Automatic control and energy sharing method and system based on user photovoltaic and electric automobile

Abstract

The invention relates to the technical field of electric vehicle energy automatic control and energy market transaction, in particular to a method and a system based on user photovoltaic and electric vehicle automatic control and energy sharing, through a vehicle-to-power grid (V2G) technology, power transmission between different electric vehicle users is automatically controlled, and reasonable benefit distribution is considered. The transaction process comprises the following steps: establishing a power transaction income model; establishing a block chain model of electric vehicle (V2G) transaction, wherein the block chain model comprises a customer satisfaction model and a block chain platform operator income model; then establishing a multi-business-subject income model, including a power grid income model, a power sales company income model and a shared energy storage operator income model; solving the model by adopting an improved northern eagle optimization algorithm; the automatic control and energy sharing method based on the user photovoltaic and the electric automobile can automatically control reasonable reutilization of electric energy of the electric automobile, and win-win of stakeholders of all parties is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle energy automatic control and energy market transaction, and in particular to a method and system for automatic control and energy sharing based on user photovoltaic and electric vehicles. Background Art

[0002] With the increasing global demand for renewable energy and concerns about carbon emissions, people are increasingly interested in using renewable energy such as solar energy. Photovoltaic power generation, as a form of clean energy, has huge development potential and can provide important support for energy transformation. At the same time, the popularity of electric vehicles has put forward higher requirements for charging facilities based on photovoltaic power generation. In many areas, in order to meet the charging needs of electric vehicle users, more charging piles and charging stations need to be built.

[0003] With the development of intelligent technology, people's demand for energy management is also increasing. Through intelligent control systems and data analysis technologies, energy production, storage and use can be managed more effectively to achieve efficient use of energy. Energy sharing and trading mode is an emerging energy management method. Through this method, users can share their remaining energy with other users, thereby realizing energy sharing and trading and improving energy utilization efficiency. Summary of the invention

[0004] In view of the above problems existing in the prior art, the present invention is proposed.

[0005] Therefore, the technical problem to be solved by the present invention is: to provide an automatic control and sharing transaction method for the surplus energy of electric vehicles based on user-side photovoltaics, to realize power transmission between different electric vehicle users through vehicle-to-grid (V2G) technology, and to consider reasonable benefit distribution; the proposed method can realize the reasonable reuse of the surplus electric energy of electric vehicles, so that all stakeholders can achieve a win-win situation.

[0006] To solve the above technical problems, the present invention provides the following technical solutions, based on the user photovoltaic and electric vehicle automatic control and energy sharing method, including: establishing an electricity trading revenue model; establishing a customer satisfaction model based on the electricity trading revenue model; establishing a blockchain platform operator revenue model; establishing a multi-operating entity revenue model based on the customer satisfaction model and the blockchain platform operator revenue model; establishing a shared energy storage operator revenue model; and solving the model using an improved Northern Goshawk optimization algorithm.

[0007] As a preferred solution of the method for automatic control and energy sharing based on user photovoltaic and electric vehicles described in the present invention, the power trading revenue model includes the conversion efficiency of photovoltaic PV, the power at time t, the constraint conditions, the buyer's electricity price, the seller's income and the buyer's cost;

[0008] The photovoltaic PV conversion efficiency is expressed as:

[0009] δ=δ0[1-r(T t -T r )]

[0010] Among them, T t represents the temperature at time t, δ represents the conversion efficiency of photovoltaic PV, δ0 represents the conversion efficiency of the PV system at the reference temperature, T r represents the reference temperature, r represents the temperature coefficient of the PV system;

[0011] The power at time t is expressed as:

[0012] P=I*A*δ

[0013] Among them, P represents the power at time t, A represents the area of ​​the photovoltaic panel, and I represents the solar radiation intensity received by the tilted photovoltaic panel;

[0014] The constraints are expressed as:

[0015]

[0016] Among them, Q s,t represents the seller’s selling price in t time periods, Q b,t represents the purchase price of the buyer in t time periods, Q G,t represents the charging price of the power grid in time period t, Q C,t represents the charging price of the sales company in t time periods, represents the upper limit of the electricity price in the tth time period, represents the lower limit of the electricity price in the tth time period;

[0017] in:

[0018]

[0019] in, Represents the intermediary service fee charged by the power grid, It represents the intermediary service fee charged by the blockchain trading platform;

[0020] If the sum of the charging and discharging power of the buyer and seller in one cycle is zero, then:

[0021]

[0022] in, represents the energy sold by the seller to the buyer in the first t time periods, represents the energy purchased by the buyer from the seller in the first t time periods;

[0023] The buyer's electricity selling price includes sales cost and expected profit, expressed as:

[0024]

[0025] Among them, Q s,t represents the electricity selling price, represents the seller’s cost function, Represents the loss cost of photovoltaic charging and discharging, represents the loss cost of charging and discharging electric vehicles, represents the expected profit of electric vehicle sellers from selling electricity;

[0026] The income of the seller is expressed as:

[0027]

[0028] Among them, R s represents the seller’s income, T represents T time periods, represents the total amount of energy sold by the seller to the buyer in the tth time period, where:

[0029]

[0030] in, represents the amount of energy sold by the i-th seller to the buyer in the t-th time period, and N represents the number of all sellers;

[0031] The buyer's cost is expressed as:

[0032]

[0033] Among them, C b represents the buyer's cost, represents the amount of electricity purchased by the buyer from the power grid, Indicates the amount of electricity purchased by the buyer from the electricity sales company. It indicates the amount of electricity sold by the seller to the buyer. represents the amount of electricity purchased by the buyer from the power grid during period t, represents the amount of electricity purchased by the jth buyer from the power sales company in the tth period, and M represents the number of all sellers; where:

[0034]

[0035] Among them, γ0 and γ1 represent Boolean logic variables.

[0036] As a preferred solution of the method for automatic control and energy sharing based on user photovoltaic and electric vehicles described in the present invention, the customer satisfaction model is expressed as:

[0037]

[0038] Among them, ΔC represents the difference between the buyer's expected value and the platform's recommended value, C max represents the maximum tolerance of the buyer; when ΔC=0, it means that the highest user satisfaction is equal to 1. When ΔC>C max When , it means that the user satisfaction is the lowest and equals to 0.

[0039] As a preferred solution of the method for automatic control and energy sharing based on user photovoltaic and electric vehicles described in the present invention, the revenue model of the blockchain platform operator is expressed as:

[0040]

[0041] Among them, R block represents the revenue of the e-commerce trading platform, μ t It represents the platform service fee collected by the trading platform in the tth time period.

[0042] As a preferred solution of the method for automatic control and energy sharing based on user photovoltaic and electric vehicles described in the present invention, wherein: the multi-operating entity income model includes a power grid income model and an electricity sales company income model;

[0043] The grid revenue model is expressed as:

[0044]

[0045] Among them, R G represents the revenue of the power grid, λ1 represents the grid-connected service cost in the tth period of a day;

[0046] The revenue model of the power sales company is expressed as:

[0047]

[0048] Among them, R C Represents the revenue of the electricity sales company.

[0049] As a preferred solution of the method for automatic control and energy sharing based on user photovoltaic and electric vehicles described in the present invention, wherein: the revenue model of the shared energy storage operator includes the fees paid by the user for storing energy, the total revenue of energy storage and constraints;

[0050] The fee paid by the user for storing energy is expressed as:

[0051]

[0052] in, represents the fee paid by the user for storing energy, represents the rental cost in the tth time period, represents the energy storage charging power of the user in the tth time period, Indicates the energy storage discharge power of the user in the tth time period;

[0053] The total revenue of the energy storage is expressed as:

[0054]

[0055] Among them, R ES represents the total revenue of energy storage, Represents the loss cost caused by energy storage charging and discharging;

[0056] When the total power of users charging and discharging at time t cannot exceed the capacity limit of the shared energy storage system within a time period T, the system capacity of T at time t satisfies:

[0057]

[0058] in, represents the minimum capacity allowed by the shared energy storage system, represents the maximum capacity allowed by the shared energy storage system, represents the system capacity of T at time t;

[0059] The constraints are expressed as:

[0060]

[0061] in, represents the system capacity at time t+1, Indicates the total charging power of the user. Indicates the total discharge power of the user, It is the maximum charging power allowed by the shared energy storage system. Indicates the maximum discharge power allowed by the shared energy storage system.

[0062] As a preferred solution of the method based on user photovoltaic and electric vehicle automatic control and energy sharing described in the present invention, the northern goshawk optimization algorithm includes an optimization algorithm that simulates the hunting behavior of the northern goshawk, and searches for the optimal solution in the solution space by simulating the goshawk's prey identification, attack, pursuit and escape behaviors; the solution ideas include initializing population position, prey identification, trailing and chasing, position update, and fitness evaluation.

[0063] Another object of the present invention is to provide an automatic control and energy sharing system based on user photovoltaic and electric vehicles, to achieve power transmission between different electric vehicle users through vehicle-to-grid (V2G) technology, to ensure efficient use of electric energy, and to promote reasonable benefit distribution in the process of power trading, to provide electric vehicle users and platform operators with a more intelligent and automated energy sharing solution.

[0064] In order to solve the above technical problems, the present invention provides the following technical solutions: Based on the user photovoltaic and electric vehicle automatic control and energy sharing system, it includes: power trading model, customer satisfaction model, blockchain revenue model, multi-subject income model, energy storage revenue model and optimization algorithm model;

[0065] The power trading model is used to establish a power trading revenue model;

[0066] The customer satisfaction model is established based on the power trading revenue model;

[0067] The blockchain revenue model establishes a revenue model for blockchain platform operators;

[0068] The multi-subject revenue model is to establish a multi-operating subject revenue model based on the customer satisfaction model and the blockchain platform operator revenue model;

[0069] The energy storage revenue model is used to establish a revenue model for shared energy storage operators;

[0070] The optimization algorithm model adopts the improved northern goshawk optimization algorithm to solve the model.

[0071] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the method based on automatic control and energy sharing of user photovoltaic and electric vehicles are implemented as described above.

[0072] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the method for automatic control and energy sharing based on user photovoltaic and electric vehicles as described above are implemented.

[0073] Beneficial effects of the present invention: Based on the different output powers of user PV in different time periods and the traffic usage characteristics of electric vehicles of different users, the present invention formulates different buyer-seller division schemes, so that buyers and sellers can achieve a win-win situation and provide more accurate services for both supply and demand sides of electric vehicle charging services.

[0074] Based on the difference between the buyer's expected value and the platform's recommended value, a customer satisfaction model for the blockchain trading platform was established, which effectively measured the customer's satisfaction with the trading platform through numerical values, which is of great significance to the proposed trading method.

[0075] The established multi-operator revenue model combines the power grid revenue model and the power sales company revenue model to form a multi-operator revenue model, which can comprehensively evaluate the profit mechanism and interactive impact of each participant in the power trading ecosystem, help optimize resource allocation, improve market efficiency, and encourage more participants to join the power trading market. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0077] Figure 1 An energy trading flow chart based on a method for automatic control and energy sharing of user photovoltaic and electric vehicles provided in one embodiment of the present invention.

[0078] Figure 2 A flowchart of an improved Beicang Eagle optimization algorithm based on a user photovoltaic and electric vehicle automatic control and energy sharing method provided for one embodiment of the present invention.

[0079] Figure 3 A PV output curve diagram of a typical day on the user side based on a method for automatic control and energy sharing of user photovoltaic and electric vehicles provided in one embodiment of the present invention.

[0080] Figure 4 A multi-operating entity electricity price diagram based on a user photovoltaic and electric vehicle automatic control and energy sharing method is provided for one embodiment of the present invention.

[0081] Figure 5 An optimal electricity purchasing strategy diagram for Class A users based on a method for automatic control and energy sharing of user photovoltaic and electric vehicles is provided in one embodiment of the present invention.

[0082] Figure 6 An optimal electricity purchasing strategy diagram for Class B users based on a method for automatic control and energy sharing of user photovoltaic and electric vehicles provided in one embodiment of the present invention.

[0083] Figure 7 An optimal electricity purchasing strategy diagram for Class C users based on a method for automatic control and energy sharing of user photovoltaic and electric vehicles is provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0084] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0085] Example 1, reference Figure 1-Figure 2 , is an embodiment of the present invention, which provides a method for automatic control and energy sharing based on user photovoltaic and electric vehicles, including:

[0086] S1: Establish a power trading revenue model.

[0087] It should be noted that the electricity trading revenue model includes the conversion efficiency of photovoltaic PV, the power at time t, the constraints, the buyer's electricity price, the seller's income and the buyer's cost;

[0088] The conversion efficiency of photovoltaic PV is expressed as:

[0089] δ=δ0[1-r(T t -T r )]

[0090] Among them, T t represents the temperature at time t, δ represents the conversion efficiency of photovoltaic PV, δ0 represents the conversion efficiency of the PV system at the reference temperature, T r represents the reference temperature, the value is 298K, r represents the temperature coefficient of the PV system;

[0091] The power at time t is expressed as:

[0092] P=I*A*δ

[0093] Among them, P represents the power at time t, A represents the area of ​​the photovoltaic panel, and I represents the solar radiation intensity received by the tilted photovoltaic panel;

[0094] Each day can be divided into T time periods, with the electricity prices of the power grid and the power sales company as the strategy, and the constraints are expressed as:

[0095]

[0096] Among them, Q s,t represents the seller’s selling price in t time periods, Q b,t represents the purchase price of the buyer in t time periods, Q G,t represents the charging price of the power grid in time period t, Q C,t represents the charging price of the sales company in t time periods, represents the upper limit of electricity price in the tth time period, represents the lower limit of the electricity price in the tth time period;

[0097] in:

[0098]

[0099] in, Represents the intermediary service fee charged by the power grid, It represents the intermediary service fee charged by the blockchain trading platform;

[0100] If the sum of the charging and discharging power of the buyer and seller in one cycle is zero, then:

[0101]

[0102] in, represents the energy sold by the seller to the buyer in the first t time periods, represents the energy purchased by the buyer from the seller in the first t time periods.

[0103] Furthermore, the buyer's electricity price includes sales costs and expected profits, so the electricity price is expressed as:

[0104]

[0105] Among them, Q s,t represents the electricity selling price, represents the seller’s cost function, Represents the loss cost of photovoltaic charging and discharging, represents the loss cost of charging and discharging electric vehicles, represents the expected profit of electric vehicle sellers from selling electricity;

[0106] The seller's income is expressed as:

[0107]

[0108] Among them, R s represents the seller’s income, T represents T time periods, represents the total amount of energy sold by the seller to the buyer in the tth time period, where:

[0109]

[0110] in, represents the amount of energy sold by the i-th seller to the buyer in the t-th time period, and N represents the number of all sellers;

[0111] The buyer's cost is expressed as:

[0112]

[0113] Among them, C b represents the buyer's cost, represents the amount of electricity purchased by the buyer from the power grid, Indicates the amount of electricity purchased by the buyer from the electricity sales company. It indicates the amount of electricity sold by the seller to the buyer. represents the amount of electricity purchased by the buyer from the power grid during period t, represents the amount of electricity purchased by the jth buyer from the power sales company in the tth period, and M represents the number of all sellers; where:

[0114]

[0115] Among them, γ0 and γ1 represent Boolean logic variables.

[0116] S2: Establish a customer satisfaction model based on the electricity trading revenue model.

[0117] It should be noted that the piecewise function is used to describe the buyer's satisfaction with the blockchain trading platform under the guidance of the seller's time-of-use electricity price, and the customer satisfaction model is expressed as:

[0118]

[0119] Among them, ΔC represents the difference between the buyer's expected value and the platform's recommended value, C max represents the maximum tolerance of the buyer; when ΔC=0, it means that the highest user satisfaction is equal to 1. When ΔC>C max When , it means that the user satisfaction is the lowest and equals to 0.

[0120] S3: Establish a revenue model for blockchain platform operators.

[0121] It should be noted that the e-commerce trading platform based on blockchain technology provides transaction protection for both buyers and sellers. The revenue model of the blockchain platform operator is expressed as:

[0122]

[0123] Among them, R block represents the revenue of the e-commerce trading platform, μ t It represents the platform service fee collected by the trading platform in the tth time period.

[0124] S4: Establish a multi-operating entity revenue model based on the customer satisfaction model and the blockchain platform operator revenue model.

[0125] The multi-operating entity revenue model includes the power grid revenue model and the power sales company revenue model;

[0126] It should be noted that the revenue of the power grid mainly consists of two parts: the service cost of power transmission generated by both buyers and sellers, and the cost incurred by the buyer when the buyer and seller have no electricity to sell. The power grid revenue model is expressed as:

[0127]

[0128] Among them, R G represents the revenue of the power grid, and λ1 represents the grid-connected service cost in the tth period of a day.

[0129] Furthermore, the revenue source of the power sales company is that the buyers directly choose this channel to purchase electricity. The revenue model of the power sales company is expressed as:

[0130]

[0131] Among them, R C Represents the revenue of the electricity sales company.

[0132] S5: Establish a revenue model for shared energy storage operators.

[0133] The revenue model for shared energy storage operators includes the fees users pay for energy storage, the total revenue from energy storage, and constraints;

[0134] It should be noted that the fee paid by users for storing energy is expressed as:

[0135]

[0136] in, represents the fee paid by the user for storing energy, represents the rental cost in the tth time period, represents the energy storage charging power of the user in the tth time period, Indicates the energy storage discharge power of the user in the tth time period;

[0137] The total revenue from energy storage is expressed as:

[0138]

[0139] Among them, R ES represents the total revenue of energy storage, Represents the loss cost caused by energy storage charging and discharging.

[0140] Furthermore, when the total power of the user charging and discharging at time t cannot exceed the capacity limit of the shared energy storage system within a time period T, the system capacity of T at time t satisfies:

[0141]

[0142] in, represents the minimum capacity allowed by the shared energy storage system, represents the maximum capacity allowed by the shared energy storage system, represents the system capacity at time t,

[0143] Furthermore, the charging and discharging power of all users in any time period is limited by the allowed power of the shared energy storage system. For time period t, the constraint is expressed as:

[0144]

[0145] in, represents the system capacity at time t+1, Indicates the total charging power of the user. Indicates the total discharge power of the user, It is the maximum charging power allowed by the shared energy storage system. Indicates the maximum discharge power allowed by the shared energy storage system.

[0146] S6: The model is solved using the improved Northern Goshawk optimization algorithm.

[0147] It should be noted that the northern goshawk optimization algorithm includes an optimization algorithm that simulates the hunting behavior of the northern goshawk. It searches for the optimal solution in the solution space by simulating the goshawk's prey recognition, attack, pursuit and escape behaviors. The solution ideas include initializing population positions, prey recognition, trailing pursuit, position update, and fitness evaluation. The algorithm has the characteristics of combining global search and local search, and can handle complex optimization problems, especially in multi-peak, nonlinear and other problems.

[0148] Furthermore, the improved northern goshawk optimization algorithm is used to solve the problem in the Matlab2021a environment. The solution steps are as follows: Figure 2 shown.

[0149] The above is a schematic scheme of the method for automatic control and energy sharing of user photovoltaic and electric vehicles in this embodiment. It should be noted that the technical scheme of the system based on the method for automatic control and energy sharing of user photovoltaic and electric vehicles and the technical scheme based on the method for automatic control and energy sharing of user photovoltaic and electric vehicles mentioned above belong to the same concept. For the details not described in detail in the technical scheme of the system based on automatic control and energy sharing of user photovoltaic and electric vehicles in this embodiment, please refer to the description of the technical scheme based on the method for automatic control and energy sharing of user photovoltaic and electric vehicles mentioned above.

[0150] Example 2, reference Figure 1, which is an embodiment of the present invention, provides a user photovoltaic and electric vehicle automatic control and energy sharing system in this embodiment, including: power trading model, customer satisfaction model, blockchain revenue model, multi-agent income model, energy storage revenue model and optimization algorithm model;

[0151] The power trading model is used to establish a power trading revenue model;

[0152] The customer satisfaction model is established based on the power trading revenue model;

[0153] The blockchain revenue model establishes a revenue model for blockchain platform operators;

[0154] The multi-subject revenue model is to establish a multi-operating subject revenue model based on the customer satisfaction model and the blockchain platform operator revenue model;

[0155] The energy storage revenue model is used to establish a revenue model for shared energy storage operators;

[0156] The optimization algorithm model adopts the improved northern goshawk optimization algorithm to solve the model.

[0157] This embodiment also provides a computing device, which is applicable to the case of a method for automatic control and energy sharing of user photovoltaic and electric vehicles, including:

[0158] Memory and processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the automatic control and energy sharing method based on user photovoltaic and electric vehicles as proposed in the above embodiment.

[0159] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for automatic control and energy sharing based on user photovoltaic and electric vehicles as proposed in the above embodiment is implemented.

[0160] The storage medium proposed in this embodiment and the method based on user photovoltaic and electric vehicle automatic control and energy sharing proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0161] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0162] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0163] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0164] Example 3, reference Figure 2-Figure 4 , which is an embodiment of the present invention, provides an automatic control and energy sharing method based on user photovoltaic and electric vehicles. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0165] This case is a regional electric vehicle user group with three different charging habits. Each electric vehicle user is equipped with corresponding photovoltaic equipment and bidirectional charging piles to realize the power supply and transmission of electric vehicles. The user-side photovoltaic system selects typical winter data, and the daily photovoltaic output curve is as follows: Figure 2 At the same time, there is a certain capacity of shared energy storage in the region, which is used to provide leasing services to sellers, realize temporary storage of electricity, and ease the use of electricity.

[0166] By analyzing the charging behavior of users, we can understand their needs and usage habits and provide more accurate services for both the supply and demand sides of electric vehicle charging services. Assume that the three types of electric vehicle users after equivalent aggregation are night (type A), early morning and late night (type B), and normal work and rest (type C). Three scenarios are set to represent three different types of electric vehicle users. Scenario 1 is mainly based on type A electric vehicle users. Scenario 2 is mainly based on type C electric vehicle users. Scenario 3 is mainly based on type B electric vehicle users. Scenario 1 sets N = [80, 50, 40] electric vehicles, Scenario 2 sets N = [30, 50, 80] electric vehicles, and Scenario 3 sets N = [40, 40, 80] electric vehicles. Table 1 lists the electric vehicle parameters after equivalent aggregation, where 0 means no charging and 1 means charging.

[0167] Table 1 Electric vehicle charging status

[0168]

[0169] As shown in Table 1, the charging periods for Class A users are: 5:00-8:00, 14:00, 17:00-24:00. The charging periods for Class B users are: 1:00-5:00, 12:00-13:00, 15:00, 18:00, 21:00-24:00. The charging periods for Class C users are: 7:00-13:00, 16:00-17:00, 19:00-23:00. The electricity prices of each operating entity are as follows: Figure 4 shown.

[0170] Example 4, reference Figure 5-Figure 7 , which is an embodiment of the present invention, provides an automatic control and energy sharing method based on user photovoltaic and electric vehicles. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0171] Scenario 1: mainly for type A users, such as Figure 5As shown in Table 1, the electricity purchase period is divided into two periods: 5:00-8:00 and 22:00-24:00. This is the opposite of the charging period because the photovoltaic power generation of Class A users rises at 14:00, so the energy is accumulated to a certain extent and there is no need to purchase electricity. From 17:00 to 21:00, the PV output gradually decreases. However, it can still meet the user's electricity demand. In the first stage, due to the charging demand, Class A users are affected by the time characteristics of photovoltaic output. Therefore, they choose to purchase electricity from the power grid or the power sales company. From 1:00 to 5:00, the price of the power sales company is lower. Therefore, Class A users choose to purchase electricity from the power sales company at 5:00, and choose to purchase electricity from the State Grid with a lower electricity price from 6:00 to 8:00. It can be seen from Table 1 that in the charging period of 14:00 and 17:00-22:00, no electricity purchase is completed because photovoltaic power generation can meet their needs. As time goes by, the photovoltaic output decreases and the remaining power decreases. Therefore, a grid with a lower price is selected to purchase electricity from 22:00 to 24:00. In scenarios 2 and 3, if Figure 5 As shown in the figure, Class C users and Class B users are dominant. Compared with Scenario 1, the number of Class A users decreases, resulting in a lower power purchase amount than Scenario 1. The power purchase cycle is consistent with Scenario 1, and the low-price operator is selected for purchase.

[0172] According to Table 1, from 1:00 to 5:00, Class B users must charge, Class C users do not need to charge, and Class A users only need to charge at 5:00. From 1:00 to 5:00, both A and C users can provide some of their own resting power. However, a large amount of electricity is purchased by the electricity selling company at a low price. The photovoltaic power generation period is 12:00-13:00, 15:00, and 18:00, which meets the charging needs of Class B users. Therefore, no power purchase occurs.

[0173] The optimal electricity purchasing strategy for Class B users is as follows: Figure 6 In scenario 1, Figure 6 As shown, Class B users purchase electricity from Class A users at 21:00, which is consistent with Figure 4 The electricity purchasing strategy for Class A users shown Figure 1 Therefore, Class B users have no demand for electricity purchase at this time. From 22:00 to 24:00, Class A and C users have no surplus electricity. Therefore, they purchase electricity from the power grid at a low price. In Scenario 2, Class B users purchase electricity from Class A users and purchase electricity from the power sales company at 21:00. Compared with Scenario 1, the number of Class A users decreases, and the insufficient electricity can be purchased from the power sales company at a lower price. In Scenario 3, compared with Scenario 2, the number of Class A users increases slightly, but compared with Scenario 1, the electricity level at this time is still insufficient, and the remaining electricity is also purchased from the power sales company.

[0174] Energy purchasing strategies for Class C users are as follows: Figure 7Table 1 shows that during the period from 1:00 to 6:00, Class C users have no charging demand, so they do not purchase electricity. From 7:00 to 13:00, Class C users have a certain charging demand. As time goes by, the power generation gradually increases, resulting in a gradual decrease in the electricity purchased by Class C users, which is reflected in Figure 7 During the period from 12:00 to 18:00, Class C users no longer purchase electricity from other channels. During the period from 19:00 to 23:00, as the photovoltaic power generation gradually decreases, Class C users have a certain demand for purchasing electricity, and the purchase of electricity gradually increases. Figure 7 In the scenario 1 shown, electricity is purchased from the grid at a low price from 7:00 to 8:00. At 9:00, part of the electricity demand is provided by Class B users, and the rest of the electricity demand is provided by the power sales company at a low price. At 10:00, as the photovoltaic power generation accumulates, only the electricity of Class B users can meet the electricity demand of Class C users. At 11:00, Class A users also have a certain amount of surplus electricity, and they and Class B users provide electricity to Class C users together. From 9:00 to 11:00, the electricity demand of Class C users tends to decrease. At 19:00, the electricity demand of Class C users is provided by Class B users and the grid. At 20:00, the electricity demand of Class C users is only provided by Class B users. From 21:00 to 23:00, no users can supply electricity, and Class C users choose to purchase electricity at a low price from the grid and the power sales company. In scenarios 2 and 3, the trend of changes in the energy purchasing strategy of Class C users is consistent with scenario 1. However, due to the change in the number of users, the electricity purchased from other users, the power grid, and the power sales company during 9:00-11:00 and 19:00-20:00 changes to a certain extent. At 11:00, Class C users in scenarios 2 and 3 do not purchase electricity from Class A users. At 19:00, Class C users in scenarios 2 and 3 only purchase electricity from the power grid, not from Class B users, which is consistent with the Figure 7 This indicates that Class B users have no electricity to sell in both scenarios.

[0175] It should be noted that the above embodiments are only used to illustrate the technical solutions 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 solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for automatic control and energy sharing based on user photovoltaic and electric vehicles, characterized by: include: Establishing a revenue model for power trading; Establish a customer satisfaction model based on the power trading revenue model; Establishing a revenue model for blockchain platform operators; Establish a multi-operator revenue model based on the customer satisfaction model and the blockchain platform operator revenue model; Establish a shared energy storage operator revenue model; The model is solved using an improved northern goshawk optimization algorithm.

2. The method for automatic control and energy sharing based on user photovoltaic and electric vehicles as claimed in claim 1, characterized in that: The power trading revenue model includes the conversion efficiency of photovoltaic PV, the power at time t, the constraint conditions, the buyer's electricity price, the seller's income and the buyer's cost; The photovoltaic PV conversion efficiency is expressed as: δ=δ0[1-r(T t -T r )] Among them, T t represents the temperature at time t, δ represents the conversion efficiency of photovoltaic PV, δ0 represents the conversion efficiency of the PV system at the reference temperature, T r represents the reference temperature, r represents the temperature coefficient of the PV system; The power at time t is expressed as: P=I*A*δ Among them, P represents the power at time t, A represents the area of ​​the photovoltaic panel, and I represents the solar radiation intensity received by the tilted photovoltaic panel; The constraints are expressed as: Among them, Q s,t represents the seller’s selling price in t time periods, Q b,t represents the purchase price of the buyer in t time periods, Q G,t represents the charging price of the power grid in time period t, Q C,t represents the charging price of the sales company in t time periods, represents the upper limit of electricity price in the tth time period, represents the lower limit of the electricity price in the tth time period; in: in, Represents the intermediary service fee charged by the power grid, It represents the intermediary service fee charged by the blockchain trading platform; If the sum of the charging and discharging power of the buyer and seller in one cycle is zero, then: in, represents the energy sold by the seller to the buyer in the first t time periods, represents the energy purchased by the buyer from the seller in the first t time periods; The buyer's electricity selling price includes sales cost and expected profit, expressed as: Among them, Q s,t represents the electricity selling price, represents the seller’s cost function, Represents the loss cost of photovoltaic charging and discharging, represents the loss cost of charging and discharging electric vehicles, represents the expected profit of electric vehicle sellers from selling electricity; The income of the seller is expressed as: Among them, R s represents the seller’s income, T represents T time periods, represents the total amount of energy sold by the seller to the buyer in the tth time period, where: in, represents the amount of energy sold by the i-th seller to the buyer in the t-th time period, and N represents the number of all sellers; The buyer's cost is expressed as: Among them, C b represents the buyer's cost, represents the amount of electricity purchased by the buyer from the power grid, Indicates the amount of electricity purchased by the buyer from the electricity sales company. It indicates the amount of electricity sold by the seller to the buyer. represents the amount of electricity purchased by the buyer from the power grid during period t, represents the amount of electricity purchased by the jth buyer from the power sales company in the tth period, and M represents the number of all sellers; where: Among them, γ0 and γ1 represent Boolean logic variables.

3. The method for automatic control and energy sharing based on user photovoltaic and electric vehicles as claimed in claim 2, characterized in that: The customer satisfaction model is expressed as: Among them, ΔC represents the difference between the buyer's expected value and the platform's recommended value, C max represents the maximum tolerance of the buyer; when ΔC=0, it means that the highest user satisfaction is equal to 1; when ΔC>C max When , it means that the user satisfaction is the lowest and equals to 0.

4. The method for automatic control and energy sharing based on user photovoltaic and electric vehicles as claimed in claim 3, characterized in that: The blockchain platform operator revenue model is expressed as: Among them, R block represents the revenue of the e-commerce trading platform, μ t It represents the platform service fee collected by the trading platform in the tth time period.

5. The method for automatic control and energy sharing based on user photovoltaic and electric vehicles as claimed in claim 4, characterized in that: The multi-operating entity revenue model includes a power grid revenue model and a power sales company revenue model; The grid revenue model is expressed as: Among them, R G represents the revenue of the power grid, λ1 represents the grid-connected service cost in the tth period of a day; The revenue model of the power sales company is expressed as: Among them, R C Represents the revenue of the electricity sales company.

6. The method for automatic control and energy sharing based on user photovoltaic and electric vehicles as claimed in claim 5, characterized in that: The shared energy storage operator revenue model includes the fees paid by users for storing energy, the total revenue from energy storage, and constraints; The fee paid by the user for storing energy is expressed as: in, represents the fee paid by the user for storing energy, represents the rental cost in the tth time period, represents the energy storage charging power of the user in the tth time period, Indicates the energy storage discharge power of the user in the tth time period; The total revenue of the energy storage is expressed as: Among them, R ES represents the total revenue of energy storage, Indicates the loss cost caused by energy storage charging and discharging; When the total power of the user charging and discharging at time t cannot exceed the capacity limit of the shared energy storage system within a time period T, the system capacity of T at time t satisfies: in, represents the minimum capacity allowed by the shared energy storage system, represents the maximum capacity allowed by the shared energy storage system, represents the system capacity of T at time t; The constraints are expressed as: in, represents the system capacity at time t+1, Indicates the total charging power of the user. Indicates the total discharge power of the user, It is the maximum charging power allowed by the shared energy storage system. Indicates the maximum discharge power allowed by the shared energy storage system.

7. The method for automatic control and energy sharing based on user photovoltaic and electric vehicles as claimed in claim 6, characterized in that: The northern goshawk optimization algorithm includes an optimization algorithm that simulates the hunting behavior of the northern goshawk. By simulating the goshawk's prey identification, attack, pursuit and escape behaviors, the optimal solution is searched in the solution space; the solution ideas include initializing population position, prey identification, trailing pursuit, position update, and fitness evaluation.

8. A system based on the user photovoltaic and electric vehicle automatic control and energy sharing method according to any one of claims 1 to 7, characterized in that: include: Power trading model, customer satisfaction model, blockchain revenue model, multi-agent revenue model, energy storage revenue model, and optimization algorithm model; The power trading model is used to establish a power trading revenue model; The customer satisfaction model is established based on the power trading revenue model; The blockchain revenue model establishes a revenue model for blockchain platform operators; The multi-subject revenue model is to establish a multi-operating subject revenue model based on the customer satisfaction model and the blockchain platform operator revenue model; The energy storage revenue model is used to establish a revenue model for shared energy storage operators; The optimization algorithm model adopts the improved northern goshawk optimization algorithm to solve the model.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for automatic control and energy sharing based on user photovoltaic and electric vehicles described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for automatic control and energy sharing based on user photovoltaic and electric vehicles described in any one of claims 1 to 7 are implemented.

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