A Method for Pushing Electric Vehicle Charging Modes Oriented to Active Response on the Demand Side

Abstract

The present invention provides a method for pushing an electric vehicle charging mode for demand-side active response, which includes the following steps: Step 1, collect the monthly data of electric vehicle users in one year, including charging attribute data and user attribute data; Step 2, divide and profile the electric vehicle users according to the collected data; Step 3, design corresponding charging modes for the profiled user groups; Step 4, calculate the profits of the electric vehicle load aggregators under different charging modes, and determine the optimal stepped electricity price and optimal stepped electricity quantity under the charging modes; Step 5, calculate the charging costs saved by the users after selecting the charging modes, and push the optimal charging mode to the users. Compared with the prior art, the charging mode formulated by the present invention according to the accurate profiling of electric vehicle users can fully improve the enthusiasm of users to participate in demand response and narrow the peak-valley difference of the power grid; it will not reduce the charging satisfaction of electric vehicle users and affect the power consumption experience.

Description

Technical Field

[0001] The present invention relates to the field of distribution network demand side management, and particularly to a method for pushing an electric vehicle charging mode for active response on the demand side. Background Technique

[0002] With the massive access of electric vehicles to the power grid, if all electric vehicles charge during a certain period, it will generate a powerful power impact on the distribution network load. It is urgent to enhance the reliability and economy of power grid operation, improve the coordinated operation ability of "source-network-load-storage", improve the real-time balance ability of system supply and demand, improve load characteristics, and increase the regulation ability on the demand side.

[0003] In the process of electric vehicles participating in demand side response, the following problems mainly exist: First, the method for evaluating the response potential of electric vehicle users is not perfect. The behavior of electric vehicle users is only analyzed from the perspectives of time and space, and big data technology is not used to establish a multi-source heterogeneous database; in actual vehicle-grid interaction, it is difficult to comprehensively evaluate the ability of electric vehicle users to participate in demand response from multiple dimensions. Second, the strategy for motivating electric vehicle users to participate in demand response is not flexible enough. An undifferentiated incentive scheme is implemented for all electric vehicle users, and in previous implementation schemes, the enthusiasm of electric vehicle users is low and the user experience is also poor, making it difficult to improve the reliability of the power grid operation with access to electric vehicles. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for pushing an electric vehicle charging mode for active response on the demand side, which can improve the user experience and the reliability of the power grid operation with access to electric vehicles, so as to overcome the defects existing in the above-mentioned prior art.

[0005] A method for pushing an electric vehicle charging mode for active response on the demand side includes the following steps:

[0006] Step 1: Collect the monthly data of electric vehicle users in one year, including charging attribute data and user attribute data;

[0007] Step 2: Divide and profile the electric vehicle users according to the collected data. The profiling refers to classifying the divided electric vehicle users by using the corresponding BIRCH clustering algorithm to obtain electric vehicle user groups;

[0008] Step 3: Design corresponding charging modes for the profiled user groups;

[0009] Step 4: Calculate the profits of the electric vehicle load aggregators under different charging modes, and determine the optimal tiered electricity price and optimal tiered electricity quantity under the charging modes;

[0010] Step 5: Calculate the charging cost saved after the user selects the charging mode and push the optimal charging mode to the user.

[0011] Further, the charging attribute data includes monthly charging volume, monthly charging frequency, and monthly charging compliance rate; the user attribute data includes user education level and user vehicle attributes.

[0012] Further, in Step 2, the users are divided into three situations of low, medium, and high electricity consumption according to their charging volume.

[0013] Further, in Step 2, obtain the sub - user groups of users with low, medium, and high electricity consumption.

[0014] Further, the charging modes include ladder mode, monthly - package mode, and monthly - rent mode.

[0015] Further, the rules of the ladder mode are as follows:

[0016]

[0017] In the formula: are the tiered electricity quantities for the 1st - 3rd tiers of the ladder mode; are the tiered electricity prices for the 1st - 3rd tiers of the ladder mode; Q is the monthly charging volume of the electric vehicle user; Q0 is the charging volume threshold for each tier of the ladder mode; P is the market charging electricity price without enjoying the mode discount.

[0018] Further, the rules of the monthly - package mode are as follows:

[0019]

[0020] In the formula: are the fixed consumption amounts for the 1st - 3rd tiers of the monthly - package mode; Q is the monthly charging volume of the electric vehicle user; Q0 is the charging volume threshold for each tier of the monthly - package mode; P is the market charging electricity price without enjoying the mode discount, are the tiered electricity quantities for the 1st - 3rd tiers of the monthly - package mode.

[0021] Further, the rules of the monthly - rent mode are as follows:

[0022]

[0023] In the formula: are the fixed fees for the 1st - 3rd tiers of the monthly - rent mode; are the tiered unit electricity prices for the 1st - 3rd tiers of the monthly - rent mode; are the tiered electricity quantities for the 1st - 3rd tiers of the monthly - rent mode; Q0 is the charging volume threshold for each tier of the ladder mode; P is the market charging electricity price without enjoying the mode discount.

[0024] Further, the profit of the electric vehicle aggregator in Step 4 is:

[0025] I = I cha,sell + I D + I g + I C - I cha,buy - I ope

[0026] In the formula: I cha,sell is the electricity sales revenue, I D is the demand response revenue, I g is the green electricity revenue, I C is the carbon trading revenue, I cha,buy is the electricity purchase cost, I ope is the mode operation cost.

[0027] Further, the saved charging cost in Step 5 is:

[0028]

[0029] In the formula: U i is the utility function for electric vehicle user i to select the optimal mode, defined as the difference between the charging cost before the user selects the mode and the charging cost after the user selects the mode; U n,i , U' n,i , U' n ' ,i are multiple utility functions respectively.

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

[0031] By analyzing the collected data of electric vehicle users, the present invention obtains the charging behavior characteristics of different categories of user groups, formulates multiple charging modes according to the user behavior characteristics, and accurately improves the enthusiasm of users to participate in demand response and the user experience.

[0032] The present invention analyzes and studies the charging characteristics of electric vehicles, uses big data technology to create personalized portraits of electric vehicle users, evaluates the potential to participate in demand response; improves the enthusiasm of users to participate in demand response, smooths the charging load curve, reduces the peak-valley difference of the power grid, and does not affect the travel plans of users while improving the reliability of electric vehicle access to the power grid operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 is the flowchart of the charging mode push based on the electric vehicle user portrait of the present invention;

[0034] Figure 2 is the clustering result diagram of the user portrait of Group A, Group a of electric vehicle A in the embodiment of the present invention;

[0035] Figure 3 It is the clustering result diagram of user portraits of Group b of Class A electric vehicles in the embodiments of the present invention;

[0036] Figure 4 It is the clustering result diagram of user portraits of Group c of Class A electric vehicles in the embodiments of the present invention;

[0037] Figure 5 It is the clustering result diagram of user portraits of Group d of Class A electric vehicles in the embodiments of the present invention;

[0038] Figure 6 It is the clustering result diagram of user portraits of Group e of Class A electric vehicles in the embodiments of the present invention;

[0039] Figure 7 It is the clustering result diagram of user portraits of Group f of Class A electric vehicles in the embodiments of the present invention;

[0040] Figure 8 It is the clustering result diagram of user portraits of Group g of Class A electric vehicles in the embodiments of the present invention;

[0041] Figure 9 It is the clustering result diagram of user portraits of Group a of Class B electric vehicles in the embodiments of the present invention;

[0042] Figure 10 It is the clustering result diagram of user portraits of Group b of Class B electric vehicles in the embodiments of the present invention;

[0043] Figure 11 It is the clustering result diagram of user portraits of Group c of Class B electric vehicles in the embodiments of the present invention;

[0044] Figure 12 It is the clustering result diagram of user portraits of Group d of Class B electric vehicles in the embodiments of the present invention;

[0045] Figure 13 It is the clustering result diagram of user portraits of Group e of Class B electric vehicles in the embodiments of the present invention;

[0046] Figure 14 It is the clustering result diagram of user portraits of Group f of Class B electric vehicles in the embodiments of the present invention;

[0047] Figure 15 It is the clustering result diagram of user portraits of Group g of Class B electric vehicles in the embodiments of the present invention;

[0048] Figure 16 It is the clustering result diagram of user portraits of Group a of Class C electric vehicles in the embodiments of the present invention;

[0049] Figure 17 It is the clustering result diagram of user portraits of Group b of Class C electric vehicles in the embodiments of the present invention;

[0050] Figure 18 This is the clustering result diagram of the user portraits of Group C of electric vehicles in the embodiments of the present invention;

[0051] Figure 19 This is the clustering result diagram of the user portraits of Group D of electric vehicles in the embodiments of the present invention;

[0052] Figure 20 This is the clustering result diagram of the user portraits of Group E of electric vehicles in the embodiments of the present invention;

[0053] Figure 21 This is the clustering result diagram of the user portraits of Group F of electric vehicles in the embodiments of the present invention;

[0054] Figure 22 This is the composition diagram of the aggregator mode of electric vehicles in the embodiments of the present invention. Detailed implementation manners

[0055] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. This embodiment is implemented on the premise of the technical solution of the present invention, and gives detailed implementation manners and specific operation processes, but the protection scope of the present invention is not limited to the following embodiments.

[0056] This embodiment provides a method for pushing an electric vehicle charging mode for active response on the demand side, including the following steps:

[0057] Step 1: First, collect electric vehicle user data, collect the monthly data of electric vehicle users in one year, and the data includes charging-related attribute data: monthly charging amount, monthly charging frequency, monthly charging compliance rate; user attribute data: user education level, vehicle attributes.

[0058] Step 2: Precise user portraits. Use the BIRCH algorithm to cluster the monthly charging frequency, monthly compliance rate, user education level, and vehicle model of users in three cases after data processing, and further display the user charging behavior characteristics of three types of users A, B, and C. Using the BIRCH clustering algorithm, cluster the monthly charging frequency, monthly compliance rate, user education level, and vehicle model in three cases after processing, and the specific clustering results are as Figures 2 - 21As shown, 7, 7, and 6 types of user clustering portrait results are obtained respectively. Taking type A users as an example, the results after clustering are denoted as type a, b, c, d, e, f, and g users. Among them, the charging times of type a and b users are concentrated in the daily peak hours. It is hoped that this group of users can actively change their charging behaviors and transfer the charging time to the low valley hours; a monthly rent model is formulated to encourage them to change their charging behaviors and transfer the charging time after 22:00, which will make a great contribution to the peak shaving and valley filling of the power grid. The charging times of type c, d, and e users are between 22:00 and 07:00. For the electric vehicle load aggregator, a monthly package model should be set up to attract these users to maintain the charging behavior during the low valley period. The charging times of type f and g users are concentrated in the evening peak period. Limited by the possible lack of charging piles at home, a flexible ladder model is set up to encourage these users to transfer the charging time to the low valley as much as possible to contribute to demand response. Compared with type B users, the charging volume of a single user is relatively low, but the number of this group of users is large, and there is still great response potential. The attractiveness of the monthly package model will decrease among this group of users; the demand response potential of type C users is low, but the proportion of users with flexible charging times is large, and a more flexible charging model will greatly stimulate the potential.

[0059] In this embodiment, within one year, taking a month as a cycle, 10,000 electric vehicle user data are collected in the charging management system and marketing system of the electric vehicle aggregator. Charge-related attribute data are collected in the charging management system: monthly charging volume, monthly charging frequency, and monthly charging compliance rate; user attribute data are collected in the marketing management system: user education level, vehicle attributes; data processing is carried out. The data obtained from the charging management system and marketing management system are integrated into a data form to fill in the missing values and correct the abnormal values that do not conform to the actual situation. The processed data are preliminarily clustered according to the monthly charging volume, and three situations are obtained. The final interval of the monthly charging volume is greater than or equal to 587 degrees, between 254 degrees and 587 degrees, and less than 254 degrees. The monthly average charging volumes are respectively denoted as the portrait results of type A users, type B users, and type C users.

[0060] Step 3: Design different charging models according to the results of the user portraits.

[0061] According to the user types obtained by clustering in step 2, refine three different models: the charging methods of the ladder model, monthly package model, and monthly rent model, and limit the usage time of the models to 22:00 - 07:00. The charging content of each type of model includes a basic package and an additional package. The basic package consists of electricity sales income and demand response income; the additional package includes income from participating in green power trading and income from participating in carbon trading.

[0062] The ladder mode is designed to attract users who charge during the evening peak hours, meet the needs of users who transfer part of their charging volume due to limitations of the charging environment, and the greater the monthly charging volume of users, the greater the electricity fee discount they enjoy. According to the division of monthly charging intervals, the charging rules are as follows:

[0063]

[0064] In the formula: are the tiered electricity quantities for the 1st - 3rd tiers of the ladder mode; are the tiered electricity prices for the 1st - 3rd tiers of the ladder mode; Q is the monthly charging volume of electric vehicle users; n1 - n3 are the numbers of users choosing the 1st - 3rd tiers of the ladder mode.

[0065] The monthly - package mode is to pay a fixed fee each month and then charge arbitrarily during that period. To attract users who charge in the early morning, a certain preferential discount is used to largely maintain users' charging during the low - peak period, and different tiers are set to meet the choices of three types of users, namely A, B, and C, with different monthly charging volumes. The charging rules for this mode are as follows:

[0066]

[0067] In the formula: are the fixed consumption amounts for the 1st - 3rd tiers of the monthly - package mode; Q is the monthly charging volume of electric vehicle users; Q0 is the charging volume threshold for each tier of the monthly - package mode; P is the market charging electricity price without enjoying the mode discount.

[0068] The monthly - rent mode fee consists of a basic fee + per - degree electricity price. Electric vehicle users pay a fixed fee each month, and the unit electricity price is slightly lower than the market price. For users whose charging time is during the daily peak, it can better meet the characteristics of large span and high flexibility, and different tiers are set to meet the choices of three types of users, namely A, B, and C, with different monthly charging volumes. The charging rules for this mode are as follows:

[0069]

[0070] In the formula: are the fixed fees for the 1st - 3rd tiers of the monthly - rent mode; are the tiered unit electricity prices for the 1st - 3rd tiers of the monthly - rent mode; are the tiered electricity quantities for the 1st - 3rd tiers of the monthly - rent mode.

[0071] Step Four: Optimize the mode, optimize the tiered electricity prices and tiered electricity quantities under different modes.

[0072] When optimizing the stepped electricity price and stepped electricity consumption in the mode design, the goal is to maximize the profit of the electric vehicle aggregator. Among them, the electricity sales revenue is the product of the electricity sales price and the electricity sales volume; the demand response revenue is the product of the demand response subsidy and the demand volume, with the peak shaving subsidy being 2.4 yuan / kW·h and the valley filling being 0.96 yuan / kW·h; the green electricity revenue is the product of the unit green electricity difference and the green electricity trading volume, and the unit green electricity difference is taken as 0.044 yuan / kW·h; the carbon trading revenue is the product of the unit carbon trading price and the carbon trading volume, and the unit carbon trading price is taken as 0.004 yuan / kW·h; the electricity purchase cost is the sum of the bilateral market electricity purchase cost and the centralized trading cost; the operation cost is the sum of the operation staff salary and the operation equipment investment cost. The profit calculation formula of the electric vehicle aggregator is as follows:

[0073] I = I cha,sell + I D + I g + I C - I cha,buy - I ope

[0074] In the formula: I cha,sell is the electricity sales revenue, I D is the demand response revenue, I g is the green electricity revenue, I C is the carbon trading revenue, I cha,buy is the electricity purchase cost, I ope is the mode operation cost.

[0075] Through the above process and formula, taking public charging pile users as an example, different types of optimization modes, stepped electricity consumption, and stepped electricity prices can be obtained respectively, as shown in Table 1:

[0076] Table 1 Optimization of Different Modes

[0077]

[0078] Step Five: Recommend a mode to select a suitable charging mode for users and increase the charging volume of users during the low valley period.

[0079] After the mode design is completed, calculate the utility function under different modes. The mode with the maximum utility function is the mode recommended to users. The utility function is as follows:

[0080]

[0081] In the formula: U i is the utility function for electric vehicle user i to select the optimal mode, which is defined as the difference between the charging cost before the user selects the mode and the charging cost after the user selects the mode; U n,i , U' n,i , U' n ',i The utility functions for user i to select the basic package, the additional green power package, and the additional carbon trading package respectively;

[0082] After implementing incentives for users, some users change their charging behaviors. The contribution amount λ is the charging amount of the selected users during the period from 22:00 to 07:00 in a monthly cycle. The defined transfer rate γ is the ratio of the absolute value of the difference in the charging amount of users before and after selecting the package during the period from 22:00 to 07:00 in a monthly cycle to the charging amount after selecting the package.

[0083] Table 2 Response capabilities under the demands of different users

[0084]

[0085]

[0086] It can be seen from Table 2 that the charging amounts of different users during the low - valley period are different, and their abilities to participate in demand response are also different. Among type A users, users of types a, b, f, and g change their charging behaviors and have relatively large transfer rates. And for users of types c, d, and e, since their original charging times are during the low - valley period, their transfer rates are relatively small and they still maintain their contributions to the power grid. Such users should be maintained to charge during the low - valley period. Compared with type A users, the transfer rates of corresponding type B users have decreased. This is because the charging amounts of this type of users are relatively small, and the driving force of electricity prices on them is not so large. However, due to the large number of such users, aggregating them still has great potential. For type C users, due to their small charging amounts, the attractiveness of the model is small, and the effect of cost savings on user electricity bills is also reduced. However, because the time of some users is relatively flexible, the transfer rate is slightly higher than that of type B users.

[0087] Therefore, the electric vehicle aggregator only needs to calculate the charging amounts of different types of users during the low - valley period according to the above process respectively, find out the portrait results in the corresponding situations, design different charging modes, and motivate users to aggregate to obtain the contributions of different types of users to the power grid demand response under the modes. This can well guide the power grid company to conduct demand - side management for users and achieve a good effect of peak shaving and valley filling. The composition diagram of the electric vehicle aggregator model is as Figure 22 shown.

[0088] When the above method is implemented in the form of software functional units and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0089] In another embodiment, an electronic device is provided, including one or more processors, a memory, and one or more programs stored in the memory. The one or more programs include instructions for executing the method for pushing the electric vehicle charging mode for active response to the demand side as described above.

[0090] The preferred specific embodiments of the present invention have been described in detail above. It should be understood that those of ordinary skill in the art can make many modifications and variations based on the concept of the present invention without creative work. Therefore, all technical solutions that can be obtained by those skilled in the art in the technical field of the present invention through logical analysis, reasoning, or limited experiments based on the concept of the present invention on the basis of the prior art should fall within the protection scope determined by the claims.

Claims

1. A method for pushing an electric vehicle charging mode for active response on the demand side, characterized in that, It includes the following steps: Step 1: Collect the monthly data of electric vehicle users within one year, including charging attribute data and user attribute data; Step 2: Divide and portrait the electric vehicle users according to the collected data. The portrait refers to classifying the divided electric vehicle users using the corresponding BIRCH clustering algorithm to obtain electric vehicle user groups; Step 3: Design corresponding charging modes for the portrait user groups; Step 4: Based on the profits of the electric vehicle load aggregator under different charging modes, determine the optimal tiered electricity price and optimal tiered electricity consumption under the charging mode; Step 5: Based on the charging cost saved by users after choosing the charging mode, push the optimal charging mode to users; The charging modes include a ladder mode, a monthly package mode, and a monthly rental mode; The rules of the ladder mode are: In the formula: is the electricity quantity for the first to third tiers of the stepped model; is the electricity price for the first to third tiers of the stepped model; Q is the monthly charging quantity of the electric vehicle user; Q0 is the charging quantity threshold for each tier of the stepped model; P is the market charging electricity price without enjoying the model discount; The rules of the monthly package mode are: In the formula: is the fixed consumption amount for the first to third gears of the monthly subscription mode; Q is the monthly charging amount of electric vehicle users; Q0 is the charging amount threshold for each gear of the monthly subscription mode; P is the market charging electricity price without enjoying the mode discount, is the tiered electricity consumption for the first to third gears of the monthly subscription mode; The rules of the monthly rental mode are: Wherein: is the fixed fee for the 1st - 3rd tiers in the monthly rental mode; is the unit electricity price for the 1st - 3rd tiers in the monthly rental mode; is the electricity consumption for the 1st - 3rd tiers in the monthly rental mode; Q0 is the charging electricity threshold for each tier of the monthly rental mode; P is the market charging electricity price without enjoying the mode discount.

2. The method for pushing an electric vehicle charging mode for active response on the demand side according to claim 1, characterized in that The charging attribute data includes monthly charging volume, monthly charging frequency, and monthly charging compliance rate; the user attribute data includes user education level and user vehicle attributes.

3. The method for pushing an electric vehicle charging mode for active response on the demand side according to claim 1, wherein In Step 2, users are divided into three situations of low, medium, and high electricity consumption according to their charging volume.

4. The method for pushing an electric vehicle charging mode for active response on the demand side according to claim 2, wherein In Step 2, obtain the subordinate branch user groups of users with low, medium, and high electricity consumption.

5. The method for pushing an electric vehicle charging mode for active response on the demand side according to claim 1, wherein The profit of the electric vehicle aggregator in Step 4 is: I = I cha,sell +I D +I g +I C -I cha,buy -I ope Where: I cha,sell is the electricity sales revenue, I D is the demand response revenue, I g is the green electricity revenue, I C is the carbon trading revenue, I cha,buy is the electricity purchase cost, I ope is the model operation cost.

6. The method for pushing an electric vehicle charging mode for active response on the demand side according to claim 1, wherein The charging cost saved in Step 5 is: where: U i is the utility function for electric vehicle user i to select the optimal mode, defined as the difference between the charging cost before the user selects the mode and the charging cost after the user selects the mode; U n,i , U' n,i , U” n,i are multiple utility functions respectively.

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