Electric vehicle non-inductive charging control method and system based on multiple targets of court

Abstract

The invention discloses an electric vehicle non-inductive charging control method and system based on multiple targets of a transformer area, and the method comprises the steps: building a first economical model with the minimum operation cost based on the charging safety of a power distribution transformer area as the target; constructing a second economic model by minimizing the power utilization cost of the electric vehicle with the regional cluster characteristic; constructing a third economic model of compensation of new energy power generation, energy storage to large power grid electricity purchase cost and electric vehicle load considering user satisfaction by minimizing new energy power generation cost, energy storage equipment cost, large power grid electricity purchase cost and electric vehicle load compensation cost; and on the basis of the first economical model, the second economical model and the third economical model, a multi-target model for non-inductive charging of the electric vehicle is constructed, a non-inductive decision-making framework for charging of the electric vehicle is established, and non-inductive charging of the electric vehicle is achieved. The operation cost of the whole charging network can be reduced, and the operation efficiency is improved.

Description

Technical Field

[0001] The present invention mainly relates to the technical field of electric vehicles, and in particular to a method and system for controlling electric vehicle non-conductive charging based on multiple objectives of a station area. Background Art

[0002] With the transformation of the global energy structure and the enhancement of environmental protection awareness, electric vehicles (EVs), as representatives of clean energy transportation, are gradually becoming an important force in promoting the sustainable development of the transportation industry. However, with the surge in the number of electric vehicles, their charging needs have posed new challenges to the stability and safety of the existing power system. In urban and rural areas, with the increasing popularity of electric vehicles, how to effectively manage the charging load of electric vehicles supplied by the substation, ensure the stable operation and power supply quality of the substation, and improve the charging experience of users has become an urgent problem to be solved. Therefore, it is imperative to design a non-sensing decision-making architecture for the charging safety boundary, charging load concurrency, and user satisfaction of the electric vehicle charging station, which can effectively promote the non-sensing charging of electric vehicles and improve the stability and safety of the power system. Summary of the invention

[0003] In view of the technical problems existing in the prior art, the present invention provides an electric vehicle non-conductive charging control method and system based on multi-objectives of the charging area, which can reduce the operating cost of the entire charging network and improve the operating efficiency.

[0004] In order to solve the above technical problems, the technical solution proposed by the present invention is:

[0005] A non-sensing charging control method for electric vehicles based on multi-objectives of a station area comprises the following steps:

[0006] Taking the minimization of the operating cost based on the charging safety of the distribution station area as the goal, considering the power balance and node voltage constraints of the system, the first economic model of the operating cost for the charging safety of the distribution station area is constructed;

[0007] Taking the electric vehicle electricity cost of regional cluster characteristics as the minimum, considering the constraints of electric vehicle state of charge, electric vehicle discharge capacity, electric vehicle charging and discharging power, and the carrying capacity of the existing distribution network, a second economic model of the electric vehicle management and control model that considers regional cluster characteristics to coordinate the electricity cost of charging behavior is constructed;

[0008] The cost of new energy power generation, energy storage equipment cost, power purchase cost from the large power grid and compensation cost of electric vehicle load are minimized, and the supply and demand balance constraints, power grid power purchase constraints and transferable load satisfaction constraints are considered to build a third economic model of new energy power generation, energy storage power purchase cost from the large power grid and compensation for electric vehicle load that takes into account user satisfaction.

[0009] Taking into account the comprehensive operating costs of electric vehicle charging area safety, charging load concurrency and user satisfaction, a multi-objective model for electric vehicle non-contact charging is constructed based on the first economic model, the second economic model and the third economic model. A non-contact decision-making architecture for electric vehicle charging is established to realize non-contact charging of electric vehicles.

[0010] Preferably, the first economic model is:

[0011]

[0012] Where: f is the comprehensive operation cost of the distribution area during the entire dispatching period; f i (i=1,2,3,4,5,6) respectively represent the cost of purchasing electricity from the superior power grid, the network loss cost of the distribution station area, the operation and maintenance cost of the photovoltaic power generation system and the energy storage device, the dispatching cost that can reduce the air-conditioning load, the dispatching cost of energy storage and the charging cost of electric vehicles.

[0013] Preferably, the constraints corresponding to the first economic model include:

[0014]

[0015] R ESS.ch,t +R ESS.dch,t ≤1 (5)

[0016]

[0017] SOC min ≤SOC t ≤SOC max (7)

[0018] E ESS ×0.1≤E ESS,t ≤E ESS ×0.9 (8)

[0019] E ESS,0 =E ESS,t (9)

[0020] P grid,min ≤P grid,t ≤P grid,max (10)

[0021] SOC EV,min ≤SOC EV,t ≤SOC EV,max (11)

[0022]

[0023] θ min ≤θ t ≤θmax (14)

[0024]

[0025] Constraints (2)-(4) are power flow equation constraints. is the set of head nodes of all branches with node j as the end node of the branch in the radial power grid; ψ(j) is the set of end nodes of all branches with node j as the head node of the branch; P ij,t , Q ij,t is the active power and reactive power at the head end of branch ij at time t; P jk,t , Q jk,t is the active power and reactive power at the head end of the branch at time t; V i,t , V′ j,t are the voltage amplitudes of nodes i and j at time t; I ij,t is the current amplitude of branch ij at time t; R ij , X ij is the resistance and reactance of the ij branch; P j,t , Q j,t represents the net active power injection and reactive power injection of node j in period t; B B is the branch set; P PV,t , P ESS,t , P grid,t are the photovoltaic power value, battery charging and discharging power, and line transmission power in period t respectively; P load,t is the active power value of the basic load in period t; They are the reduced power of the load and the charging capacity of EV at time t respectively;

[0026] Constraints (5)-(9) are constraints on the battery of the electric vehicle, including the power constraint, state of charge constraint and charge constraint in the initial state during the charging and discharging process of the battery; where P ESS.ch,t , P ESS.dch,t are the charging power and discharging power of the energy storage device at time t respectively; R ESS.ch,t , R ESS.dch,t Indicates the charging and discharging status of energy storage; SOC is the maximum power allowed for charging and discharging of energy storage; max is the maximum state of charge value; E ESS is the rated capacity of the battery, E ESS,0 represents the initial storage energy of energy storage, E ESS,t Indicates the remaining energy after a scheduling;

[0027] Constraint (10) is the transmission power capacity limit of the tie line; where P grid,max and P grid,minThe maximum and minimum values ​​of the power of the upper power grid tie line;

[0028] Constraints (11)-(12) are EV constraints; where SOC EV,max , SOC EV,min are the upper and lower limits of EV’s SOC, is the charge amount of EV at time t;

[0029] Constraints (13)-(14) are the load reduction AC constraints, the upper and lower limit constraints of the adjustable power, and the user comfort constraints; where: Respectively represent the upper and lower limits of load reduction; θ max ,θ min They are the upper and lower limits of user comfort, respectively;

[0030] Constraints (15)-(16) are the upper and lower limits of voltage and current; where V j (t) is the voltage value of the jth node in the t period; V j,min 、V j,max is the lower and upper limits of the node voltage in the substation; I ij,max ,I ij,min are the upper and lower bounds of the current; B N A collection of nodes.

[0031] Preferably, the objective function expression of the second economic model is as follows:

[0032]

[0033] In the formula, Represents the electricity price at different times; Represents the i-th electric car in the T i The charging power at each moment is the control variable of this model; g(x) represents the fitting function of harmonic component and power; F 0 stands for harmonic coefficient, which expresses the harmonic component as the cost of electricity; Represents the total load at different times.

[0034] Preferably, the constraints of the second economic model include the state of charge constraint of the electric vehicle, the discharge capacity constraint of the electric vehicle, the charge and discharge power constraint of the electric vehicle, and the carrying capacity constraint of the existing distribution network, specifically:

[0035]

[0036] Constraints (20)-(21) are constraints on the state of charge of the electric vehicle; T end Represents the last dispatch time; They represent the state of charge and the upper limit of the state of charge of the ith electric vehicle when it leaves the charging pile; represents the initial grid-connected charge state of the ith electric vehicle; E 0,i represents the rated capacity of the i-th electric vehicle;

[0037] Constraints (22)-(23) are constraints on the discharge capacity of electric vehicles; where: Represents the electric car with the ith number in T i The state of charge at the moment; S OC.min Represents the minimum state of charge limit for electric vehicles in emergency situations;

[0038] Constraints (24)-(25) are the charging and discharging power constraints of electric vehicles; where: Represents T i The number of electric vehicles being charged and discharged at any given moment;

[0039] Constraint (26) is the carrying capacity constraint of the existing distribution network; where, Represents T i The carrying capacity of the distribution network at a certain moment.

[0040] Preferably, the satisfaction of the transferable load represented by electric vehicles is measured by two aspects, namely, the transfer span satisfaction and the profit improvement rate; the transfer span satisfaction of the transferable load is expressed by the following formula (27):

[0041]

[0042] In the formula, Transfer span satisfaction for transferable class loads; is the starting time before the transfer of the bth transferable load; is the starting time after the transfer of the bth transferable load;

[0043] After the load is transferred, the electricity price after the transfer is different from that before the transfer, and the power department will receive electricity fee compensation under the current electricity price mechanism. The profit after the transfer is compared with the profit before the transfer to calculate the profit improvement rate. The profit improvement rate is shown in the following formula (28):

[0044]

[0045] In the formula, is the efficiency improvement rate of the bth transferable load; P b is the rated power of the bth transferable load; ΔC E is the price difference between before and after the transfer; b 2 Compensation unit price for the power sector; Loss b Transfer unit time loss benefit for the bth transferable load; J bGenerates benefit for the bth load unit power.

[0046] Preferably, the objective function corresponding to the third economic model is expressed as follows:

[0047]

[0048] Where P w (t) is the wind power generation power in period t; C w is the unit power generation cost of wind power; P Pv (t) is the photovoltaic power generation power during period t; C Pv is the unit power generation cost of photovoltaic power generation; P g (t) is the amount of electricity purchased from the large power grid during period t; C g (t) is the electricity purchase price of the large power grid during period t; The load a can be reduced and the power is reduced during the period t; b 1 is the compensation price for reducing unit power of the reducible load; Δt b is the transfer time of transferable load b; P b is the rated power of the transferable load; b 2 is the transferable load compensation price; C c (t) is the cost of the electric vehicle energy storage equipment during period t.

[0049] Preferably, the constraints corresponding to the third economic model include:

[0050] Supply and demand balance constraints, specifically:

[0051]

[0052] Where, F(t) is the load condition during period t; P w (t) is the wind power generation power in period t; P Pv (t) is the photovoltaic power generation power in period t; P E (t) is the power generation of the electric vehicle energy storage battery during period t; P g (t) is the power purchased from the large power grid during period t;

[0053] The power purchase constraints of the power grid are as follows:

[0054] P g (t)≤P g,max (34)

[0055] Where P g,max To purchase the maximum amount of electricity from the large power grid.

[0056] The transferable load satisfaction constraints are as follows:

[0057] According to equations (27) and (28), the transferable load satisfaction constraint is as follows:

[0058]

[0059] In the formula, γ is the lower limit of the transfer span satisfaction of the transferable load; ρ is the lower limit of the profit improvement rate of the transferable load;

[0060] From equations (35) and (36), we can see that the transfer time range under the constraint of user satisfaction is:

[0061]

[0062] Preferably, the multi-objective model of electric vehicle non-inductive charging is:

[0063] MinC=C 台 +C 负 +C 用 (38)

[0064] Among them, MinC has the lowest comprehensive cost for electric vehicle non-conductive charging, C 台 To ensure the lowest comprehensive operating cost of the charging station area, C 负 In order to minimize the electricity cost of coordinating charging behaviors in the electric vehicle control model considering regional cluster characteristics, C 用 Electric vehicle charging costs are the lowest when user experience is considered.

[0065] The present invention also discloses an electric vehicle non-inductive charging control system based on multi-target areas, comprising a memory and a processor connected to each other, wherein a computer program is stored on the memory, and when the computer program is run by the processor, the steps of the method described above are executed.

[0066] Compared with the prior art, the advantages of the present invention are:

[0067] The present invention can effectively predict and evaluate the impact of charging facilities on distribution stations through the operation cost model, ensure the stable operation of the power system, and reduce the risk of power grid accidents caused by charging; the management and control model considering the characteristics of regional clusters can coordinate charging behaviors, avoid charging peaks, reduce power grid pressure, and thus reduce electricity costs; the third economic model takes into account user satisfaction, so that new energy power generation, energy storage, electricity purchase costs and electric vehicle load compensation are closer to user needs, thereby improving user satisfaction with the use of electric vehicles; the multi-objective model can balance factors such as charging safety, charging load simultaneity, and user satisfaction, and realize non-sensing charging of electric vehicles, that is, completing charging optimization and adjustment without the user's knowledge.

[0068] The present invention can effectively utilize new energy power generation, increase its proportion in the large power grid, and promote the consumption of clean energy by reasonably allocating the charging needs of electric vehicles; the model that comprehensively considers various factors helps to reduce the operating cost of the entire charging network and improve operational efficiency; reasonable charging strategies and management measures can improve energy utilization efficiency and reduce energy waste; the model that considers cluster characteristics and user behavior can improve the adaptability of the power grid to charging loads and enhance the flexibility of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 The present invention is a flow chart of an embodiment of the electric vehicle non-sensing charging control method based on multi-objectives of the station area. DETAILED DESCRIPTION

[0070] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments.

[0071] like Figure 1 As shown, the electric vehicle non-sensing charging control method based on multi-objective of the station area provided by the embodiment of the present invention includes the following steps:

[0072] S1. Construct an economic model for the operating cost of charging safety in the distribution station area (the first economic model);

[0073] Taking into account the cost of purchasing electricity from the upper power grid, network loss cost, energy storage dispatch cost, and electric vehicle charging cost, and considering the constraints such as system power balance and node voltage, an economic model of charging safety in the distribution area is introduced with the goal of minimizing the operating cost based on the charging safety of the distribution area. The model is constructed as follows:

[0074]

[0075] R ESS.ch,t +R ESS.dch,t ≤1 (5)

[0076]

[0077] SOC min ≤SOC t ≤SOC max (7)

[0078] E ESS ×0.1≤E ESS,t ≤E ESS ×0.9 (8)

[0079] E ESS,0 =E ESS,t (9)

[0080] P grid,min ≤Pgrid,t ≤P grid,max (10)

[0081] SOC EV,min ≤SOC EV,t ≤SOC EV,max (11)

[0082]

[0083] θ min ≤θ t ≤θ max (14)

[0084]

[0085] Where: f is the comprehensive operation cost of the distribution area during the entire dispatching period; f i (i=1,2,3,4,5,6) are respectively the cost of purchasing electricity from the upper power grid, the network loss cost of the distribution station area, the operation and maintenance cost of the photovoltaic power generation system and the energy storage device, the dispatching cost of reducing the air conditioning load, the dispatching cost of energy storage and the charging cost of electric vehicles;

[0086] Constraints (2)-(4) are power flow equation constraints. is the set of head nodes of all branches with node j as the end node of the branch in the radial power grid; ψ(j) is the set of end nodes of all branches with node j as the head node of the branch; P ij,t , Q ij,t is the active power and reactive power at the head end of branch ij at time t; P jk,t , Q jk,t is the active power and reactive power at the head end of the branch at time t; V i,t , V′ j,t are the voltage amplitudes of nodes i and j at time t; I ij,t is the current amplitude of branch ij at time t; R ij , X ij is the resistance and reactance of the ij branch; P j,t , Q j,t represents the net active power injection and reactive power injection of node j in period t; B B is the branch set; P PV,t , P ESS,t , P grid,t are the photovoltaic power value, battery charging and discharging power, and line transmission power in period t respectively; P load,t is the active power value of the basic load in period t; They are the load reduction power and EV charging capacity at time t.

[0087] Constraints (5)-(9) are constraints on the battery of electric vehicles, mainly including the power constraint, state of charge constraint and charge constraint in the initial state during the charging and discharging process of the battery. ESS.ch,t , P ESS.dch,t are the charging power and discharging power of the energy storage device at time t respectively; R ESS.ch,t , R ESS.dch,t Indicates the charging and discharging status of energy storage; The maximum power allowed for charging and discharging of energy storage; SOC max is the maximum state of charge value, which is set to 0.9, and the minimum state of charge value SOC min is set to 0.1; E ESS is the rated capacity of the battery, E ESS,0 represents the initial storage energy of energy storage, E ESS,t Indicates the remaining energy after a scheduling.

[0088] Constraint (10) is the transmission power capacity limit of the tie line. In order to ensure the normal operation of the tie line between the distribution station area and the upper power grid, the power transmission amount should be strictly limited to meet the constraint of power transmission capacity. grid,max and P grid,min It is the maximum and minimum value of the power of the upper power grid interconnection line.

[0089] Constraints (11)-(12) are EV constraints. In the formula, SOC EV,max , SOC EV,min are the upper and lower limits of EV’s SOC, is the charge amount of EV at time t.

[0090] Constraints (13)-(14) are AC constraints for load reduction, mainly the upper and lower limits of power regulation and the user comfort constraints. Respectively represent the upper and lower limits of load reduction; θ max ,θ min They are the upper and lower limits of user comfort respectively.

[0091] Constraints (15)-(16) are the upper and lower limits of voltage and current. j (t) is the voltage value of the jth node in the t period, V j,min 、V j,max is the lower and upper limits of the node voltage in the substation; I ij,max ,I ij,min are the upper and lower bounds of the current; B N A collection of nodes.

[0092] S2. Construct an economic model (second economic model) that coordinates the electricity cost of charging behavior by considering the characteristics of regional clusters in the electric vehicle management and control model;

[0093] The essence of coordinating charging behavior in the electric vehicle control model considering regional cluster characteristics is to reduce electricity costs. The specific objective function expression is shown in formula (17):

[0094]

[0095] In the formula, Represents the electricity price at different times. In order to reduce the overlap between the grid load and the total charging load of electric vehicles, the grid-connected electricity price is set to change with the load. Represents the i-th electric car in the T i The charging power at each moment is the control variable of this model; g(x) represents the fitting function of harmonic component and power; F 0 stands for harmonic coefficient, which expresses the harmonic component as the cost of electricity; Represents the total load at different times.

[0096] The constraints of the electric vehicle grid-connected power control model include electric vehicle state of charge constraints, electric vehicle discharge capacity constraints, electric vehicle charging and discharging power constraints, and existing distribution network carrying capacity constraints; specifically:

[0097]

[0098] Constraints (20)-(21) are constraints on the state of charge of electric vehicles. The state of charge of electric vehicles determines the charging comfort of users and is a key indicator reflecting the charging process of electric vehicles. In order to ensure that users do not feel the charging, the state of charge of electric vehicles with different numbers must meet the minimum limit at the end of charging. Among them, T end Represents the last dispatch time; They represent the state of charge and the upper limit of the state of charge of the ith electric vehicle when it leaves the charging pile; represents the initial grid-connected charge state of the ith electric vehicle; E 0,i Represents the rated capacity of the i-th electric vehicle.

[0099] Constraints (22)-(23) are the discharge capacity constraints of electric vehicles. The grid-connected discharge behavior of electric vehicles will cause the state of charge of the vehicle battery to decrease, but the state of charge at any time should satisfy the constraint of not less than 0. Therefore, the discharge capacity constraint is set to limit the grid-connected discharge behavior of electric vehicles. In the formula, Represents the electric car with the ith number in T i The state of charge at the moment; S OC.min Represents the minimum state of charge limit of electric vehicles in emergency situations, with a value of 0.3.

[0100] Constraints (24)-(25) are constraints on the charging and discharging power of electric vehicles. When the power is too high, the heat generated by the electric vehicle battery is large, the loss is large, and the battery life is reduced; when the power is too low, the charging time of the electric vehicle battery is long, and the user's charging experience is reduced. Therefore, there are upper and lower limits on the grid-connected power of electric vehicles. In the formula, Represents T i The number of electric vehicles being charged and discharged at any given moment.

[0101] Constraint (26) is the carrying capacity constraint of the existing distribution network. When the total charging load of large-scale electric vehicles exceeds the carrying capacity of the distribution network, the stability of the distribution network operation will decrease, and even cause a large-scale power outage. Therefore, considering the carrying capacity constraint of the existing distribution network, the total charging load of electric vehicles is kept below the limit. In the formula, Represents T i The carrying margin of the distribution network at a certain moment is taken as the difference between 1.2 times the maximum value of the basic load of the power grid and the basic load of the power grid at the current moment.

[0102] S3. Construct an economic model (third economic model) that takes into account user satisfaction, renewable energy generation, energy storage, electricity purchase costs from the large power grid, and compensation for electric vehicle loads.

[0103] The satisfaction of transferable loads represented by electric vehicles is measured by two aspects, namely, the transfer span satisfaction and the profit improvement rate. When users receive dispatching requirements from the power department, they will transfer their own loads. The transfer span is used as a measure of power comfort. The transfer span satisfaction of transferable loads can be expressed by the following formula (27):

[0104]

[0105] In the formula, Transfer span satisfaction for transferable class loads; is the starting time before the transfer of the bth transferable load; It is the starting time after the transfer of the bth transferable load.

[0106] After the load is transferred, the electricity price after the transfer is different from that before the transfer, and the power department will receive electricity fee compensation under the current electricity price mechanism. The profit after the transfer is compared with the profit before the transfer to calculate the profit improvement rate. The profit improvement rate is shown in the following formula (28):

[0107]

[0108] In the formula, is the efficiency improvement rate of the bth transferable load; P b is the rated power of the bth transferable load; ΔCE is the price difference between before and after the transfer; b 2 Compensation unit price for the power sector; Loss b Transfer unit time loss benefit for the bth transferable load; J b Generates benefit for the bth load unit power.

[0109] By scheduling electric vehicles under satisfaction constraints, the total cost of power generation can be minimized. The objective function of the model is to minimize the cost of new energy power generation, energy storage equipment cost, power purchase cost from the large power grid, and compensation cost of electric vehicle load. The objective function is expressed as follows:

[0110]

[0111] Where P w (t) is the wind power generation power in period t; C w is the unit power generation cost of wind power; P Pv (t) is the photovoltaic power generation power during period t; C Pv is the unit power generation cost of photovoltaic power generation; P g (t) is the amount of electricity purchased from the large power grid during period t; C g (t) is the electricity purchase price of the large power grid during period t; The load a can be reduced and the power is reduced during the period t; b 1 is the compensation price for reducing unit power of the reducible load; Δt b is the transfer time of transferable load b; P b is the rated power of the transferable load; b 2 is the transferable load compensation price; C c (t) is the cost of the electric vehicle energy storage equipment in period t. The cost of the electric vehicle is expressed by the following formulas (30) and (31):

[0112] When an electric car is charging:

[0113]

[0114] When an electric vehicle is discharging:

[0115] C c (t) = Q E C EA,F (31)

[0116] In the formula, C EA,F The loss cost of discharging electric vehicles.

[0117] The constraints of the economic model of renewable energy generation, energy storage, electricity purchase cost from the large power grid and electric vehicle load compensation considering user satisfaction are as follows:

[0118] (1) Supply and demand balance constraints

[0119]

[0120] Where, F(t) is the load condition during period t; P w (t) is the wind power generation power in period t; P Pv (t) is the photovoltaic power generation power in period t; P E (t) is the power generation of the electric vehicle energy storage battery during period t; P g (t) is the power purchased from the large power grid during period t.

[0121] (2) Constraints on power purchases from the power grid

[0122] P g (t)≤P g,max (34)

[0123] Where P g,max To purchase the maximum amount of electricity from the large power grid.

[0124] (3) Transferable load satisfaction constraints

[0125] According to equations (27) and (28), the transferable load satisfaction constraint is as follows:

[0126]

[0127] Where γ is the lower limit of the transfer span satisfaction of the transferable load; ρ is the lower limit of the profit improvement rate of the transferable load.

[0128] From equations (35) and (36), we can see that the transfer time range under the constraint of user satisfaction is:

[0129]

[0130] S4. Based on the above cost-economic model that takes into account charging safety, charging load concurrency, and user satisfaction, a multi-objective model for contactless charging of electric vehicles is constructed.

[0131] In the above content, the corresponding economic model has been established for the operating cost of charging safety in the distribution station area, the electric vehicle control model considering the characteristics of regional clusters to coordinate the electricity cost of charging behavior, the new energy generation, energy storage, the cost of purchasing electricity from the large power grid and the compensation cost of electric vehicle load considering user satisfaction. The multi-objective model of electric vehicle non-conductive charging mainly takes into account the comprehensive operating costs including the safety of electric vehicle charging area, the simultaneous rate of charging load and user satisfaction. The specific model is as follows:

[0132] MinC=C 台 +C 负 +C用 (38)

[0133]

[0134] Among them, MinC has the lowest comprehensive cost for electric vehicle non-conductive charging, C 台 To ensure the lowest comprehensive operating cost of the charging station area, C 负 In order to minimize the electricity cost of coordinating charging behaviors in the electric vehicle control model considering regional cluster characteristics, C 用 The charging cost of electric vehicles is the lowest when considering user experience, and the remaining parameters and constraints are explained in the economic modeling of the above three factors.

[0135] The electric vehicle non-conductive charging multi-objective model constructed by the present invention is based on a non-preference multi-objective planning strategy. In order to facilitate intuitive comparison, the concept of unit time cost is also introduced. The objective function form of the model is:

[0136]

[0137] Based on the non-preference multi-objective planning strategy, a non-inferior solution set (Pareto frontier) is generated, and each solution corresponds to a charging scheduling scheme.

[0138] In contrast to preference-based multi-objective optimization, the idea of ​​the non-preference multi-objective planning strategy of the present invention is "search first, then decide". Its advantage is that it can provide a series of non-inferior solutions for decision makers to refer to, and decision makers can choose from them according to actual conditions, thereby improving the flexibility of decision-making.

[0139] The present invention can effectively predict and evaluate the impact of charging facilities on distribution stations through the operation cost model, ensure the stable operation of the power system, and reduce the risk of power grid accidents caused by charging; the management and control model considering the characteristics of regional clusters can coordinate charging behaviors, avoid charging peaks, reduce power grid pressure, and thus reduce electricity costs; the third economic model takes into account user satisfaction, so that new energy power generation, energy storage, electricity purchase costs and electric vehicle load compensation are closer to user needs, thereby improving user satisfaction with the use of electric vehicles; the multi-objective model can balance factors such as charging safety, charging load simultaneity, and user satisfaction, and realize non-sensing charging of electric vehicles, that is, the optimization and adjustment of charging are completed without the user's knowledge.

[0140] The present invention can effectively utilize new energy power generation, increase its proportion in the large power grid, and promote the consumption of clean energy by reasonably allocating the charging needs of electric vehicles; the model that comprehensively considers various factors helps to reduce the operating cost of the entire charging network and improve operational efficiency; reasonable charging strategies and management measures can improve energy utilization efficiency and reduce energy waste; the model that considers cluster characteristics and user behavior can improve the adaptability of the power grid to charging loads and enhance the flexibility of the power grid.

[0141] The present invention also discloses a non-inductive charging control system for electric vehicles based on multi-target areas, comprising a memory and a processor connected to each other, wherein a computer program is stored on the memory, and when the computer program is run by the processor, the steps of the above method are executed. The control system of the present invention corresponds to the above control method and also has the advantages described in the above control method.

[0142] The present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by hardware related to computer program instructions. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiment when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. Computer-readable storage media include: any entity or device that can carry computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. The memory is used to store computer programs and / or modules, and the processor implements various functions by running or executing computer programs and / or modules stored in the memory, and calling data stored in the memory. The memory may include high-speed random access memory and may also include non-volatile memory, such as a hard disk, an internal memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0143] The above are only preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should be regarded as the protection scope of the present invention.

Claims

1. A non-sensing charging control method for electric vehicles based on multi-objectives in the substation, characterized in that: Includes steps: Taking the minimization of the operating cost based on the charging safety of the distribution station area as the goal, considering the power balance and node voltage constraints of the system, the first economic model of the operating cost for the charging safety of the distribution station area is constructed; Taking the electric vehicle electricity cost of regional cluster characteristics as the minimum, considering the constraints of electric vehicle state of charge, electric vehicle discharge capacity, electric vehicle charging and discharging power, and the carrying capacity of the existing distribution network, a second economic model of the electric vehicle management and control model that considers regional cluster characteristics to coordinate the electricity cost of charging behavior is constructed; The cost of new energy power generation, energy storage equipment cost, power purchase cost from the large power grid and compensation cost of electric vehicle load are minimized, and the supply and demand balance constraints, power grid power purchase constraints and transferable load satisfaction constraints are considered to build a third economic model of new energy power generation, energy storage power purchase cost from the large power grid and compensation for electric vehicle load that takes into account user satisfaction. Taking into account the comprehensive operating costs of electric vehicle charging area safety, charging load concurrency and user satisfaction, a multi-objective model for electric vehicle non-contact charging is constructed based on the first economic model, the second economic model and the third economic model. A non-contact decision-making architecture for electric vehicle charging is established to realize non-contact charging of electric vehicles.

2. The electric vehicle non-sensing charging control method based on multi-objectives of the station area according to claim 1 is characterized in that: The first economic model is: Where: f is the comprehensive operation cost of the distribution area during the entire dispatching period; f i (i=1,2,3,4,5,6) respectively represent the cost of purchasing electricity from the superior power grid, the network loss cost of the distribution station area, the operation and maintenance cost of the photovoltaic power generation system and the energy storage device, the dispatching cost that can reduce the air-conditioning load, the dispatching cost of energy storage and the charging cost of electric vehicles.

3. The electric vehicle non-sensing charging control method based on multi-objectives in the substation according to claim 2 is characterized in that: The constraints corresponding to the first economic model include: R ESS.ch,t +R ESS.dch,t ≤1 (5) SOC min ≤SOC t ≤SOC max (7) AND ESS ×0.1≤E ESS,t ≤E ESS ×0.9 (8) AND ESS,0 =And ESS,t (9) P grid,min ≤P grid,t ≤P grid,max (10) SOC EV,min ≤SOC EV,t ≤SOC EV,max (11) i min ≤θ t ≤θ max (14) Constraints (2)-(4) are power flow equation constraints. is the set of head nodes of all branches with node j as the end node of the branch in the radial power grid; ψ(j) is the set of end nodes of all branches with node j as the head node of the branch; P ij,t , Q ij,t is the active power and reactive power at the head end of branch ij at time t; P jk,t , Q jk,t is the active power and reactive power at the head end of the branch at time t; V i,t , V′ j,t are the voltage amplitudes of nodes i and j at time t; I ij,t is the current amplitude of branch ij at time t; R ij , X ij is the resistance and reactance of the ij branch; P j,t , Q j,t represents the net active power injection and reactive power injection of node j in period t; B B is the branch set; P PV,t , P ESS,t , P grid,t are the photovoltaic power value, battery charging and discharging power, and line transmission power in period t respectively; P load,t is the active power value of the basic load in period t; They are the reduced power of the load and the charging capacity of EV at time t respectively; Constraints (5)-(9) are constraints on the battery of the electric vehicle, including the power constraint, state of charge constraint and charge constraint in the initial state during the charging and discharging process of the battery; where P ESS.ch,t , P ESS.dch,t are the charging power and discharging power of the energy storage device at time t respectively; R ESS.ch,t , R ESS.dch,t Indicates the charging and discharging status of energy storage; SOC is the maximum power allowed for charging and discharging of energy storage; max is the maximum state of charge value; E ESS is the rated capacity of the battery, E ESS,0 represents the initial storage energy of energy storage, E ESS,t Indicates the remaining energy after a scheduling; Constraint (10) is the transmission power capacity limit of the tie line; where P grid,max and P grid,min The maximum and minimum values ​​of the power of the upper power grid tie line; Constraints (11)-(12) are EV constraints; SOC EV,max , SOC EV,min are the upper and lower limits of EV’s SOC, is the charge amount of EV at time t; Constraints (13)-(14) are load reduction AC constraints, power upper and lower limit constraints, and user comfort constraints; where: Respectively represent the upper and lower limits of load reduction; θ max ,θ min They are the upper and lower limits of user comfort, respectively; Constraints (15)-(16) are the upper and lower limits of voltage and current; where V j (t) is the voltage value of the jth node in the t period; V j,min 、V j,max is the lower and upper limits of the node voltage in the substation; I ij,max ,I ij,min are the upper and lower limits of the current; B N A collection of nodes.

4. The electric vehicle non-sensing charging control method based on multi-objectives of the station area according to claim 1, 2 or 3 is characterized in that: The objective function expression of the second economic model is as follows: In the formula, Represents the electricity price at different times; Represents the i-th electric car in the T i The charging power at each moment is the control variable of this model; g(x) represents the fitting function of harmonic component and power; F0 represents the harmonic coefficient, which represents the harmonic component as the electricity cost; Represents the total load at different times.

5. The electric vehicle non-sensing charging control method based on multi-objectives of the station area according to claim 4 is characterized in that: The constraints of the second economic model include the state of charge constraint of electric vehicles, the discharge capacity constraint of electric vehicles, the charge and discharge power constraint of electric vehicles, and the carrying capacity constraint of the existing distribution network, which are as follows: Constraints (20)-(21) are constraints on the state of charge of the electric vehicle; T end Represents the last dispatch time; They represent the state of charge and the upper limit of the state of charge of the ith electric vehicle when it leaves the charging pile; represents the initial grid-connected charge state of the ith electric vehicle; E 0,i represents the rated capacity of the i-th electric vehicle; Constraints (22)-(23) are constraints on the discharge capacity of electric vehicles; where: Represents the electric car with the ith number in T i The state of charge at the moment; S OC.min Represents the minimum state of charge limit for electric vehicles in emergency situations; Constraints (24)-(25) are the charging and discharging power constraints of electric vehicles; where: Represents T i The number of electric vehicles being charged and discharged at any given moment; Constraint (26) is the carrying capacity constraint of the existing distribution network; where, Represents T i The carrying capacity of the distribution network at a certain moment.

6. The electric vehicle non-sensing charging control method based on multi-objectives in the area according to claim 1, 2 or 3, characterized in that: The satisfaction of transferable loads represented by electric vehicles is measured by two aspects, namely, the transfer span satisfaction and the profit improvement rate. The transfer span satisfaction of transferable loads is expressed by the following formula (27): In the formula, Transfer span satisfaction for transferable class loads; is the starting time before the transfer of the bth transferable load; is the starting time after the transfer of the bth transferable load; After the load is transferred, the electricity price after the transfer is different from that before the transfer, and the power department will receive electricity fee compensation under the current electricity price mechanism. The profit after the transfer is compared with the profit before the transfer to calculate the profit improvement rate. The profit improvement rate is shown in the following formula (28): In the formula, is the efficiency improvement rate of the bth transferable load; P b is the rated power of the bth transferable load; ΔC E is the price difference between before and after the transfer; b2 is the compensation unit price of the power sector; Loss b Transfer unit time loss benefit for the bth transferable load; J b Generates benefit for the bth load unit power.

7. The electric vehicle non-sensing charging control method based on multi-objectives of the station area according to claim 6 is characterized in that: The objective function corresponding to the third economic model is expressed as follows: Where P w (t) is the wind power generation power in period t; C w is the unit power generation cost of wind power; P Pv (t) is the photovoltaic power generation power during period t; C Pv is the unit power generation cost of photovoltaic power generation; P g (t) is the amount of electricity purchased from the large power grid during period t; C g (t) is the electricity purchase price of the large power grid during period t; is the power reduction of the curtailable load a during period t; b1 is the compensation price for the curtailable load unit power reduction; Δt b is the transfer time of transferable class load b; P b is the rated power of the transferable load; b2 is the compensation price of the transferable load; C c (t) is the cost of the electric vehicle energy storage equipment during period t.

8. The electric vehicle non-sensing charging control method based on multi-objectives of the station area according to claim 7 is characterized in that: The constraints corresponding to the third economic model include: Supply and demand balance constraints, specifically: Where, F(t) is the load condition during period t; P w (t) is the wind power generation power in period t; P Pv (t) is the photovoltaic power generation power in period t; P E (t) is the power generation of the electric vehicle energy storage battery during period t; P g (t) is the power purchased from the large power grid during period t; The power purchase constraints of the power grid are as follows: P g (t)≤P g,max (34) Where P g,max To purchase the maximum amount of electricity from the large power grid; The transferable load satisfaction constraints are as follows: According to equations (27) and (28), the transferable load satisfaction constraint is as follows: In the formula, γ is the lower limit of the transfer span satisfaction of the transferable load; ρ is the lower limit of the profit improvement rate of the transferable load; From equations (35) and (36), we can see that the transfer time range under the constraint of user satisfaction is:

9. The electric vehicle non-sensing charging control method based on multi-objectives of the station area according to claim 1, 2 or 3, characterized in that: The multi-objective model of electric vehicle non-inductive charging is: MinC=C 台 +C 负 +C 用 (38) Among them, MinC has the lowest comprehensive cost for electric vehicle non-conductive charging, C 台 To ensure the lowest comprehensive operating cost of the charging station area, C 负 In order to minimize the electricity cost of coordinating charging behaviors in the electric vehicle control model considering regional cluster characteristics, C 用 Electric vehicle charging costs are the lowest when user experience is considered.

10. An electric vehicle non-conductive charging control system based on multi-target areas, comprising a memory and a processor connected to each other, wherein a computer program is stored in the memory, characterized in that: When the computer program is executed by a processor, the computer program performs the steps of the method according to any one of claims 1 to 9.

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