Aggregator charging pile cluster control system and method based on price guidance mechanism

Abstract

The invention discloses an aggregator charging pile cluster control system and method based on a price guiding mechanism, relates to the technical field of charging pile cluster control, and solves the problem that a fixed price charging strategy cannot effectively guide a user to select a proper charging place and time. And therefore, the technical problems of non-uniform distribution of charging resources and reduction of overall benefits can be solved. Electric vehicle information and charging pile information are collected in a set area; obtaining a basic price of an aggregator charging pile, and calculating an initial charging price of the charging pile based on the basic price; judging the influence factor sensitivity type of the user on the charging pile of the aggregator; calculating the probability that the user selects the aggregator charging pile, and determining the adjustment price of the aggregator charging pile through the probability; calculating aggregator revenue maximization under the condition of minimizing the dissatisfaction degree of the user by using a multi-objective planning algorithm, and recommending a corresponding charging pile to the user; according to the invention, the electric vehicle group is guided to be charged dispersedly, and revenue maximization of the charging pile aggregator is realized.

Description

Technical Field

[0001] The present invention belongs to the field of new energy and energy conservation, and relates to charging pile cluster control technology, specifically to an aggregator charging pile cluster control system and method based on a price guidance mechanism. Background Art

[0002] An aggregator charging pile cluster control system based on a price guidance mechanism is suitable for aggregators of electric vehicle charging stations and power operators, especially for cities or regions with large fluctuations in electricity market prices and high charging demand. Through this system, operators can better guide user behavior and achieve efficient use of charging facilities and maximize profits.

[0003] In the existing technology, with the increase in the number of electric vehicles and the widespread layout of charging piles, the traditional fixed-price charging strategy has failed to effectively guide users to choose appropriate charging locations and times, resulting in uneven distribution of charging resources, local congestion and reduced overall benefits.

[0004] The present invention provides a charging pile cluster control system and method based on a price guidance mechanism to solve the above technical problems. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a control system and method for an aggregator charging pile cluster based on a price guidance mechanism, which is used to solve the technical problem that the traditional fixed-price charging strategy fails to effectively guide users to choose a suitable charging location and time, resulting in uneven distribution of charging resources, local congestion and reduced overall efficiency.

[0006] To achieve the above-mentioned object, the first aspect of the present invention provides a control system for charging pile clusters of aggregators based on a price guidance mechanism, comprising: a data acquisition module, an initial pricing module, a dynamic pricing module and a revenue evaluation module;

[0007] Data collection module: collects electric vehicle information and charging pile information in the set area;

[0008] Initial pricing module: obtains the basic price of charging piles from aggregators, and calculates the initial charging price of charging piles based on the basic price and real-time market data;

[0009] Dynamic pricing module: Determine the user's sensitivity to factors affecting the charging piles of aggregators; calculate the probability of users choosing charging piles of aggregators, and determine the adjusted price of charging piles of aggregators based on the probability; the influencing factors include: the price of a single charge and the distance from the order location to the charging pile;

[0010] Revenue evaluation module: uses a multi-objective programming algorithm to calculate the maximum revenue of the aggregator under the condition of minimizing user dissatisfaction, and recommends corresponding charging piles to users;

[0011] Database: used to store the service life data of several charging piles, construction costs, site costs, infrastructure costs, operating costs, device maintenance costs, depreciation rates of charging piles, policy subsidies and weather forecasts.

[0012] Preferably, the system comprises: a user interface module: showing the number of vehicles queuing at the current charging pile to the user; showing the number of vehicles queuing at the current charging pile to the user; showing the current forecast weather conditions to the user, and providing the user with a text box to input the current real-time weather conditions.

[0013] Preferably, the obtaining of the basic price of the charging pile of the aggregator includes:

[0014] The total purchase cost of a charging device is marked as CZF, and the corresponding service life data of several charging piles are obtained from the database and marked as PJS k ; Wherein, k is the number of charging pile devices obtained, k=1, 2, 3, ..., m; m is a positive integer; the charging device includes: a number of charging pile devices and supporting power facilities;

[0015] By formula Calculate the base price of the charging station per hour.

[0016] It should be noted that the service life data of the corresponding type of charging pile equipment obtained in the database is statistically measured in years.

[0017] Preferably, the calculating of the initial charging price of the charging pile based on the base price and the real-time market data includes:

[0018] Get the basic price P of the charging pile base ; The adjustment coefficient of charging price is marked as γ, and the spot price of electricity is marked as P market , the number of vehicles queuing at the current charging pile is marked as Q j , the maximum queue capacity of the charging pile is marked as Q max ;

[0019] By formula The initial charging price per hour of charging pile j is calculated; wherein j represents the number of the charging pile, j=1, 2, 3, ..., n; and n is a positive integer.

[0020] Preferably, the method for obtaining the adjustment coefficient includes:

[0021] S1: Obtain the construction cost of the charging pile from the database, marked as SGF, the site cost as CDF, the infrastructure cost as JSF, the operating cost as YYF, the device maintenance cost as ZZF, the depreciation rate of the charging pile as CZF, and the policy subsidy as ZCB;

[0022] S2: By formula Calculate the adjustment coefficient; μ is the geometric coefficient, set μ = 10 c , c is a natural number;

[0023] S3: Substitute c into the calculation formula of the adjustment coefficient to determine whether the hourly charging cost of the corresponding charging pile is lower than the highest charging cost on the market during the same period; if yes, stop updating the adjustment coefficient; if no, re-substitute c±1 into the calculation formula of the adjustment coefficient;

[0024] S4: In the two cases of c+x and c-x, determine whether there is a situation where the hourly charging cost of the charging pile is lower than the highest charging cost on the market during the same period; if yes, stop judging; if no, repeat S4; wherein x is a positive integer greater than 0.

[0025] Preferably, the method of determining the user's sensitivity type to factors affecting the charging piles of the aggregator includes:

[0026] Extracting electric vehicle information; the electric vehicle information includes: the order location and historical charging records of each electric vehicle; the historical charging records include: the total number of historical charging times, the total charging time for each time, the total cost for each charging; the distance from the user's order location to the charging pile each time;

[0027] The unit price of several single charges is obtained by dividing the total single charging cost of the user by the corresponding total single charging time;

[0028] Retrieve the user's total number of historical charging times and mark it as LSZ, count the number of times the user uses the same charging unit price and mark it as CDD L ; Count the number of times the user places an order at the same distance and mark it as TYX r Wherein, L represents the number of different charging unit prices, and r represents the number of different order distances; the value ranges of L and r are both positive integers;

[0029] By formula Calculate the proportion of different unit prices;

[0030] By formula Calculate the proportion of different order distances;

[0031] Set the sensitivity threshold of the influencing factors to determine whether the highest proportion values ​​of the influencing factors are all higher than the sensitivity threshold of the influencing factors; if yes, the corresponding influencing factors are in the low sensitivity range of the user; if no, the corresponding influencing factors are in the high sensitivity range of the user.

[0032] Preferably, the calculating the probability of the user selecting the charging pile of the aggregator includes:

[0033] Obtain the forecast weather conditions from the meteorological database. The user provides the real-time weather conditions before selecting the charging pile. Determine whether the forecast weather conditions are consistent with the real-time weather conditions. If yes, the set probability is P1. If no, determine whether the user replaces the forecast weather conditions with the real-time weather conditions. If yes, the set probability is P2. If no, the set probability is P3. Among them, 0.4≥P1≥0, 0.4≥P2≥0, 0.4≥P3≥0.

[0034] The selection probability of users with high sensitivity to the price of a single charge is set to P4; the selection probability of users with low sensitivity to the price of a single charge is set to P5; where 0.3≥P5>P4≥0;

[0035] The selection probability of users with high sensitivity to the distance from the order location to the charging pile is set to P6; the selection probability of users with low sensitivity to the distance from the order location to the charging pile is set to P7; where 0.3≥P7>P6≥0;

[0036] The probability that user i chooses charging station j is calculated by the formula P(choosej|i)=(P1+P2+P3)+(P4+P5)+(P6+P7); where 1≥P(choosej|i)≥0.

[0037] It should be noted that when one of P1, P2, and P3 is a positive number, the other is 0; and P2 and P3 are not 0 at the same time; the specific value of P1 depends on the weather conditions of the day, and the probability of sunny weather is greater than the probability of rainy and snowy weather.

[0038] Preferably, the step of determining the adjusted price of the charging pile of the aggregator by probability includes:

[0039] When the probability is [a, 1], then by formula TZJ j =CF j ×1(1±a)×100% to calculate the adjusted price;

[0040] When the probability is [b, a), then by the formula Calculate the adjusted price;

[0041] When the probability is [0, b), the adjusted price is CF j ; Among them, a and Both are expressed as price change rates; where a and b are expressed as interval change rates, and 1>a>b>0;

[0042] Determine whether the proportion of the real-time available interfaces of the corresponding charging pile to all interfaces is less than 30%; if yes, the price change rate is a positive value, otherwise, the price change rate is a negative value.

[0043] It should be noted that the highest charging fee of charging piles on the market during the same period is retrieved, and the initial charging price CF is retrieved. j , taking the highest charging fee in the market during the same period as the adjustment price TZJ, then

[0044] Retrieve the lowest charging fee of charging piles on the market during the same period, count the number of times the lowest charging fee of charging piles on the market appears in a day and the number of charging piles on the market with different prices during the same period, and take the quotient of the number of times the lowest charging fee of charging piles on the market divided by the number of charging piles on the market with different prices during the same period as b.

[0045] Preferably, the method of calculating the maximum aggregator profit under the condition of minimizing user dissatisfaction by using a multi-objective programming algorithm includes:

[0046] By formula The aggregator profit coefficient is calculated; where N is the total number of electric vehicle users;

[0047] By formula U i =min(CF j )subjecttomaxP(choosej|i) calculates the dissatisfaction function of user i;

[0048] By formula The target optimization function is calculated; where: represents the total revenue of the aggregator, Represents the total dissatisfaction of all users;

[0049] Use the ideal solution through third-party software to obtain the charging station number recommended to the user.

[0050] To achieve the above object, the second aspect of the present invention provides a method for controlling a charging pile cluster of an aggregator based on a price guidance mechanism, comprising:

[0051] Collect electric vehicle information and charging pile information in the set area;

[0052] Obtain the base price of the charging pile from the aggregator, and calculate the initial charging price of the charging pile based on the base price and real-time market data;

[0053] Determine the user's sensitivity to factors affecting the charging piles of aggregators; calculate the probability of the user choosing the charging piles of aggregators, and determine the adjusted price of the charging piles of aggregators based on the probability; the influencing factors include: the single charging price and the distance from the ordering location to the charging pile;

[0054] A multi-objective programming algorithm is used to calculate the maximum profit of the aggregator under the condition of minimizing user dissatisfaction, and recommend corresponding charging piles to users.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] 1. The present invention guides users to choose appropriate charging stations through reasonable dynamic price adjustments to optimize the overall operational benefits of the charging pile cluster; by combining the real-time fluctuations of the electricity market, the charging prices in different regions are dynamically adjusted to encourage users to choose different charging stations to achieve vehicle diversion and maximize profits; the present invention increases the charging price to achieve reasonable diversion of vehicles to be charged, but the price will still not be higher than the highest charging fee on the market during the same period, so that the charging piles of the aggregators in this system maintain a certain price competitiveness.

[0057] 2. The present invention dynamically adjusts the charging price according to the real-time fluctuation of the power market and the load of the charging piles through the system to guide users to choose different charging piles, thereby optimizing the distribution of charging loads and maximizing overall benefits; the benchmark price is dynamically adjusted according to the real-time power market price and the queue status of the charging piles; the revenue coefficient ρ of the aggregator is used to calculate the charging price of the charging piles. j Reflects the profitability of a charging pile j; the choice of electric vehicles will be affected by the adjustment of charging prices, thereby guiding users to disperse loads and optimize resource allocation. According to the real-time fluctuations of the power market and regional electricity prices, the charging price is dynamically adjusted, which is conducive to the reasonable diversion of vehicles, reducing congestion during peak hours, and improving the utilization efficiency of charging piles. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0059] Figure 1 This is a schematic diagram of the relationship between modules included in the present invention;

[0060] Figure 2 A schematic diagram of the initial pricing step of the present invention;

[0061] Figure 3A schematic diagram of the dynamic pricing steps of the present invention;

[0062] Figure 4 It is a schematic diagram of the control steps of the charging pile cluster of the aggregator of the present invention. DETAILED DESCRIPTION

[0063] The technical scheme of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0064] See also Figure 1 , the first aspect of the present invention provides an aggregator charging pile cluster control system based on a price guidance mechanism, including: a data acquisition module, an initial pricing module, a dynamic pricing module and a revenue evaluation module;

[0065] Data collection module: collects electric vehicle information and charging pile information in the set area;

[0066] Initial pricing module: obtains the basic price of charging piles from aggregators, and calculates the initial charging price of charging piles based on the basic price and real-time market data;

[0067] Dynamic pricing module: Determine the user's sensitivity to factors affecting the charging piles of aggregators; calculate the probability of users choosing charging piles of aggregators, and determine the adjusted price of charging piles of aggregators based on the probability; the influencing factors include: the price of a single charge and the distance from the order location to the charging pile;

[0068] Revenue evaluation module: Use a multi-objective planning algorithm to maximize the aggregator's revenue while minimizing user dissatisfaction, and recommend corresponding charging stations to users.

[0069] See also Figure 2 In the initial pricing step, the total purchase cost of a charging device is marked as CZF, and the corresponding service life data of several charging piles are obtained from the database and marked as PJS. k ; Wherein, k is the number of charging pile devices obtained, k=1, 2, 3, ..., m; m is a positive integer; the charging device includes: a number of charging pile devices and supporting power facilities;

[0070] By formula Calculate the basic price of the charging station per hour;

[0071] Get the basic price P of the charging pile base ; The adjustment coefficient of charging price is marked as γ, and the spot price of electricity is marked as P market, the number of vehicles queuing at the current charging pile is marked as Q j , the maximum queue capacity of the charging pile is marked as Q max ;

[0072] By formula The initial charging price per hour of charging pile j is calculated; wherein j represents the number of the charging pile, j=1, 2, 3, ..., n; and n is a positive integer.

[0073] How to obtain the adjustment coefficient:

[0074] S1: Obtain the construction cost of the charging pile from the database, marked as SGF, the site cost as CDF, the infrastructure cost as JSF, the operating cost as YYF, the device maintenance cost as ZZF, the depreciation rate of the charging pile as CZF, and the policy subsidy as ZCB;

[0075] S2: By formula Calculate the adjustment coefficient; μ is the geometric coefficient, set μ = 10 c , c is a natural number;

[0076] S3: Substitute c into the calculation formula of the adjustment coefficient to determine whether the hourly charging cost of the corresponding charging pile is lower than the highest charging cost on the market during the same period; if yes, stop updating the adjustment coefficient; if no, re-substitute c±1 into the calculation formula of the adjustment coefficient;

[0077] S4: In the two cases of c+x and c-x, determine whether there is a situation where the hourly charging cost of the charging pile is lower than the highest charging cost on the market during the same period; if yes, stop judging; if no, repeat S4; wherein x is a positive integer greater than 0.

[0078] For example, a charging pile is priced. The total cost of purchasing a charging device CZF is 100,000 yuan; a charging device contains 10 charging piles, and the average service life data of a charging pile is PJS. k For 20 years,

[0079] By formula The basic price of the charging station per hour is calculated to be 0.57 yuan;

[0080] From the database, we get the construction cost SGF of the charging pile as 46,500 yuan, the site cost CDF as 100,000 yuan, the infrastructure cost JSF as 50,000 yuan, the operating cost YYF as 50,000 yuan, the device maintenance cost ZZF as 20,000 yuan, the depreciation rate CZF of the charging pile within 20 years as 15%, and the policy subsidy ZCB as 32,800 yuan;

[0081] By formula The adjustment coefficient is calculated; where μc is the geometric coefficient, set μ = 10 c , c is a natural number; the adjustment coefficient is used to remove the dimension;

[0082] The highest charging fee in the market during the same period is 1.1.

[0083] If c=1, then μ=10, then the adjustment coefficient of charging price γ=24.73×10%=2.473

[0084] Get the electricity spot price P market The current number of vehicles queuing at the charging station is Q j The maximum queuing capacity of the charging pile is marked as Q. max 10 vehicles;

[0085] By formula The initial charging price per hour of charging pile j is calculated to be 2.14; because 2.14 is greater than 1.1, c=-1, μ=0.1, and the adjustment coefficient of charging price γ=24.73×10%=0.02473;

[0086] By formula The calculated initial charging price per hour for charging pile j is 0.59 yuan.

[0087] See also Figure 3 , dynamic pricing step, extracting electric vehicle information; wherein the electric vehicle information includes: the order location of each electric vehicle, historical charging records, the historical charging records include: the total number of historical charging times, the total charging time for each time, the total cost for each charging; the distance from the user's order location to the charging pile each time;

[0088] The unit price of several single charges is obtained by dividing the total single charging cost of the user by the corresponding total single charging time;

[0089] Retrieve the user's total number of historical charging times and mark it as LSZ, count the number of times the user uses the same charging unit price and mark it as CDD L ; Count the number of times the user places an order at the same distance and mark it as TYX r Wherein, L represents the number of different charging unit prices, and r represents the number of different order distances; the value ranges of L and r are both positive integers;

[0090] By formula Calculate the proportion of different unit prices;

[0091] By formula Calculate the proportion of different order distances;

[0092] Set the sensitivity threshold of the influencing factors to determine whether the highest proportion of the influencing factors is higher than the sensitivity threshold of the influencing factors; if yes, the corresponding influencing factors are in the low sensitivity range of the user; if no, the corresponding influencing factors are in the high sensitivity range of the user;

[0093] Obtain the forecast weather conditions from the meteorological database. The user provides the real-time weather conditions before selecting the charging pile. Determine whether the forecast weather conditions are consistent with the real-time weather conditions. If yes, the set probability is P1. If no, determine whether the user replaces the forecast weather conditions with the real-time weather conditions. If yes, the set probability is P2. If no, the set probability is P3. Among them, 0.4≥P1≥0, 0.4≥P2≥0, 0.4≥P3≥0.

[0094] The selection probability of users with high sensitivity to the price of a single charge is set to P4; the selection probability of users with low sensitivity to the price of a single charge is set to P5; where 0.3≥P5>P4≥0;

[0095] The selection probability of users with high sensitivity to the distance from the order location to the charging pile is set to P6; the selection probability of users with low sensitivity to the distance from the order location to the charging pile is set to P7; where 0.3≥P7>P6≥0;

[0096] The probability that user i chooses charging station j is calculated by the formula P(choosej|i)=(P1+P2+P3)+(P4+P5)+(P6+P7); where 1≥P(choosej|i)≥0;

[0097] When the probability is [a, 1], then by formula TZJ j =CF j ×(1±a)×100% to calculate the adjusted price;

[0098] When the probability is [b, a), then by the formula Calculate the adjusted price;

[0099] When the probability is [0, b), the adjusted price is CF j ; Among them, a and Both are expressed as price change rates; where a and b are expressed as interval change rates, and 1>a>b>0;

[0100] Determine whether the proportion of the real-time available interfaces of the corresponding charging pile to all interfaces is less than 30%; if yes, the price change rate is a positive value, otherwise, the price change rate is a negative value.

[0101] It should be noted that the highest charging fee of charging piles on the market during the same period is retrieved, and the initial charging price CF is retrieved. j , taking the highest charging fee in the market during the same period as the adjustment price TZJ, then

[0102] Retrieve the lowest charging fee of charging piles on the market during the same period, count the number of times the lowest charging fee of charging piles on the market appears in a day and the number of charging piles on the market with different prices during the same period, and divide the number of times the lowest charging fee of charging piles on the market by the number of charging piles on the market with different prices during the same period as b.

[0103] For example, existing user A obtains the user's historical charging records; there are 8 historical charging records in total, and the total cost of a single charge is 3.23 yuan, 4.59 yuan, 2.25 yuan, 2.25 yuan, 3.21 yuan, 1.59 yuan, 1.89 yuan, and 2.25 yuan respectively; the corresponding total duration of a single charge is 4.59h, 4.25h, 3.89h, 3.65h, 3.9h, 2.58h, 2.1h, and 3.89h respectively;

[0104] By dividing the total single charging cost of user A by the corresponding total single charging time, the unit prices of several single charges are: 0.7, 1.08, 0.58, 0.62, 0.82, 0.62, 0.9, 0.58; the maximum number of times the charging unit price is the same is 2;

[0105] Statistics show that user A placed orders 3 times within a distance of 2.5 km, 2 times within a distance of 1.2 km, 2 times within a distance of 0.25 km, and 1 time within a distance of 5.2 km.

[0106] Set the unit price ratio threshold to 30%, and set the different order distance ratio threshold to 40%.

[0107] By formula The highest percentage of different unit prices calculated is 25%, which is lower than 30%;

[0108] By formula The highest number of different order placement distances was calculated to account for 37.5%, which is lower than 40%;

[0109] If the highest percentage values ​​of the influencing factors are all lower than the influencing factor sensitivity threshold, the corresponding influencing factors are in the high sensitivity range of user A;

[0110] The user A determines through the user interface module whether to consider the charging price factor of the charging pile when selecting the charging pile;

[0111] Obtain the forecast weather conditions from the meteorological database. The user provides the real-time weather conditions before selecting the charging pile. Determine whether the forecast weather conditions are consistent with the real-time weather conditions. If yes, the set probability is 30%. If no, determine whether the user replaces the forecast weather conditions with the real-time weather conditions. If yes, the set probability is 35%. If no, the set probability is 10%.

[0112] At this time, the weather forecast is sunny, but the actual weather is light rain, so the weather forecast is inconsistent with the real-time weather. User A inputs the current actual weather conditions through the user interface module, and the probability that user A selects the charging pile of this platform is 35%;

[0113] The probability of selecting the option with high sensitivity to the price of a single charge is set to 20%; the probability of selecting the option with low sensitivity to the price of a single charge is set to 30%; where 0.3≥P5>P4≥0;

[0114] The probability of selecting the user who is highly sensitive to the distance from the order location to the charging pile is set to 20%; the probability of selecting the user who is less sensitive to the distance from the order location to the charging pile is set to 30%;

[0115] Since the single charging price and the distance from the order location to the charging station are in the high sensitivity range of user A;

[0116] The formula P(choosej|i)=(P1+P2+P3)+(P4+P5)+(P6+P7)=(0+35%+0)+(20%+0)+(20%+0)=75% calculates that the probability of user A choosing charging station j is 75%;

[0117] The highest charging fee of the charging pile in the market during the same period is 1.1, the initial charging price is 0.59, and the highest charging fee of the charging pile in the market during the same period is used as the adjustment price TZJ, then

[0118] The lowest charging fee of the charging piles on the market in the same period is retrieved as 0.49. The number of times the lowest charging fee of the charging piles on the market appears in one day is counted as 4 times, and the number of different prices of the charging piles on the market in the same period is 8. The number of times the lowest charging fee of the charging piles on the market is divided by the number of different prices of the charging piles on the market in the same period is taken as b, b = 50%;

[0119] The charging pile on this platform has 5 real-time available interfaces, and there are 20 interfaces in all the charging piles. If the proportion of real-time available interfaces of the charging pile is less than 30% of all interfaces, the price change rate is positive.

[0120] When the probability is [86.4%, 1], then by formula TZJ j =CF j ×(1±86.4)×100% to calculate the adjusted price;

[0121] When the probability is [50%, 86.4%), then by the formula Calculate the adjusted price;

[0122] When the probability is [0, 50%), the adjusted price is CF j; Among them, a and All are expressed as price change rates;

[0123] The probability that user A chooses charging station j is 75%, which is in the interval [50%, 86.4%), so the price is adjusted

[0124]

[0125] See also Figure 4 The second aspect of the present invention provides a method for controlling a cluster of charging piles of an aggregator based on a price guidance mechanism, including:

[0126] Collect electric vehicle information and charging pile information in the set area; real-time data includes: electric vehicle information, charging pile information and real-time fluctuation of electricity prices; electric vehicle information includes: order location and historical charging records of each electric vehicle; charging pile information includes: number of available sockets for each charging pile;

[0127] Obtain the base price of the charging pile from the aggregator, and calculate the initial charging price of the charging pile based on the base price and real-time market data;

[0128] Determine the user's sensitivity to factors affecting the charging piles of aggregators; calculate the probability of the user choosing the charging piles of aggregators, and determine the adjusted price of the charging piles of aggregators based on the probability; the influencing factors include: the single charging price and the distance from the ordering location to the charging pile;

[0129] A multi-objective programming algorithm is used to calculate the maximum profit of the aggregator under the condition of minimizing user dissatisfaction, and recommend corresponding charging piles to users.

[0130] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0131] Working principle of the present invention: The present invention obtains the basic price of the charging pile of the aggregator by collecting real-time data in a set area, calculates the initial charging price of the charging pile based on the basic price and real-time market data; determines the type of sensitivity of users to the influencing factors of the charging pile of the aggregator; calculates the probability of users choosing the charging pile of the aggregator, and determines the adjusted price of the charging pile of the aggregator by probability; wherein the influencing factors include: the single charging price and the distance from the ordering location to the charging pile; uses a multi-objective planning algorithm to calculate the maximum profit of the aggregator under the condition of minimizing user dissatisfaction, and recommends the corresponding charging pile to the user.

[0132] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. The control system of charging pile cluster of aggregators based on price guidance mechanism is characterized by: include: Data collection module, initial pricing module, dynamic pricing module and revenue evaluation module; Data collection module: collects electric vehicle information and charging pile information in the set area; Initial pricing module: obtains the basic price of charging piles from aggregators, and calculates the initial charging price of charging piles based on the basic price and real-time market data; Dynamic pricing module: determines the user's sensitivity to factors affecting the charging piles of aggregators; Calculate the probability of the user choosing the charging pile of the aggregator, and determine the adjusted price of the charging pile of the aggregator based on the probability; the influencing factors include: the single charging price and the distance from the ordering location to the charging pile; Revenue evaluation module: Use a multi-objective planning algorithm to maximize the aggregator's revenue while minimizing user dissatisfaction, and recommend corresponding charging stations to users.

2. The charging pile cluster control system based on the price guidance mechanism of the aggregator according to claim 1 is characterized in that: Also includes: User interface module: displays to the user the number of vehicles currently queuing at the charging station; displays to the user the current weather forecast, and provides the user with a text box to input the current real-time weather conditions.

3. The charging pile cluster control system based on the price guidance mechanism of claim 1 is characterized in that: The basic price of the charging pile of the aggregator is obtained, including: The total purchase cost of a charging device is marked as CZF, and the corresponding service life data of several charging piles are obtained from the database and marked as PJS k ; Wherein, k is the number of charging pile devices obtained, k=1, 2, 3, ..., m; m is a positive integer; the charging device includes: a number of charging pile devices and supporting power facilities; By formula Calculate the base price of the charging station per hour.

4. The charging pile cluster control system based on the price guidance mechanism of claim 1 is characterized in that: The initial charging price of the charging pile is calculated based on the base price and the real-time market data, including: Extracting charging pile information, wherein the charging pile information includes: the number of vehicles currently queuing at the charging pile, the maximum queuing capacity of the charging pile, and the spot price of electricity; Get the basic price P of the charging pile base ; The adjustment coefficient of charging price is marked as γ, and the spot price of electricity is marked as P market , the number of vehicles queuing at the current charging pile is marked as Q j , the maximum queue capacity of the charging pile is marked as Q max ; By formula The initial charging price per hour of charging pile j is calculated; wherein j represents the number of the charging pile, j=1, 2, 3, ..., n; and n is a positive integer.

5. The charging pile cluster control system based on the price guidance mechanism of the aggregator according to claim 4 is characterized in that: The method for obtaining the adjustment coefficient includes: S1: Obtain the construction cost of the charging pile from the database, marked as SGF, the site cost as CDF, the infrastructure cost as JSF, the operating cost as YYF, the device maintenance cost as ZZF, the depreciation rate of the charging pile as CZF, and the policy subsidy as ZCB; S2: By formula Calculate the adjustment coefficient; μ is the geometric coefficient, set μ = 10 c , c is a natural number; S3: Substitute c into the calculation formula of the adjustment coefficient to determine whether the hourly charging cost of the corresponding charging pile is lower than the highest charging cost on the market during the same period; if yes, stop updating the adjustment coefficient; if no, re-substitute c±1 into the calculation formula of the adjustment coefficient; S4: In the two cases of c+x and c-x, determine whether there is a situation where the hourly charging cost of the charging pile is lower than the highest charging cost on the market during the same period; if yes, stop judging; if no, repeat S4; wherein x is a positive integer greater than 0.

6. The charging pile cluster control system based on the price guidance mechanism of the aggregator according to claim 1 is characterized in that: The type of determining the user's sensitivity to factors affecting the charging piles of the aggregator includes: Extracting electric vehicle information; the electric vehicle information includes: the order location and historical charging records of each electric vehicle; the historical charging records include: the total number of historical charging times, the total charging time for each time, the total cost for each charging; the distance from the user's order location to the charging pile each time; The unit price of several single charges is obtained by dividing the total single charging cost of the user by the corresponding total single charging time; Retrieve the user's total number of historical charging times and mark it as LSZ, count the number of times the user uses the same charging unit price and mark it as CDD L ; Count the number of times the user places an order at the same distance and mark it as TYX r Wherein, L represents the number of different charging unit prices, and r represents the number of different order distances; the value ranges of L and r are both positive integers; By formula Calculate the proportion of different unit prices; By formula Calculate the proportion of different order distances; Set the sensitivity threshold of the influencing factors to determine whether the highest proportion values ​​of the influencing factors are all higher than the sensitivity threshold of the influencing factors; if yes, the corresponding influencing factors are in the low sensitivity range of the user; if no, the corresponding influencing factors are in the high sensitivity range of the user.

7. The charging pile cluster control system based on the price guidance mechanism of claim 1, characterized in that: The calculating the probability of the user selecting the charging pile of the aggregator includes: Obtain the forecast weather conditions from the meteorological database. The user provides the real-time weather conditions before selecting the charging pile. Determine whether the forecast weather conditions are consistent with the real-time weather conditions. If yes, the set probability is P1. If no, determine whether the user replaces the forecast weather conditions with the real-time weather conditions. If yes, the set probability is P2. If no, the set probability is P3. Among them, 0.4≥P1≥0, 0.4≥P2≥0, 0.4≥P3≥0. The selection probability of users with high sensitivity to the price of a single charge is set as P4; the selection probability of users with low sensitivity to the price of a single charge is set as P5; where 0.3≥P5>P4≥0; The selection probability of users with high sensitivity to the distance from the order location to the charging pile is set to P6; the selection probability of users with low sensitivity to the distance from the order location to the charging pile is set to P7; where 0.3≥P7>P6≥0; The probability that user i chooses charging station j is calculated by the formula P(choosej|i)=(P1+P2+P3)+(P4+P5)+(P6+P7); where 1≥P(choosej|i)≥0.

8. The charging pile cluster control system based on the price guidance mechanism of the aggregator according to claim 1 is characterized in that: The method of determining the adjusted price of the charging pile of the aggregator through probability includes: When the probability is [a, 1], then by formula TZJ j =CF j ×(1±a)×100% to calculate the adjusted price; When the probability is [b, a), then by the formula Calculate the adjusted price; When the probability is [0, b), the adjusted price is CF j ; Among them, a and Both are expressed as price change rates; where a and b are expressed as interval change rates, and 1>a>b>0; Determine whether the proportion of the real-time available interfaces of the corresponding charging pile to all interfaces is less than 30%; if yes, the price change rate is a positive value, otherwise, the price change rate is a negative value.

9. The charging pile cluster control system based on the price guidance mechanism of the aggregator according to claim 1 is characterized in that: The method of calculating the maximum aggregator profit by using a multi-objective programming algorithm under the condition of minimizing user dissatisfaction includes: By formula The aggregator profit coefficient is calculated; where N is the total number of electric vehicle users; By formula U i =min(CF j )subjecttomaxP(choosej|i) calculates the dissatisfaction function of user i; By formula The target optimization function is calculated; where: represents the total revenue of the aggregator, Represents the total dissatisfaction of all users; Use the ideal solution through third-party software to obtain the charging station number recommended to the user.

10. A method for controlling aggregator charging pile clusters based on a price guidance mechanism, applied to aggregator charging pile cluster control system based on a price guidance mechanism as claimed in any one of claims 1 to 9, characterized in that: include: Collect electric vehicle information and charging pile information in the set area; Obtain the base price of the charging pile from the aggregator, and calculate the initial charging price of the charging pile based on the base price and real-time market data; Determine the type of user sensitivity to factors affecting charging piles of aggregators; Calculate the probability of the user choosing the charging pile of the aggregator, and determine the adjusted price of the charging pile of the aggregator based on the probability; the influencing factors include: the single charging price and the distance from the ordering location to the charging pile; A multi-objective programming algorithm is used to calculate the maximum profit of the aggregator under the condition of minimizing user dissatisfaction, and recommend corresponding charging piles to users.

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