A power supply method and system for electric vehicles based on multi-objective of transformer area

Abstract

The application discloses a kind of based on the electric vehicle non-inductive charging control method and system of district multi-objective, method includes the following steps: with the minimum operating cost based on the charging safety of distribution district as the goal, first economic model is constructed;With the minimum electric cost of regional cluster characteristics electric vehicle, second economic model is constructed;With the minimum new energy generation cost, energy storage equipment cost, the cost of purchasing electricity from large power grid and the compensation cost of electric vehicle load, third economic model is constructed considering user satisfaction new energy generation, energy storage to the cost of purchasing electricity from large power grid and the compensation of electric vehicle load;Based on first economic model, second economic model and third economic model, the multi-objective model of electric vehicle non-inductive charging is constructed, the non-inductive decision architecture of electric vehicle charging is established, and electric vehicle non-inductive charging is realized.The application can reduce the operating cost of the whole charging network and improve the operating efficiency.

Description

TECHNICAL FIELD

[0001] The present application mainly relates to the technical field of electric vehicles, specifically to an electric vehicle non-sensing charging control method and system based on a transformer area multi-objective. BACKGROUND

[0002] With the transformation of global energy structure and the enhancement of environmental protection awareness, electric vehicles (EV) as a representative of clean energy transportation are gradually becoming an important force to promote the sustainable development of the transportation industry. However, with the rapid increase in the number of electric vehicles, their charging demand has posed new challenges to the stability and safety of the existing power system. In urban and rural areas, with the continuous popularization of electric vehicles, how to effectively manage the charging load of electric vehicles in transformer areas, ensure the stable operation and power supply quality of transformer areas, and improve the charging experience of users has become a problem to be solved. Therefore, it is imperative to design a non-sensing decision framework for the charging safety boundary, charging load simultaneous rate and user satisfaction of electric vehicle charging transformer areas, which can effectively promote the non-sensing charging of electric vehicles and improve the stability and safety of the power system. SUMMARY

[0003] To solve the technical problems existing in the prior art, the present application provides an electric vehicle non-sensing charging control method and system based on a transformer area multi-objective, which can reduce the operating cost of the entire charging network and improve the operating efficiency.

[0004] To solve the above technical problems, the technical solution provided by the present application is as follows:

[0005] An electric vehicle non-sensing charging control method based on a transformer area multi-objective, comprising the steps of:

[0006] Based on the charging safety of the distribution transformer area, the first economic model for the operating cost of the charging safety of the distribution transformer area is constructed, considering the power balance and node voltage constraints of the system;

[0007] The second economic model for the electric vehicle control model considering the regional cluster characteristics is constructed to coordinate the charging behavior and the cost of electricity consumption, considering the constraints of the state of charge of the electric vehicle, the discharge capacity of the electric vehicle, the charging and discharging power of the electric vehicle and the carrying capacity of the inventory distribution network;

[0008] The third economic model for the cost of new energy generation, energy storage device, power purchase from the grid and compensation of electric vehicle load is constructed, considering the constraints of supply and demand balance, grid power purchase and transferable load satisfaction, to consider the user satisfaction of new energy generation, energy storage and power purchase from the grid and the compensation of electric vehicle load;

[0009] The multi-objective model of the non-inductive charging of the electric vehicle is constructed based on a first economic model, a second economic model and a third economic model, a non-inductive charging decision framework of the electric vehicle is established, and the non-inductive charging of the electric vehicle is realized.

[0010] Preferably, the first economic model is:

[0011]

[0012] In the formula, f is the integrated operation cost of the distribution area in the whole scheduling period; f i (i=1, 2, 3, 4, 5, 6) are respectively the power purchase cost of the upper-level power grid, the network loss cost of the distribution area, the operation and maintenance cost of the photovoltaic power generation system and the energy storage device, the scheduling cost of the cuttable air conditioner load, the scheduling cost of the energy storage and the charging cost of the electric vehicle.

[0013] Preferably, the constraint condition corresponding to the first economic model comprises:

[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 all branch head nodes of all branches with j node as the branch end node; ψ(j) is the set of all branch end nodes of all branches with j node as the branch head node; P ij,t , Q ij,t are the head end active power and reactive power of branch ij at time t; P jk,t , Q jk,t are the head end active power and reactive power of branch at time t; V i,t , V′ j,t are the voltage amplitudes of i and j nodes at time t, respectively; I ij,t is the current amplitude of branch ij at time t; R ij , X ij are the resistance and reactance of branch ij; P j,t , Q j,t represent the net active power injection and the net reactive power injection of node j at time t; B B is the set of branches; P PV,t , P ESS,t , P grid,t are the photovoltaic power value, the charge-discharge power of the battery and the line transmission power at time t, respectively; P load,t is the basic load active power value at time t; are the curtailed power of the curtailed load and the charging amount of the EV at time t, respectively;

[0026] Constraints (5)-(9) are the electric vehicle battery constraints, including the power constraints, the state of charge constraints and the initial state of charge constraints of the battery during the charging and discharging process; wherein, P ESS.ch,t , P ESS.dch,t are the charging power and the discharging power of the energy storage device at time t; R ESS.ch,t , R ESS.dch,t represent the charging and discharging state of the energy storage; is the maximum power allowed for charging and discharging of the energy storage; SOC 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 the energy storage, E ESS,t represents the remaining energy after a scheduling is performed;

[0027] Constraint (10) is the transmission power capacity limit of the tie line; wherein, P grid,max and P grid,minMax and min values of power of the superior power grid tie line;

[0028] The constraint conditions (11)-(12) are EV constraints; in the formula, SOC EV,max , SOC EV,min are upper and lower limits of the SOC of the EV respectively, is the charging amount of the EV at the time t;

[0029] The constraint conditions (13)-(14) are the curtaillable load AC constraints, the upper and lower limits of the regulating power constraints and the user comfort constraints; in the formula, respectively represent the upper and lower limits of the curtaillable load; θ max , θ min are upper and lower limits of the user comfort respectively;

[0030] The constraint conditions (15)-(16) are the upper and lower limits of the voltage and current; in the formula, V j (t) is the voltage value of the jth node at the t period; V j,min , V j,max are the lower and upper limits of the node voltage; I ij,max , I ij,min are the upper and lower limits of the current; B N is the node set.

[0031] Preferably, the target function expression of the second economic model is as shown in the following formula:

[0032]

[0033] In the formula, represents the electricity price at different times; represents the charging power of the ith electric vehicle at the T i th time, that is, the control variable of the model; g(x) represents a fitting function of harmonic components and power; F0 represents a harmonic coefficient, which represents the electricity cost of the harmonic component; represents the total load at different times.

[0034] Preferably, the constraint conditions of the second economic model include the electric vehicle state of charge constraint, the electric vehicle discharging capacity constraint, the electric vehicle charging and discharging power constraint and the inventory power grid carrying capacity constraint, and specifically are:

[0035]

[0036] The constraint conditions (20)-(21) are the electric vehicle state of charge constraints; T end represents the last scheduling time; respectively represent the state of charge and the upper limit of the state of charge of the ith electric vehicle when leaving the charging pile; the initial state of charge of the i th electric vehicle connected to the grid; E 0,i the rated capacity of the i th electric vehicle;

[0037] The constraints (22)-(23) are the discharge capacity constraints of the electric vehicles; wherein, the state of charge of the i th electric vehicle at the T i th time; S OC.min the minimum state of charge limit of the electric vehicle in emergency situations;

[0038] The constraints (24)-(25) are the charge-discharge power constraints of the electric vehicles; wherein, the number of the electric vehicles charging and discharging at the T i th time; and

[0039] The constraint (26) is the inventory power grid carrying capacity constraint; wherein, the carrying capacity of the power grid at the T i th time.

[0040] Preferably, the satisfaction degree of the transferable load represented by the electric vehicle is measured by two aspects, i.e., the transfer span satisfaction degree and the profit increase rate. The transfer span satisfaction degree of the transferable load is represented by the following formula (27):

[0041]

[0042] wherein, is the transfer span satisfaction degree of the transferable load; is the starting time before the transfer of the b th transferable load; is the starting time after the transfer of the b th transferable load;

[0043] When the price of electricity at the time after the transfer of the load is different from that at the time before the transfer, and the power department will compensate the electricity fee under the current price mechanism, the profit increase rate is obtained by comparing the profit after the transfer with the profit before the transfer. The profit increase rate is represented by the following formula (28):

[0044]

[0045] wherein, is the benefit increase rate of the b th transferable load; P b is the rated power of the b th transferable load; ΔC E is the difference between the price of electricity after the transfer and that before the transfer; b2 is the compensation unit price of the power department; Loss b is the loss benefit per unit time of the b th transferable load; J b is the benefit per unit power of the b th load.

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

[0047]

[0048] wherein P w (t) is the wind power at time t; C w is the unit cost of wind power generation; P Pv (t) is the photovoltaic power at time t; C Pv is the unit cost of photovoltaic power generation; P g (t) is the power purchased from the large power grid at time t; C g (t) is the price of power purchased from the large power grid at time t; is the power cut by the cuttable load a at time t; b1 is the unit compensation price of power cut by the cuttable load; Δt b is the transfer time of the transferable 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 device at time t.

[0049] Preferably, the constraint condition corresponding to the third economic model comprises:

[0050] a supply-demand balance constraint, specifically:

[0051]

[0052] wherein F(t) is the load at time t; P w (t) is the wind power at time t; P Pv (t) is the photovoltaic power at time t; P E (t) is the power generated by the electric vehicle energy storage battery at time t; P g (t) is the power purchased from the large power grid at time t;

[0053] a power grid purchase constraint, specifically:

[0054] P g (t)≤P g,max (34)

[0055] wherein P g,max is the maximum power purchased from the large power grid.

[0056] a transferable load satisfaction constraint, specifically:

[0057] The transferable load satisfaction constraint is as follows according to equations (27) and (28):

[0058]

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

[0060] As can be seen from the formula (35) and (36), the transfer time range under the user satisfaction constraint is:

[0061]

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

[0063] Min C=C 台 +C 负 +C 用 (38)

[0064] Wherein, MinC is the lowest comprehensive cost of the electric vehicle non-inductive charging, C 台 C is the lowest comprehensive operation cost of the charging area safety, C 负 C is the lowest cost of the electric vehicle management and control model considering the regional cluster characteristics to coordinate the charging behavior, C 用 C is the lowest electric vehicle charging cost considering user experience.

[0065] The application also discloses an electric vehicle non-inductive charging control system based on the multi-objective of the area, which comprises a memory and a processor connected with each other, the memory is stored with a computer program, and the computer program performs the steps of the method as described above when being run by the processor.

[0066] Compared with the prior art, the application has the following advantages:

[0067] The application can effectively predict and evaluate the influence of the charging facility on the distribution area by the operation cost model, ensure the stable operation of the power system, reduce the risk of power grid accidents caused by charging, the management and control model considering the regional cluster characteristics can coordinate the charging behavior, avoid the charging peak, reduce the pressure of the power grid, and thus reduce the power consumption cost, the third economic model makes the new energy generation, energy storage, power purchase cost and electric vehicle load compensation more close to the user demand by considering the user satisfaction, and thus improves the user satisfaction of the electric vehicle use, and the multi-objective model can balance the charging safety, charging load simultaneous rate, user satisfaction and other factors, and realize the non-inductive charging of the electric vehicle, that is, the optimization and adjustment of the charging are completed in the case that the user is unaware.

[0068] The application can effectively utilize new energy generation, improve the proportion in the large power grid, and promote clean energy consumption by reasonably arranging the charging demand of the electric vehicle. The model considering various factors can help reduce the operation cost of the entire charging network and improve the operation efficiency. The reasonable charging strategy and control measures can improve the energy utilization efficiency and reduce energy waste. The model considering the cluster characteristics and user behavior can improve the adaptability of the power grid to the charging load and enhance the flexibility of the power grid. BRIEF DESCRIPTION OF DRAWINGS

[0069] Figure 1 The flowchart of the electric vehicle non-inductive charging control method based on the transformer area multi-objective of the application in the embodiment. DETAILED DESCRIPTION

[0070] The application will be further described below in combination with the drawings and specific embodiments.

[0071] As shown in the figure, the electric vehicle non-inductive charging control method based on the transformer area multi-objective provided by the embodiment of the application includes the following steps: Figure 1 S1. Construct an economic model of the operation cost of the charging safety of the distribution transformer area (first economic model);

[0072] The purchase cost of the upper-level power grid, the network loss cost, the dispatching cost of the energy storage, and the charging cost of the electric vehicle are comprehensively considered, the power balance and the node voltage of the system are considered as constraint conditions, the economic model of the charging safety of the transformer area based on the minimum operation cost of the charging safety of the distribution transformer area is introduced, and the model is constructed as follows:

[0073]

[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 ≤P​grid,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] In the formula: f is the integrated operation cost of the distribution area in the entire scheduling period; f i (i = 1, 2, 3, 4, 5, 6) are respectively the power purchase cost of the upper-level power grid, the network loss cost of the distribution area, the operation and maintenance cost of the photovoltaic power generation system and the energy storage device, the scheduling cost of the cuttable air conditioner load, the scheduling cost of the energy storage, and the charging cost of the electric vehicle;

[0086] Constraints (2)-(4) are power flow equation constraints, is the set of all branch head nodes of all branches with j node as the branch end node in the radial power grid; ψ(j) is the set of all branch end nodes of all branches with j node as the branch head node; P ij,t , Q ij,t are the head active power and reactive power of branch ij at time t; P jk,t , Q jk,t are the head active power and reactive power of the branch at time t; V i,t , V' j,t are respectively the voltage amplitudes of i and j nodes at time t; I ij,t is the current amplitude of branch ij at time t; R ij , X ij are the resistance and reactance of ij branch; P j,t , Q j,t represent the net active power injection and the net reactive power injection of node j at time t; B B is the branch set; P PV,t , P ESS,t , P grid,t are respectively the photovoltaic power value, the charge and discharge power of the battery, and the line transmission power at time t; P load,t is the basic load active power value at time t; are respectively the cuttable load reduction power and the EV charging amount at time t.

[0087] The constraint conditions (5)-(9) are the constraints of the electric vehicle battery, mainly including the power constraint, the state of charge constraint and the initial state of charge constraint of the battery during the charging and discharging process. Among them, P ESS.ch,t , P ESS.dch,t are the charging power and the discharging power of the energy storage device at time t; R ESS.ch,t , R ESS.dch,t represent the charging and discharging state of the energy storage; is the maximum power of the charging and discharging allowed by the 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 the energy storage, E ESS,t represents the remaining energy after a scheduling is performed.

[0088] The constraint condition (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 area and the upper-level power grid, the power transmission amount should be strictly limited to meet the constraint condition of the power transmission capacity. In the formula, P grid,max and P grid,min are the maximum and minimum values of the power of the tie line of the upper-level power grid.

[0089] The constraint conditions (11)-(12) are the EV constraints. In the formula, SOC EV,max , SOC EV,min are the upper and lower limits of the SOC of the EV, is the charging amount of the EV at time t.

[0090] The constraint conditions (13)-(14) are the adjustable load AC constraints, mainly including the upper and lower limit constraints of the adjustable load and the comfort constraints of the user. In the formula, respectively represent the upper and lower limits of the adjustable load; θ max , θ min are the upper and lower limits of the user comfort.

[0091] The constraint conditions (15)-(16) are the upper and lower limit constraints of the voltage and current. In the formula, V j (t) is the voltage value of the jth node at the t period, V j,min , V j,max are the lower and upper limits of the node voltage; I ij,max , I ij,min are the upper and lower limits of the current; B N is the node set.

[0092] S2. Construct an electric vehicle management and control model considering the regional cluster characteristics to coordinate the charging behavior and the economy of the electricity cost model (the second economy model).

[0093] The coordination charging behavior of the electric vehicle management and control model considering the regional cluster characteristics is essentially to reduce the electricity cost, and the specific objective function expression is shown in equation (17):

[0094]

[0095] In the formula, represents the electricity price at different times, in order to reduce the coincidence degree of the total charging load of the electric vehicle and the load of the power grid, the grid-connected electricity price is set to change with the load here; represents the charging power of the i-th electric vehicle at the T i -th time, which is the control variable of the model; g(x) represents the fitting function of harmonic components and power; F0 represents the harmonic coefficient, which expresses the harmonic component as the electricity cost; represents the total load at different times.

[0096] The constraint conditions of the electric vehicle grid-connected power management and control model include the state of charge constraint of the electric vehicle, the discharge capacity constraint of the electric vehicle, the charging and discharging power constraint of the electric vehicle, and the carrying capacity constraint of the power grid; Specifically:

[0097]

[0098] Constraints (20)-(21) are the state of charge constraints of the electric vehicle. The state of charge of the electric vehicle determines the charging comfort of the user, and is a key indicator reflecting the charging process of the electric vehicle. In order to ensure the realization of the user's no feeling, the state of charge of the electric vehicle with different numbers must meet the minimum limit value at the end of charging. Among them, T end represents the last scheduling time; respectively represent the state of charge and the upper limit of the state of charge of the i-th electric vehicle when leaving the charging pile; represents the initial grid-connected state of charge of the i-th 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 the electric vehicle. The grid-connected discharge behavior of the electric vehicle will cause the state of charge of the battery to decrease, but the state of charge at any time should meet the constraint of not less than 0. Therefore, the discharge capacity constraint is set to limit the grid-connected discharge behavior of the electric vehicle. In the formula, represents the state of charge of the i-th numbered electric vehicle at the T i -th time; S OC.min represents the minimum state of charge limit value of the electric vehicle in emergency situations, which is 0.3.

[0100] The constraints (24)-(25) are the electric vehicle charging and discharging power constraints. When the grid-connected power is too high, the electric vehicle battery will generate a large amount of heat and consume a large amount of power, which will reduce the battery life. When the grid-connected power is too low, the charging time of the electric vehicle battery will be long, and the user's charging experience will be poor. Therefore, the grid-connected power of the electric vehicle has upper and lower limit constraints. In the formula, respectively represent T i the number of charging and discharging electric vehicles at the time.

[0101] The constraint (26) is the inventory distribution network carrying capacity constraint. When the total charging load of the large-scale electric vehicle exceeds the carrying capacity of the distribution network, it will cause the operation stability of the distribution network to decline, and even cause a large-scale power outage. Therefore, considering the carrying capacity constraint of the inventory distribution network, the total charging load of the electric vehicle is kept below the limit value. In the formula, represent the carrying capacity of the distribution network at the T i time, which is the difference between the maximum value of 1.2 times the basic load of the power grid and the basic load of the power grid at the current time.

[0102] S3. Build an economic model considering user satisfaction, new energy power generation, energy storage, and compensation for electric vehicle load (third economic model).

[0103] The satisfaction of the transferable load represented by the electric vehicle is measured by two aspects, namely the transfer span satisfaction and the profit improvement rate. When the user receives the dispatching requirement from the power department, the user will transfer the load. The transfer span is a measure of the comfort level of electricity consumption, and the transfer span satisfaction of the transferable load can be represented by the following formula (27):

[0104]

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

[0106] When the load is transferred, the electricity price after transfer is different from the electricity price before transfer, and the user will get the electricity fee compensation under the current electricity price mechanism. The profit improvement rate is obtained by comparing the profit after transfer with the profit before transfer, and the profit improvement rate is shown in the following formula (28):

[0107]

[0108] In the formula, is the benefit 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; b2 is the unit price of compensation of the power department; Loss b is the loss benefit of the bth transferable load per unit time; J b is the benefit of the bth load per unit power.

[0109] By scheduling the electric vehicles under the satisfaction constraint, the purpose of minimizing the total generation cost is achieved. The objective function of the model is to minimize the new energy generation cost, the energy storage device cost, the cost of purchasing electricity from the large power grid, and the compensation cost of the electric vehicle load, which is expressed as follows:

[0110]

[0111] In the formula, P w (t) is the wind power at period t; C w is the unit generation cost of wind power; P Pv (t) is the photovoltaic power at period t; C Pv is the unit generation cost of photovoltaic power; P g (t) is the amount of electricity purchased from the large power grid at period t; C g (t) is the electricity price of the large power grid at period t; is the cuttable load a at period t; b1 is the compensation price of the cuttable load per unit power; Δt b is the transfer time of the transferable 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 device at period t, and the cost of the electric vehicle is represented by the following formulas (30) and (31):

[0112] When the electric vehicle is charging:

[0113]

[0114] When the electric vehicle is discharging:

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

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

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

[0118] (1) Supply and demand balance constraint

[0119]

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

[0121] (2) Power grid power purchase constraint

[0122] P g (t)≤P g,max (34)

[0123] In the formula, P g,max is the maximum power purchased from the large power grid.

[0124] (3) Satisfaction constraint of transferable load

[0125] The satisfaction constraint of transferable load is as follows according to formula (27), (28):

[0126]

[0127] 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.

[0128] It is known from formula (35), (36) that the transfer time range under the satisfaction constraint of the user is:

[0129]

[0130] S4. On the basis of the above cost economy model considering the charging safety of the distribution area, the simultaneous rate of the charging load, and the user satisfaction, a multi-objective model of the non-inductive charging of the electric vehicle is constructed.

[0131] In the above content, the corresponding economy models are established for the operation cost of the charging safety of the distribution area, the electric vehicle management and control model coordinating the charging behavior cost considering the regional cluster characteristics, the new energy generation, the energy storage, the power purchase cost from the large power grid considering the user satisfaction, and the compensation cost of the electric vehicle load. The multi-objective model of the non-inductive charging of the electric vehicle mainly takes into account the comprehensive operation cost including the safety of the area of the electric vehicle charging, the simultaneous rate of the charging load, and the user satisfaction. The specific model is as follows:

[0132] Min C=C 台 +C 负 +C 用 (38)

[0133]

[0134] Wherein, MinC is the lowest comprehensive cost of electric vehicle inductive charging, C 台 To ensure the lowest comprehensive operation cost of charging area safety, C 负 To consider the electric vehicle management and control model coordinated charging behavior use electricity cost, C 用 The electric vehicle charging cost considering user experience is the lowest, and the remaining parameters and constraint conditions are described in the above three factors economic modeling.

[0135] The multi-objective model of electric vehicle inductive charging constructed by the application is based on the non-preference multi-objective planning strategy, in order to facilitate intuitive comparison, the concept of unit time cost is also introduced, and the objective function form of the model is:

[0136]

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

[0138] Contrary to the multi-objective optimization based on preference, the idea of the non-preference multi-objective planning strategy of the application is "search first, decision-making later", and its advantage lies in that it can provide a series of non-inferior solutions for decision-makers to select according to actual situation, thereby improving the flexibility of decision-making.

[0139] Through the operation cost model, the application can effectively predict and evaluate the influence of charging facilities on the distribution area, 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 regional cluster characteristics can coordinate the charging behavior, avoid the charging peak, reduce the pressure of the power grid, and thus reduce the electricity cost; the third economic model considers the user satisfaction, so that the new energy generation, energy storage, electricity purchase cost and compensation of electric vehicle load are closer to the user demand, thereby improving the user's satisfaction with the use of electric vehicles; the multi-objective model can balance the charging safety, charging load simultaneous rate, user satisfaction and other factors, and realize the inductive charging of electric vehicles, that is, the optimization and adjustment of charging are completed without the user's awareness.

[0140] The application can effectively utilize new energy generation, improve its proportion in the large power grid, and promote the consumption of clean energy by reasonably allocating the charging demand of electric vehicles; the model considering various factors helps to reduce the operation cost of the entire charging network and improve the operation efficiency; reasonable charging strategy and control measures can improve energy utilization efficiency and reduce energy waste; the model considering cluster characteristics and user behavior can improve the adaptability of the power grid to the charging load and enhance the flexibility of the power grid.

[0141] The application also discloses a power-driven vehicle non-inductive charging control system based on a transformer area multi-target, which comprises a memory and a processor connected with each other, and the memory stores a computer program which executes the steps of the above method when the processor runs.

[0142] The application realizes all or part of the processes in the above-mentioned embodiment methods, and can also be completed by computer program instruction related hardware. The computer program can be stored in a computer readable storage medium, and the computer program can realize the steps of the above-mentioned method embodiment when executed by a processor. The computer program includes computer program code, which can be in the form of source code, object code, executable files or some intermediate forms, etc. The computer readable storage medium includes any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. The memory is used for storing computer programs and / or modules, and the processor realizes various functions by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory. The memory can include high-speed random access memory and can also include non-volatile memory such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one magnetic disk storage device, flash memory device or other volatile solid-state storage device, etc.

[0143] The above is only the preferred embodiment of the application, and the protection scope of the application is not limited to the above-mentioned embodiment. Any technical solution falling within the idea of the application belongs to the protection scope of the application. It should be noted that some improvements and decorations without departing from the principle of the application are considered to be within the protection scope of the application.

Claims

1. A method for contactless charging control of electric vehicles based on multi-objective distribution areas, characterized in that, Including the following steps: With the goal of minimizing operating costs based on the charging safety of distribution transformer areas, and considering the power balance of the system and node voltage constraints, a first economic model for the operating costs of charging safety in distribution transformer areas is constructed. To minimize the electricity cost of electric vehicles with regional cluster characteristics, and considering constraints such as the state of charge of electric vehicles, the discharge capacity of electric vehicles, the charging and discharging power of electric vehicles, and the carrying capacity of the existing distribution network, a second economic model for coordinating the electricity cost of charging behavior is constructed based on the electric vehicle management model considering regional cluster characteristics. To minimize the costs of new energy power generation, energy storage equipment, electricity purchase from the main grid, and compensation costs for electric vehicle load, a third economic model is constructed that considers user satisfaction constraints, including supply and demand balance constraints, grid purchase constraints, and transferable load satisfaction constraints. Taking into account the comprehensive operating costs of electric vehicle charging area safety, charging load simultaneity rate and user satisfaction, a multi-objective model for seamless electric vehicle charging is constructed based on the first economic model, the second economic model and the third economic model. A seamless decision-making architecture for electric vehicle charging is established to realize seamless electric vehicle charging. The first economic model is: (1) In the formula: The total operating cost of the distribution area for the entire dispatch cycle; These include the cost of purchasing electricity from the upstream power grid, the grid loss cost of the distribution substation, the operation and maintenance cost of the photovoltaic power generation system and energy storage device, the dispatch cost that can reduce air conditioning load, the dispatch cost of energy storage, and the charging cost of electric vehicles. The objective function corresponding to the third economic model is expressed as follows: (29) In the formula, yes Wind power generation capacity during a given time period; This refers to the unit cost of generating electricity from wind power. yes Photovoltaic power generation during a given period; This refers to the unit cost of photovoltaic power generation. yes Electricity purchased from the main power grid during specific time periods; yes The electricity purchase price from the main power grid during the specified time period; It is a load that can be reduced. exist Power reduction during certain periods; It is the price for compensation per unit power of load reduction that can be reduced; It is a transferable load. Transfer time; It is the rated power of a transferable load; It is the price for compensation of transferable loads; It is an energy storage device for electric vehicles. Time-based costs.

2. The method for contactless charging control of electric vehicles based on multi-objective distribution areas according to claim 1, characterized in that, The constraints corresponding to the first economic model include: (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) Constraints (2)-(4) are power flow equality constraints. It is a radiation power grid with A node is the set of the starting nodes of all branches that are the ending nodes of a branch. Therefore A node is the set of all end nodes of all branches that are the starting node of a branch. , branch road exist Active and reactive power at the beginning of the time; , For the branch road Active and reactive power at the beginning of the time; They are respectively time and Voltage amplitude at the node; branch road exist Current amplitude at time; , for The resistance and reactance of the branch circuit; Represents a node exist Net active power injection and net reactive power injection during the time period; For branch set; , , They are respectively Photovoltaic power output, battery charging and discharging power, and line transmission power during the specified time period; for Active power value of basic load during the time period; , They are respectively The power reduction of the load and the charging amount of the EV can be reduced at any time; T represents the set of scheduling cycle times; Constraints (5)-(9) are constraints on the electric vehicle battery, including power constraints, state of charge constraints, and initial charge constraints during the charging and discharging process; among them, , They are respectively The charging and discharging power of the energy storage device at any time; , Indicates the charge / discharge state of energy storage; , The maximum allowable charging and discharging power for energy storage; This is the maximum state of charge value; This refers to the rated capacity of the battery. This represents the initial stored energy. This represents the remaining energy after a scheduling operation; Indicates the state of charge of the energy storage during time period t; This is the minimum state of charge value; Constraint (10) is the limit on the transmission power capacity of the tie line; where, and These are the maximum and minimum power values ​​of the upstream power grid interconnection lines; This represents the line transmission power during time period t; Constraints (11)-(12) are EV constraints; , These are the upper and lower limits of the SOC for EV. For EVs Current charging level; This indicates the state of charge of the electric vehicle during time period t; This represents the maximum charging power of the electric vehicle during time period t; Constraints (13)-(14) are the load reduction AC constraint, the upper and lower limits of the adjustable power constraint, and the user comfort constraint; where, , These represent the upper and lower limits of the load that can be reduced, respectively. , These represent the upper and lower limits of user comfort, respectively. This indicates that the load can be reduced during period t; This represents the user comfort level during time period t; Constraints (15)-(16) are upper and lower limits of voltage and current; where, For the first Each node Voltage value during the time period; , These are the lower and upper limits of the voltage at the transformer substation nodes; , These are the upper and lower bounds of the current. A set of nodes; This represents the current value of branch ij during time period t.

3. The method for contactless charging control of electric vehicles based on multi-objective distribution areas according to claim 1 or 2, characterized in that, Satisfaction with transferable loads, represented by electric vehicles, is measured by two aspects: satisfaction with the transfer span and profit improvement rate; the satisfaction with the transfer span of transferable loads is expressed by the following formula (27): (27) In the formula, Satisfaction with the transfer span of transferable loads; For the first The start time before the transfer of each transferable load class; For the first The start time after the transferable load is transferred; After the load is transferred, the electricity price after the transfer is different from that before the transfer, and the electricity department will provide compensation under the current electricity price mechanism. The profit after the transfer is compared with the profit before the transfer to calculate the profit increase rate, which is shown by the following formula (28): (28) In the formula, For the first The efficiency improvement rate of each transferable load; For the first Rated power of each transferable load; This represents the price difference between the electricity before and after the transfer. Compensation for the unit price of the power sector; For the first The loss of profit per unit time for each transferable type of load; For the first Each unit of load generates benefits per unit of power.

4. The method for contactless charging control of electric vehicles based on multi-objective distribution areas according to claim 1 or 2, characterized in that, The multi-objective model for contactless charging of electric vehicles is as follows: (38) in, The overall cost of seamless charging for electric vehicles is the lowest. To ensure the safety of the charging station area and minimize overall operating costs, To minimize electricity costs in the electric vehicle management model that considers regional cluster characteristics and coordinates charging behavior, Electric vehicles have the lowest charging costs, taking user experience into consideration.

5. A multi-target, contactless charging control system for electric vehicles, comprising an interconnected memory and a processor, wherein the memory stores a computer program, characterized in that... The computer program, when run by a processor, performs the steps of the method as described in any one of claims 1-4.

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