Load-side trading method, system and related equipment based on the peak-valley difference rate of the power grid

Abstract

The present invention discloses a load-side trading method, system and related equipment based on the peak-valley difference rate of the power grid. The method includes: S1, collecting load index data related to the peak-valley difference of the load, the load index system, and obtaining historical unified regulation load data, and determining the evaluation index type by using the correlation analysis method; S2, establishing a load prediction model according to the evaluation index type, and the load prediction model is used to calculate and obtain the multi-scenario trading scale, trading date, and monthly average peak-valley difference rate through decomposition calculation in the trading cycle; S3, obtaining the current annual peak-valley difference rate reduction target, and using the load prediction model to predict the electricity load within a predetermined time and then calculating and outputting the planned average peak-valley difference rate; S4, constructing an automatic trading plan model based on the peak-valley difference rate according to the planned average peak-valley difference rate and conducting load trading according to the automatic trading plan model. Through big data analysis, a multi-element fusion high-elasticity load-side trading trigger mechanism is designed to reduce the peak-valley difference rate of the user load.

Description

Technical Field

[0001] The present invention relates to the technical field of power trading, and particularly to a load-side trading method, system and related equipment based on the grid peak-valley difference rate. Background Art

[0002] With the continuous economic development and the improvement of people's living standards, the highest electricity load of the whole society has been rising steadily, which has also led to the continuous increase of the maximum peak-valley difference of the unified regulated load in recent years. For example, the maximum peak-valley difference rates in the recent three years are 47.89%, 48.35%, and 49.14% respectively.

[0003] While the electricity load is increasing continuously and the peak-valley difference of electricity consumption is continuously increasing, due to the continuous increase of new energy in the energy proportion and the irregularity of new energy power generation itself, the conventional peak regulation methods mainly based on traditional thermal power and pumped storage can no longer meet the grid peak regulation requirements. The difficulty and cost of peak regulation are increasing day by day.

[0004] After establishing the strategic goal of building a multi-element integrated high-elasticity power grid with massive resources awakened, full interaction among the power grid, load, and energy storage, and double improvement of safety and efficiency, around the four core links of the power system of the power grid, load, and energy storage, through eight aspects such as flexible grid planning, strong grid, grid-guided multi-energy interconnection, safe bearing, tolerance and anti-interference, equipment potential tapping, efficient operation, awakening and aggregation of resources on each side, elastic balance of the power grid, load, and energy storage, reform mechanism matching, and scientific and technological innovation leading digital intelligence empowerment, etc., to achieve the specific implementation of promoting multi-element integration. With the orderly progress of the construction of the supporting market mechanism of the high-elasticity power grid, it provides more diversified subjects, more diverse contents, and more flexible choices for grid peak regulation.

[0005] Among them, diversified resources such as the load side, power source side, and energy storage side are important factors in integrating into the high-elasticity power grid market. Therefore, it is necessary to study the trigger mechanism for load-side resources to participate in the trading of the multi-element integrated high-elasticity power grid, expand the scale and regulation ability of load-side resources participating in the market, gradually reduce the peak-valley load difference, and ensure the safe and economic operation of the power grid. Summary of the Invention

[0006] The purpose of the present invention is to provide a load-side trading method, system and related equipment based on the grid peak-valley difference rate to solve the deficiencies of the existing conventional peak regulation methods, improve the peak regulation efficiency, and reduce the difficulty and cost of peak regulation.

[0007] To solve the above technical problems, an embodiment of the present invention provides a load-side trading method based on the grid peak-valley difference rate, including:

[0008] S1. Collect load index data related to the peak-valley difference of load, the load index system, and obtain historical unified adjusted load data. After analyzing the correlation between the load index system and load resource regulation based on the historical unified adjusted load data using the correlation analysis method, determine the evaluation index type;

[0009] S2. Establish a load forecasting model according to the evaluation index type. The load forecasting model is used to calculate and obtain the multi-scenario trading scale, trading date, and monthly average peak-valley difference rate through decomposition calculation by trading cycle;

[0010] S3. Obtain the current annual target for reducing the peak-valley difference rate, and use the load forecasting model to forecast the electricity load within a predetermined time, and then calculate and output the planned average peak-valley difference rate;

[0011] S4. Construct an automatic trading plan model based on the peak-valley difference rate according to the planned average peak-valley difference rate and conduct load trading according to the automatic trading plan model;

[0012] Among them, the automatic trading plan model includes taking the minimum of the target monthly average peak-valley difference rate after participating in the load-side trading and the planned average peak-valley difference rate as the objective function and the corresponding constraints, with the trading date, peak shaving capacity, and valley filling capacity as variables;

[0013] The objective function is: min|target monthly average peak-valley difference rate - the planned average peak-valley difference rate|;

[0014] The constraints include daily peak-valley difference rate constraint and target monthly average peak-valley difference rate constraint:

[0015] Among them, the daily peak-valley difference rate = [(daily maximum load - daily peak shaving capacity) - (daily minimum load - daily valley filling capacity)] / (daily maximum load - daily peak shaving capacity);

[0016] The target monthly average peak-valley difference rate constraint = Σ daily peak-valley difference rate / number of days in a month.

[0017] Among them, the correlation analysis method includes comparative analysis method, structural analysis method, trend analysis method, and factor analysis method.

[0018] Among them, the load index data is typical load index data constructed based on dimensions such as load characteristics, load curve changes, and monthly load fluctuations and screened by preset typical load indexes.

[0019] Among them, the load index data includes the annual maximum load, the annual minimum load, the daily maximum load, the daily minimum load, the annual maximum peak-valley difference rate, the monthly maximum peak-valley difference rate, the annual average peak-valley difference rate, the monthly average peak-valley difference rate, and the daily peak-valley difference rate. The load index system includes a load dimension and a peak-valley difference dimension. Among them, the load dimension includes the annual maximum load, the annual minimum load, the daily maximum load, and the daily minimum load. The peak-valley difference dimension includes the annual average peak-valley difference rate, the annual maximum peak-valley difference rate, the monthly average peak-valley difference rate, the monthly maximum peak-valley difference rate, and the daily peak-valley difference rate.

[0020] Among them, the S4 further includes:

[0021] Solve the automatic trading plan model by using the reverse dynamic programming method or the forward dynamic programming method.

[0022] In addition, the embodiments of the present application also provide a load-side trading system based on the grid peak-valley difference rate, including:

[0023] An evaluation index type determination module, configured to collect load index data related to the load peak-valley difference, a load index system, and obtain historical unified regulation load data, and determine the evaluation index type after analyzing the correlation between the load index system and the load resource regulation according to the historical unified regulation load data by using the correlation analysis method;

[0024] A load prediction model construction module, configured to establish a load prediction model according to the evaluation index type. The load prediction model is used to calculate the multi-scenario trading scale and trading date through decomposition calculation in a trading cycle, and obtain the monthly average peak-valley difference rate;

[0025] A peak-valley difference rate calculation module, configured to obtain the current annual peak-valley difference rate reduction target, and calculate and output the planned average peak-valley difference rate after predicting the electricity load within a predetermined time by using the load prediction model;

[0026] An automatic trading plan model construction module, configured to construct an automatic trading plan model based on the peak-valley difference rate according to the planned average peak-valley difference rate and perform load trading according to the automatic trading plan model;

[0027] Among them, the automatic trading plan model includes an objective function with the minimum of the target monthly average peak-valley difference rate after participating in the load-side trading and the planned average peak-valley difference rate and corresponding constraint conditions, and uses the trading date, peak shaving capacity, and valley filling capacity as variables;

[0028] The objective function is: min|target monthly average peak-valley difference rate - the planned average peak-valley difference rate|;

[0029] The constraint conditions include the daily peak-valley difference rate constraint and the target monthly average peak-valley difference rate constraint:

[0030] Among them, the daily peak-valley difference rate = [(daily maximum load - daily peak shaving capacity) - (daily minimum load - daily valley filling capacity)] / (daily maximum load - daily peak shaving capacity);

[0031] The target monthly average peak-valley difference rate constraint = Σ daily peak-valley difference rate / number of days in a month.

[0032] Among them, there is also a method selection module connected to the evaluation index type determination module and the automatic trading plan model construction module, which is used to determine the specific types of the correlation analysis method and the dynamic programming method. Among them, the correlation analysis method includes the comparative analysis method, the structural analysis method, the trend analysis method and the factor analysis method, and the dynamic programming method includes the reverse dynamic programming method and the sequential dynamic programming method.

[0033] Among them, there is also a load index determination module connected to the evaluation index type determination module, which is used to determine the types of the load index data and the load index system. Among them, the load index data is typical load index data constructed based on load characteristics, load curve changes, and monthly load fluctuations and screened by preset typical load indexes. The load index data includes the annual maximum load, the annual minimum load, the daily maximum load, the daily minimum load, the annual maximum peak-valley difference rate, the monthly maximum peak-valley difference rate, the annual average peak-valley difference rate, the monthly average peak-valley difference rate, and the daily peak-valley difference rate. The load index system includes a load dimension and a peak-valley difference dimension; among them, the load dimension includes: the annual maximum load, the annual minimum load, the daily maximum load and the daily minimum load; the peak-valley difference dimension includes: the annual average peak-valley difference rate, the annual maximum peak-valley difference rate, the monthly average peak-valley difference rate, the monthly maximum peak-valley difference rate, and the daily peak-valley difference rate.

[0034] In addition, an embodiment of the present application also provides a device for load-side trading based on the grid peak-valley difference rate, including:

[0035] A memory for storing a computer program;

[0036] A processor for executing the computer program to implement the steps of the load-side trading method based on the grid peak-valley difference rate as described above.

[0037] In addition, an embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored, and the computer program is executed by a processor to implement the steps of the load-side trading method based on the grid peak-valley difference rate as described above.

[0038] The load - side trading method, system and related equipment based on the peak - valley difference rate of the power grid provided by the embodiments of the present invention have the following advantages compared with the prior art:

[0039] The load - side trading method, system and related equipment based on the peak - valley difference rate of the power grid collect load index data, load index systems, and historical unified - regulation load data related to the peak - valley difference of the load for power big - data analysis. After studying the correlation between the peak - valley difference of the power grid and the regulation ability of the load - side resources, a load forecasting model is established according to the type of evaluation index, the current annual peak - valley difference rate reduction target is obtained, and the load forecasting model is used to forecast the electricity load within a predetermined time, and then the planned average peak - valley difference rate is calculated and output. Finally, an automatic trading plan model based on the peak - valley difference rate is constructed according to the planned average peak - valley difference rate, and load trading is carried out according to the automatic trading plan model. That is, by designing a multi - element fusion high - elasticity load - side trading trigger mechanism based on the peak - valley difference of the power grid, a long - term grid - security market trigger mechanism is established to reduce the user's load peak - valley difference rate, and the preset value of the peak - valley difference rate of the power grid is reduced through the market mechanism; coordinate the load - side trading plan with the control center, reduce the positive and negative reserves of the power grid, and reduce the power - grid operation cost to solve the deficiencies of the existing conventional peak - shaving methods, improve the peak - shaving efficiency, and reduce the difficulty and cost of peak - shaving. Brief Description of the Drawings

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following - described drawings are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0041] Figure 1 It is a schematic diagram of the step - by - step process of a specific implementation manner of the load - side trading method based on the peak - valley difference rate of the power grid provided by the embodiments of the present invention;

[0042] Figure 2 It is a schematic diagram of the comparison between load changes and the peak - valley difference rate in an embodiment of the load - side trading method based on the peak - valley difference rate of the power grid provided by the embodiments of the present invention;

[0043] Figure 3 It is a planned graph of the average peak - valley difference rate for each month in an embodiment of the load - side trading method based on the peak - valley difference rate of the power grid provided by the embodiments of the present invention;

[0044] Figure 4 It is a trading - plan graph for January in an embodiment of the load - side trading method based on the peak - valley difference rate of the power grid provided by the embodiments of the present invention;

[0045] Figure 5It is a schematic structural diagram of a specific implementation manner of the load - side trading method and system based on the peak - valley difference rate of the power grid provided by the embodiments of the present invention. Specific implementation manner

[0046] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0047] Please refer to Figures 1 to 5 , Figure 1 It is a schematic step - flow diagram of a specific implementation manner of the load - side trading method based on the peak - valley difference rate of the power grid provided by the embodiments of the present invention; Figure 2 It is a schematic diagram of the comparison between load changes and peak - valley difference rates in an embodiment of the load - side trading method based on the peak - valley difference rate of the power grid provided by the embodiments of the present invention; Figure 3 It is a monthly average peak - valley difference rate plan diagram in an embodiment of the load - side trading method based on the peak - valley difference rate of the power grid provided by the embodiments of the present invention; Figure 4 It is a trading plan diagram for January in an embodiment of the load - side trading method based on the peak - valley difference rate of the power grid provided by the embodiments of the present invention; Figure 5 It is a schematic structural diagram of a specific implementation manner of the load - side trading method and system based on the peak - valley difference rate of the power grid provided by the embodiments of the present invention.

[0048] In a specific implementation manner, the load - side trading method based on the peak - valley difference rate of the power grid includes:

[0049] S1. Collect load index data, a load index system related to the peak - valley difference of the load, and obtain historical unified - adjusted load data. After analyzing the correlation between the load index system and load resource regulation according to the historical unified - adjusted load data by using the correlation analysis method, determine the type of evaluation index;

[0050] S2. Establish a load forecasting model according to the type of evaluation index. The load forecasting model is used to calculate and obtain multi - scenario trading scales, trading dates, and monthly average peak - valley difference rates through decomposition calculation in a trading cycle;

[0051] S3. Obtain the current annual peak - valley difference rate reduction target, and use the load forecasting model to predict the electricity load within a predetermined time and then calculate and output the planned average peak - valley difference rate;

[0052] S4. Construct an automatic trading plan model based on the peak-valley difference rate according to the planned average peak-valley difference rate, and conduct load trading according to the automatic trading plan model;

[0053] Among them, the automatic trading plan model includes taking the minimum of the target monthly average peak-valley difference rate after participating in the load-side trading and the planned average peak-valley difference rate as the objective function and the corresponding constraints, with the trading date, peak shaving capacity, and valley filling capacity as variables;

[0054] The objective function is: min|target monthly average peak-valley difference rate - the planned average peak-valley difference rate|;

[0055] The constraints include daily peak-valley difference rate constraints and target monthly average peak-valley difference rate constraints:

[0056] Among them, the daily peak-valley difference rate = [(daily maximum load - daily peak shaving capacity) - (daily minimum load - daily valley filling capacity)] / (daily maximum load - daily peak shaving capacity);

[0057] The target monthly average peak-valley difference rate constraint = Σ daily peak-valley difference rate / number of days in a month.

[0058] Among them, the daily peak-valley difference rate constraint reflects the daily peak-valley difference rate. If the peak shaving capacity is 0, it means that no peak shaving trading is carried out on that day. If the valley filling capacity is 0, it means that no valley filling trading is carried out on that day; the target monthly average peak-valley difference rate constraint reflects the monthly average peak-valley difference rate after participating in the load-side trading.

[0059] Through collecting load index data related to the load peak-valley difference, the load index system, and historical unified adjusted load data for power big data analysis, studying the correlation between the grid peak-valley difference and the load-side resource regulation ability, establishing a load forecasting model according to the evaluation index type, obtaining the current annual peak-valley difference rate reduction target, using the load forecasting model to forecast the electricity load within a predetermined time, calculating and outputting the planned average peak-valley difference rate, and finally constructing an automatic trading plan model based on the peak-valley difference rate according to the planned average peak-valley difference rate and conducting load trading according to the automatic trading plan model. That is, by designing a multi-element fusion high-elastic load-side trading trigger mechanism based on the grid peak-valley difference, establishing a long-term grid security market trigger mechanism, reducing the user load peak-valley difference rate, and achieving the preset value of the grid peak-valley difference rate reduction through the market mechanism; coordinating the load-side trading plan with the control center, reducing the positive and negative reserves of the grid, and reducing the grid operation cost to solve the deficiencies of the existing conventional peak regulation methods, improving the peak regulation efficiency, and reducing the difficulty and cost of peak regulation.

[0060] In this application, typical load indicators are first collected. For the purpose of the safe operation of the power grid, a load indicator system is screened out, historical unified dispatching load data is obtained, and the correlation analysis method is used to analyze the correlation between the load indicator system and the load resource regulation ability according to the historical unified dispatching load data, so as to determine evaluation indicators and establish a load forecasting model. The load forecasting model conducts unified dispatching load forecasting for the trading cycle. According to the annual peak-valley difference reduction target, the load-side trading target is decomposed into months, and the multi-scenario trading scale and trading dates are calculated based on the monthly target and load forecasting. Then, an automatic trading plan model based on the peak-valley difference rate is constructed according to the planned average peak-valley difference rate.

[0061] In one embodiment, the comparison between load changes and the peak-valley difference rate is as Figure 2 shown. From the comparison of load and peak-valley difference rate, using the comparative analysis method, if the indicator system only includes indicators reflecting the peak-valley difference rate, it cannot well reflect the changes in the maximum load and minimum load, which is not conducive to the development of load-side trading. Therefore, the indicator system should include two load indicators, namely the maximum load and the minimum load.

[0062] This application specifically defines the correlation analysis method. The correlation analysis method includes the comparative analysis method, the structural analysis method, the trend analysis method, and the factor analysis method, and may also include other methods.

[0063] To facilitate an in-depth and comprehensive analysis of the research object and fully grasp the development and change law of the research object, in addition to establishing a complete set of indicator systems, a scientific indicator analysis method should also be available to deeply excavate the indicator system. The indicator system analysis method in this application is summarized as follows:

[0064] The comparative analysis method is to compare objective things to understand the essence and law of things and make a correct evaluation. Usually, two interrelated indicator data are compared to show and explain the size of the scale, the level of the research object, the speed of change, and whether various relationships are coordinated in terms of quantity. In the process of comparison, the time standard can be selected, that is, the indicator values at different times are selected for comparison. The most commonly used is to compare with the same period of the previous year, that is, "year-on-year", and it can also be compared with the previous period. In addition, it can also be compared with the period when the historical best level is reached or some key periods in history. The space standard can also be selected, that is, different spatial indicator data are selected for comparison. It can be compared with similar objects, such as the comparison of electricity sales levels between different cities; it can also be compared with the average level, such as the comparison of the electricity sales volume of a certain city with the average electricity sales volume of the whole province. The plan standard can also be selected, that is, the actual execution result is compared with the planned indicator, etc.

[0065] The structural analysis method refers to the analysis of the components in a system and the changing laws of their comparison relationships. Based on statistical grouping, it calculates the proportions of each component, and then analyzes the internal structural characteristics of a certain overall phenomenon, the nature of the overall, and the changing laws shown by the internal structure of the overall over time. The main applications of the structural analysis method include identifying the characteristics of the overall composition, revealing the changing trends of each component of the overall, studying the process of overall structural changes, revealing the law that the overall phenomenon gradually transforms from quantitative change to qualitative change, and revealing the dependence relationships between parts, etc.

[0066] The trend analysis method, also known as the horizontal analysis method, is a method of analyzing the historical data of similar indicators in a time series to determine the direction, amount, and amplitude of their increase or decrease, so as to observe the long-term trend and volatility of the indicator. In practical applications, there are usually two analysis methods. One is the absolute number trend analysis, which reflects the current situation and future development changes of the enterprise's indicators by comparing the indicators with historical periods; the second is the relative number trend analysis, which reflects the development speed of the indicators through various percentage indicators, such as change rate, contribution rate, ratio, etc.

[0067] The factor analysis method is a statistical analysis method used to measure the direction and degree of influence of each factor in the total change of a certain phenomenon affected by multiple factors. The advantage of this method is that it can quantitatively grasp the influence degree of each relevant factor affecting the analysis index, which is conducive to distinguishing the reasons and responsibilities, can point out the direction for the further development of the work, and can also objectively evaluate the work effect of the enterprise. The main principle of this method is to first determine the main factors affecting a certain index; then establish an analysis calculation formula according to the internal relationship between these factors; finally, conduct factor substitution in a certain order to determine the influence degree of each factor.

[0068] In this application, the acquisition method of the load index data is not limited. In one embodiment, the load index data is typical load index data constructed with load characteristics, load curve changes, and monthly load fluctuations as dimensions and screened by preset typical load indexes.

[0069] Load characteristics refer to the law that the active power and reactive power drawn by an electrical load from the power source of the power system change with the voltage at the load end and the system frequency.

[0070] The specific load characteristic indicators are as follows:

[0071] Annual / (semi-annual, quarterly, monthly, weekly, daily) maximum load; Annual / (semi-annual, quarterly, monthly, weekly, daily) minimum load; Annual / (semi-annual, quarterly, monthly, weekly, daily) average load; Annual / (semi-annual, quarterly, monthly, weekly, daily) load factor; Daily minimum load factor; Annual / (semi-annual, quarterly, monthly, weekly) minimum load factor; Daily peak-valley difference; Daily peak-valley difference rate; Annual / (semi-annual, quarterly, monthly, weekly) maximum peak-valley difference; Annual / (semi-annual, quarterly, monthly, weekly) maximum peak-valley difference rate; Annual / (semi-annual, quarterly, monthly, weekly) average peak-valley difference; Annual / (semi-annual, quarterly, monthly, weekly) average peak-valley difference rate; Monthly imbalance coefficient; Annual load probability distribution.

[0072] The load curve refers to the curve showing the variation law of load over time. Compared with load characteristics, the load curve can better reflect the original law of load variation. The load curve indicators are as follows.

[0073] Annual load curve; Annual duration load curve; Daily load curve; Typical working day curve; Saturday curve; Sunday curve; Semi-annual / (quarterly, monthly) maximum electricity day curve; Semi-annual / (quarterly, monthly) minimum electricity day curve; Semi-annual / (quarterly, monthly) maximum load day curve; Semi-annual / (quarterly, monthly) minimum load day curve; Semi-annual / (quarterly, monthly) maximum peak-valley difference day curve; Semi-annual / (quarterly, monthly) minimum peak-valley difference day curve.

[0074] Monthly load fluctuation refers to the fluctuating characteristics of load over time. Compared with the load curve, monthly load fluctuation can use probability statistical indicators to calculate the fluctuation of the load curve over a period of time, and the result is more intuitive. The monthly load fluctuation indicators are as follows.

[0075] Maximum load fluctuation coefficient; Minimum load fluctuation coefficient; Average load fluctuation coefficient; Load factor fluctuation coefficient; Peak-valley difference rate fluctuation coefficient.

[0076] In one embodiment, the calculation method of the load indicators is shown in Table 1.

[0077] Table 1 Calculation methods of typical indicators

[0078]

[0079]

[0080] According to the analysis of typical load indicator data, the indicators with the highest correlation with the grid peak-valley difference include nine technical indicators such as annual maximum load, annual minimum load, daily maximum load, daily minimum load, annual maximum peak-valley difference rate, monthly maximum peak-valley difference rate, annual average peak-valley difference rate, monthly average peak-valley difference rate, and daily peak-valley difference rate.

[0081] In some embodiments, the typical load index data includes: maximum load, minimum load, average load, load factor, daily minimum load factor, minimum load factor, daily peak-valley difference, daily peak-valley difference rate, maximum peak-valley difference, maximum peak-valley difference rate, average peak-valley difference rate, monthly imbalance coefficient, annual probability distribution, maximum load fluctuation coefficient, minimum load fluctuation coefficient, average load fluctuation coefficient, and load factor fluctuation coefficient;

[0082] The load index system includes a load dimension and a peak-valley difference dimension; wherein, the load dimension includes: annual maximum load, annual minimum load, daily maximum load, and daily minimum load; the peak-valley difference dimension includes: annual average peak-valley difference rate, annual maximum peak-valley difference rate, monthly average peak-valley difference rate, monthly maximum peak-valley difference rate, and daily peak-valley difference rate.

[0083] This application uses the dynamic programming method to solve the automatic trading plan model, and no limitation is imposed on the specific solution process. In one embodiment, S4 further includes:

[0084] Solving the automatic trading plan model using the reverse-order dynamic programming method or the forward-order dynamic programming method.

[0085] The calculation method of the annual maximum load is the annual maximum load, which reflects the peak load situation of the year.

[0086] The calculation method of the annual minimum load is the annual minimum load, which reflects the valley load situation of the year.

[0087] The calculation method of the daily maximum load is the maximum load within the day, which reflects the peak load situation of the day.

[0088] The calculation method of the daily minimum load is the minimum load within the day, which reflects the valley load situation of the day.

[0089] The calculation method of the annual maximum peak-valley difference rate is the maximum value of the daily peak-valley difference rates within the year, which reflects the maximum value of the annual load distribution imbalance.

[0090] The calculation method of the monthly maximum peak-valley difference rate is the maximum value of the daily peak-valley difference rates within the month, which reflects the maximum value of the monthly load distribution imbalance.

[0091] The calculation method of the annual average peak-valley difference rate is the average value of the daily peak-valley difference rates within the year, which reflects the average situation of the annual load distribution imbalance.

[0092] The calculation method of the monthly average peak-valley difference rate is the average value of the daily peak-valley difference rates within the month, which reflects the average situation of the monthly load distribution imbalance.

[0093] The calculation method of the daily peak-valley difference rate is (daily maximum load - daily minimum load) / daily maximum load, which reflects the imbalance of the daily load distribution and affects the safe operation of the power grid.

[0094] In one embodiment, this application analyzes the correlation based on the historical load data of unified dispatching in a certain place. The calculation results of indicators such as the minimum load, maximum load, maximum peak-valley difference rate, and average peak-valley difference rate in each month of 2018 in a certain place are shown in Table 2.

[0095] Table 2 Monthly unified dispatching load statistics table in a certain place in 2018

[0096]

[0097]

[0098] According to Table 2, the monthly unified dispatching load statistics in a certain place in 2018 are as follows: the average load in a certain place in 2018 is 61.45 million kilowatts; the maximum load occurs in September, with a maximum load of 100 million kilowatts; the minimum load occurs in February, with a minimum load of 13.424 million kilowatts; the maximum peak-valley difference occurs in September, with a maximum peak-valley difference of 46.88 million kilowatts.

[0099] From the perspective of the changes in load and peak-valley difference rate, using the trend analysis method, due to the two factors of the Spring Festival holiday and relatively low temperature from January to March in a certain place, the maximum load and minimum load are significantly lower than those in other months, and the maximum and average peak-valley difference rates are larger than those in other months; from April to June, due to the rising temperature and the resumption of normal industrial production, the maximum load and minimum load increase compared with January to March, and the average and maximum peak-valley difference rates are basically equal to those in other months; from July to September, due to the relatively high temperature, the maximum load and minimum load are larger than those in other months, and the maximum and average peak-valley difference rates are smaller than those in other months; from October to December, due to the decreasing temperature, the maximum load and minimum load decrease compared with July to September, and the average and maximum peak-valley difference rates are basically equal to those in other months.

[0100] As a specific embodiment, according to the calculation, the annual average peak-valley difference rate in a certain place in 2018 is 37%. Assuming that the annual average peak-valley difference rate in 2021 is 2 percentage points lower than that in 2018, then the planned annual average peak-valley difference rate in 2021 is 35%. The index of the decrease in the average peak-valley difference rate is evenly decomposed into each month, and the planned average peak-valley difference rate for each month in 2021 is as Figure 3 shown.

[0101] In one embodiment, this application first obtains the results of the daily maximum load, daily minimum load, and daily peak-valley difference rate before participating in the load-side transaction in January 2021, as shown in Table 3.

[0102] Table 3 Peak-valley difference rate data in January

[0103]

[0104]

[0105] Then sort them in descending order according to the daily peak-valley difference rate, as shown in Table 4.

[0106] Table 4 Daily peak-valley difference rate after sorting

[0107]

[0108]

[0109] Taking the trading date, peak shaving capacity, and valley filling capacity as variables, using the automatic trading plan model, the trading date on the load side in January, the load side capacity participating in peak shaving, and the load side capacity participating in valley filling are generated, as shown in Table 5.

[0110] Table 5 Calculation results of the trading scale in January

[0111]

[0112]

[0113] From the calculation results, it can be known the number of days of the load at the substation measured by long-duration trading in January. According to the calculation method in January, the calculations for February to December are carried out, as shown in Table 6.

[0114] Table 6 Trading situations in each month of 2021

[0115] Month Number of trading days (days) Maximum load after trading Minimum load after trading January 17 70406 27461 February 13 64880 14690 March 15 68877 32127 April 15 95160 35119 May 19 95160 37139 June 12 91411 44645 July 31 95788 51067 August 31 97428 49310 September 30 99518 26988 October 17 82707 34873 November 14 76526 39807 December 19 75227 39620 Total 233 / /

[0116] Using the analysis of historical load curves, it is recommended that the peak shaving trading period be set as [14:00, 17:00), and the valley filling trading period be [3:00, 6:00) in the early morning.

[0117] According to the unified dispatching load forecast results, sort the daily maximum load and the daily minimum load, and select the top 10 as the maximum load trigger threshold and the minimum load trigger threshold. According to the above index calculation method, calculate the maximum load and minimum load index trigger thresholds. The maximum load trigger value is 96003 MW, and the minimum load trigger value is 16941 MW. The number of trading days triggered by the maximum load and minimum load in each month is shown in Table 7.

[0118] Table 7 Number of trading days in each month

[0119]

[0120]

[0121] It can be seen that for the load - side transactions triggered by the maximum load, the peak - shaving trading capacity is arranged according to the maximum load capacity participating in the load - side transactions; for the load - side transactions triggered by the minimum load, the valley - filling trading capacity is arranged according to the maximum load capacity participating in the load - side transactions. By analyzing the historical load curve, it is recommended that the peak - shaving trading period be set as [14:00, 17:00), and the valley - filling trading period be set as [3:00, 6:00) in the early morning. Therefore, by combining the triggering mechanism based on the peak - valley difference rate and the triggering mechanism based on the maximum and minimum loads, an annual trading plan based on the average peak - valley difference target can be generated, as shown in Table 8.

[0122] Table 8 Monthly trading plans in 2021

[0123]

[0124]

[0125] The specific trading plans for each month are as Figure 4 shown.

[0126] In addition, the embodiments of the present application also provide a load - side trading system based on the peak - valley difference rate of the power grid, including:

[0127] An evaluation index type determination module 10, configured to collect load index data, a load index system related to the peak - valley difference of the load, and obtain historical unified - adjusted load data, and determine the evaluation index type after analyzing the correlation between the load index system and load resource regulation according to the historical unified - adjusted load data by using the correlation analysis method;

[0128] A load prediction model construction module 20, configured to establish a load prediction model according to the evaluation index type, and the load prediction model is used to calculate and obtain multi - scenario trading scales, trading dates, and monthly average peak - valley difference rates through decomposition calculation in a trading cycle;

[0129] A peak - valley difference rate calculation module 30, configured to obtain the current annual peak - valley difference rate reduction target, and calculate and output the planned average peak - valley difference rate after predicting the power consumption load within a predetermined time by using the load prediction model;

[0130] An automatic trading plan model construction module 40, configured to construct an automatic trading plan model based on the peak - valley difference rate according to the planned average peak - valley difference rate and conduct load trading according to the automatic trading plan model;

[0131] Among them, the automatic trading plan model includes an objective function with the minimum of the target monthly average peak - valley difference rate after participating in the load - side transaction and the planned average peak - valley difference rate and corresponding constraint conditions, with trading dates, peak - shaving capacity, and valley - filling capacity as variables;

[0132] The objective function is: min|target monthly average peak-valley difference rate - planned average peak-valley difference rate|;

[0133] The constraint conditions include daily peak-valley difference rate constraint and target monthly average peak-valley difference rate constraint:

[0134] Among them, the daily peak-valley difference rate = [(daily maximum load - daily peak shaving capacity) - (daily minimum load - daily valley filling capacity)] / (daily maximum load - daily peak shaving capacity);

[0135] The target monthly average peak-valley difference rate constraint = Σ daily peak-valley difference rate / number of days in a month.

[0136] Since the load-side trading system based on the grid peak-valley difference rate is the system corresponding to the load-side trading method based on the grid peak-valley difference rate and has the same beneficial effects, this application will not elaborate on this.

[0137] Since in the system of this application, in actual calculations, there are several methods such as the correlation analysis method and the dynamic programming method. The load-side trading system based on the grid peak-valley difference rate further includes a method selection module connected to the evaluation index type determination module 10 and the automatic trading plan model construction module 40, which is used to determine the specific types of the correlation analysis method and the dynamic programming method. Among them, the correlation analysis method includes the comparative analysis method, the structural analysis method, the trend analysis method, and the factor analysis method, and the dynamic programming method includes the reverse dynamic programming method and the forward dynamic programming method.

[0138] This application includes but is not limited to the above methods. Before the staff realizes the calculation, they can select a suitable calculation method to achieve the optimal calculation method selection. Moreover, by selecting different methods, the optimal method group can be judged, and the calculation optimization can be improved by selecting the calculation speed, accuracy, etc.

[0139] In this application, no specific limitations are imposed on the load index data and the load index system. In one embodiment, the load-side trading system based on the peak-valley difference rate of the power grid further includes a load index determination module connected to the evaluation index type determination module, which is used to determine the types of the load index data and the load index system. Among them, the load index data is typical load index data constructed based on load characteristics, load curve changes, and monthly load fluctuations and screened by preset typical load indexes. The load index data includes the annual maximum load, the annual minimum load, the daily maximum load, the daily minimum load, the annual maximum peak-valley difference rate, the monthly maximum peak-valley difference rate, the annual average peak-valley difference rate, the monthly average peak-valley difference rate, and the daily peak-valley difference rate. The load index system includes a load dimension and a peak-valley difference dimension. Among them, the load dimension includes: the annual maximum load, the annual minimum load, the daily maximum load, and the daily minimum load; the peak-valley difference dimension includes: the annual average peak-valley difference rate, the annual maximum peak-valley difference rate, the monthly average peak-valley difference rate, the monthly maximum peak-valley difference rate, and the daily peak-valley difference rate.

[0140] Staff members can choose to add or reduce specific data types according to needs, and this application does not limit this.

[0141] In addition, the embodiments of this application also provide a device for load-side trading based on the peak-valley difference rate of the power grid, including:

[0142] A memory for storing a computer program;

[0143] A processor for executing the computer program to implement the steps of the load-side trading method based on the peak-valley difference rate of the power grid as described above.

[0144] Since the processor in the device for load-side trading based on the peak-valley difference rate of the power grid is used to execute the computer program to implement the steps of the load-side trading method based on the peak-valley difference rate of the power grid as described above, and has the same beneficial effects, this application will not elaborate on this.

[0145] The device memory and processor for load-side trading based on the peak-valley difference rate of the power grid may further include a network interface. The memory stores a computer program. The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, etc., such as read-only memory (ROM) or flash memory (flashRAM). This computer device stores an operating system, and the memory is an example of a computer-readable medium.

[0146] In one embodiment, the load-side trading method based on the peak-valley difference rate of the power grid provided by this application can be implemented in the form of a computer program, and the computer program can run on a computer device.

[0147] In some embodiments, when the computer program is executed by the processor, the processor is caused to perform the following steps: collecting typical load index data to obtain a load index system related to the peak-valley difference of the load; obtaining historical unified regulation load data, and using the correlation analysis method to analyze the correlation between the load index system and the load resource regulation ability according to the historical unified regulation load data to determine an evaluation index; establishing a load prediction model according to the evaluation index, where the load prediction model is used to decompose a trading cycle, calculate a multi-scenario trading scale and trading dates, and obtain a monthly average peak-valley difference rate; using the load prediction model to predict the power consumption load based on a preset annual peak-valley difference rate reduction target to calculate a planned average peak-valley difference rate; and constructing an automatic trading plan model based on the peak-valley difference rate according to the planned average peak-valley difference rate.

[0148] In addition, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, and the computer program is executed by a processor to implement the steps of the load-side trading method based on the grid peak-valley difference rate as described above.

[0149] Similarly, the computer-readable storage medium has the same technical effects as above.

[0150] The storage medium of the computer includes, but is not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tape storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device.

[0151] In summary, for the load-side trading method, system and related devices based on the grid peak-valley difference rate provided by the embodiments of the present invention, by collecting load index data, load index systems, and historical unified regulation load data related to the grid peak-valley difference for power big data analysis, after studying the correlation between the grid peak-valley difference and the load-side resource regulation ability, a load prediction model is established according to the type of evaluation index, the current annual peak-valley difference rate reduction target is obtained, and the planned average peak-valley difference rate is calculated and output after predicting the power consumption load within a predetermined time using the load prediction model. Finally, an automatic trading plan model based on the peak-valley difference rate is constructed according to the planned average peak-valley difference rate, and load trading is carried out according to the automatic trading plan model. That is, by designing a multi-source fusion high-elasticity load-side trading trigger mechanism based on the grid peak-valley difference, a long-term grid security market trigger mechanism is established to reduce the user's load peak-valley difference rate, and the preset value of the grid peak-valley difference rate reduction is achieved through the market mechanism; coordinating the load-side trading plan with the control center, reducing the positive and negative reserves of the grid, and reducing the grid operation cost, so as to solve the deficiencies of the existing conventional peak regulation methods, improve the peak regulation efficiency, and reduce the difficulty and cost of peak regulation.

[0152] The load-side trading method, system and related devices based on the grid peak-valley difference rate provided by the present invention have been introduced in detail above. Specific examples are used in this article to elaborate on the principle and implementation manner of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention. It should be noted that for those of ordinary skill in the art in this technical field, without departing from the principle of the present invention, several improvements and modifications can be made to the present invention, and these improvements and modifications also fall within the protection scope of the claims of the present invention.

Claims

1. A load-side trading method based on the peak-valley difference rate of the power grid, characterized in that Including: S1. Collect load index data related to the peak-valley difference of load, the load index system, and obtain historical unified adjusted load data. After analyzing the correlation between the load index system and load resource regulation according to the historical unified adjusted load data by using the correlation analysis method, determine the evaluation index type; S2. Establish a load forecasting model according to the evaluation index type. The load forecasting model is used to calculate the multi-scenario trading scale and trading date through decomposition calculation in the trading cycle, and obtain the monthly average peak-valley difference rate; S3. Obtain the current annual peak-valley difference rate reduction target, and use the load forecasting model to forecast the electricity load within a predetermined time, and then calculate and output the planned average peak-valley difference rate; S4. Construct an automatic trading plan model based on the peak-valley difference rate according to the planned average peak-valley difference rate, and conduct load trading according to the automatic trading plan model; Among them, the automatic trading plan model includes the objective function and corresponding constraint conditions with the minimum of the target monthly average peak-valley difference rate after participating in the load-side trading and the planned average peak-valley difference rate as the objective function, and uses the trading date, peak shaving capacity, and valley filling capacity as variables; The objective function is: min|target monthly average peak-valley difference rate - the planned average peak-valley difference rate|; The constraint conditions include daily peak-valley difference rate constraint and target monthly average peak-valley difference rate constraint: Among them, the daily peak-valley difference rate = 【(daily maximum load - daily peak shaving capacity) - (daily minimum load - daily valley filling capacity)】 / (daily maximum load - daily peak shaving capacity); The target monthly average peak-valley difference rate constraint = Σ daily peak-valley difference rate / number of days in a month; The correlation analysis method includes comparative analysis method, structural analysis method, trend analysis method, and factor analysis method; The load index data is typical load index data constructed based on the dimensions of load characteristics, load curve changes, and monthly load fluctuations and screened by preset typical load indexes.

2. The load-side trading method based on the peak-valley difference rate of the power grid according to claim 1, wherein The load index data includes annual maximum load, annual minimum load, daily maximum load, daily minimum load, annual maximum peak-valley difference rate, monthly maximum peak-valley difference rate, annual average peak-valley difference rate, monthly average peak-valley difference rate, and daily peak-valley difference rate. The load index system includes a load dimension and a peak-valley difference dimension; among them, the load dimension includes: annual maximum load, annual minimum load, daily maximum load, and daily minimum load; the peak-valley difference dimension includes: annual average peak-valley difference rate, annual maximum peak-valley difference rate, monthly average peak-valley difference rate, monthly maximum peak-valley difference rate, and daily peak-valley difference rate.

3. The load-side trading method based on the peak-valley difference rate of the power grid according to claim 2, wherein S4 further includes: Solving the automatic trading plan model by using the reverse dynamic programming method or the sequential dynamic programming method.

4. A load - side trading system based on the peak - valley difference rate of the power grid, characterized in that, Including: An evaluation index type determination module, which is used to collect load index data related to the peak-valley difference of load, the load index system, and obtain historical unified adjusted load data. After analyzing the correlation between the load index system and load resource regulation according to the historical unified adjusted load data by using the correlation analysis method, determine the evaluation index type; A load forecasting model construction module, which is used to establish a load forecasting model according to the type of evaluation index. The load forecasting model is used to calculate and obtain the multi-scenario trading scale, trading date, and monthly average peak-valley difference rate through decomposition calculation by trading cycle; A peak-valley difference rate calculation module, which is used to obtain the current annual peak-valley difference rate reduction target, and calculate and output the planned average peak-valley difference rate after predicting the electricity load within a predetermined time by using the load forecasting model; An automatic trading plan model construction module, which is used to construct an automatic trading plan model based on the peak-valley difference rate according to the planned average peak-valley difference rate and conduct load trading according to the automatic trading plan model; Among them, the automatic trading plan model includes an objective function with the minimum of the target monthly average peak-valley difference rate after participating in the load-side trading and the planned average peak-valley difference rate and corresponding constraint conditions, with the trading date, peak shaving capacity, and valley filling capacity as variables; The objective function is: min|target monthly average peak-valley difference rate - the planned average peak-valley difference rate|; The constraint conditions include daily peak-valley difference rate constraint and target monthly average peak-valley difference rate constraint: Among them, the daily peak-valley difference rate = 【(daily maximum load - daily peak shaving capacity) - (daily minimum load - daily valley filling capacity)】 / (daily maximum load - daily peak shaving capacity); The target monthly average peak-valley difference rate constraint = Σ daily peak-valley difference rate / number of days in a month; It also includes a method selection module connected to the evaluation index type determination module and the automatic trading plan model construction module, which is used to determine the specific types of the correlation analysis method and the dynamic programming method. Among them, the correlation analysis method includes comparative analysis method, structural analysis method, trend analysis method, and factor analysis method, and the dynamic programming method includes reverse dynamic programming method and sequential dynamic programming method.

5. The load-side trading system based on the grid peak-valley difference rate according to claim 4, characterized in that It also includes a load index determination module connected to the evaluation index type determination module, which is used to determine the type of the load index data and the load index system. Among them, the load index data is typical load index data constructed based on the dimensions of load characteristics, load curve changes, and monthly load fluctuations and screened by preset typical load indexes. The load index data includes annual maximum load, annual minimum load, daily maximum load, daily minimum load, annual maximum peak-valley difference rate, monthly maximum peak-valley difference rate, annual average peak-valley difference rate, monthly average peak-valley difference rate, and daily peak-valley difference rate. The load index system includes a load dimension and a peak-valley difference dimension; among them, the load dimension includes: annual maximum load, annual minimum load, daily maximum load, and daily minimum load; the peak-valley difference dimension includes: annual average peak-valley difference rate, annual maximum peak-valley difference rate, monthly average peak-valley difference rate, monthly maximum peak-valley difference rate, and daily peak-valley difference rate.

6. A device for load-side trading based on the peak-valley difference rate of the power grid, characterized in that, It includes: A memory, which is used to store computer programs; A processor, which is used to execute the computer program to implement the steps of the load-side trading method based on the grid peak-valley difference rate according to any one of claims 1 to 3.

7. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, and the computer program is executed by the processor to implement the steps of the load-side trading method based on the grid peak-valley difference rate according to any one of claims 1 to 3.

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