Electric vehicle charging bearing capacity calculation method and system based on interval affine algorithm

Abstract

The invention discloses an interval affine algorithm-based electric vehicle charging bearing capacity calculation method and system. The method comprises the following steps of: obtaining an uncertainty factor data set of an electric vehicle charging load; performing interval uncertainty modeling of the charging load of the electric vehicle to obtain interval representation of the charging load of the electric vehicle; calculating load flow by using an affine algorithm to obtain affine representation of node voltage; according to the affine representation of the node voltage and the set value of the voltage violation index VVI, constructing an interval undervoltage probability IUP model, and calculating to obtain the upper and lower bounds of the interval undervoltage probability; and according to the upper and lower bounds of the interval undervoltage probability and the maximum interval undervoltage probability set by the power distribution network, the accessible number range of the electric vehicles is calculated to serve as the finally obtained electric vehicle charging bearing capacity of the power distribution network. The method aims at achieving accurate calculation of the electric vehicle charging bearing capacity of the power distribution network, and scientific planning is provided for power distribution network optimization so as to achieve flexible operation of the power distribution network.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network dispatching, and in particular to a method and system for calculating the charging carrying capacity of an electric vehicle based on an interval affine algorithm. Background Art

[0002] The rapidly growing electric vehicle and charging station market has a considerable impact on the safe operation of distribution networks. Electric vehicles have uncertainties in arrival time, departure time, battery state of charge (SOC), and charging station selection. These factors work together to affect the reflection of electric vehicle uncertainty in distribution network modeling. How to accurately calculate the electric vehicle charging capacity of the distribution network has become a key technical issue that needs to be solved urgently. Summary of the invention

[0003] Technical problem to be solved by the present invention: In view of the above-mentioned problems in the prior art, a method and system for calculating the charging carrying capacity of electric vehicles based on an interval affine algorithm are provided. The present invention aims to accurately calculate the charging carrying capacity of electric vehicles in a distribution network in view of the uncertainties in the arrival time, departure time, battery state of charge (SOC) and charging station selection of electric vehicles, so as to provide scientific planning for the optimization of the distribution network and realize the flexible operation of the distribution network.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is: A method for calculating the charging carrying capacity of an electric vehicle based on an interval affine algorithm comprises the following steps: S1, obtain the uncertainty factor dataset of electric vehicle charging load; S2, interval uncertainty modeling of electric vehicle charging load based on uncertainty factor data set, and the interval representation of electric vehicle charging load obtained; S3, based on the interval representation of the electric vehicle charging load, the power flow is calculated using an affine algorithm to obtain an affine representation of the node voltage; S4, constructing the upper and lower bounds of the interval undervoltage probability calculated by the interval undervoltage probability IUP model according to the affine representation of the node voltage and the set value of the voltage violation index VVI; S5, the range of the number of electric vehicles that can be connected is calculated according to the upper and lower bounds of the interval undervoltage probability and the maximum interval undervoltage probability set by the distribution network as the final electric vehicle charging carrying capacity of the distribution network.

[0005] Optionally, the uncertainty factors in the uncertainty factor data set in step S2 include part or all of the number of electric vehicles, arrival time, battery state of charge, and charging station selection results.

[0006] Optionally, the interval uncertainty modeling of the electric vehicle charging load based on the uncertainty factor data set in step S2 includes constructing an interval representation of the electric vehicle charging load of each bus in the distribution network according to the following formula: , , In the above formula, and are the upper and lower limits of the interval of electric vehicle charging load in time period t, is the electric vehicle charging load on the dth day in period t, is the set of days in which the electric vehicle charging load is greater than or equal to the average value in period t, is the set of days in which the electric vehicle charging load is less than the average value in period t, is the set of days for Monte Carlo sampling, , , Respectively represent , and The collection size of is the average value of the electric vehicle charging load in period t, is the first The daily EV charging load is greater than or equal to the average value. is the first Daily, below-average EV charging load.

[0007] Optionally, step S3 includes: S3.1, the initial voltage of the terminal busbar of the distribution network is determined by the affine form of the current: , In the above formula, is the node current, is the apparent power of the node, including active power and reactive power, is the node voltage on the bus, ~ are the node current obtained by dividing the power passing through the node by the node voltage, ~ are error variables in the range of [-1,1], and the It indicates that it is represented by affine variables, the b in the subscript indicates that the voltage, current and power are represented by affine form, and the n in the subscript indicates the branch or node number in the distribution network; S3.2, starting from the terminal bus, calculate the branch current using Kirchhoff's current law according to the following formula: , In the above formula, is the branch current of the m+1th node, is the set of all upstream nodes directly connected to node n, For the j The branch current of each node, ~ are the currents of each branch respectively; the node voltages of the starting bus and the terminal bus are calculated according to the following formula: , In the above formula, and are the node voltages of the m+1th and mth nodes respectively, is the impedance between the m+1th and mth nodes, is the branch current of the mth node, ~ are the voltages of each branch respectively; S3.3, determine whether the following formula is valid for stopping iteration: , In the above formula, To obtain the maximum value, is the upper limit of the node voltage. The voltage of the downstream adjacent node is the upper limit of the node voltage. is the lower limit of the node voltage. is the voltage of the downstream adjacent node of the node voltage upper limit, represents a preset small positive number as the threshold for stopping iteration; if not true, jump to step S3.2 to continue iteration, otherwise, jump to step S4.

[0008] Optionally, in step S4, the function expressions of the upper and lower bounds of the interval undervoltage probability calculated by constructing the interval undervoltage probability IUP model according to the affine representation of the node voltage and the set value of the voltage violation index VVI are: , , , , in, For the Charging scenarios When the electric vehicle is charged, the voltage lower limit is less than the preset minimum interval voltage. Number of busbars If the voltage is greater than the voltage violation index VVI, is the number of buses, is the voltage violation index VVI, where m, i, j and k in the subscripts represent the indexes of electric vehicles, buses, situations and charging scenarios, respectively. For the Charging scenarios The lower limit of the voltage between all busbars under the condition of electric vehicle charging Less than the preset minimum interval voltage situation, For calculation The decision variables are in the range of [0, 1]; For the Charging scenarios The lower limit of the voltage between all busbars when charging electric vehicles is Expressed as the total number of "absolute low voltage" values ​​used to assess the carrying capacity in interval form; Expressed as the total number of "partial low voltage" values ​​used to assess the carrying capacity in interval form; represents the total number of Monte Carlo simulations for the number of electric vehicles at the mth station and the access to the jth charging station; represents the total number of Monte Carlo simulations for the mth electric car.

[0009] Optionally, in step S5, the accessible number range of electric vehicles calculated according to the upper and lower bounds of the interval undervoltage probability and the maximum interval undervoltage probability set by the distribution network is used as a function expression of the electric vehicle charging carrying capacity of the distribution network finally obtained: , , , , In the above formula, , Respectively represent The interval form of the interval undervoltage probability model under the condition of selecting a charging station, Expressed as the total number of "absolute low voltage" values ​​used to assess the carrying capacity in interval form; Expressed as the total number of "partial low voltage" values ​​used to assess the carrying capacity in interval form; represents the total number of Monte Carlo simulations for the number of electric vehicles at the mth station and the access to the jth charging station; , Respectively The interval form of the interval undervoltage probability model for the case of electric vehicles; is the total number of charging stations that can be selected, and are the upper and lower bounds of the range of the number of electric vehicles that can be connected, is the maximum value of the electric vehicle quantity index, is the minimum number of electric vehicles, The maximum value of the interval undervoltage probability set for the distribution network.

[0010] In addition, the present invention also provides an electric vehicle charging carrying capacity calculation system based on an interval affine algorithm, comprising: A data acquisition program unit, used for acquiring an uncertainty factor data set of an electric vehicle charging load; An electric vehicle charging load interval uncertainty modeling program unit is used to perform interval uncertainty modeling of the electric vehicle charging load based on an uncertainty factor data set, and obtain an interval representation of the electric vehicle charging load; A power flow calculation program unit, used for calculating the power flow using an affine algorithm based on the interval representation of the electric vehicle charging load to obtain an affine representation of the node voltage; An interval undervoltage calculation program unit is used to construct the upper and lower bounds of the interval undervoltage probability calculated by the interval undervoltage probability IUP model according to the affine representation of the node voltage and the set value of the voltage violation index VVI; The charging carrying capacity calculation program unit is used to calculate the accessible number range of electric vehicles according to the upper and lower bounds of the interval undervoltage probability and the maximum interval undervoltage probability set by the distribution network as the final electric vehicle charging carrying capacity of the distribution network.

[0011] In addition, the present invention also provides an electric vehicle charging carrying capacity calculation system based on an interval affine algorithm, comprising an interconnected microprocessor and a memory, wherein the microprocessor is programmed or configured to execute the electric vehicle charging carrying capacity calculation method based on the interval affine algorithm.

[0012] In addition, the present invention also provides a computer-readable storage medium, which stores a computer program or instruction, and the computer program or instruction is programmed or configured to execute the electric vehicle charging carrying capacity calculation method based on the interval affine algorithm through a processor.

[0013] In addition, the present invention also provides a computer program product, including a computer program or instructions, which are programmed or configured to execute the electric vehicle charging carrying capacity calculation method based on the interval affine algorithm through a processor.

[0014] Compared with the prior art, the present invention has the following advantages: The rapidly growing electric vehicle and charging pile market has a considerable impact on the operation of the distribution network. Electric vehicles have uncertainties in arrival time, departure time, battery state of charge (SOC) and charging station selection. These factors work together to affect the embodiment of electric vehicle uncertainty in distribution network modeling. The method of the present invention proposes a calculation method that can use the interval affine algorithm to evaluate the carrying capacity of the distribution network, that is, how many electric vehicles can be carried. First, the interval affine algorithm is used to model the electric vehicle load considering uncertainty. Secondly, in order to analyze the influence of voltage, the same algorithm is extended to the power flow calculation. Thirdly, the interval undervoltage probability (IUP) is introduced to evaluate the electric vehicle carrying capacity of the distribution network. On this basis, the voltage violation index (VVI) is introduced, and an evaluation method based on the voltage violation index is proposed, which is helpful for the flexible operation of the power grid. The calculation method of electric vehicle charging carrying capacity based on the interval affine algorithm is of great significance in that it provides a more accurate and flexible evaluation method for distribution network planning and efficient operation. With the rapid growth of the number of electric vehicles and charging facilities, the load fluctuations and uncertainties faced by the distribution network have been significantly aggravated. How to scientifically evaluate and effectively respond to this challenge has become the key to ensuring the stable operation of the power grid. By integrating the interval algorithm and the affine algorithm, the present invention comprehensively considers multiple uncertain factors such as the number of electric vehicles, arrival time, battery charge state and charging station selection, and constructs an electric vehicle load model that is closer to reality. The model can accurately reflect the immediate impact of electric vehicle charging behavior on the distribution network, and can also provide forward-looking data support for power grid planning by predicting possible future load changes. More importantly, the method introduces two key indicators, the interval undervoltage probability (IUP) and the voltage violation index (VVI), to quantitatively evaluate the robustness and safety of the distribution network when bearing the charging load of electric vehicles. This not only helps power grid managers to quickly identify potential operating risks, but also provides a scientific basis for formulating targeted optimization measures, thereby effectively avoiding the occurrence of problems such as power grid overload and voltage violation. In addition, the calculation method based on the interval affine algorithm can give a more conservative but reliable solution compared to the traditional method, which reduces the decision-making risk caused by uncertain factors to a certain extent. By flexibly setting the maximum interval undervoltage probability, power grid managers can adjust the access strategy of electric vehicles according to actual needs to achieve the optimal balance between system operation flexibility and economy. In summary, the present invention is of great significance for improving the intelligence, flexibility and safety of the distribution network, and is an important technical support for meeting the challenge of rapid growth of electric vehicle charging load and promoting the development of green energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 Schematic diagram of the basic flow of the method of the embodiment of the present invention. DETAILED DESCRIPTION

[0016] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] like Figure 1 As shown, the electric vehicle charging carrying capacity calculation method based on the interval affine algorithm in this embodiment includes the following steps: S1, obtain the uncertainty factor dataset of electric vehicle charging load; S2, interval uncertainty modeling of electric vehicle charging load based on uncertainty factor data set, and the interval representation of electric vehicle charging load obtained; S3, based on the interval representation of the electric vehicle charging load, the power flow is calculated using an affine algorithm to obtain an affine representation of the node voltage; S4, constructing the upper and lower bounds of the interval undervoltage probability calculated by the interval undervoltage probability IUP model according to the affine representation of the node voltage and the set value of the voltage violation index VVI; S5, the range of the number of electric vehicles that can be connected is calculated according to the upper and lower bounds of the interval undervoltage probability and the maximum interval undervoltage probability set by the distribution network as the final electric vehicle charging carrying capacity of the distribution network.

[0018] This embodiment proposes an innovative method for calculating the charging capacity of electric vehicles. The core of this method adopts an interval affine algorithm, which has far-reaching and practical significance for the planning and operation of modern distribution networks. Faced with the uncertainty challenges brought about by the growing charging load of electric vehicles, this method provides unprecedented decision support for power grid managers by accurately evaluating multiple uncertain factors in the charging process of electric vehicles, including but not limited to the fluctuation of the number of electric vehicles, the uncertainty of charging time, the difference in battery status, and the flexibility of charging station selection. The key to this method is to introduce two quantitative indicators, namely the interval undervoltage probability and the voltage violation index. These two indicators can comprehensively and deeply evaluate the robustness and safety of the distribution network when accepting the charging load of electric vehicles. By scientifically calculating these indicators, power grid managers can intuitively understand the bearing capacity of the power grid in different operating scenarios, thereby effectively reducing the risks caused by improper decision-making and ensuring the stable operation of the power grid. This method gives a conservative but reliable solution, helping power grid managers to flexibly adjust the electric vehicle access strategy and balance the flexibility and economy of system operation. This technology is an effective support for coping with the challenges of growing charging load of electric vehicles and promoting the development of green energy.

[0019] The uncertainty factors in the uncertainty factor data set in step S2 of this embodiment include the number of electric vehicles, arrival time, battery charge state, and charging station selection results.

[0020] In this embodiment, an improved interval overvoltage probability method (IOP) is used to evaluate the penetration rate of electric vehicles. First, according to the existing vehicle entry and exit records, the number of electric vehicles entering and exiting the charging station is found according to the time. According to the average value of the existing vehicle traffic records, the case number is generated using a normal distribution random number. Secondly, the embodiment determines the SOC of each electric vehicle using a normal distribution random number between the minimum and maximum SOC. Third, the embodiment determines the charging station where each electric vehicle will be deployed. For electric vehicles, the charging connection time is determined according to the SOC; if the number of electric vehicles at the charging station exceeds the maximum capacity, no more electric vehicles can be connected. Then, the embodiment calculates the interval charging load of each electric vehicle deployed at each charging station and converts the obtained interval charging load into an affine form. Subsequently, after executing the power flow using the affine form charge load to obtain the voltage in the affine form, the embodiment converts the voltage into the interval form. Finally, the embodiment uses the IUP method to evaluate the carrying capacity of the distribution network for electric vehicles. In the interval uncertainty modeling of the electric vehicle charging load, the state of charge SOC of each electric vehicle is generated by a random number. If the upper and lower limits of the SOC are determined based on this randomly generated SOC, the interval load of the electric vehicle can be determined in proportion to the output of the electric vehicle charger. The interval uncertainty modeling of the electric vehicle charging load based on the uncertainty factor data set in step S2 includes constructing an interval representation of the electric vehicle charging load of each bus in the distribution network according to the following formula: , (1) , (2) In the above formula, and are the upper and lower limits of the interval of electric vehicle charging load in time period t, is the electric vehicle charging load on the dth day in period t, is the set of days in which the electric vehicle charging load is greater than or equal to the average value in period t, is the set of days in which the electric vehicle charging load is less than the average value in period t, is the set of days for Monte Carlo sampling, , , Respectively represent , and The collection size of is the average value of the electric vehicle charging load in period t, is the first The daily EV charging load is greater than or equal to the average value. is the first Based on the above functional expressions (1) and (2), the method of this embodiment converts the result into an affine form: : , (3) In the above formula, and are the upper and lower limits of the interval of electric vehicle charging load in time period t, is the error of the electric vehicle. If this formula is used for power flow calculation, the power flow is calculated according to the affine algorithm, and the output variables are shown as follows: , (4) , (5) in, For the The bus and The voltage interval form of each node is: and are the real and imaginary parts of the voltage interval form, and are the uncertainties of the real and imaginary parts of the voltage of each branch, is the number of parameters in the affine form, is the noise element representing the uncertainty of each branch, To represent the error element is the uncertainty of electric vehicle charging power The uncertainty of each branch is caused by the uncertainty of the electric vehicle charging power. Therefore, the interval can be obtained by the following formula: : , (6) , (7) , (8) , (9) , (10) , (11) , (12) in, and is expressed as a real value of voltage expressed in interval form; and The voltage imaginary value expressed in interval form, is the maximum value of the lower limit of the minimum voltage interval in all cases j at all times t, all buses i, and all cases in the network under the kth scenario, is the maximum value of the time period index, is the number of buses, is the total number of cases, indicating how many different EV charging situations are considered.

[0021] For radial low-voltage distribution networks, the net power injection of the power flow is expressed in complex affine variables as: , (13) In the above formula, is the apparent power of the node, including active power and reactive power, are error variables in the range of [-1,1], and are the apparent powers of each branch respectively. It is based on the general forward and backward sweep power flow, in which the power flow using affine variables is calculated by the following three steps. Step S3 of this embodiment includes: S3.1, the initial voltage of the terminal busbar of the distribution network is determined by the affine form of the current: , (14) In the above formula, is the node current, is the apparent power of the node, including active power and reactive power, is the node voltage on the bus, ~ are the node current obtained by dividing the power passing through the node by the node voltage, ~ are error variables in the range of [-1,1], and the It indicates that it is represented by affine variables, the b in the subscript indicates that the voltage, current and power are represented by affine form, and the n in the subscript indicates the branch or node number in the distribution network; S3.2, starting from the terminal bus, calculate the branch current using Kirchhoff's current law according to the following formula: , (15) In the above formula, is the branch current of the m+1th node, is the set of all upstream nodes directly connected to node n, For the j The branch current of each node, ~ are the currents of each branch respectively; the node voltages of the starting bus and the terminal bus are calculated according to the following formula: , (16) In the above formula, and are the node voltages of the m+1th and mth nodes respectively, is the impedance between the m+1th and mth nodes, is the branch current of the mth node, ~ are the voltages of each branch respectively; S3.3, determine whether the following formula is valid for stopping iteration: , (17) In the above formula, To obtain the maximum value, is the upper limit of the node voltage. The voltage of the downstream adjacent node is the upper limit of the node voltage. is the lower limit of the node voltage. is the voltage of the downstream adjacent node of the node voltage upper limit, represents a preset small positive number as the threshold for stopping iteration; if not, jump to step S3.2 to continue iteration, otherwise, jump to step S4.

[0022] In step S4 of this embodiment, the function expressions of the upper and lower bounds of the interval undervoltage probability calculated by constructing the interval undervoltage probability IUP model according to the affine representation of the node voltage and the set value of the voltage violation index VVI are as follows: , (18) , (19) , (20) ,(twenty one) in, For the Charging scenarios When the electric vehicle is charged, the voltage lower limit is less than the preset minimum interval voltage. Number of busbars If the voltage is greater than the voltage violation index VVI, is the number of buses, is the voltage violation index VVI, where m, i, j and k in the subscripts represent the indexes of electric vehicles, buses, situations and charging scenarios, respectively. For the Charging scenarios The lower limit of the voltage between all busbars under the condition of electric vehicle charging Less than the preset minimum interval voltage situation, For calculation The decision variables are in the range of [0, 1]; For the Charging scenarios The lower limit of the voltage between all busbars when charging electric vehicles is Expressed as the total number of "absolute low voltage" values ​​used to assess the carrying capacity in interval form; Expressed as the total number of "partial low voltage" values ​​used to assess the carrying capacity in interval form; represents the total number of Monte Carlo simulations for the number of electric vehicles at the mth station and the access to the jth charging station; represents the total number of Monte Carlo simulations for the mth electric car.

[0023] In step S5 of this embodiment, the range of the number of electric vehicles that can be connected is calculated based on the upper and lower bounds of the interval undervoltage probability and the maximum interval undervoltage probability set by the distribution network as the function expression of the electric vehicle charging carrying capacity of the distribution network finally obtained: ,(twenty two) ,(twenty three) ,(twenty four) , (25) In the above formula, , Respectively represent The interval form of the interval undervoltage probability model under the condition of selecting a charging station, Expressed as the total number of "absolute low voltage" values ​​used to assess the carrying capacity in interval form; Expressed as the total number of "partial low voltage" values ​​used to assess the carrying capacity in interval form; represents the total number of Monte Carlo simulations for the number of electric vehicles at the mth station and the access to the jth charging station; , Respectively The interval form of the interval undervoltage probability model for the case of electric vehicles; is the total number of charging stations that can be selected, and are the upper and lower bounds of the range of the number of electric vehicles that can be connected, is the maximum value of the electric vehicle quantity index, is the minimum number of electric vehicles, is the maximum value of the interval undervoltage probability set for the distribution network. The upper bound of the whole scene is Probability of the situation occurring; probability of undervoltage in the interval The lower bound of The probability of occurrence is the situation where the minimum lower voltage on each bus violates the voltage constraint. The upper limit of the electric vehicle carrying capacity is determined by the distribution network as the interval undervoltage probability, and the lower limit is greater than The minimum number of electric vehicles. According to the above mathematical formula, the method of this embodiment adopts the modeling of the interval undervoltage probability. Taking into account the uncertainty of electric vehicles accessing the distribution network, the charging load will be expressed in the form of an interval. By gradually modifying the value of VVI, the system can evaluate the charging carrying capacity of electric vehicles according to the change of the upper limit. VVI can be set by the distribution network. The smaller the value of VVI, the closer it is to the lower limit, so the more conservative the distribution network's carrying capacity assessment is. The evaluation method of the distribution network charging carrying capacity using the interval undervoltage probability shows that within a given range, the distribution network can carry the number of electric vehicles that can be connected. Therefore, when the interval undervoltage probability is set according to network simulation and applied to distribution network system planning, the degree of impact of EVs with greater uncertainty on the network can be more sensitively evaluated.

[0024] In addition, this embodiment also provides an electric vehicle charging carrying capacity calculation system based on an interval affine algorithm, including: A data acquisition program unit, used for acquiring an uncertainty factor data set of an electric vehicle charging load; An electric vehicle charging load interval uncertainty modeling program unit is used to perform interval uncertainty modeling of the electric vehicle charging load based on an uncertainty factor data set, and obtain an interval representation of the electric vehicle charging load; A power flow calculation program unit, used for calculating the power flow using an affine algorithm based on the interval representation of the electric vehicle charging load to obtain an affine representation of the node voltage; An interval undervoltage calculation program unit is used to construct the upper and lower bounds of the interval undervoltage probability calculated by the interval undervoltage probability IUP model according to the affine representation of the node voltage and the set value of the voltage violation index VVI; The charging carrying capacity calculation program unit is used to calculate the accessible number range of electric vehicles according to the upper and lower bounds of the interval undervoltage probability and the maximum interval undervoltage probability set by the distribution network as the final electric vehicle charging carrying capacity of the distribution network.

[0025] In addition, this embodiment also provides an electric vehicle charging carrying capacity calculation system based on an interval affine algorithm, comprising a microprocessor and a memory connected to each other, wherein the microprocessor is programmed or configured to execute the electric vehicle charging carrying capacity calculation method based on the interval affine algorithm.

[0026] In addition, this embodiment also provides a computer-readable storage medium, which stores a computer program or instruction, and the computer program or instruction is programmed or configured to execute the electric vehicle charging carrying capacity calculation method based on the interval affine algorithm through a processor.

[0027] In addition, this embodiment also provides a computer program product, including a computer program or instructions, which are programmed or configured to execute the electric vehicle charging carrying capacity calculation method based on the interval affine algorithm through a processor.

[0028] Those skilled in the art should understand that the technical solutions provided by the embodiments of the present application may be in the form of methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the process Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer-readable memory produce a product including an instruction device, which implements the functions specified in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing the process in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0029] The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A method for calculating the charging capacity of electric vehicles based on interval affine algorithm, characterized in that: The steps include: S1, obtain the uncertainty factor dataset of electric vehicle charging load; S2, interval uncertainty modeling of electric vehicle charging load based on uncertainty factor data set, and the interval representation of electric vehicle charging load obtained; S3, based on the interval representation of the electric vehicle charging load, the power flow is calculated using an affine algorithm to obtain an affine representation of the node voltage; S4, constructing the upper and lower bounds of the interval undervoltage probability calculated by the interval undervoltage probability IUP model according to the affine representation of the node voltage and the set value of the voltage violation index VVI; S5, the range of the number of electric vehicles that can be connected is calculated according to the upper and lower bounds of the interval undervoltage probability and the maximum interval undervoltage probability set by the distribution network as the final electric vehicle charging carrying capacity of the distribution network.

2. The method for calculating the charging capacity of electric vehicles based on interval affine algorithm according to claim 1 is characterized in that: The uncertainty factors in the uncertainty factor data set in step S2 include part or all of the number of electric vehicles, arrival time, battery charge state, and charging station selection results.

3. The method for calculating the charging carrying capacity of electric vehicles based on interval affine algorithm according to claim 2 is characterized in that: The interval uncertainty modeling of the electric vehicle charging load based on the uncertainty factor data set in step S2 includes constructing an interval representation of the electric vehicle charging load of each bus in the distribution network according to the following formula: ， ， In the above formula, and are the upper and lower limits of the interval of electric vehicle charging load in time period t, is the electric vehicle charging load on the dth day in period t, is the set of days in which the electric vehicle charging load is greater than or equal to the average value in period t, is the set of days in which the electric vehicle charging load is less than the average value in period t, is the set of days for Monte Carlo sampling, , , Respectively represent , and The collection size of is the average value of the electric vehicle charging load in period t, is the first The daily EV charging load is greater than or equal to the average value. is the first Daily, below-average EV charging load.

4. The method for calculating the charging carrying capacity of electric vehicles based on interval affine algorithm according to claim 3 is characterized in that: Step S3 includes: S3.1, the initial voltage of the terminal busbar of the distribution network is determined by the affine form of the current: ， In the above formula, is the node current, is the apparent power of the node, including active power and reactive power, is the node voltage on the bus, ~ are the node current obtained by dividing the power passing through the node by the node voltage, ~ are error variables in the range of [-1,1], and the It indicates that it is represented by affine variables, the b in the subscript indicates that the voltage, current and power are represented by affine form, and the n in the subscript indicates the branch or node number in the distribution network; S3.2, starting from the terminal bus, calculate the branch current using Kirchhoff's current law according to the following formula: ， In the above formula, is the branch current of the m+1th node, is the set of all upstream nodes directly connected to node n, For the j The branch current of each node, ~ are the currents of each branch respectively; the node voltages of the starting bus and the terminal bus are calculated according to the following formula: ， In the above formula, and are the node voltages of the m+1th and mth nodes respectively, is the impedance between the m+1th and mth nodes, is the branch current of the mth node, ~ are the voltages of each branch respectively; S3.3, determine whether the following formula is valid for stopping iteration: ， In the above formula, To obtain the maximum value, is the upper limit of the node voltage. The voltage of the downstream adjacent node is the upper limit of the node voltage. is the lower limit of the node voltage. is the voltage of the downstream adjacent node of the node voltage upper limit, represents a preset small positive number as the threshold for stopping iteration; if not, jump to step S3.2 to continue iteration, otherwise, jump to step S4.

5. The method for calculating the charging capacity of electric vehicles based on interval affine algorithm according to claim 4 is characterized in that: In step S4, the function expressions of the upper and lower bounds of the interval undervoltage probability calculated by constructing the interval undervoltage probability IUP model according to the affine representation of the node voltage and the set value of the voltage violation index VVI are as follows: ， ， ， ， in, For the Charging scenarios When the electric vehicle is charged, the voltage lower limit is less than the preset minimum interval voltage. Number of busbars If the voltage is greater than the voltage violation index VVI, is the number of buses, is the voltage violation index VVI, where m, i, j and k in the subscripts represent the indexes of electric vehicles, buses, situations and charging scenarios, respectively. For the Charging scenarios The lower limit of the voltage between all busbars under the condition of electric vehicle charging Less than the preset minimum interval voltage situation, For calculation The decision variables are in the range of [0, 1]; For the Charging scenarios The lower limit of the voltage between all busbars in the case of electric vehicle charging is: Expressed as the total number of "absolute low voltage" values ​​used to assess the carrying capacity interval format; Expressed as the total number of "partial low voltage" values ​​used to assess the carrying capacity in interval form; represents the total number of Monte Carlo simulations for the number of electric vehicles at the mth station and the access to the jth charging station; represents the total number of Monte Carlo simulations for the mth electric car.

6. The method for calculating the charging capacity of electric vehicles based on interval affine algorithm according to claim 5 is characterized in that: In step S5, the accessible number range of electric vehicles calculated according to the upper and lower bounds of the interval undervoltage probability and the maximum interval undervoltage probability set by the distribution network is used as the function expression of the electric vehicle charging carrying capacity of the distribution network finally obtained: ， ， ， ， In the above formula, , Respectively represent The interval form of the interval undervoltage probability model under the condition of selecting a charging station, Expressed as the total number of "absolute low voltage" values ​​used to assess the carrying capacity interval format; Expressed as the total number of "partial low voltage" values ​​used to assess the carrying capacity in interval form; represents the total number of Monte Carlo simulations for the number of electric vehicles at the mth station and the access to the jth charging station; , Respectively The interval form of the interval undervoltage probability model for the case of electric vehicles; is the total number of charging stations that can be selected, and are the upper and lower bounds of the range of the number of electric vehicles that can be connected, is the maximum value of the electric vehicle quantity index, is the minimum number of electric vehicles, The maximum value of the interval undervoltage probability set for the distribution network.

7. An electric vehicle charging capacity calculation system based on interval affine algorithm, characterized in that: include: A data acquisition program unit, used for acquiring an uncertainty factor data set of an electric vehicle charging load; An electric vehicle charging load interval uncertainty modeling program unit is used to perform interval uncertainty modeling of the electric vehicle charging load based on an uncertainty factor data set, and obtain an interval representation of the electric vehicle charging load; A power flow calculation program unit, used for calculating the power flow using an affine algorithm based on the interval representation of the electric vehicle charging load to obtain an affine representation of the node voltage; An interval undervoltage calculation program unit is used to construct the upper and lower bounds of the interval undervoltage probability calculated by the interval undervoltage probability IUP model according to the affine representation of the node voltage and the set value of the voltage violation index VVI; The charging carrying capacity calculation program unit is used to calculate the accessible number range of electric vehicles according to the upper and lower bounds of the interval undervoltage probability and the maximum interval undervoltage probability set by the distribution network as the final electric vehicle charging carrying capacity of the distribution network.

8. An electric vehicle charging capacity calculation system based on interval affine algorithm, comprising a microprocessor and a memory connected to each other, characterized in that: The microprocessor is programmed or configured to execute the electric vehicle charging carrying capacity calculation method based on the interval affine algorithm as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program or instruction stored therein, characterized in that: The computer program or instruction is programmed or configured to execute the electric vehicle charging carrying capacity calculation method based on the interval affine algorithm as described in any one of claims 1 to 6 through a processor.

10. A computer program product comprising a computer program or instructions, characterized in that The computer program or instruction is programmed or configured to execute the electric vehicle charging carrying capacity calculation method based on the interval affine algorithm as described in any one of claims 1 to 6 through a processor.

Images