Electric vehicle charging optimization method and system considering distributed charging stations and electricity price guidance

Abstract

The invention relates to an electric vehicle charging optimization method and system considering distributed charging stations and electricity price guidance, and the method comprises the following steps: building an electric vehicle EV charging model which comprises a single EV charging model and an EV cluster charging model; according to the single EV charging model and the EV cluster charging model, considering an electricity price guiding factor, and establishing a power distribution network model containing a distributed charging station; and based on the power distribution network model containing the distributed charging station, setting an optimization objective function by taking the maximum charging amount and the minimum charging cost as optimization objectives to obtain an optimal charging scheme.

Description

Technical Field

[0001] The present application relates to the technical field of electric vehicle charging optimization, and more specifically, to an electric vehicle charging optimization method and system taking into account distributed charging stations and electricity price guidance. Background Art

[0002] With the implementation and promotion of the "dual carbon" policy, the new energy vehicle industry has developed rapidly, and countries around the world are vigorously supporting the manufacture of electric vehicles. Electric vehicles have the advantages of energy conservation and emission reduction, and alleviating the energy crisis. At the same time, their batteries have the function of energy storage, which makes electric vehicles both a power source and a load. When electric vehicles are used as power sources, they can transmit electricity to the power grid through charging piles to achieve energy mutual assistance between electric vehicles and the power grid; when electric vehicles are used as loads, their charging power can be regulated by the power grid and are regarded as flexible loads. However, with the increase in the penetration rate of electric vehicles, the planning of charging stations has also become one of the important factors restricting their rapid development. The planning of electric vehicle charging stations will not only affect the operating costs and benefits of charging stations and the user experience of car owners, but may even affect the power quality of the power grid, aggravate the peak-to-valley difference of the power grid load, and endanger the safety of the power system. At the same time, reasonable electricity price guidance can also achieve orderly charging of the power grid, reduce the charging costs of users, and improve the stability of the power grid.

[0003] However, most existing studies plan the location and scale of charging stations from the perspective of charging station construction costs and user experience, without considering the impact of changes in their scale on the power grid, and without fully considering the impact of electricity price guidance on the power grid.

[0004] Prior art, such as the Chinese patent application with publication number "CN115860352A", discloses an electric vehicle charging and swapping method considering space-time dual-layer optimization. The method includes setting up a space-time dual-layer charging optimization model for a battery swap station: in the upper model, the EV system autonomously searches for a battery swap station, and then obtains the traffic conditions of the battery swap station throughout the day, and transmits the results to the lower model. According to steps one to four of the upper optimization model, the problem of optimal selection of battery swap stations on a spatial scale is solved; in the optimization of the lower model, the operation economy optimization of the battery swap station as a market entity, charging optimization and the operation efficiency of the battery swap station, and the load characteristic control function of the distribution network are effectively realized by minimizing the charging cost and through the grid incentive method; under the joint action of the upper and lower model optimization, it provides a basis for the capacity planning and economic operation of the battery swap station, and the benefits and coordinated cooperation between the conversion station and the grid enterprises.

[0005] The problem with the above-mentioned prior art is that this method only considers the optimization of the operation economy of the battery swap station, including minimization of charging costs, control of distribution network load changes and peak-to-valley differences, and does not involve the optimization of electric vehicle charging capacity. Other aspects of grid stability, such as power flow distribution and node voltage, are not considered comprehensively. Summary of the invention

[0006] In order to solve the above technical problems, the present invention proposes an electric vehicle charging optimization method and system taking into account distributed charging stations and electricity price guidance.

[0007] The technical solution of the present invention is as follows:

[0008] The present invention proposes an electric vehicle charging optimization method considering distributed charging stations and electricity price guidance, comprising the following steps:

[0009] Step S1, establishing an electric vehicle EV charging model, including: a single EV charging model and an EV cluster charging model;

[0010] Step S2, establishing a distribution network model including distributed charging stations based on a single EV charging model and an EV cluster charging model and taking into account electricity price guidance factors;

[0011] Step S3, based on the distribution network model including distributed charging stations, an optimization objective function is set with maximum charging capacity and minimum charging cost as optimization goals to obtain an optimal charging plan.

[0012] As a preferred implementation, the single EV charging model is:

[0013]

[0014] Where D is the charging demand of a single EV; is the charge state of a single EV before it is connected to the grid; C is the battery capacity of a single EV; P ch,t is the charging power of a single EV during period t; η is the charging efficiency of a single EV; t start and t end are the time when a single EV connects to and leaves the grid; Δt is the time interval; P pilemax is the maximum charging power of a single EV; SOC t is the charge state of a single EV at time t.

[0015] As a preferred implementation, the EV cluster charging model is:

[0016]

[0017] Where: P ag,t is the charging power of the EV cluster in period t; P ag,maxis the maximum charging power of the EV cluster; η′ is the charging efficiency of the EV cluster; E t E is the accumulated electric energy received by the EV cluster from the grid at time t; min,t E is the minimum electric energy accumulated by the EV cluster from the grid at time t; max,t E is the maximum electric energy received by the EV cluster from the grid at time t; t-1 The accumulated electric energy received by the EV cluster from the grid at time t-1.

[0018] As a preferred implementation, the establishment of a distribution network model including distributed charging stations specifically includes: constructing charging station constraints and constructing power system operation constraints; wherein:

[0019] Construct charging station constraints, including: minimum charging capacity constraint, charging EV quantity constraint, EVSOC constraint and EV charging constraint;

[0020] Construct power system operation constraints, including: power system operation constraints, power upper and lower limit constraints, power flow constraints and node voltage constraints.

[0021] As a preferred implementation, the construction of charging station constraints includes: minimum charging capacity constraint, charging EV quantity constraint, EVSOC constraint and EV charging constraint; wherein:

[0022] The minimum charge capacity constraint is:

[0023]

[0024] Where: G is the minimum charging capacity constraint of EV charging;

[0025] The number of charging EVs is constrained as follows:

[0026]

[0027]

[0028] Where: n station 、n EV 、n pile are the number of charging stations, EVs and charging piles, respectively, e∈n EV , s∈n station ;n e,t,s is the number of EVs charging at charging station s at time t;

[0029] The EVSOC constraints are:

[0030] SOC min ≤SOC≤SOC max

[0031] Where: SOC min , SOCmax They are the minimum and maximum values ​​allowed for EVSOC respectively;

[0032] The EV charging constraints are:

[0033]

[0034] Where: is the charging status of the EV; is the maximum charging power of the EV.

[0035] As a preferred implementation, the construction of power system operation constraints includes: power system operation constraints, power upper and lower limit constraints, power flow constraints and node voltage constraints, wherein:

[0036] The power system operation constraints are:

[0037]

[0038]

[0039] In the formula, and is the active power and reactive power transmitted by line ij at time t; and are the active power and reactive power delivered by the generator to node i at time t, respectively; and are the active power and reactive power of the load connected to node i at time t, is the total charging power of the EV connected to node i at time t; D L is the set of power grid line nodes;

[0040] The upper and lower power limits are:

[0041]

[0042]

[0043] Where: and are the upper limits of active and reactive power output of generators respectively; N is the set of power grid nodes;

[0044] The power flow constraint is:

[0045]

[0046]

[0047] In the formula, and are the maximum active and reactive power allowed to flow through line ij respectively;

[0048] The node voltage constraints are:

[0049]

[0050] U min ≤U i,t ≤U max ,

[0051] Where: U i,t and U j,t are the squares of the bus voltage amplitudes at node i and node j at time t respectively; r ij is the equivalent resistance of line ij; x ij is the equivalent reactance of line ij; U min is the minimum voltage allowed at node i; U max is the maximum voltage allowed at node i.

[0052] As a preferred implementation, the optimization objective function is set with the maximum charging amount and the minimum charging cost as the optimization goal. The specific optimization objective function is:

[0053]

[0054] Where: cost is the weight coefficient of charging cost, δ SOC is the weight coefficient of the charge amount; c cost is the unit electricity price; T is the total time period; P ag,t is the EV charging power at time t; Δt is the charging time; is the charge state of the electric vehicle e when it leaves the grid, It is the charge state of electric vehicle e before connecting to the grid.

[0055] On the other hand, the present invention also provides an electric vehicle charging optimization system taking into account distributed charging stations and electricity price guidance, comprising:

[0056] EV charging model building module, to build an electric vehicle EV charging model, including: single EV charging model and EV cluster charging model;

[0057] The distribution network model building module builds a distribution network model including distributed charging stations based on the single EV charging model and the EV cluster charging model and taking into account the electricity price guidance factors;

[0058] The charging optimization module, based on the distribution network model containing distributed charging stations, sets the optimization objective function with the maximum charging amount and the minimum charging cost as the optimization goals to obtain the optimal charging plan.

[0059] On the other hand, the present invention further provides an electronic device having a computer program stored thereon, wherein when the computer program is executed by a processor, an electric vehicle charging optimization method taking into account distributed charging stations and electricity price guidance as described in any embodiment of the present invention is implemented.

[0060] On the other hand, the present invention also provides a computer-readable medium for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement an electric vehicle charging optimization method that takes into account distributed charging stations and electricity price guidance as described in any embodiment of the present invention.

[0061] The present invention has the following beneficial effects:

[0062] 1. By setting the maximum charging amount and the minimum charging cost as the optimization goals, a comprehensive optimization objective function is set to guide users to reduce charging during peak load periods and increase charging during non-peak periods. This can not only meet users' charging needs, but also reduce charging costs, make the power flow distribution in the distribution network more uniform, and achieve comprehensive optimization of the charging plan.

[0063] 2. Fully consider the constraints of charging stations and power system operation. These constraints can effectively deal with the impact of electric vehicle charging on the power grid, ensure the stable operation of the power grid, and avoid problems such as increased peak-to-valley differences in distribution networks, intensified load fluctuations, increased network losses, and decreased voltage quality caused by large-scale disorderly charging of electric vehicles.

[0064] 3. Taking into account the factors guiding distributed charging stations and electricity prices, while optimizing the charging plan, it also provides a scientific basis for the planning and layout of charging stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0066] Figure 1 It is a schematic diagram of the process of the present invention;

[0067] Figure 2 This is a distributed charging mode diagram;

[0068] Figure 3 This is a radial network topology diagram. DETAILED DESCRIPTION

[0069] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not 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.

[0070] It should be understood that the step numbers used in this document are only for convenience of description and are not intended to limit the order in which the steps are executed.

[0071] It should be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0072] The terms “include” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0073] The term "and / or" means and includes any and all possible combinations of one or more of the associated listed items.

[0074] Embodiment 1:

[0075] In order to make the purpose, technical solutions and advantages of the present invention more clear, the following will be combined with the specific embodiments of the present application and refer to the attached Figure 1 , clearly and completely describe the technical solution of the present invention.

[0076] In order to solve the problems in the prior art, the present invention provides an electric vehicle charging optimization method considering distributed charging stations and electricity price guidance, comprising the following steps:

[0077] Step S1, establishing an electric vehicle EV charging model, including: a single EV charging model and an EV cluster charging model;

[0078] like Figure 2 As shown in the figure, the distributed charging mode can make more EV users in the distribution network covered by the charging service area, and can also disperse EVs to different areas, thereby reducing the load pressure on a single node in the distribution network and helping to balance and stabilize the distribution network flow and load.

[0079] Step S101, establish a single EV charging model, the specific formula is as follows:

[0080]

[0081] Where D is the charging demand of a single EV; is the charge state of a single EV before it is connected to the grid; C is the battery capacity of a single EV; P ch,t is the charging power of a single EV during period t; η is the charging efficiency of a single EV; t start and t end are the time when a single EV connects to and leaves the grid; Δt is the time interval; P pilemax is the maximum charging power of a single EV; SOC t is the charge state of a single EV at time t.

[0082] Step S102: Establish an EV cluster charging model. The specific formula is as follows:

[0083]

[0084] Where: P ag,t is the charging power of the EV cluster in period t; P ag,max is the maximum charging power of the EV cluster; η′ is the charging efficiency of the EV cluster; E t E is the accumulated electric energy received by the EV cluster from the grid at time t; min,t E is the minimum electric energy accumulated by the EV cluster from the grid at time t; max,t E is the maximum electric energy received by the EV cluster from the grid at time t; t-1 The accumulated electric energy received by the EV cluster from the grid at time t-1.

[0085] Step S2, establishing a distribution network model including distributed charging stations based on a single EV charging model and an EV cluster charging model and taking into account electricity price guidance factors;

[0086] Based on the established EV cluster charging model, considering that with the continuous increase in the number of EVs, the disorderly access of large-scale EVs to the distribution network will increase the peak-to-valley difference of the distribution network, resulting in increased load fluctuations, increased uncertainty, increased network losses, and reduced voltage quality. Therefore, in order to address these problems, in addition to relying on the grid's own regulation capabilities, peak-to-valley electricity prices can also be used to guide EV charging time, reduce EV access during peak load periods, and encourage EV charging during low load periods, thereby alleviating the operating pressure of the distribution network, and establishing a distribution network model containing distributed charging stations.

[0087] Distribution network model with distributed charging stations, including: charging station constraints and power system operation constraints;

[0088] Among them: charging station constraints, including: minimum charging amount constraint, charging EV number constraint, EVSOC constraint and EV charging constraint; power system operation constraints, including: power system operation constraints, power upper and lower limit constraints, power flow constraints and node voltage constraints.

[0089] The minimum charge capacity constraint is specifically expressed by the following formula:

[0090]

[0091] Where: G is the minimum charging capacity constraint of EV; in this embodiment, G is 0.3, and the constraint restricts that the electric vehicle must be charged at least 30% before leaving the charging station.

[0092] The constraint on the number of charging EVs is specifically expressed by the following formula:

[0093]

[0094]

[0095] Where: n station 、n EV 、n pile are the number of charging stations, EVs and charging piles, respectively, e∈n EV , s∈n station ;n e,t,s is the number of EVs charged at charging station s at time t; this constraint limits the number of electric vehicles charged to no more than the number of charging piles, and each electric vehicle can only be charged in one charging station at most.

[0096] The EVSOC constraint is specifically expressed by the following formula:

[0097] SOC min ≤SOC≤SOC max

[0098] Where: SOC min , SOC max are the minimum and maximum values ​​allowed by EVSOC respectively; in this embodiment, they are set to 0.1 and 0.9 respectively.

[0099] The EV charging constraint is specifically expressed by the following formula:

[0100]

[0101] Where: is the charging status of the EV, When the EV is charging, When EV is not charging; is the maximum charging power of the EV.

[0102] The power system operation constraints are specifically expressed by the following formula:

[0103]

[0104]

[0105] In the formula, and is the active power and reactive power transmitted by line ij at time t; and are the active power and reactive power delivered by the generator to node i at time t, respectively; and are the active power and reactive power of the load connected to node i at time t, is the total charging power of the EV connected to node i at time t; D L is the set of power grid line nodes; this constraint ensures the balance of active power and reactive power in the distribution network.

[0106] The power upper and lower limit constraints are specifically expressed by the following formula:

[0107]

[0108]

[0109] Where: and are the upper limits of active and reactive power output of generators respectively; N is the set of power grid nodes;

[0110] The power flow constraint is:

[0111]

[0112]

[0113] In the formula, and are the maximum active and reactive power values ​​allowed to flow through line ij respectively; this constraint limits the maximum active and reactive power values ​​flowing through the line.

[0114] The node voltage constraint is specifically expressed by the following formula:

[0115]

[0116] U min ≤U i,t ≤U max ,

[0117] Where: U i,t and U j,tare the squares of the bus voltage amplitudes at node i and node j at time t respectively; r ij is the equivalent resistance of line ij; x ij is the equivalent reactance of line ij; U min is the minimum voltage allowed at node i; U max is the maximum voltage allowed at node i.

[0118] Step S3, based on the distribution network model including distributed charging stations, an optimization objective function is set with maximum charging capacity and minimum charging cost as optimization goals to obtain an optimal charging plan.

[0119] The specific optimization objective function is:

[0120]

[0121] Where: cost is the weight coefficient of charging cost, δ SOC is the weight coefficient of the charge amount; c cost is the unit electricity price; T is the total time period; P ag,t is the EV charging power at time t; Δt is the charging time; is the charge state of the electric vehicle e when it leaves the grid, It is the charge state of electric vehicle e before connecting to the grid.

[0122] This objective function guides users to reduce charging during peak load periods and increase charging during off-peak periods to meet users' charging needs while making the power flow distribution in the distribution network as uniform as possible.

[0123] Embodiment 2:

[0124] Compared with Example 1, this embodiment provides a specific application scenario for example analysis, which has verified the effectiveness of the optimization method. Specifically:

[0125] This embodiment uses the IEEE 33-node distribution network test system for example analysis. Figure 3 The network is a radial network, using a distribution network with a base capacity of 100MW. The simulation time span is 7:00-22:00, each period Δt = 15min, and there are 61 periods in total. The EV battery capacity is 30kWh, the maximum charging power is 10KW, and the number of electric vehicle clusters is 10. The load demand of each node is shown in Table 1.

[0126] Table 1 Load demand table for each region

[0127]

[0128] This embodiment sets four different scenarios, which are as follows:

[0129] 1) Scenario 1: Disordered charging at centralized charging stations (charging is done at a fixed price across the distribution network, and only one charging station is configured in each area);

[0130] 2) Scenario 2: Centralized charging station with fixed time-of-use electricity price (referring to EV charging optimization with time-of-use electricity price in the entire distribution network);

[0131] 3) Scenario 3: Orderly charging at distributed charging stations (EV charging optimization with time-of-use electricity prices is adopted in the entire distribution network, and multiple charging stations are configured in each area, with a total of 6 charging stations);

[0132] 4) Scenario 4: Orderly charging at distributed charging stations (the distribution network adopts EV charging optimization with time-of-use electricity prices throughout the region, and each area is equipped with multiple charging stations, with a total of 9 charging stations).

[0133] Through the dual-objective hybrid nonlinear programming model, the evaluation indicators of the system under different scenarios are obtained, as shown in Table 2 below.

[0134] Table 2 Comparison of power system operating parameters under different scenarios

[0135]

[0136] It can be seen from Table 2 that the maximum and minimum differences in the power flow entropy of Case 3 and Case 4 are reduced by 0.4601 and 0.1374 respectively compared with Case 2; the power flow entropy variance is reduced by 0.1026 and 0.0856 respectively compared with Case 2. This shows that the power flow distribution in the distribution network has become more uniform and the system runs more smoothly. Using Case 4 proposed in this application, its voltage offset and the charging price of the entire network are the lowest among all cases, and the power flow entropy variance does not increase much compared with Case 3. Therefore, this strategy can ensure the operation of the power system in a relatively stable state while ensuring the charging price. Therefore, the strategy of considering distributed charging stations and electricity price guidance can effectively improve the stability of system operation.

[0137] Embodiment three:

[0138] This embodiment provides an electric vehicle charging optimization system taking into account distributed charging stations and electricity price guidance, including:

[0139] An EV charging model building module is used to build an electric vehicle EV charging model, including: a single EV charging model and an EV cluster charging model; this module is used to implement the function of step S1 in embodiment 1, which will not be described in detail here;

[0140] A distribution network model building module, based on a single EV charging model and an EV cluster charging model, takes into account the electricity price guidance factor, to establish a distribution network model including distributed charging stations; this module is used to implement the function of step S2 in embodiment 1, which will not be described in detail here;

[0141] The charging optimization module is based on a distribution network model including distributed charging stations, sets an optimization objective function with maximum charging capacity and minimum charging cost as optimization goals, and obtains an optimal charging plan; this module is used to implement the function of step S3 in Example 1, which will not be repeated here.

[0142] Embodiment 4:

[0143] This embodiment provides an electronic device having a computer program stored thereon, wherein when the computer program is executed by a processor, an electric vehicle charging optimization method considering distributed charging stations and electricity price guidance as described in any embodiment of the present invention is implemented.

[0144] Embodiment five:

[0145] This embodiment provides a computer-readable medium for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement an electric vehicle charging optimization method that takes into account distributed charging stations and electricity price guidance as described in any embodiment of the present invention.

[0146] In the embodiments of the present application, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can be represented by: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, c can be single or multiple.

[0147] Those of ordinary skill in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented in a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0148] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0149] In several embodiments provided in the present application, if any function is implemented in the form of a software functional unit 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 application, 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, which is stored in a storage medium and includes 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 method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory; hereinafter referred to as: ROM), random access memory (Random Access Memory; hereinafter referred to as: RAM), disk or optical disk, and other media that can store program codes.

[0150] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. An electric vehicle charging optimization method considering distributed charging stations and electricity price guidance, characterized in that: The following steps are involved: Step S1, establishing an electric vehicle EV charging model, including: a single EV charging model and an EV cluster charging model; Step S2, establishing a distribution network model including distributed charging stations based on a single EV charging model and an EV cluster charging model and taking into account electricity price guidance factors; Step S3, based on the distribution network model including distributed charging stations, an optimization objective function is set with maximum charging capacity and minimum charging cost as optimization goals to obtain an optimal charging plan.

2. The electric vehicle charging optimization method considering distributed charging stations and electricity price guidance according to claim 1 is characterized in that: The single EV charging model is: Where D is the charging demand of a single EV; is the charge state of a single EV before it is connected to the grid; C is the battery capacity of a single EV; P ch,t is the charging power of a single EV during period t; η is the charging efficiency of a single EV; t start and t end are the time when a single EV connects to and leaves the grid; Δt is the time interval; P pilemax is the maximum charging power of a single EV; SOC t is the charge state of a single EV at time t.

3. The electric vehicle charging optimization method considering distributed charging stations and electricity price guidance according to claim 1 is characterized in that: The EV cluster charging model is: Where: P ag,t is the charging power of the EV cluster in period t; P ag,max is the maximum charging power of the EV cluster; η′ is the charging efficiency of the EV cluster; E t E is the accumulated electric energy received by the EV cluster from the grid at time t; min,t E is the minimum electric energy accumulated by the EV cluster from the grid at time t; max,t E is the maximum electric energy received by the EV cluster from the grid at time t; t-1 The accumulated electric energy received by the EV cluster from the grid at time t-1.

4. The electric vehicle charging optimization method considering distributed charging stations and electricity price guidance according to claim 1 is characterized in that: The establishment of a distribution network model including distributed charging stations specifically includes: constructing charging station constraints and constructing power system operation constraints; wherein: Construct charging station constraints, including: minimum charging capacity constraint, charging EV quantity constraint, EVSOC constraint and EV charging constraint; Construct power system operation constraints, including: power system operation constraints, power upper and lower limit constraints, power flow constraints and node voltage constraints.

5. The electric vehicle charging optimization method considering distributed charging stations and electricity price guidance according to claim 4 is characterized in that: The construction of charging station constraints includes: minimum charging capacity constraint, charging EV quantity constraint, EVSOC constraint and EV charging constraint; wherein: The minimum charge capacity constraint is: Where: G is the minimum charging capacity constraint of EV charging; The number of charging EVs is constrained as follows: Where: n station 、n EV 、n pile are the number of charging stations, EVs and charging piles, respectively, e∈n EV , s∈n station ;n e,t,s is the number of EVs charging at charging station s at time t; The EVSOC constraints are: SOC min ≤SOC≤SOC max Where: SOC min , SOC max They are the minimum and maximum values ​​allowed for EVSOC respectively; The EV charging constraints are: Where: is the charging status of the EV; is the maximum charging power of the EV.

6. The electric vehicle charging optimization method considering distributed charging stations and electricity price guidance according to claim 4 is characterized in that: The construction of power system operation constraints includes: power system operation constraints, power upper and lower limit constraints, power flow constraints and node voltage constraints, wherein: The power system operation constraints are: In the formula, and is the active power and reactive power transmitted by line ij at time t; and are the active power and reactive power delivered by the generator to node i at time t, respectively; and are the active power and reactive power of the load connected to node i at time t, is the total charging power of the EV connected to node i at time t; D L is the set of power grid line nodes; The upper and lower power limits are: Where: and are the upper limits of active and reactive power output of the generator respectively; N is the set of power grid nodes; The power flow constraint is: In the formula, and are the maximum active and reactive power allowed to flow through line ij respectively; The node voltage constraints are: Where: U i,t and U j,t are the squares of the bus voltage amplitudes at node i and node j at time t respectively; r ij is the equivalent resistance of line ij; x ij is the equivalent reactance of line ij; U min is the minimum voltage allowed at node i; U max is the maximum voltage allowed at node i.

7. The electric vehicle charging optimization method considering distributed charging stations and electricity price guidance according to claim 1 is characterized in that: The optimization objective function is set with the maximum charging amount and the minimum charging cost as the optimization goal. The specific optimization objective function is: Where: cost is the weight coefficient of charging cost, δ SOC is the weight coefficient of the charge amount; c cost is the unit electricity price; T is the total time period; P ag,t is the EV charging power at time t; Δt is the charging time; is the charge state of the electric vehicle e when it leaves the grid, It is the charge state of electric vehicle e before connecting to the grid.

8. An electric vehicle charging optimization system considering distributed charging stations and electricity price guidance, characterized in that: include: EV charging model building module, to build an electric vehicle EV charging model, including: single EV charging model and EV cluster charging model; The distribution network model building module builds a distribution network model including distributed charging stations based on the single EV charging model and the EV cluster charging model and taking into account the electricity price guidance factors; The charging optimization module, based on the distribution network model containing distributed charging stations, sets the optimization objective function with the maximum charging amount and the minimum charging cost as the optimization goals to obtain the optimal charging plan.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, an electric vehicle charging optimization method taking into account distributed charging stations and electricity price guidance as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, an electric vehicle charging optimization method taking into account distributed charging stations and electricity price guidance is implemented as described in any one of claims 1 to 7.

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