t is the time series of the load factor observation period, B is the lag operation, and the above formula is lagged by n time steps, that is:

[0091] B n y t =y t-n

[0092] Using hysteresis operation, we can express the first-order difference and n-order difference, where the first-order difference is the difference between adjacent label values, which can be expressed as:

[0093] Δy t =y t -y t-1 =y t -By t =(1-B)y t

[0094] Then, the n-order difference is to subtract n-1 label values ​​from each other:

[0095] Δ n y t =y t -y t-n =y t -B n y t =(1-B n )y t

[0096] The d-order difference can be expressed as:

[0097] d_order_y=(1-B) d y t

[0098] According to the above steps, the comparison between the annual residential area load forecast results and the actual results is shown in Table 1.

[0099] Table 1 Annual load forecast and actual value of a large residential area

[0100] years 2018 2019 2020 2021 2022 2023 Actual value / kW 139 771 3728 8509 10498 10964 Predicted value / kW 137 801 3600 8193 10302 10746 Percent error -1.44 3.89 -3.43 -3.71 -1.87 -1.99

[0101] It can be seen from the above comparison results that the deviation between the annual load forecast value obtained by the method in this embodiment and the actual value is less than 5%, indicating the rationality of the forecast method.

[0102] The step S3 is specifically as follows: according to the residential area load, industrial user load, commercial user load, data center load and charging load prediction results obtained in step S2, the load rate prediction values ​​of the summer peak and winter peak are calculated respectively, and compared with the actual summer peak and winter peak load rate values ​​to complete the evaluation.

[0103] like Figure 3 As shown, in this embodiment, a power grid main equipment load rate dynamic management system is also provided, including:

[0104] The data acquisition module is used to obtain power consumption data through the dispatching SCADA system and the middle station, and pre-process the power consumption data to obtain the load rate database of the main equipment of the power grid;

[0105] The data processing module is used to classify the data in the load rate database of the main equipment of the power grid according to the user type, and analyze the load growth trend thereof. Based on the analysis results, the peak load is further automatically predicted before the summer peak and winter peak.

[0106] The data output module is used to complete the load analysis output in combination with the forecast results after the summer peak and winter peak loads appear.

[0107] The contents of the above method embodiments are all applicable to the present system embodiments. The functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0108] The present invention also provides a device, comprising:

[0109] at least one processor;

[0110] At least one memory for storing at least one program;

[0111] When the at least one program is executed by at least one processor, the at least one processor implements a method for dynamically managing the load rate of a main device in a power grid.

[0112] An embodiment of the present invention further provides a storage medium, wherein the storage medium stores a program, and the program is executed by a processor to implement a method for dynamically managing the load rate of a main device in a power grid.

[0113] Embodiment 2:

[0114] In this embodiment, take the Shinan area of ​​Shanghai as an example:

[0115] The characteristics of industrial load are large consumption, irregular peak-valley distribution, and great influence by industry type. The peak-valley distribution of industrial loads of different industrial types is obviously different. According to relevant literature, the peak-valley characteristics of industrial loads are summarized into four typical curves: three-peak type, two-peak type, stable type and peak-avoiding type, such as Figure 4As shown, its distribution characteristics are as follows:

[0116] (1) Three-peak type: It has two peaks in the morning and afternoon and a relatively low peak in the evening. It corresponds to industries that implement three-shift production and work schedules. Large equipment manufacturing industries such as electrical, mechanical, instrumentation, and furniture generally meet this requirement.

[0117] (2) Bimodal type: It has two distinct peaks, corresponding to enterprises that implement a two-shift production schedule. Light industries such as textiles and clothing generally meet this requirement.

[0118] (3) Stable type: It remains basically stable throughout the day, with no obvious peaks and valleys, generally in high-tech industries such as electronics, communications, and computers.

[0119] (4) Peak avoidance: Peak demand occurs at night when electricity prices are low, usually in labor-intensive industries such as cement plants and steel mills.

[0120] The Shinan Power Grid has heavy industrial loads in areas such as Minhang Development Zone, Wujing Industrial Zone, and Xinzhuang Industrial Zone. The project team collected monthly load data from industrial users within the Shinan Power Grid for the past three years and established a model to predict the monthly electricity consumption data of customers in the next year. The prediction period is determined to be monthly. The impact of economic factors on the load of large industrial customers is complex, far-reaching and long-lasting, and generally needs to be considered in long-term forecasts. The electricity price factor is mainly the peak and valley time-of-use electricity price, which generally has a greater impact on daily load forecasts. Therefore, the impact of climate factors on the load of large industrial customers is mainly considered.

[0121] like Figure 5 As shown, it can be analyzed that the industrial electricity load curve of the 35 kV industrial user has little fluctuation in the remaining months except for the Spring Festival month when there is a significant drop due to the holiday and suspension of work. It is basically not affected by the season or temperature, but is related to its production plan and business development.

[0122] By adopting the prediction method described in Example 1, the annual load of industrial users can be predicted.

[0123] Embodiment 3:

[0124] In this embodiment, take the Shinan area of ​​Shanghai as an example:

[0125] The commercial load of Shinan Power Grid is mainly concentrated in Xujiahui Business District, Caohejing Development Zone and other places. The commercial power load mainly refers to the sum of the power loads of various electrical equipment in commercial buildings. Commercial buildings are classified by function into the following types: shopping malls, office buildings, hotels, gyms, entertainment venues, various types of restaurants and more than ten other functional types. Compared with the system load, the commercial load has a single composition, and the main loads include: air conditioning, lighting, elevators, water supply equipment, office appliances, kitchen appliances, etc.

[0126] The characteristics of commercial load changes are closely related to its business hours. The business hours of shopping malls are generally between 8:00 and 22:30, and the business hours can reach more than 13 hours a day. Figure 6 As shown in the figure, the mall has load demand all day long, with obvious time-division characteristics and large load differences in different time periods. 11:00-20:00 is the peak period of electricity consumption, because the lighting, elevators and other electrical equipment in the mall are all turned on, and it is also the peak period of cooling load (air conditioning load). During the rest of the time outside business hours, the total load is at a low level, about 11%-17% of the maximum load.

[0127] The load characteristic curve of an office building is similar to that of a shopping mall. Its load variation curve is related to the working hours and activity patterns of the staff in the building. The working hours are generally from Monday to Friday, 8-10 hours a day, and overtime is sometimes required on Saturdays and Sundays. Figure 7 As shown in the figure, when people start working from 8:00 to 9:00, the total load quickly rises to the peak, and the cooling load (air conditioning load) also increases at the same time. From 12:00 to 13:00, due to the lunch break, the total load and cooling load decrease. From 17:00 to 19:00, the total load gradually decreases after get off work, and drops to the lowest level at 22:00, only maintaining the operation of the basic equipment in the building and the power consumption of the building lighting. It can be seen from the figure that the cooling load rises to the peak around 11:00 and 15:00, and the total load also rises to the peak accordingly.

[0128] Unlike shopping malls and office buildings, which have a higher total cooling load ratio during the day and zero at night, hotel buildings have a relatively stable load 24 hours a day and maintain a higher load demand. Figure 8 As shown in the figure, the hotel load can be divided into the daytime stage and the night stage. 8:00-23:00 is the daytime stage, and 24:00-8:00 the next day is the night stage. The load in the daytime stage gradually increases, and the peak period is 2:00-20:00. Unlike the load of shopping malls and office buildings, the hotel building load at night still has a medium power load, which is about a quarter of the daytime load. The change of the hotel's cooling load throughout the day is the same as the change of the total load. It increases with the temperature during the day, and the cooling load value is lower at night compared to the daytime temperature, which is about 20%-35% of the daytime cooling load.

[0129] By adopting the prediction method described in Example 1, the annual load of commercial users can be predicted.

[0130] Embodiment 4:

[0131] The power usage effectiveness (PUE) of a data center is highly correlated with its load. PUE is a commonly used industry metric that can be used to evaluate the effectiveness of data center power usage. The energy consumption of a data center can be roughly divided into three parts: IT equipment, cooling system, and power transmission equipment. Fig. 9 As shown in Figure 1, in the data center infrastructure support, cooling devices bear a large part of the power consumption, up to 40% of the data center load is consumed by cooling; another 45% of the data center load is consumed by IT equipment. Therefore, the IT equipment load and cooling system load have a significant impact on PUE.

[0132] The PUE value is defined as the ratio of the total load of the data center to the load provided to the computing equipment. Assuming PUE = 2, it means that for every 1W of power consumed by the IT equipment to perform the required calculations, the supporting electrical and cooling infrastructure of the data center will incur an additional cost of 1W, so the ideal value of this indicator is 1.

[0133] On the basis of the prediction method described in Example 1, adding the influencing factor of PUE is beneficial to improving the accuracy of data center load prediction.

[0134] Embodiment 5:

[0135] In this embodiment, take the Shinan area of ​​Shanghai as an example:

[0136] Electric vehicles have both load and energy storage characteristics. Their charging and discharging behaviors are random and volatile in time and space. Accurate prediction of the spatiotemporal distribution of electric vehicle charging loads is the basis for studying the impact of electric vehicle access to the grid, grid planning and operation, and interaction with the grid. When a large number of charging loads are connected to the distribution network, it will have a negative impact on the safe and stable operation of the urban distribution system.

[0137] Charging load forecasting is affected by multiple factors such as weather, date type, load fluctuation trend, electricity price, etc. These factors affect the charging load by changing the starting charging time, charging amount and charging time of EV.

[0138] Multiple factors have an impact on load forecasting. The following analysis lists several of the more important factors as input features of the preliminary forecasting model.

[0139] Meteorological factors such as season, temperature, and weather type will greatly affect the amount of electric vehicle charging. Among them, season is the biggest influencing factor due to the use of air conditioning and the owner's daily routine. In addition, the activity of ions inside the EV battery is also related to the season. In summer, ions are more active and the EV charging time is short, while in winter, the opposite is true. It can be seen that the season affects the charging load prediction by affecting the charging amount and charging time.

[0140] The date type will affect the use of the car, thus causing the charging time to be advanced or delayed. For example, most of the trips on weekdays are for commuting to school or work, and the peak hours of getting off work and school in the afternoon and evening are accompanied by a sharp increase in charging load in the future period; most of the trips on holidays are for tourism and shopping, and the charging load time is not very regular. It can be seen that the date type affects the charging load prediction by affecting the starting charging time of EV.

[0141] Load fluctuation trend is also one of the important aspects to be considered in charging load forecasting. Charging load has strong time series and continuity, and will show regular changes in time periods such as months, weeks, days, and hours. Therefore, according to the predicted time scale, the corresponding recent load can be input for model training. The recent loads for medium-term, short-term and ultra-short-term forecasts are the loads of the previous month, the previous week (day) and the previous few hours, respectively. It can be seen that the load fluctuation trend is helpful for the study of load change laws.

[0142] Electricity prices also have an impact on charging load. At present, many provinces in my country have introduced a policy mechanism for peak and valley time-of-use electricity prices, dividing the 24 hours of each day into three periods: peak, flat, and valley, corresponding to three levels of high, medium, and low electricity prices. As an economic regulation method, electricity prices will affect the owner's starting charging time (for example, most car owners prefer to charge during low-price periods), thereby affecting the daily load distribution curve of the charging station.

[0143] At present, several 10 kV large-scale charging stations have been connected within the Shinan power grid, which has a certain impact on the load rate of the corresponding main transformers and lines.

[0144] Vanke Station’s subordinate Wan17 Huqingping No. 4A – 79 Hu61 Huqingping Charging Station;

[0145] Vanke Station’s subordinate Wan 12 Qishen 8th A – 250,000 9 Huqing 1st Charging Station;

[0146] The charging piles of Hua 33 Huqingping No. 1B-Huqingping No. 10-Huqing #2 at the lower level of Hanghua Station;

[0147] The subordinate stations of the new Qibao Station are Town 1 Caobao No. 4A and Town 20 Caobao No. 4B.

[0148] Analyze the load of Hua 33 Shanghai-Qingping No. 1B line. What will the load be like on June 1, 2023? Fig.10 As shown in the figure, the load of Hua 33 Huqingping No. 1B line is relatively flat in the morning, starts to rise suddenly around 11 am, and starts to fall around 15 pm. Except for this period, it remains at a low level.

[0149] The load curve of Hua 33 Shanghai-Qingping No. 1B Line from June 1 to June 21, 2023 is as follows: Fig.11As shown, the horizontal axis represents one sampling point per minute from 00:00 to 23:59 a day, for a total of 1440 sampling points. The vertical axis represents the 3 weeks from June 1 to 21, 2023, a total of 21 days. The z-axis represents the load value of each sampling point every day. Looking at the load data of a certain day alone, the charging behavior of large charging stations mainly occurs from 11 am to 3 pm. After three consecutive weeks of observation, it can be seen that the same week type also shows a certain similarity, which is manifested as a peak on the vertical axis.

[0150] By adopting the prediction method as described in Example 1, the prediction of charging load throughout the year can be achieved.

[0151] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the described embodiments. Those skilled in the art may make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A method for dynamically managing the load rate of a main power grid device, characterized in that: The following steps are involved: S1: Obtain power consumption data through the dispatching SCADA system and the middle station, and pre-process the power consumption data to obtain a power grid main equipment load rate database, where the classification of the power grid main equipment load rate database includes the substation load rate, main transformer load rate and line load rate; S2: classify the data in the load rate database of the main equipment of the power grid according to the user type and analyze the load growth trend; S3: Based on the analysis results in step S2, the peak load forecast is further automatically performed before the summer peak and winter peak; S4: After the summer peak and winter peak loads appear, the load analysis output is completed in combination with the prediction results of step S3.

2. The method for dynamic management of load rate of main equipment of power grid according to claim 1 is characterized in that: The step S2 is classified according to user type, specifically: the data in the power grid main equipment load rate database is divided into: residential area load analysis, industrial user load analysis, commercial user load analysis, data center load analysis and charging load analysis.

3. The method for dynamic management of load rate of main equipment of power grid according to claim 2 is characterized in that: The residential area load analysis, industrial user load analysis, commercial user load analysis, data center load analysis and charging load analysis specifically include the following steps: Select a reference period, where the reference period is specifically: the days with the highest load or the month with the highest load in the power consumption data obtained from the SCADA system and the middle station; Calculate the load coefficients of different time periods according to the load curve of the reference period and the actual load data, the coefficients including: daily load coefficient and monthly load coefficient; Based on the load factor and benchmark load data, the load at a certain period in the future is predicted.

4. The method for dynamic management of load rate of main equipment of power grid according to claim 3 is characterized in that: The load prediction for a certain period of time in the future is specifically: S21: Establish an ARIMA model and calculate the lagged value of the load factor in the model: A t =α+ω1A t-1 +ω2A t-2 +…+oh p A t-p +r t Among them, ω1, ω2, …, ω p is the model parameter, α is a constant, ρ t is white noise, p is a fixed value of 12; S22: Calculate the lagged values ​​of the statistically incorrect load factor used in the model: A t =b+ρ t+ ω1ρ t-1 +ω2ρ t-2 +…+oh q r t-q Where b is a constant, ω1ρ t-1 ,ω2ρ t-2 ,…,ω q ρ t-q , is the white noise in the past, and q is a fixed value of 12; S23: Calculate the difference order in the model.

5. The method for dynamic management of load rate of main equipment of power grid according to claim 4 is characterized in that: The calculation of the difference order in the model specifically includes the following steps: Convert the load factor at a certain time into the load factor at the previous moment: By t =y t-1 Among them, y t is the time series of the load factor observation period, B is the lag operation, and the above formula is lagged by n time steps, that is: B n and t =and t-n Using hysteresis operation, we can express the first-order difference and n-order difference, where the first-order difference is the difference between adjacent label values, which can be expressed as: Δy t =y t -y t-1 =y t -By t =(1-B)y t Then, the n-order difference is to subtract n-1 label values ​​from each other: Δ n and t =and t -and t-n =and t -B n and t (1-B) n )and t The d-order difference can be expressed as: d_order_y=(1-B) d y t。 6. The method for dynamic management of load rate of main equipment of power grid according to claim 5 is characterized in that: The step S3 is specifically as follows: according to the residential area load, industrial user load, commercial user load, data center load and charging load prediction results obtained in step S2, the load rate prediction values ​​of the summer peak and winter peak are calculated respectively, and compared with the actual summer peak and winter peak load rate values ​​to complete the evaluation.

7. A power grid main equipment load rate dynamic management system, the system is applicable to the power grid main equipment load rate dynamic management method proposed in claims 1-6, characterized in that: include: The data acquisition module is used to obtain power consumption data through the dispatching SCADA system and the middle station, and pre-process the power consumption data to obtain the load rate database of the main equipment of the power grid; The data processing module is used to classify the data in the load rate database of the main power grid equipment according to user type, and analyze the load growth trend thereof. Based on the analysis results, the peak load is further automatically predicted before the summer peak and winter peak.

8. A power grid main equipment load rate dynamic management system, according to claim 7, characterized in that: include: The data output module is used to complete the load analysis output in combination with the forecast results after the summer peak and winter peak loads appear.

9. A device for dynamically managing the load rate of a main power grid device, the device being applicable to the method for dynamically managing the load rate of a main power grid device proposed in claims 1 to 6, characterized in that: The method comprises a processor and a memory for storing a computer program, wherein when the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium, which is applicable to the method for dynamically managing the load rate of a main device of a power grid as proposed in claims 1-6, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by one or more processors, the steps of the method according to any one of claims 1 to 6 are implemented.

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