A method and device for coordinated operation of electric vehicle charging in multiple zones

Abstract

This invention discloses a method and apparatus for coordinated operation of electric vehicle charging in multiple distribution substations, addressing the technical problem of achieving interconnection and mutual power supply between low-voltage distribution substations under large-scale electric vehicle access, thereby improving the economic efficiency of substation operation. The invention includes: acquiring basic parameter information of multiple distribution substations associated with intelligent soft switches and the charging parameters of electric vehicles; establishing an optimization model for coordinated operation of electric vehicle charging in multiple distribution substations based on flexible interconnection using the basic parameter information and charging parameters; solving the coordinated operation model of electric vehicle charging in multiple distribution substations to obtain electric vehicle charging and intelligent soft switch scheduling strategies; and using the electric vehicle charging and intelligent soft switch scheduling strategies to perform charging and intelligent soft switch scheduling for electric vehicles in multiple distribution substations.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle charging technology, and in particular to a method and apparatus for coordinated operation of multi-zone electric vehicle charging. Background Technology

[0002] With the high proportion of distributed power sources and electric vehicles integrated into distribution networks, the operating conditions of low-voltage distribution substations are becoming increasingly complex and variable. These substations are facing multiple severe challenges, including dynamic capacity expansion, power balancing, and power supply reliability. In the traditional radial distribution network model, each substation operates independently, with power interaction limited to within the substation itself. This leads to low resource utilization and insufficient power supply reliability. In particular, with the widespread integration of electric vehicle charging loads, the remaining transformer capacity and available source-load resources of neighboring substations cannot be effectively shared, further exacerbating the load imbalance problem between adjacent substations. Against this backdrop, the charging needs of electric vehicle users within the distribution substation may not be fully met, and the safe operation of the distribution network faces potential threats. Summary of the Invention

[0003] This invention provides a method and apparatus for coordinated operation of electric vehicle charging in multiple distribution areas, which is used to solve the technical problem of how to achieve interconnection and mutual power supply between low-voltage distribution network distribution areas under the large-scale access of electric vehicles and improve the economic efficiency of distribution area operation.

[0004] This invention provides a method for coordinated operation of charging multiple electric vehicles in different areas, comprising:

[0005] Acquire basic parameter information of multiple distribution radio zones associated with smart soft switches and charging parameters of electric vehicles;

[0006] Using the aforementioned basic parameter information and charging parameters, and with the objective function being the minimum sum of electric vehicle charging costs, user charging incomplete compensation costs, distribution transformer overload rate penalty costs, system network loss costs, and intelligent soft switch power loss costs, and with the constraints of distribution substation operation constraints, distribution transformer operation constraints, node three-phase imbalance constraints, electric vehicle charging constraints, distributed power source operation constraints, and intelligent soft switch operation constraints, a multi-distribution substation electric vehicle charging collaborative operation optimization model based on flexible interconnection is established.

[0007] Solve the electric vehicle charging collaborative operation model of the multi-distribution station area to obtain the electric vehicle charging and intelligent soft switching scheduling strategy;

[0008] The electric vehicle charging and intelligent soft-switching scheduling strategy is used to schedule the charging and intelligent soft-switching of electric vehicles in multiple distribution radio areas.

[0009] Optionally, the objective function is:

[0010] min f = f EV +f C +f DT +f L +f E

[0011]

[0012]

[0013]

[0014]

[0015]

[0016]

[0017]

[0018] Where f is the objective function, f EV The cost of charging electric vehicles, f C Compensation for incomplete charging for users, f DT For the penalty fee for exceeding the load rate limit of the distribution transformer, f L For system network loss costs, f E Cost of power loss for intelligent soft switching; It represents any one of the three phases A, B, and C; Indicates the tth time period The charging power of the nth electric vehicle in phase c; t Represents time-of-use pricing; Δt represents the optimized time interval; N EV N represents the total number of electric vehicles in the distribution area. DT Indicates the number of transformer substations; N T Indicates the total optimized scheduling period; C represents the compensation coefficient corresponding to the state of charge of the nth electric vehicle when it leaves the distribution station area; EV,max This represents the maximum value of the compensation coefficient; s represents the state of charge of the nth electric vehicle when it leaves the distribution station area; exp This represents the user's expected value for the state of charge; Let represent the expected battery capacity of the nth electric vehicle; This represents the penalty coefficient corresponding to the load rate of the distribution transformer in the k-th distribution area during time period t; β represents the base load of the k-th transformer area during time period t; k,t This represents the load factor of the distribution transformer in the k-th distribution area during time period t; This indicates the limit value corresponding to the penalty fee for exceeding the load rate limit of the distribution transformer; cL Indicates the unit cost of line loss; Ω b Represents the set of branches; Represents branch ij Phase resistance; This indicates the flow from node i to node j during time period t. The square of the phase current amplitude; c E N represents the unit cost of power loss in SOP equipment. SOP Indicates the number of smart soft switch SOPs; This represents the power loss of the l-th SOP during time period t.

[0019] Optionally, the operating constraints of the distribution radio area are:

[0020]

[0021]

[0022]

[0023]

[0024]

[0025]

[0026]

[0027]

[0028]

[0029] in, and Representing the branches ij respectively Phase resistance and Phase reactance; and These represent the flow from node i to node j during time period t. Phase active power and Phase reactive power; and These represent the flow from node j to node h during time period t. Active power and reactive power of each phase; and These represent the injections at node j during time period t. The sum of active power of each phase and The sum of phase reactive power; and These represent the distributed power injection, electric vehicle consumption, and load consumption at node j during time period t, respectively. Phase active power; and These represent the distributed power injection and load consumption at node j during time period t, respectively. Phase reactive power; This indicates the flow from node i to node j during time period t. The square of the phase current amplitude; and Represent the values ​​of node i and node j in time period t, respectively. The square of the phase voltage amplitude; V and These represent the lower and upper limits of the node voltage amplitude, respectively.

[0030] Optionally, the operating constraints of the distribution transformer are:

[0031]

[0032]

[0033]

[0034]

[0035] Where e represents the node where the transformer in area 1 is located; m represents the node where the transformer in area 2 is located; and These represent the active power passing through distribution transformers in area 1 and area 2 during time period t, respectively. and These represent the active power transmitted via SOP on transformer area 1 and transformer area 2 during time period t, respectively. and These represent the rated power of the distribution transformers in transformer substation 1 and transformer substation 2, respectively. and These represent the total active power consumed by each phase in transformer substation 1 and transformer substation 2 during time period t.

[0036] Optionally, the node three-phase unbalance constraint is:

[0037]

[0038] VUR j <VUR max

[0039] Among them, VUR j VUR represents the voltage imbalance at node j. max This represents the maximum permissible value for the three-phase voltage imbalance in the distribution network. This indicates the node j in time period t. Phase voltage amplitude.

[0040] Optionally, the electric vehicle charging constraint is:

[0041]

[0042]

[0043]

[0044] in, Indicates time period t The state of charge of the nth electric vehicle in phase; Indicates time period t The state of charge of the nth electric vehicle in phase t-1; express The initial state of charge of the nth electric vehicle selected; and They represent The charging start time and charging end time of the selected nth electric vehicle; s and These represent the lower and upper limits of the state of charge, respectively.

[0045] Optionally, the operating constraints of the distributed power source are:

[0046]

[0047]

[0048] in, This represents the predicted active power output of the distributed generation; cosθ DG This refers to the power factor of the distributed power source.

[0049] Optionally, the operating constraints of the intelligent soft switch are:

[0050]

[0051]

[0052]

[0053]

[0054]

[0055]

[0056]

[0057]

[0058]

[0059]

[0060] in, and These represent the active and reactive power transmitted via SOP on transformer 1 during time period t, respectively. and These represent the active power and reactive power transmitted via SOP on transformer substation 2 during time period t, respectively. and This represents the power loss of the SOP connected to transformer area 1 and transformer area 2 during time period t; This represents the DC fast charging power during time period t; and These represent the SOP capacity connected to area 1 and area 2, respectively. The SOP capacity is connected between area 1 and area 2; and These represent the minimum active power and minimum reactive power injected by SOP connected to transformer area 1, respectively. and These represent the minimum active power and the maximum reactive power injected by SOP connected to transformer area 1, respectively.

[0061] Optionally, the step of solving the multi-distribution station area electric vehicle charging cooperative operation model to obtain the electric vehicle charging and intelligent soft-switching scheduling strategy includes:

[0062] The second-order cone programming method is used to solve the electric vehicle charging collaborative operation model of the multi-distribution station area, and the electric vehicle charging and intelligent soft switching scheduling strategy is obtained.

[0063] The present invention also provides a multi-zone electric vehicle charging collaborative operation device, comprising:

[0064] The parameter acquisition module is used to acquire basic parameter information of multiple distribution radio zones associated with intelligent soft switches and charging parameters of electric vehicles;

[0065] The model building module is used to establish an optimization model for the coordinated operation of electric vehicle charging in multiple distribution substations based on flexible interconnection, using the basic parameter information and the charging parameters, with the objective function being the minimum sum of electric vehicle charging cost, user charging incomplete compensation cost, distribution transformer overload rate penalty cost, system network loss cost and intelligent soft switch power loss cost, and with the constraints being distribution substation operation constraints, distribution transformer operation constraints, node three-phase imbalance constraints, electric vehicle charging constraints, distributed power source operation constraints, and intelligent soft switch operation constraints.

[0066] The solution module is used to solve the electric vehicle charging collaborative operation model of the multi-distribution station area to obtain the electric vehicle charging and intelligent soft switching scheduling strategy.

[0067] The scheduling module is used to perform charging and intelligent soft-switching scheduling of electric vehicles in multiple distribution radio zones using the electric vehicle charging and intelligent soft-switching scheduling strategy.

[0068] As can be seen from the above technical solutions, the present invention has the following advantages: The present invention establishes an optimization model for the coordinated operation of electric vehicle charging in multiple distribution substations based on flexible interconnection, thereby realizing the interconnection and mutual supply between substations, optimizing the load distribution of substations, improving the electric vehicle acceptance capacity of distribution substations, and further realizing the coordinated scheduling of electric vehicles and intelligent soft switches in multiple distribution substations, thereby improving the operation economy and safety of distribution substations. Attached Figure Description

[0069] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0070] Figure 1 A flowchart illustrating the steps of a multi-zone electric vehicle charging collaborative operation method provided in an embodiment of the present invention;

[0071] Figure 2 A structural diagram of the distribution station area associated with the intelligent soft switch;

[0072] Figure 3 This is a schematic diagram of the load power variation curve;

[0073] Figure 4 This is a time-of-use electricity price curve;

[0074] Figure 5 A comparison diagram of the active power of the system in substation 1 among the three schemes;

[0075] Figure 6 A comparison diagram of the system active power in substation 2 among the three schemes;

[0076] Figure 7 This is a structural block diagram of a multi-zone electric vehicle charging collaborative operation device provided in an embodiment of the present invention. Detailed Implementation

[0077] This invention provides a method and apparatus for coordinated operation of electric vehicle charging in multiple distribution areas, which addresses the technical problem of how to achieve interconnection and mutual power supply between low-voltage distribution network areas under large-scale electric vehicle access, thereby improving the economic efficiency of distribution area operation.

[0078] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0079] Please see Figure 1 , Figure 1 A flowchart illustrating the steps of a multi-zone electric vehicle charging collaborative operation method provided in an embodiment of the present invention.

[0080] The present invention provides a method for coordinated operation of charging multiple electric vehicles in different areas, which may specifically include the following steps:

[0081] Step 101: Obtain basic parameter information of multiple distribution radio zones associated with the smart soft switch and charging parameters of the electric vehicle;

[0082] A transformer substation is a term used in power economic operation and management, referring to the power supply range and area of ​​a single transformer.

[0083] In this embodiment of the invention, the basic parameter information of the distribution substation may include: the distribution substation topology and parameter information, the access location and capacity of the smart soft switch, the access location and power change curve of the load, and the access location and capacity of the distributed power source; the charging parameters of the electric vehicle may include: the number of electric vehicles, the charging location, the maximum charging power, the battery capacity, the charging start time, the charging end time, and the initial state of charge.

[0084] In one example, the structure diagram of the distribution area associated with the smart soft switch is as follows: Figure 2 As shown, the topology and parameter information of the distribution substation are presented in Tables 1 and 2. The rated voltage level of the substation is 0.38kV, and the base power is 1MVA. The load in the low-voltage distribution substation mainly consists of residential and commercial loads, and the load power variation curve is shown in Table 2. Figure 3As shown in Table 3, the access locations and capacities of the intelligent soft switches are as follows. The three-phase imbalance problem in the system is mainly caused by the imbalance of the three-phase load. Substation 1 and Substation 3 are mainly residential substations, with 60 and 90 electric vehicles respectively. The access locations and numbers of electric vehicles are shown in Table 4. The charging method in Substation 1 and Substation 3 is slow charging. The charging start time, charging end time, and initial state of charge of electric vehicles are all generated by Monte Carlo sampling and follow a normal distribution. The average values ​​of the charging start time, charging end time, and initial state of charge are 17.47, 8.92, and 0.216, respectively, with standard deviations of 3.41, 3.24, and 0.118. The slow charging battery capacity of electric vehicles is set to 48kWh, and the charging power is set to 7kW. The fast charging battery capacity is set to 64.8kWh, and the charging power is set to 60kW. The charging efficiency of electric vehicles is 0.95, and the user's expected state of charge value is 0.95. Distribution areas 2 and 4 are primarily commercial areas. In addition to basic commercial load, area 2 includes 10 60kW fast charging piles and commercial fast charging loads, while area 4 includes slow charging loads for electric vehicles. The rated capacities of the distribution transformers T1-T4 are set at 630kVA and 8kVA respectively. 0 0kVA, 800kVA, and 800kVA, all with a power factor of 0.95. (Settings) V and The values ​​are 0.93 and 1.07 respectively, and β is 0.8. SOP1 and SOP2 are set to be connected to the low-voltage side of distribution transformers T1 and T2, and T3 and T4 respectively, with a rated capacity of 400kVA and a loss factor of 0.01 for each.

[0085] Table 1. Improved Calculation Example of Actual Low-Voltage Distribution Substation Area: Load Connection Location and Power

[0086]

[0087] Table 2. Improved Line Parameters for Actual Low-Voltage Distribution Substation Example

[0088]

[0089]

[0090] Table 3. Photovoltaic grid connection locations and capacity

[0091] 1 5 A 30 30 2 5 B 30 30 3 5 C 30 30

[0092] Table 4. Locations and Number of Electric Vehicles Connected

[0093] 1 3 A 20 4 8 A 21 2 3 B 20 5 8 B 22 3 3 C 20 6 8 C 47

[0094] Step 102: Using basic parameter information and charging parameters, with the objective function being the minimum sum of electric vehicle charging costs, user charging incomplete compensation costs, distribution transformer overload rate penalty costs, system network loss costs, and intelligent soft switch power loss costs, and with the constraints of distribution substation operation constraints, distribution transformer operation constraints, node three-phase imbalance constraints, electric vehicle charging constraints, distributed power source operation constraints, and intelligent soft switch operation constraints, a multi-distribution substation electric vehicle charging collaborative operation optimization model based on flexible interconnection is established.

[0095] After obtaining the basic parameter information of the distribution station and the charging parameters of the electric vehicle, an optimization model for the coordinated operation of electric vehicle charging in multiple distribution stations based on flexible interconnection can be established.

[0096] The objective function established by minimizing the sum of electric vehicle charging costs, user incomplete charging compensation costs, distribution transformer overload rate penalty costs, system network loss costs, and intelligent soft switching power loss costs is as follows:

[0097] min f = f EV +f C +f DT +f L +f E

[0098]

[0099]

[0100]

[0101]

[0102]

[0103]

[0104]

[0105] Where f is the objective function, f EV The cost of charging electric vehicles, f C Compensation for incomplete charging for users, f DT For the penalty fee for exceeding the load rate limit of the distribution transformer, f L For system network loss costs, f E Cost of power loss for intelligent soft switching; It represents any one of the three phases A, B, and C; Indicates the tth time period The charging power of the nth electric vehicle in phase c; t Indicates time-of-use electricity pricing, such as Figure 4 As shown; Δt represents the optimization time interval; N EV N represents the total number of electric vehicles in the distribution area. DT Indicates the number of transformer substations; N T Indicates the total optimized scheduling period; C represents the compensation coefficient corresponding to the state of charge of the nth electric vehicle when it leaves the distribution station area; EV,max This represents the maximum value of the compensation coefficient; s represents the state of charge of the nth electric vehicle when it leaves the distribution station area; exp This represents the user's expected value for the state of charge; Let represent the expected battery capacity of the nth electric vehicle; This represents the penalty coefficient corresponding to the load rate of the distribution transformer in the k-th distribution area during time period t; β represents the base load of the k-th transformer area during time period t; k,t This represents the load factor of the distribution transformer in the k-th distribution area during time period t; This indicates the limit value corresponding to the penalty fee for exceeding the load rate limit of the distribution transformer; c L Indicates the unit cost of line loss; Ω b Represents the set of branches; Represents branch ij Phase resistance; This indicates the flow from node i to node j during time period t. The square of the phase current amplitude; c E N represents the unit cost of power loss in SOP equipment. SOP Indicates the number of smart soft switch SOPs; This represents the power loss of the l-th SOP during time period t.

[0106] In this embodiment of the invention, the constraints of the multi-distribution transformer area electric vehicle charging collaborative operation optimization model may include distribution transformer area operation constraints, distribution transformer operation constraints, node three-phase imbalance constraints, electric vehicle charging constraints, distributed power source operation constraints, and intelligent soft-switching operation constraints. Specifically, as follows:

[0107] The operating constraints of the distribution radio area are:

[0108]

[0109]

[0110]

[0111]

[0112]

[0113]

[0114]

[0115]

[0116]

[0117] in, and Representing the branches ij respectively Phase resistance and Phase reactance; and These represent the flow from node i to node j during time period t. Phase active power and Phase reactive power; and These represent the flow from node j to node h during time period t. Active power and reactive power of each phase; and These represent the injections at node j during time period t. The sum of active power of each phase and The sum of phase reactive power; and These represent the distributed power injection, electric vehicle consumption, and load consumption at node j during time period t, respectively. Phase active power; and These represent the distributed power injection and load consumption at node j during time period t, respectively. Phase reactive power; This indicates the flow from node i to node j during time period t. The square of the phase current amplitude; and Represent the values ​​of node i and node j in time period t, respectively. The square of the phase voltage amplitude; V and These represent the lower and upper limits of the node voltage amplitude, respectively.

[0118] The operating constraints of the distribution transformer are:

[0119]

[0120]

[0121]

[0122]

[0123] Where e represents the node where the transformer in area 1 is located; m represents the node where the transformer in area 2 is located; and These represent the active power passing through distribution transformers in area 1 and area 2 during time period t, respectively. and These represent the active power transmitted via SOP on transformer area 1 and transformer area 2 during time period t, respectively. and These represent the rated power of the distribution transformers in transformer substation 1 and transformer substation 2, respectively. and These represent the total active power consumed by each phase in transformer substation 1 and transformer substation 2 during time period t.

[0124] The three-phase unbalance constraint at the node is:

[0125]

[0126] VUR j <VUR max

[0127] Among them, VUR j VUR represents the voltage imbalance at node j. max This represents the maximum permissible value for the three-phase voltage imbalance in the distribution network. This indicates the node j in time period t. Phase voltage amplitude.

[0128] Electric vehicle charging constraints are:

[0129]

[0130]

[0131]

[0132] in, Indicates time period t The state of charge of the nth electric vehicle in phase; Indicates time period t The state of charge of the nth electric vehicle in phase t-1; express The initial state of charge of the nth electric vehicle selected; and They represent The charging start time and charging end time of the selected nth electric vehicle; s and These represent the lower and upper limits of the state of charge, respectively.

[0133] The operating constraints of distributed power sources are:

[0134]

[0135]

[0136] in, This represents the predicted active power output of the distributed generation; cosθ DG This refers to the power factor of the distributed power source.

[0137] The operating constraints of the intelligent soft switch are:

[0138]

[0139]

[0140]

[0141]

[0142]

[0143]

[0144]

[0145]

[0146]

[0147]

[0148] in, and These represent the active and reactive power transmitted via SOP on transformer 1 during time period t, respectively. and These represent the active power and reactive power transmitted via SOP on transformer substation 2 during time period t, respectively. and This represents the power loss of the SOP connected to transformer area 1 and transformer area 2 during time period t; This represents the DC fast charging power during time period t; and These represent the SOP capacity connected to area 1 and area 2, respectively. The SOP capacity is connected between area 1 and area 2; and These represent the minimum active power and minimum reactive power injected by SOP connected to transformer area 1, respectively. and These represent the minimum active power and the maximum reactive power injected by SOP connected to transformer area 1, respectively.

[0149] Step 103: Solve the electric vehicle charging collaborative operation model of multiple distribution stations to obtain the electric vehicle charging and intelligent soft switching scheduling strategy.

[0150] In this embodiment of the invention, the solution to the multi-distribution station area electric vehicle charging collaborative operation model can be achieved through the following steps: the second-order cone programming method is used to solve the multi-distribution station area electric vehicle charging collaborative operation model to obtain the electric vehicle charging and intelligent soft switching scheduling strategy.

[0151] In practical implementation, the electric vehicle charging collaborative operation model of the distribution station area can be linearized by second-order cone transformation to improve the solution efficiency and accuracy of the model.

[0152] In one example, the process of linearizing the compensation fee for users who have not completed charging is as follows:

[0153] To eliminate compensation costs f C The nonlinear form introduces auxiliary variables. To perform linearization, i.e.:

[0154]

[0155] Meanwhile, auxiliary variables The following constraints should be met:

[0156]

[0157]

[0158] The process of linearizing the penalty cost for exceeding the load rate limit of a distribution transformer is as follows:

[0159] To eliminate penalty costs f DT The nonlinear form introduces auxiliary variables. To perform linearization, i.e.:

[0160]

[0161] Meanwhile, auxiliary variables The following constraints should be met:

[0162]

[0163] β k,t ≥0

[0164] The process of linearizing the nodal three-phase unbalance constraint is as follows:

[0165] To eliminate the nonlinear terms in the three-phase unbalance constraint, the following is introduced: right By performing variable substitution, we can obtain:

[0166]

[0167]

[0168] in, This indicates the node j in time period t. The square of the phase voltage amplitude; This indicates that node j is in time period t. Phase voltage imbalance; λ max The maximum value of three-phase voltage imbalance

[0169] Step 104: Electric vehicle charging and intelligent soft-switching scheduling strategies are adopted to perform charging and intelligent soft-switching scheduling for electric vehicles in multiple distribution areas.

[0170] After obtaining the electric vehicle charging and smart soft-switching scheduling strategy, the scheduling strategy can be sent to the edge computing terminal for execution to optimize electric vehicle charging and smart soft-switching in multi-distribution station areas.

[0171] This invention establishes a flexible interconnected multi-distribution transformer area electric vehicle charging collaborative operation optimization model to achieve interconnection and mutual supply between transformer areas, thereby optimizing transformer area load distribution, improving the electric vehicle acceptance capacity of distribution transformer areas, and further realizing the collaborative scheduling of electric vehicles and intelligent soft switches in multiple distribution transformer areas, thus improving the operation economy and safety of distribution transformer areas.

[0172] To verify the feasibility and effectiveness of the multi-zone electric vehicle charging collaborative operation method provided in this embodiment of the invention, the following three scenarios are used for verification and analysis:

[0173] Option 1: Without considering the effect of intelligent soft switching, obtain the independent operation status and charging results of the distribution station area under the condition of disordered charging of electric vehicles;

[0174] Option 2: Without considering the role of intelligent soft switching, but taking into account user participation, obtain the independent operation status and charging results of the distribution station area under the optimized scheduling of electric vehicles;

[0175] Option 3: Considering the role of intelligent soft switching and user participation, the collaborative optimization operation status and charging results of distribution radio areas under the optimized scheduling of electric vehicles are obtained.

[0176] The solution was obtained using YALMIP programming within the MATLAB environment and the CPLEX algorithm package. The computer hardware environment for performing the test calculations was an Intel(R) Core(TM) i7-10700 CPU @ 2.90GHz with 16GB of memory; the software environment was a Windows 11 operating system.

[0177] Table 5 shows the optimization results of the three schemes above. Figure 5 and Figure 6 Table 6 shows a comparison of the system active power of transformer substation 1 and transformer substation 3 under the three schemes, and a comparison of the electric vehicle charging results.

[0178] Table 5. Optimization results for each scheme

[0179] Intelligent soft switching loss cost / / 104.27 System network loss cost 697.57 640.95 574.04 Electric vehicle charging costs 5884.52 3769.33 3995.66 Penalty fee for incomplete charging / 432.21 0.00 Penalty for exceeding the transformer load rate limit 29374.58 7461.43 0.00 Total cost 35956.64 12303.92 4673.97

[0180] Table 6 Comparison of charging results under different schemes

[0181]

[0182]

[0183] As shown in Table 5, under the independent operation of the distribution substation in Scheme 1 without intelligent soft-switching control and electric vehicle optimized scheduling, the penalty cost for exceeding the load limit of the distribution transformer is extremely high, and the system network loss cost and electric vehicle charging cost are also high, resulting in poor overall operating economy of the substation. In Scheme 2, electric vehicles participate in optimized scheduling, which greatly reduces the penalty cost for exceeding the load limit of the distribution transformer, and the electric vehicle charging cost and system network loss cost also decrease significantly. In Scheme 3, through the proposed intelligent soft-switching and electric vehicle coordinated optimized control, the penalty cost for exceeding the load limit of the distribution transformer is reduced to zero, system losses are further reduced, and the electric vehicle charging cost is also significantly lower than that of Scheme 1. Overall, the total cost of Scheme 3 is 87% lower than that of Scheme 1 and 62% lower than that of Scheme 2, and the operating economy of the substation is significantly improved under Scheme 3.

[0184] from Figure 5 , 6 Comparing the active power of the systems, for transformer substations 1 and 3, Scheme 2, which only optimizes the scheduling to control the charging behavior of electric vehicles, will lead to overload in the distribution transformer substations, and the risk of the distribution transformer exceeding the limit is still relatively high. However, Scheme 3, based on the intelligent soft switch and electric vehicle collaborative optimization method proposed in this chapter, can effectively reduce the risk of the distribution transformer load rate exceeding the limit, and keep the load in the distribution transformer substation at an ideal level.

[0185] As shown in Table 6, compared to Scheme 2, Scheme 3 reduces the maximum load rate of the distribution transformers in transformer substation 1 by 16.05% and in transformer substation 3 by 11.96%, indicating that Scheme 3 can further reduce the transformer load rate. In terms of charging satisfaction rate analysis, in transformer substation 1, the charging satisfaction rate of Scheme 3 is significantly higher than that of Scheme 2, increasing by 53%. This demonstrates that under the implementation of Scheme 3, the distribution transformer substations can meet the charging needs of more electric vehicle users and accommodate more electric vehicles for charging.

[0186] The above comparative analysis demonstrates the effectiveness of the multi-zone electric vehicle charging collaborative operation method proposed in this invention in avoiding heavy overload of distribution transformers and improving the electric vehicle acceptance capacity, which is more conducive to the safe and efficient operation of distribution zones.

[0187] Please see Figure 7 , Figure 7 This is a structural block diagram of a multi-zone electric vehicle charging collaborative operation device provided in an embodiment of the present invention.

[0188] This invention provides a multi-zone electric vehicle charging collaborative operation device, comprising:

[0189] The parameter acquisition module 701 is used to acquire basic parameter information of multiple distribution radio zones associated with the smart soft switch and the charging parameters of the electric vehicle.

[0190] The model building module 702 is used to establish an optimization model for the coordinated operation of electric vehicle charging in multiple distribution substations based on flexible interconnection by using basic parameter information and charging parameters, with the objective function being the minimum sum of electric vehicle charging costs, user charging incomplete compensation costs, distribution transformer overload rate penalty costs, system network loss costs, and intelligent soft switch power loss costs. The model is based on the constraints of distribution substation operation constraints, distribution transformer operation constraints, node three-phase imbalance constraints, electric vehicle charging constraints, distributed power source operation constraints, and intelligent soft switch operation constraints.

[0191] Solver module 703 is used to solve the electric vehicle charging cooperative operation model of multiple distribution stations and obtain the electric vehicle charging and intelligent soft switching scheduling strategy.

[0192] The scheduling module 704 is used to perform charging and intelligent soft-switching scheduling of electric vehicles in multiple distribution substations using electric vehicle charging and intelligent soft-switching scheduling strategies.

[0193] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0194] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0195] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, embodiments of the present invention can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of the present invention can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0196] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0197] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0198] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0199] Although preferred embodiments of the present invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present invention.

[0200] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0201] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for coordinated operation of electric vehicle charging in multiple zones, characterized in that, include: Acquire basic parameter information of multiple distribution radio zones associated with smart soft switches and charging parameters of electric vehicles; Using the aforementioned basic parameter information and charging parameters, and with the objective function being the minimum sum of electric vehicle charging costs, user charging incomplete compensation costs, distribution transformer overload rate penalty costs, system network loss costs, and intelligent soft switch power loss costs, and with the constraints of distribution substation operation constraints, distribution transformer operation constraints, node three-phase imbalance constraints, electric vehicle charging constraints, distributed power source operation constraints, and intelligent soft switch operation constraints, a multi-distribution substation electric vehicle charging collaborative operation optimization model based on flexible interconnection is established. Solve the multi-distribution station area electric vehicle charging cooperative operation optimization model to obtain the electric vehicle charging and intelligent soft switching scheduling strategy; The electric vehicle charging and intelligent soft-switching scheduling strategy is used to perform charging and intelligent soft-switching scheduling for electric vehicles in multiple distribution radio areas. The objective function is: in, Let be the objective function. The cost of charging electric vehicles, Compensation will be provided for users whose charging is not completed. Penalty fees for exceeding the load limit of distribution transformers. For system network loss costs, Cost of power loss for intelligent soft switching; It represents any one of the three phases A, B, and C; express Time period The first phase Charging power of electric vehicles; Indicates time-of-use electricity pricing; This indicates an optimized time interval; This indicates the total number of electric vehicles in the distribution area; Indicates the number of transformer substations; Indicates the total optimized scheduling period; Indicates the first The compensation coefficient corresponding to the state of charge of an electric vehicle when it leaves the distribution station area. This represents the maximum value of the compensation coefficient; Indicates the first The state of charge of an electric vehicle when it leaves the distribution station area; This represents the user's expected value for the state of charge; Indicates the first Expected battery capacity for a vehicle; Indicates the first In each distribution transformer area The penalty coefficient corresponding to the load rate during a given time period; Indicates the first Each district Base load for a given period; Indicates the first Each district Time period distribution transformer load rate; This indicates the limit value corresponding to the penalty fee for exceeding the load rate limit of the distribution transformer; Indicates the unit cost of line loss; Represents the set of branches; Indicates a branch of Phase resistance; express Time period nodes Flow to Node of The square of the phase current amplitude; The unit cost representing the power loss of SOP equipment; Indicates the number of smart soft switch SOPs; Indicates the first One SOP in Power loss during a given time period; This indicates the maximum penalty coefficient corresponding to different load rates of the distribution transformer; The operating constraints of the distribution transformer are as follows: in, This indicates the node where the transformer in distribution area 1 is located; This indicates the node where the transformer in area 2 is located; and They represent The active power passing through the distribution transformers of area 1 and area 2 during the time period; and They represent The active power transmitted by SOP on transformer area 1 and transformer area 2 during the time period; and These represent the rated power of the distribution transformers in transformer substation 1 and transformer substation 2, respectively. and They represent The total active power consumed by each phase in transformer area 1 and transformer area 2 during the time period.

2. The method according to claim 1, characterized in that, The operating constraints of the distribution radio area are as follows: in, and Representing branches of Phase resistance and Phase reactance; and They represent Time period nodes Flow to Node of Phase active power and Phase reactive power; and They represent Time period nodes Flowing towards node h Active power and reactive power; and They represent Time period nodes Injected The sum of active power of each phase and The sum of phase reactive power; , and They represent Time period nodes Distributed power injection, electric vehicle consumption, and load consumption Phase active power; and They represent Time period nodes Distributed power injection and load consumption Phase reactive power; express Time period nodes Flow to Node of The square of the phase current amplitude; and They represent Time period nodes and nodes of The square of the phase voltage amplitude; and These represent the lower and upper limits of the node voltage amplitude, respectively.

3. The method according to claim 1, characterized in that, The three-phase unbalance constraint at the node is: in, For nodes Voltage imbalance; This represents the maximum permissible value for the three-phase voltage imbalance in the distribution network. express Time period nodes Place Phase voltage amplitude.

4. The method according to claim 3, characterized in that, The electric vehicle charging constraint is: in, express Time period The first phase The state of charge of an electric vehicle; express Time period The first phase electric vehicles State of charge over a period of time; express The first The initial state of charge of an electric vehicle; and They represent The first The start and end times of charging for each electric vehicle; and These represent the lower and upper limits of the state of charge, respectively; Indicates time period t The charging power of the nth EV; Indicates the maximum charging power allowed for the EV; Indicates the charging efficiency of the EV; Let n be the battery capacity of the nth EV.

5. The method according to claim 4, characterized in that, The operating constraints of the distributed power source are: in, This represents the predicted active power output of distributed generation sources. The power factor of the distributed power source; This represents the actual active power output of the distributed generation. This represents the predicted reactive power output of distributed generation.

6. The method according to claim 5, characterized in that, The operating constraints of the intelligent soft switch are: in, and They represent The active and reactive power transmitted by the SOP on transformer area 1 during the time period; and They represent The active and reactive power transmitted by the SOP on transformer area 2 during the time period; and express The power loss of the SOP connected to the transformer area 1 and transformer area 2 during the time period; express DC fast charging power during the specified time period; and These represent the SOP capacity connected to area 1 and area 2, respectively. The SOP capacity is connected between area 1 and area 2; and These represent the minimum active power and minimum reactive power injected by SOP connected to transformer area 1, respectively. and These represent the minimum active power and the maximum reactive power injected by SOP connected to transformer area 1, respectively. The power loss factor of the SOP on transformer substation 1; The power loss factor of the SOP on transformer substation 2; This represents the minimum active power injected by SOP connected to transformer substation 2; This indicates the minimum reactive power injected into the SOP connected to transformer area 2; This indicates the maximum active power injected by SOP connected to transformer area 2; This indicates the maximum reactive power injected into the SOP connected to transformer area 2.

7. The method according to claim 6, characterized in that, The steps of solving the multi-distribution station area electric vehicle charging cooperative operation optimization model to obtain the electric vehicle charging and intelligent soft-switching scheduling strategy include: The second-order cone programming method is used to solve the electric vehicle charging collaborative operation optimization model of the multi-distribution station area, and the electric vehicle charging and intelligent soft switching scheduling strategy is obtained.

8. A multi-zone electric vehicle charging collaborative operation device, characterized in that, include: The parameter acquisition module is used to acquire basic parameter information of multiple distribution radio zones associated with intelligent soft switches and charging parameters of electric vehicles; The model building module is used to establish an optimization model for the coordinated operation of electric vehicle charging in multiple distribution substations based on flexible interconnection, using the basic parameter information and the charging parameters, with the objective function being the minimum sum of electric vehicle charging cost, user charging incomplete compensation cost, distribution transformer overload rate penalty cost, system network loss cost and intelligent soft switch power loss cost, and with the constraints being distribution substation operation constraints, distribution transformer operation constraints, node three-phase imbalance constraints, electric vehicle charging constraints, distributed power source operation constraints, and intelligent soft switch operation constraints. The solution module is used to solve the electric vehicle charging collaborative operation optimization model of the multi-distribution station area to obtain the electric vehicle charging and intelligent soft switching scheduling strategy. The scheduling module is used to perform charging and intelligent soft-switching scheduling of electric vehicles in multiple distribution substations using the electric vehicle charging and intelligent soft-switching scheduling strategy. The objective function is: in, Let be the objective function. The cost of charging electric vehicles, Compensation will be provided for users whose charging is not completed. Penalty fees for exceeding the load limit of distribution transformers. For system network loss costs, Cost of power loss for intelligent soft switching; It represents any one of the three phases A, B, and C; express Time period The first phase Charging power of electric vehicles; Indicates time-of-use electricity pricing; This indicates an optimized time interval; This indicates the total number of electric vehicles in the distribution area; Indicates the number of transformer substations; Indicates the total optimized scheduling period; Indicates the first The compensation coefficient corresponding to the state of charge of an electric vehicle when it leaves the distribution station area. This represents the maximum value of the compensation coefficient; Indicates the first The state of charge of an electric vehicle when it leaves the distribution station area; This represents the user's expected value for the state of charge; Indicates the first Expected battery capacity for a vehicle; Indicates the first In each distribution transformer area The penalty coefficient corresponding to the load rate during a given time period; Indicates the first Each district Base load for a given period; Indicates the first Each district Time period distribution transformer load rate; This indicates the limit value corresponding to the penalty fee for exceeding the load rate limit of the distribution transformer; Indicates the unit cost of line loss; Represents the set of branches; Indicates a branch of Phase resistance; express Time period nodes Flow to Node of The square of the phase current amplitude; The unit cost representing the power loss of SOP equipment; Indicates the number of smart soft switch SOPs; Indicates the first One SOP in Power loss during a given time period; This indicates the maximum penalty coefficient corresponding to different load rates of the distribution transformer; The operating constraints of the distribution transformer are: in, This indicates the node where the transformer in distribution area 1 is located; This indicates the node where the transformer in area 2 is located; and They represent The active power passing through the distribution transformers of area 1 and area 2 during the time period; and They represent The active power transmitted by SOP on transformer area 1 and transformer area 2 during the time period; and These represent the rated power of the distribution transformers in transformer substation 1 and transformer substation 2, respectively. and They represent The total active power consumed by each phase in transformer area 1 and transformer area 2 during the time period.

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