A market factor and resource allocation optimization-based power selling strategy optimization method

Abstract

The application discloses a kind of based on market factors and resource allocation optimization's electricity selling strategy optimization method, it is related to electric power analysis technical field, including: the relationship model of electricity consumption and price is established;Potential consumption model of electric quantity is obtained;The potential promotion of electric quantity consumption is calculated;The growth coefficient of potential promotion is calculated, and the critical value of growth coefficient is formed;Electricity resource load coefficient is calculated, and the preset value of electricity resource load coefficient is formed;When growth coefficient is greater than critical value, then carry out electricity selling price reduction optimization;First adjustment price is calculated;Floating electricity price is adjusted;When electricity resource load coefficient is greater than preset value, carry out electricity selling price increase optimization;Second adjustment price is calculated;Floating electricity price is adjusted.The relationship model of electricity consumption and price is established, and potential consumption model of electric quantity is obtained, can stimulate the potential electricity demand of user, and then improve the electricity satisfaction of user, avoid the line fault caused by high load.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power analysis, in particular to a power selling strategy optimization method based on market factors and resource allocation optimization. BACKGROUND

[0002] The power selling company improves market competitiveness, meets user demand and enhances user satisfaction through flexible sales mode, diversified products and price strategy. In today's increasingly open power market, how to efficiently and accurately sell power has become the focus of attention in the industry.

[0003] The prior art generates a power selling strategy for different power users by analyzing the power load characteristics of the power users and combining the power selling revenue of the power selling company, but the power load characteristics of the users cannot reflect the potential power consumption capacity of the users, resulting in that the set electricity price cannot stimulate potential power consumption, and thus the potential power demand of the users cannot be met, and the user satisfaction has certain room for improvement. In addition, the prior art lacks control over power load, which is easy to cause power transmission failure. SUMMARY

[0004] To solve the above technical problems, a power selling strategy optimization method based on market factors and resource allocation optimization is provided. The technical solution solves the problem that the prior art generates a power selling strategy for different power users by analyzing the power load characteristics of the power users and combining the power selling revenue of the power selling company, but the power load characteristics of the users cannot reflect the potential power consumption capacity of the users, resulting in that the set electricity price cannot stimulate potential power consumption, and thus the potential power demand of the users cannot be met, and the user satisfaction has certain room for improvement. In addition, the prior art lacks control over power load, which is easy to cause power transmission failure.

[0005] To achieve the above purpose, the technical solution adopted by the present application is:

[0006] A power selling strategy optimization method based on market factors and resource allocation optimization, comprising:

[0007] Obtaining the real-time power generation of a power plant and the real-time power consumption of at least one user, wherein the real-time power consumption is the total power consumption of all users within a preset time from the current time;

[0008] According to the supply and demand relationship of power consumption, a relationship model of power consumption and price is established;

[0009] The power selling price is divided into two parts, namely the basic electricity price and the floating electricity price, wherein the basic electricity price is a fixed value, and the floating electricity price is a variable value. Based on historical data, the variable range of the floating electricity price is obtained;

[0010] Analyze the market's potential electricity consumption capacity and obtain a potential electricity consumption model;

[0011] Calculate the potential increase in power consumption based on the potential power consumption model;

[0012] Calculate the growth coefficient of potential improvement and form the critical value of the growth coefficient;

[0013] Calculating the power resource load factor to form a preset value of the power resource load factor;

[0014] When the growth coefficient is greater than the critical value, the electricity price reduction optimization is carried out;

[0015] When optimizing electricity price reduction, the first adjusted electricity price is calculated based on the relationship model between electricity consumption and price;

[0016] Adjusting the floating electricity price based on the first adjusted electricity price;

[0017] When the load factor of electricity resources is greater than the preset value, the electricity price will be optimized;

[0018] When optimizing electricity price increases, the second adjusted electricity price is calculated based on the relationship model between electricity consumption and price;

[0019] The floating electricity price is adjusted based on the second adjusted electricity price.

[0020] Preferably, establishing a relationship model between electricity consumption and price based on the supply and demand relationship of electricity consumption includes the following steps:

[0021] Obtain the value range of the electricity selling price, divide the value range of the real-time electricity consumption into equal intervals, and obtain at least one identification point;

[0022] Obtain the value range of the electricity price regulation range, divide the value range of the electricity price regulation range into (-k, k) with equal intervals, and obtain at least one test point, where k is the maximum value of the electricity price regulation range in the historical data. If the electricity price is increased, the electricity price regulation range is positive, and if the electricity price is reduced, the electricity price regulation range is negative;

[0023] Under the condition that the electricity selling price is equal to the value at the identification point, the first electricity consumption is obtained; under the condition that the electricity selling price is equal to the sum of the value at the identification point and the value at the test point, the second electricity consumption is obtained;

[0024] Using the proportional formula, calculate the change ratio of the first power consumption;

[0025] Pair the change ratio, the value at the identification point and the value at the test point, and fit at least one set of the paired change ratio, the value at the identification point and the value at the test point to obtain a supply-demand fitting function, and take the supply-demand fitting function as a relationship model between electricity and price, wherein the change ratio and the value at the identification point are independent variables, and the value at the test point is a dependent variable;

[0026] The proportional formula is as follows:

[0027]

[0028] Wherein, A is the change ratio, a is the second electricity consumption, and b is the first electricity consumption.

[0029] Preferably, the analysis of the potential electricity consumption capacity of the market obtains an electricity consumption potential consumption model, and the analysis comprises the following steps:

[0030] The whole day period is evenly divided to obtain at least one sub-period, and the length of the sub-period is equal to 2 times the preset time;

[0031] The incomes of at least one user are obtained, the users are divided according to the incomes to obtain at least one user set, and the income gap of the users in the user set is less than a preset amount;

[0032] A user in the user set is randomly selected as a sample user;

[0033] At least one power consumption device used by the sample user is obtained;

[0034] Based on big data, the use time of the power consumption device is obtained;

[0035] When the use time of the power consumption device intersects with the sub-period, the power consumption device and the sub-period are paired;

[0036] The rated power of the power consumption device paired with the sub-period is superimposed to obtain a total rated power;

[0037] The total rated power is multiplied by the length of the sub-period to obtain the potential upper limit of electricity consumption of the sample user in the sub-period;

[0038] The potential upper limits of electricity consumption of at least one sample user in at least one sub-period are summarized to obtain an electricity consumption potential consumption model.

[0039] Preferably, the calculation of the potential increase of electricity consumption based on the electricity consumption potential consumption model comprises the following steps:

[0040] The current actual time is obtained, and the sub-period in which the current actual time is located is taken as a target sub-period;

[0041] The overall upper limit of electricity consumption of at least one user is calculated using a power comprehensive formula.

[0042] Subtracting the real-time power consumption from the real-time power generation, a power idle value is obtained;

[0043] Subtracting the real-time power consumption from the total power consumption upper limit, a power potential value is obtained;

[0044] Taking the smaller value between the power idle value and the power potential value as the potential increase of power consumption;

[0045] The power comprehensive formula is as follows:

[0046]

[0047] Wherein, B is the total power consumption upper limit, i is the subscript, n is the total number of at least one user set, c i is the potential power consumption upper limit of the sample user of the i-th user set in the target partition period, d i is the number of elements in the i-th user set.

[0048] Preferably, the calculation of the growth coefficient of the potential increase includes the following steps:

[0049] In the previous hour of the current time, at least one time point is taken, and the potential increase at the time point is obtained as the characteristic potential increase;

[0050] The time point and the characteristic potential increase are paired and fitted to obtain a promotion fitting function;

[0051] Deriving the promotion fitting function to obtain a growth fitting function;

[0052] Substituting the current time into the growth fitting function to obtain the growth coefficient of the potential increase;

[0053] At least one sample time is obtained, and the sample time is substituted into the growth fitting function to obtain a sample growth coefficient;

[0054] Using the minimum and maximum values of at least one sample growth coefficient, a growth interval is formed;

[0055] In the growth interval, at least one growth point is evenly taken, and under the condition that the potential increase is equal to the value of the growth point, the increase value of the power consumption is obtained when the selling price changes by a preset price;

[0056] When the increase value of the power consumption is greater than the preset power, the growth point is taken as the target growth point;

[0057] Taking the minimum value of at least one target growth point as the critical value of the growth coefficient.

[0058] Preferably, the calculating the electricity resource load factor comprises the following steps:

[0059] Dividing the real-time electricity consumption by the real-time power generation to obtain the electricity resource load factor;

[0060] Based on historical data, obtaining at least one historical electricity resource load factor;

[0061] Obtaining the failure rate of the power transmission line under the condition of the historical electricity resource load factor;

[0062] When the failure rate is greater than the preset proportion, the historical electricity resource load factor is taken as the risk value;

[0063] Taking the minimum value of the at least one risk value as the preset value of the electricity resource load factor.

[0064] Preferably, the calculating the first adjusted electricity price based on the electricity-price relationship model comprises the following steps:

[0065] Dividing the potential increase in electricity consumption by the real-time electricity consumption to obtain the actual proportion;

[0066] Substituting the actual proportion and the electricity selling price into the supply-demand fitting function to obtain the first adjusted electricity price.

[0067] Preferably, the adjusting the floating electricity price based on the first adjusted electricity price comprises the following steps:

[0068] Adding the first adjusted electricity price to the floating electricity price to obtain a first change value;

[0069] When the first change value belongs to the variable range, the floating electricity price is adjusted to the first change value, otherwise, the floating electricity price is adjusted to the maximum value in the variable range.

[0070] Preferably, the calculating the second adjusted electricity price based on the electricity-price relationship model comprises the following steps:

[0071] Multiplying the real-time power generation by the preset value to obtain a target electricity consumption;

[0072] Dividing the difference between the target electricity consumption and the real-time electricity consumption by the real-time electricity consumption to obtain a target proportion;

[0073] Substituting the target proportion and the electricity selling price into the supply-demand fitting function to obtain the second adjusted electricity price.

[0074] Preferably, the adjusting the floating electricity price based on the second adjusted electricity price comprises the following steps:

[0075] Adding the second adjusted electricity price to the floating electricity price to obtain a second change value;

[0076] When the second change value belongs to the variable range, the floating electricity price is adjusted to the second change value, otherwise, the floating electricity price is adjusted to the maximum value in the variable range.

[0077] Compared with the prior art, the present application has the advantages that:

[0078] By establishing the relationship model between electricity and price, obtaining the potential electricity consumption model, judging the growth coefficient and the electricity resource load coefficient, and adjusting the electricity price accordingly, the potential electricity consumption of the user can be estimated, and the electricity price can be reasonably adjusted to stimulate the potential electricity demand of the user, thereby improving the electricity satisfaction of the user. At the same time, the electricity load is calculated, and the price is adjusted in time according to the situation of the electricity load, thereby avoiding the line fault caused by the too high load. BRIEF DESCRIPTION OF DRAWINGS

[0079] Figure 1 It is a flowchart of the electricity selling strategy optimization method based on market factors and resource allocation optimization of the present application;

[0080] Figure 2 It is a flowchart of establishing the relationship model between electricity and price according to the supply and demand relationship of electricity of the present application;

[0081] Figure 3 It is a flowchart of analyzing the potential electricity consumption capacity of the market and obtaining the potential electricity consumption model of the present application;

[0082] Figure 4 It is a flowchart of calculating the potential increase of electricity consumption based on the potential electricity consumption model of the present application;

[0083] Figure 5 It is a flowchart of calculating the growth coefficient of the potential increase and forming the critical value of the growth coefficient of the present application;

[0084] Figure 6 It is a flowchart of calculating the electricity resource load coefficient and forming the preset value of the electricity resource load coefficient of the present application;

[0085] Figure 7 It is a flowchart of calculating the first adjusted electricity price based on the relationship model between electricity and price of the present application;

[0086] Figure 8 It is a flowchart of adjusting the floating electricity price based on the first adjusted electricity price of the present application;

[0087] Figure 9 It is a flowchart of calculating the second adjusted electricity price based on the relationship model between electricity and price of the present application;

[0088] Figure 10 A flowchart for adjusting the floating electricity price based on the second adjustment electricity price of the present application. DETAILED DESCRIPTION

[0089] The following description is provided to enable those skilled in the art to practice the present application. The preferred embodiments described below are only examples of the present application and other obvious variants can be conceived by those skilled in the art.

[0090] Referring to Figure 1 As shown in the figure, a power selling strategy optimization method based on market factors and resource allocation optimization includes:

[0091] Obtain real-time power generation of power plants, and obtain real-time power consumption of at least one user, wherein the real-time power consumption is the total power consumption of all users within a preset time from the current time;

[0092] According to the supply and demand relationship of power consumption, a power consumption and price relationship model is established;

[0093] The power selling price is divided into two parts, namely the basic electricity price and the floating electricity price, wherein the basic electricity price is a fixed value, and the floating electricity price is a variable value, and based on historical data, the variable range of the floating electricity price is obtained;

[0094] The potential power consumption capacity of the market is analyzed to obtain a power consumption potential model;

[0095] Based on the power consumption potential model, the potential increase of power consumption is calculated;

[0096] The growth coefficient of the potential increase is calculated to form a critical value of the growth coefficient;

[0097] The power consumption resource load coefficient is calculated to form a preset value of the power consumption resource load coefficient;

[0098] When the growth coefficient is greater than the critical value, power selling price reduction optimization is performed;

[0099] When the power selling price reduction optimization is performed, the first adjustment electricity price is calculated based on the power consumption and price relationship model;

[0100] Based on the first adjustment electricity price, the floating electricity price is adjusted;

[0101] When the power consumption resource load coefficient is greater than the preset value, power selling price increase optimization is performed;

[0102] When the power selling price increase optimization is performed, the second adjustment electricity price is calculated based on the power consumption and price relationship model;

[0103] Based on the second adjustment electricity price, the floating electricity price is adjusted.

[0104] The electric power service is a public service, so profit is not a main factor when selling electricity, but the cost is controlled to prevent excessive loss, so in the scheme, when adjusting the price, the selling electricity price is divided into two parts, the basic electricity price and the floating electricity price, and the variable range of the floating electricity price is set, the basic electricity price accounts for the main part of the selling electricity price, and the floating electricity price is the part used for adjustment, so the adjustment can be controlled within a reasonable range to prevent extreme prices;

[0105] In the scheme, the user's power consumption experience is mainly improved. Due to the factor of price, some users will reduce the use of some electrical appliances, but in fact, the existing power can meet the current potential power consumption demand, and the user actually has the demand for use, and these power will be wasted if not used, so the price needs to be reasonably adjusted to increase the user's satisfaction.

[0106] The real-time power consumption is the total power consumption of all users within a preset time from the current time. When the preset time is small enough, the real-time power consumption can be approximately regarded as the instantaneous power consumption. According to the definition, it is easy to know that the real-time power consumption is the power consumption of 2 times the preset time length.

[0107] Referring to Figure 2 As shown in the figure, according to the supply and demand relationship of power consumption, a power consumption and price relationship model is established, including the following steps:

[0108] Obtain the value range of the selling electricity price, equally divide the value range of the real-time power consumption to obtain at least one identification point;

[0109] Obtain the value range of the selling electricity price control range, equally divide the value range of the selling electricity price control range (-k, k) to obtain at least one test point, wherein k is the maximum value of the selling electricity price control range in the historical data, the selling electricity price control range is positive when the price is increased, and the selling electricity price control range is negative when the price is reduced;

[0110] Under the condition that the selling electricity price is equal to the value of the identification point, obtain the first power consumption, and under the condition that the selling electricity price is equal to the sum of the value of the identification point and the value of the test point, obtain the second power consumption;

[0111] Using the proportional formula, calculate the change proportion of the first power consumption;

[0112] Pair the change proportion, the value of the identification point and the value of the test point, and fit at least one group of change proportion, identification point value and test point value after pairing to obtain a supply and demand fitting function, wherein the change proportion and the identification point value are independent variables, and the test point value is the dependent variable;

[0113] The proportional formula is as follows:

[0114]

[0115] wherein A is the change proportion, a is the second power consumption, and b is the first power consumption.

[0116] The demand for electricity has a strong correlation with the price, but it should be noted that the demand for electricity is related not only to the change range of the price, but also to the price itself. Obviously, the change in the demand for electricity caused by the price difference of 1 yuan and 1.1 yuan is different from the change in the demand for electricity caused by the price difference of 0.5 yuan and 0.6 yuan. Therefore, when constructing the model, both factors are considered, and at the same time, in order to facilitate subsequent calculation, the change proportion and the value at the identification point are set as independent variables when the supply-demand fitting function is set, because the required price change value needs to be calculated in actual calculation.

[0117] Referring to Figure 3 , the potential power consumption capacity of the market is analyzed to obtain a power consumption potential model, including the following steps:

[0118] The whole day period is evenly divided to obtain at least one division period, and the length of the division period is equal to 2 times the preset time;

[0119] The income of at least one user is obtained, the users are divided according to the income, and at least one user set is obtained, and the income difference of the users in the user set is less than a preset amount;

[0120] A user in the user set is randomly selected as a sample user;

[0121] At least one power consumption device used by the sample user is obtained;

[0122] Based on big data, the use time of the power consumption device is obtained;

[0123] When the use time of the power consumption device intersects with the division period, the power consumption device and the division period are paired;

[0124] The rated power of the power consumption device paired with the division period is superimposed to obtain a total rated power;

[0125] The total rated power is multiplied by the length of the division period to obtain the potential upper limit of power consumption of the sample user in the division period;

[0126] The potential upper limit of power consumption of at least one sample user in at least one division period is summarized to obtain a power consumption potential model.

[0127] The partition period is set to 2 times the preset time, so as to keep consistency with the calculation method of the real-time power consumption when calculating the potential power consumption upper limit, that is, the time length is consistent;

[0128] The users are classified because the users with different incomes use different electrical appliances, and thus the power consumption is different. By using the classification, the power consumption of the sample users in the user set can represent all the users in the user set, and thus it is not necessary to calculate each user, thereby reducing the load of the algorithm;

[0129] When calculating the potential power consumption upper limit, the calculation needs to be performed according to the actual situation because each power consumption device has a relatively fixed use time. For example, the light and the air conditioner have basically consistent use time under big data statistics, and thus the power consumption can be calculated according to the use time in the partition period, thereby obtaining the potential power consumption upper limit.

[0130] Referring to Figure 4 , the calculation of the potential power consumption upper limit based on the power consumption potential consumption model includes the following steps:

[0131] The current actual time is obtained, and the partition period in which the current actual time is located is taken as the target partition period;

[0132] The total power consumption upper limit of at least one user is calculated by using the power comprehensive formula;

[0133] The real-time power generation is subtracted from the real-time power consumption to obtain the power idle value;

[0134] The total power consumption upper limit is subtracted from the real-time power consumption to obtain the power potential value;

[0135] The smaller one of the power idle value and the power potential value is taken as the potential power consumption;

[0136] The power comprehensive formula is as follows:

[0137]

[0138] Wherein, B is the total power consumption upper limit, i is the subscript, n is the total number of at least one user set, c i is the potential power consumption upper limit of the sample user of the i-th user set in the target partition period, d i is the number of elements in the i-th user set.

[0139] Because the user should use a certain power consumption device, but does not use the power consumption device due to the price, the situation needs to be identified, and thus the power potential value is calculated;

[0140] The reason for selecting the smaller value between the power idle value and the power potential value is that the power potential value cannot be greater than the power idle value, otherwise, it will cause the load to be too high and prone to failure.

[0141] Referring to Figure 5 As shown in the figure, the growth coefficient of the potential promotion amount is calculated, and the critical value of the growth coefficient is formed including the following steps:

[0142] In the last hour of the current time, at least one time point is taken, and the potential promotion amount at the time point is obtained as the characteristic potential promotion amount;

[0143] The time point is paired with the characteristic potential promotion amount and fitted to obtain a promotion fitting function;

[0144] The promotion fitting function is differentiated to obtain a growth fitting function;

[0145] The current time is substituted into the growth fitting function to obtain the growth coefficient of the potential promotion amount;

[0146] At least one sample time is obtained, and the sample time is substituted into the growth fitting function to obtain a sample growth coefficient;

[0147] The minimum and maximum values of the at least one sample growth coefficient are used to form a growth interval;

[0148] At least one growth point is uniformly taken in the growth interval, and the increase value of the electricity consumption is obtained under the condition that the potential promotion amount is equal to the value of the growth point. The magnitude of the change in the electricity sales price is preset;

[0149] When the increase value of the electricity consumption is greater than the preset electricity consumption, the growth point is taken as a target growth point;

[0150] The minimum value of the at least one target growth point is taken as the critical value of the growth coefficient.

[0151] Since the present scheme is uninterrupted, the potential promotion amount actually adjusted by the present scheme is a value, so there is no value to adjust the potential promotion amount, and the growth coefficient of the potential promotion amount is calculated. The growth coefficient reflects the future change of the potential promotion amount, so subsequent adjustment of the potential promotion amount can be made accordingly;

[0152] The critical value of the growth coefficient is set according to the adjustment of the electricity consumption, and when the growth coefficient is small, the increase value of the electricity consumption caused by the change in the preset price of the price change is very small and can be ignored. Therefore, no adjustment is made to the growth coefficient, which saves computing power.

[0153] Referring to Figure 6 As shown in the figure, the electricity resource load coefficient is calculated, and the preset value of the electricity resource load coefficient is formed including the following steps:

[0154] Real-time power consumption divided by real-time power generation, to obtain the power consumption resource load coefficient;

[0155] Based on historical data, obtain at least one historical power consumption resource load coefficient;

[0156] Obtain the failure rate of the power transmission line under the condition of the historical power consumption resource load coefficient;

[0157] When the failure rate is greater than the preset proportion, the historical power consumption resource load coefficient is taken as the risk value;

[0158] The minimum value of at least one risk value is taken as the preset value of the power consumption resource load coefficient.

[0159] The preset value of the power consumption resource load coefficient is set according to the failure rate. When the failure cannot be ignored, the corresponding power consumption resource load coefficient is taken as the preset value.

[0160] Referring to Figure 7 , based on the relationship model between power consumption and price, the first adjustment price is calculated, including the following steps:

[0161] The potential increase in power consumption is divided by the real-time power consumption to obtain the actual proportion;

[0162] The actual proportion and the electricity selling price are substituted into the supply-demand fitting function to obtain the first adjustment price.

[0163] When calculating, the proportion of power adjustment and the current price need to be known. The current price is the electricity selling price, and the actual proportion can be calculated in a manner consistent with the proportion formula.

[0164] Referring to Figure 8 , based on the first adjustment price, the floating price is adjusted, including the following steps:

[0165] The first adjustment price and the floating price are added to obtain the first change value;

[0166] When the first change value belongs to the variable range, the floating price is adjusted to the first change value, otherwise, the floating price is adjusted to the maximum value in the variable range.

[0167] Here, the adjustment of the floating price cannot exceed the variable range, otherwise it may cause excessive loss of cost. The maximum value in the variable range is the numerical value of the right endpoint of the variable range.

[0168] Referring to Figure 9 , based on the relationship model between power consumption and price, the second adjustment price is calculated, including the following steps:

[0169] The real-time power generation is multiplied by the preset value to obtain the target power consumption;

[0170] The target proportion is obtained by subtracting the target electricity consumption from the real-time electricity consumption and dividing the result by the real-time electricity consumption.

[0171] The second adjustment electricity price is obtained by substituting the target proportion and the electricity selling price into the supply-demand fitting function.

[0172] The target electricity consumption is a target that the real-time electricity consumption is expected to reach after price adjustment.

[0173] In the calculation, the proportion of electricity adjustment and the current price are needed.

[0174] Referring to Figure 10 Based on the second adjustment electricity price, the floating electricity price is adjusted by the following steps.

[0175] The second adjustment electricity price is added to the floating electricity price to obtain a second change value.

[0176] When the second change value belongs to the variable range, the floating electricity price is adjusted to the second change value, otherwise, the floating electricity price is adjusted to the maximum value in the variable range.

[0177] Further, the application further provides a storage medium having a computer readable program stored thereon.

[0178] It can be understood that the storage medium can be a magnetic medium, such as a floppy disk, a hard disk, a magnetic tape, an optical medium such as a DVD, or a semiconductor medium such as a solid state disk (SSD).

[0179] In summary, the application has the following advantages: by establishing a relationship model between electricity consumption and price, obtaining an electricity potential consumption model, judging the growth coefficient and the electricity resource load coefficient, and adjusting the electricity price accordingly, the potential electricity consumption of users can be estimated, the electricity price can be reasonably adjusted, the potential electricity demand of users can be stimulated, and the electricity satisfaction of users can be improved. At the same time, the electricity load is calculated, and the price is adjusted in time according to the situation of the electricity load, so as to avoid line faults caused by high load.

[0180] The above shows and describes the basic principles, main features and advantages of the present application. Those skilled in the art should understand that the present application is not limited to the above-mentioned embodiments, and the above-mentioned embodiments and descriptions in the specification are only the principles of the present application. Various changes and improvements can be made without departing from the spirit and scope of the present application, and these changes and improvements all fall within the scope of the claimed present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing a power selling strategy based on market factors and resource allocation optimization, characterized in that, The method comprises the following steps: obtaining real-time power generation of a power plant and real-time power consumption of at least one user, wherein the real-time power consumption is the total power consumption of all users within a preset time from the current time; establishing a relationship model between power consumption and price according to the supply-demand relationship of power consumption; dividing the electricity selling price into two parts, namely, a basic price and a floating price, wherein the basic price is a fixed value, and the floating price is a variable value, and obtaining the variable range of the floating price based on historical data; analyzing the potential power consumption capacity of the market to obtain a power consumption potential model; calculating the potential increase of power consumption based on the power consumption potential model; calculating the growth coefficient of the potential increase to form a critical value of the growth coefficient; calculating the power resource load coefficient to form a preset value of the power resource load coefficient; when the growth coefficient is greater than the critical value, performing electricity selling price reduction optimization; when the electricity selling price reduction optimization is performed, calculating a first adjusted price based on the relationship model between power consumption and price; adjusting the floating price based on the first adjusted price; when the power resource load coefficient is greater than the preset value, performing electricity selling price increase optimization; when the electricity selling price increase optimization is performed, calculating a second adjusted price based on the relationship model between power consumption and price; adjusting the floating price based on the second adjusted price; the calculation of the growth coefficient of the potential increase to form the critical value of the growth coefficient comprises the following steps: in the last hour of the current time, taking at least one time point, obtaining the potential increase at the time point as a characteristic potential increase; pairing the time point with the characteristic potential increase and fitting to obtain a fitting function of the increase; deriving the fitting function of the increase to obtain a fitting function of the growth; substituting the current time into the fitting function of the growth to obtain the growth coefficient of the potential increase; obtaining at least one sample time, and substituting the sample time into the fitting function of the growth to obtain a sample growth coefficient; using the minimum value and the maximum value of the at least one sample growth coefficient to form a growth interval; in the growth interval, evenly taking at least one growth point, and under the condition that the potential increase is equal to the value of the growth point, obtaining the increase value of the power consumption when the amplitude of the preset price of the electricity selling price change is obtained; when the increase value of the power consumption is greater than a preset power consumption, the growth point is taken as a target growth point; taking the minimum value of the at least one target growth point as the critical value of the growth coefficient. 2.The market factor and resource allocation optimization-based electricity selling strategy optimization method according to claim 1, wherein, the establishment of the relationship model between power consumption and price according to the supply-demand relationship of power consumption comprises the following steps: obtaining the value range of the electricity selling price, equally spacing the value range of the real-time power consumption to obtain at least one identification point; obtaining the value range of the electricity selling price regulation amplitude, equally spacing the value range of the electricity selling price regulation amplitude (-k, k) to obtain at least one test point, wherein k is the maximum value of the electricity selling price regulation amplitude in the historical data, and the electricity selling price regulation amplitude is positive when the electricity price is increased, and the electricity selling price regulation amplitude is negative when the electricity price is reduced; under the condition that the electricity selling price is equal to the value of the identification point, obtaining a first power consumption, and under the condition that the electricity selling price is equal to the sum of the value of the identification point and the value of the test point, obtaining a second power consumption; using a proportional formula to calculate the change proportion of the first power consumption; Pair the change ratio, the value at the identification point and the value at the test point, and fit at least one set of the paired change ratio, the value at the identification point and the value at the test point to obtain a supply-demand fitting function, wherein the change ratio and the value at the identification point are independent variables, and the value at the test point is a dependent variable. The proportional formula is as follows: Wherein, A is the change ratio, a is the second power consumption, and b is the first power consumption. 3.The market factor and resource allocation optimization-based electricity selling strategy optimization method according to claim 2, characterized in that, The analysis of the potential power consumption capacity of the market to obtain the power potential consumption model comprises the following steps: Divide the whole day period uniformly to obtain at least one sub-period, and the length of the sub-period is equal to 2 times the preset time; Obtain the income of at least one user, divide the users according to the income pairs to obtain at least one user set, and the income gap of the users in the user set is less than a preset amount; Randomly select a user in the user set as a sample user; Obtain at least one power consumption equipment used by the sample user; Based on big data, obtain the use time of the power consumption equipment; When the use time of the power consumption equipment intersects with the sub-period, pair the power consumption equipment with the sub-period; Superimpose the rated power of the power consumption equipment paired with the sub-period to obtain the total rated power; Multiply the total rated power by the length of the sub-period to obtain the potential upper limit of power consumption of the sample user in the sub-period; Summarize the potential upper limit of power consumption of at least one sample user in at least one sub-period to obtain the power potential consumption model. 4.The market factor and resource allocation optimization-based electricity selling strategy optimization method of claim 3, wherein, The calculation of the potential improvement of power consumption based on the power potential consumption model comprises the following steps: Obtain the current actual time, and take the sub-period in which the current actual time is located as a target sub-period; Use the power comprehensive formula to calculate the overall upper limit of power consumption of at least one user; Subtract the real-time power consumption from the real-time power generation to obtain the power idle value; Subtract the real-time power consumption from the overall upper limit of power consumption to obtain the power potential value; Take the smaller value between the power idle value and the power potential value as the potential improvement of power consumption; The power comprehensive formula is as follows: where B is an overall upper limit of electricity consumption, i is a subscript, n is a total number of at least one user set, c i is a potential upper limit of electricity consumption of a sample user of the i-th user set in a target partition period, d i is a number of elements in the i-th user set.

5. The market factor and resource allocation optimization-based power selling strategy optimization method according to claim 4, characterized in that, The calculation of the power resource load coefficient to form the preset value of the power resource load coefficient comprises the following steps: Divide the real-time power consumption by the real-time power generation to obtain the power resource load coefficient; Based on historical data, obtain at least one historical power resource load coefficient; Obtain the failure rate of the power transmission line under the condition of the historical power resource load coefficient; When the failure rate is greater than a preset proportion, take the historical power resource load coefficient as a risk value; Take the minimum value of at least one risk value as the preset value of the power resource load coefficient.

6. The market factor and resource allocation optimization-based power selling strategy optimization method according to claim 5, characterized in that, The calculation of the first adjusted electricity price based on the relationship model between power consumption and price comprises the following steps: Divide the potential improvement of power consumption by the real-time power consumption to obtain the actual ratio; Substitute the actual ratio and the electricity sales price into the supply-demand fitting function to obtain the first adjusted electricity price.

7. The market factor and resource allocation optimization-based power selling strategy optimization method according to claim 6, characterized in that, The adjustment of the floating electricity price based on the first adjusted electricity price comprises the following steps: Add the first adjusted electricity price to the floating electricity price to obtain a first change value; When the first change value belongs to a variable range, adjust the floating electricity price to the first change value, otherwise, adjust the floating electricity price to the maximum value in the variable range. 8.The market factor and resource allocation optimization-based electricity selling strategy optimization method according to claim 7, wherein, The second adjustment electricity price is calculated based on the electricity consumption and price relationship model, and includes the following steps: The real-time power generation is multiplied by a preset value to obtain a target electricity consumption; The target electricity consumption is subtracted from the real-time electricity consumption, and the result is divided by the real-time electricity consumption to obtain a target proportion; The target proportion and the electricity selling price are substituted into the supply-demand fitting function to obtain the second adjustment electricity price. 9.The market factor and resource allocation optimization-based power selling strategy optimization method of claim 8, wherein, The floating electricity price is adjusted based on the second adjustment electricity price, and includes the following steps: The second adjustment electricity price is added to the floating electricity price to obtain a second change value; When the second change value belongs to a variable range, the floating electricity price is adjusted to the second change value, otherwise, the floating electricity price is adjusted to the maximum value in the variable range.

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