A power distribution network electric vehicle charging load carrying capacity robust evaluation method and system

Abstract

The application discloses a kind of power distribution network electric vehicle charging load carrying capacity robust evaluation method and system, comprising: step one: obtaining power distribution network parameters and new energy output parameter;Step two: according to power distribution network parameters, with the maximum of accessible electric vehicle charging load as target, considering the safety constraints such as equipment capacity, node voltage, establish charging load carrying capacity evaluation model;Step three: based on step two, combined with step one new energy output parameter, with the maximum of worst new energy output scene accessible charging load as target, establish the charging load carrying capacity robust evaluation model with min-max double-layer structure;Step four: for the robust evaluation model established in step three, the inner problem is equivalent transformed and merged with the outer layer by using dual theory, and the carrying capacity evaluation result is obtained by solving the transformed model.The present application can make up the deficiency that the traditional electric vehicle charging load carrying capacity evaluation does not consider the uncertainty of new energy output, resulting in poor adaptability of the results.

Description

Technical Field

[0001] This invention belongs to the field of power system operation, and specifically relates to a robust assessment method and system for the charging load carrying capacity of electric vehicles in a power distribution network. Background Technology

[0002] With rapid economic development and accelerating globalization, energy shortages and climate change are becoming increasingly severe worldwide. Electric vehicles, characterized by low emissions and low energy consumption, offer significant advantages in reducing carbon dioxide emissions and promoting renewable energy development. However, the rapid growth in electric vehicle numbers has led to substantial charging demand, resulting in issues such as voltage exceeding limits and equipment overload in power distribution networks. This poses a serious challenge to the network's carrying capacity. Accurate and rapid calculation of the charging load capacity of power distribution networks is crucial for ensuring safe network operation and promoting the continued development of electric vehicles.

[0003] However, current assessments of the charging load carrying capacity of distribution networks primarily employ comprehensive evaluation methods and simulation calculations. These methods treat the impact of charging loads connecting to the distribution network as evaluation indicators, evaluating and scoring different load scenarios and selecting the best approach, or continuously increasing the charging load scale for distribution network power flow simulation calculations until the indicators exceed limits, resulting in cumbersome calculation processes. Mathematical optimization methods typically aim to maximize the penetration rate of electric vehicles in the distribution network, considering network security constraints, and obtaining the carrying capacity result by solving an optimization model. However, as the penetration rate of distributed power sources such as photovoltaics in the distribution network continues to increase, while they can provide power support to alleviate the insufficient charging load carrying capacity of the distribution network, their significant uncertainties also have a significant impact on the charging load carrying capacity results. Currently, there is no robust assessment research on the charging load carrying capacity of distribution networks that considers the uncertainty of renewable energy output. This research aims to obtain accurate and adaptable assessment results of the charging load carrying capacity of distribution networks by considering the intermittency and volatility of renewable energy output, based on mathematical optimization methods. Summary of the Invention

[0004] The purpose of this invention is to provide a robust evaluation method and system for the charging load carrying capacity of electric vehicles in power distribution networks, so as to make up for the shortcomings of traditional methods that do not consider the uncertainty of new energy output, resulting in poor adaptability of evaluation results.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A robust assessment method for the charging load carrying capacity of electric vehicles in a power distribution network includes the following steps:

[0007] Step 1: Obtain power distribution network parameters and new energy output parameters;

[0008] Step 2: Based on the distribution network parameters in Step 1, establish a load capacity assessment model for electric vehicle charging in the distribution network. This model aims to maximize the electric vehicle charging load that the distribution network can access, and takes into account the capacity of distribution equipment, node voltage, power flow equations, and power balance safety operation constraints.

[0009] Step 3: Based on the distribution network electric vehicle charging load carrying capacity assessment model in Step 2, establish an uncertainty set characterizing the uncertainty of new energy output based on the new energy output parameters in Step 1. With the maximum electric vehicle charging load that the distribution network can access under the worst output scenario as the objective, establish a robust assessment model of the distribution network electric vehicle charging load carrying capacity with a min-max dual-layer structure.

[0010] Step 4: For the robust evaluation model of electric vehicle charging load carrying capacity of the distribution network with the min-max dual-layer structure established in Step 3, the inner layer problem is equivalently transformed and merged with the outer layer using duality theory, and the robust evaluation result of electric vehicle charging load carrying capacity of the distribution network considering the uncertainty of new energy output is obtained.

[0011] A further improvement of this invention is that, in step two, an evaluation model for the electric vehicle charging load carrying capacity of the distribution network is established. This model aims to maximize the electric vehicle charging load that the distribution network can access. The objective function of the model is shown in formula (1):

[0012]

[0013] Where t is the time identifier, with values ​​of 1, 2, ..., T; T is the total number of time periods; i is the distribution network node identifier, with values ​​of 1, 2, ..., N; N is the total number of nodes; This represents the charging load (in MW) that can be connected to distribution network node i at time t.

[0014] A further improvement of this invention is that, in step two, the evaluation model for the carrying capacity of electric vehicle charging load in the distribution network should also consider the capacity of the distribution equipment, node voltage, power flow equations, and power balance safety operation constraints; Formula (2) indicates that the active and reactive power output of the distribution network transformer should not exceed its capacity limit; Formula (3) indicates that the actual output of new energy power sources is determined by the available power and the abandoned power; The linearized Dist-flow model is used to calculate the power flow of the distribution network with radial characteristics, as shown in Formula (4); Formula (5) indicates that the node voltage and line transmission power of the distribution network should not exceed the limit; Formula (6) is the active and reactive power balance constraint of the distribution network; Formula (7) indicates that the charging load connected to the distribution network is non-negative;

[0015]

[0016] In the formula, p gi,tp represents the active power output (MW) of the distribution transformer gi at time t. gi,max and p gi,min These represent the upper and lower limits of the active power of the distribution transformer gi (in MW); q gi,t q represents the zero power output (in MW) of the distribution transformer gi at time t; gi,max and q gi,min These represent the upper and lower limits of reactive power of the distribution transformer (gi) in MW, respectively. and These represent the upper and lower limits of the power factor gi of the distribution transformer, respectively; p drg,t and q drg,t These represent the active and reactive power output (in MW) of the new energy power source drg at time t; pa drg,t and PD drg,t These represent the available power and power curtailment (in MW) of the new energy power source drg at time t, respectively. The drg power factor of a new energy power source; v i,t This represents the voltage amplitude (kV) of distribution network node i at time t; v i,max and v i,min Represents the upper and lower limits of the voltage amplitude at node i in kV, respectively; R ij and X ij These represent the resistance and reactance values ​​of the line ij in kΩ, respectively; p ij,t and q ij,t These represent the active power and reactive power flowing through line ij in MW, respectively; p ij,max p represents the maximum active power of line ij in MW. i,t and q i,t Let represent the active and reactive loads (in MW) of node i at time t, respectively.

[0017] A further improvement of this invention is that, in step three, an uncertainty set characterizing the uncertainty of new energy power output is established. Considering that the available power of new energy sources is affected by weather conditions and has significant uncertainty, in order to ensure the robustness of the evaluation results of the electric vehicle charging load carrying capacity of the distribution network, it is necessary to consider the worst-case scenario of new energy power output. In this case, the uncertain parameter values ​​are at the boundary, so it can be represented by the uncertainty set as shown in formula (8):

[0018]

[0019] in, The predicted available power of new energy sources; δ drg,t Uncertainty, used to characterize robustness, represents the range of fluctuation of the uncertain parameter. The larger the value, the more offset predictions there are, and the stronger the robustness of the result. and It is a 0-1 variable that characterizes whether drg takes the maximum output upward or the minimum output downward at time t;

[0020] Since an uncertain parameter cannot simultaneously take both maximum and minimum values, it needs to satisfy formula (9). In addition, in actual operation, it is rare for all new energy power sources to have maximum / minimum output at all times. Therefore, the "budget constraint" shown in formula (10) is also needed to limit the conservatism of the uncertain parameter values.

[0021]

[0022]

[0023] A robust evaluation model for the charging load carrying capacity of the distribution network considering the uncertainty of new energy output is established by adopting a min-max dual-layer structure. The aim is to obtain the charging load carrying capacity of the distribution network under the worst new energy output scenario. Its objective function is shown in formula (11), and the constraints are distribution network operation constraints and uncertainty set constraints (2)-(10).

[0024]

[0025] A further improvement of this invention is that, in step three, with the goal of maximizing the electric vehicle charging load that the distribution network can access under the worst-case power output scenario, a robust evaluation model for the electric vehicle charging load carrying capacity of the distribution network with a min-max dual-layer structure is established, as shown in equation (11):

[0026]

[0027] A further improvement of this invention is that, in step four, for the established robust evaluation model of electric vehicle charging load carrying capacity of the distribution network with a min-max dual-layer structure, the inner layer problem is equivalently transformed and merged with the outer layer using dual theory, and the robust evaluation result of electric vehicle charging load carrying capacity of the distribution network considering the uncertainty of new energy output is obtained. For ease of expression, the established robust evaluation model of electric vehicle charging load carrying capacity of the distribution network with a dual-layer min-max structure (11) can be written in a compact format as shown in formula (12):

[0028]

[0029] In the formula, u is the uncertain parameter of the new energy output; C T h, W are the coefficient matrices of each variable; X is the optimization decision variable, specifically expressed as:

[0030] X = [p] c ,p g ,q g ,p drg ,pd drg ,q drg ,v,ppl ,q pl (13)

[0031] The model's two-layer structure makes it impossible to solve directly. Since the inner "max" problem is a linear programming problem, its dual problem (14) can be obtained by introducing the dual variable λ:

[0032]

[0033] The two-layer min-max problem is transformed into a min-min problem, which is equivalent to the single-layer min problem (15). For the nonlinear characteristics of the multiplication of variables u and λ in the equivalent single-layer min problem, it can be linearized by the big M method to obtain the robust evaluation results of the electric vehicle charging load carrying capacity of the distribution network considering the uncertainty of new energy output.

[0034]

[0035] A robust evaluation system for the charging load carrying capacity of electric vehicles in a power distribution network includes:

[0036] The parameter acquisition unit acquires power distribution network parameters and new energy output parameters.

[0037] The first model building unit obtains the distribution network parameters in the unit based on the parameters and establishes a distribution network electric vehicle charging load carrying capacity assessment model. This model aims to maximize the electric vehicle charging load that the distribution network can access, and takes into account the capacity of power distribution equipment, node voltage, power flow equations and power balance safe operation constraints.

[0038] The second model building unit, based on the distribution network electric vehicle charging load carrying capacity assessment model in the first model building unit, establishes an uncertainty set representing the uncertainty of new energy output based on the new energy output parameters in the parameter acquisition unit. With the goal of maximizing the electric vehicle charging load that the distribution network can access under the worst output scenario, a robust assessment model of the distribution network electric vehicle charging load carrying capacity with a min-max dual-layer structure is established.

[0039] The solution unit, for the robust evaluation model of the electric vehicle charging load carrying capacity of the distribution network with a min-max dual-layer structure established in the second model establishment unit, adopts duality theory to equivalently transform the inner layer problem and merge it with the outer layer, and solves to obtain the robust evaluation result of the electric vehicle charging load carrying capacity of the distribution network taking into account the uncertainty of new energy output.

[0040] A further improvement of this invention is that, in the first model establishment unit, a power distribution network electric vehicle charging load carrying capacity assessment model is established. This model aims to maximize the electric vehicle charging load that the power distribution network can access. The objective function of the model is shown in formula (1):

[0041]

[0042] Where t is the time identifier, with values ​​of 1, 2, ..., T; T is the total number of time periods; i is the distribution network node identifier, with values ​​of 1, 2, ..., N; N is the total number of nodes; This represents the charging load (in MW) that can be connected to distribution network node i at time t.

[0043] A further improvement of this invention is that, in the first model establishment unit, the electric vehicle charging load carrying capacity assessment model of the distribution network is established, and the capacity of the distribution equipment, node voltage, power flow equation and power balance safe operation constraints are also considered; Formula (2) indicates that the active and reactive power output of the distribution network transformer should not exceed its capacity limit; Formula (3) indicates that the actual output of the new energy power source is determined by the available power and the abandoned power; The linearized Dist-flow model is used to calculate the power flow of the distribution network with radial characteristics, as shown in Formula (4); Formula (5) indicates that the node voltage and line transmission power of the distribution network should not exceed the limit; Formula (6) is the active and reactive power balance constraint of the distribution network; Formula (7) indicates that the charging load connected to the distribution network is non-negative;

[0044]

[0045] In the formula, p gi,t p represents the active power output (MW) of the distribution transformer gi at time t. gi,max and p gi,min These represent the upper and lower limits of the active power of the distribution transformer gi (in MW); q gi,t q represents the zero power output (in MW) of the distribution transformer gi at time t; gi,max and q gi,min These represent the upper and lower limits of reactive power of the distribution transformer (gi) in MW, respectively. and These represent the upper and lower limits of the power factor gi of the distribution transformer, respectively; p drg,t and q drg,t These represent the active and reactive power output (in MW) of the new energy power source drg at time t; pa drg,t and PD drg,t These represent the available power and power curtailment (in MW) of the new energy power source drg at time t, respectively. The drg power factor of a new energy power source; v i,t This represents the voltage amplitude (kV) of distribution network node i at time t; v i,max and v i,min Represents the upper and lower limits of the voltage amplitude at node i in kV, respectively; R ij and X ij These represent the resistance and reactance values ​​of the line ij in kΩ, respectively; p ij,t and q ij,tThese represent the active power and reactive power flowing through line ij in MW, respectively; p ij,max p represents the maximum active power of line ij in MW. i,t and q i,t Let represent the active and reactive loads (in MW) of node i at time t, respectively.

[0046] A further improvement of this invention is that, in the second model building unit, an uncertain set characterizing the uncertainty of new energy power output is established. Considering that the available power of new energy sources is affected by weather conditions and has significant uncertainty, in order to ensure the robustness of the evaluation results of the electric vehicle charging load carrying capacity of the distribution network, it is necessary to consider the worst-case scenario of new energy power output. In this case, the uncertain parameter values ​​are at the boundary, so it can be represented by the uncertain set as shown in formula (8):

[0047]

[0048] in, The predicted available power of new energy sources; δ drg,t Uncertainty, used to characterize robustness, represents the range of fluctuation of the uncertain parameter. The larger the value, the more offset predictions there are, and the stronger the robustness of the result. and It is a 0-1 variable that characterizes whether drg takes the maximum output upward or the minimum output downward at time t;

[0049] Since an uncertain parameter cannot simultaneously take both maximum and minimum values, it needs to satisfy formula (9). In addition, in actual operation, it is rare for all new energy power sources to have maximum / minimum output at all times. Therefore, the "budget constraint" shown in formula (10) is also needed to limit the conservatism of the uncertain parameter values.

[0050]

[0051]

[0052] A robust evaluation model for the charging load carrying capacity of the distribution network considering the uncertainty of new energy output is established by adopting a min-max dual-layer structure. The aim is to obtain the charging load carrying capacity of the distribution network under the worst new energy output scenario. Its objective function is shown in formula (11), and the constraints are distribution network operation constraints and uncertainty set constraints (2)-(10).

[0053]

[0054] Compared with the prior art, the present invention has at least the following beneficial technical effects:

[0055] This invention provides a robust assessment method and system for the electric vehicle charging load carrying capacity of a distribution network. The method aims to maximize the electric vehicle charging load that the distribution network can connect to. It considers safety constraints such as distribution equipment capacity, node voltage, power flow equations, and power balance to establish an assessment model for the electric vehicle charging load carrying capacity of the distribution network. Furthermore, it takes into account the uncertainty of renewable energy output and establishes a robust assessment model with a min-max two-layer structure, aiming to maximize the electric vehicle charging load that the distribution network can connect to under the worst-case renewable energy output scenario. Finally, it uses dual transformation to equivalently transform the inner layer problem and merge it with the outer layer, solving for the robust assessment result of the electric vehicle charging load carrying capacity of the distribution network considering the uncertainty of renewable energy output. This invention, by establishing a robust assessment model with a min-max two-layer structure, considers the impact of the significant uncertainty of renewable energy output on the electric vehicle charging load carrying capacity of the distribution network, making the assessment result more adaptable. Attached Figure Description

[0056] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0057] Figure 1 This is the overall flowchart of the present invention.

[0058] Figure 2 This is the photovoltaic load power curve.

[0059] Figure 3 This is a schematic diagram showing the evaluation results of charging load carrying capacity under different optimization methods.

[0060] Figure 4 This is a schematic diagram comparing the actual and predicted power output of photovoltaic systems obtained using a robust evaluation method.

[0061] Figure 5 This is a structural block diagram of a robust evaluation system for the charging load capacity of electric vehicles in a power distribution network, according to the present invention. Detailed Implementation

[0062] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the invention. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0063] In the description of this invention, it should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0064] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0065] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0066] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0067] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0068] Example 1

[0069] like Figure 1 As shown, the present invention provides a robust assessment method for the charging load carrying capacity of electric vehicles in a power distribution network, comprising the following steps:

[0070] Step 1: Obtain power distribution network parameters and new energy output parameters;

[0071] Step 2: Based on the distribution network parameters in Step 1, establish a load capacity assessment model for electric vehicle charging in the distribution network. This model aims to maximize the electric vehicle charging load that the distribution network can access, and takes into account safety operation constraints such as distribution equipment capacity, node voltage, power flow equations, and power balance.

[0072] Step 3: Based on the distribution network electric vehicle charging load carrying capacity assessment model in Step 2, establish an uncertainty set characterizing the uncertainty of new energy output based on the new energy output parameters in Step 1. With the maximum electric vehicle charging load that the distribution network can access under the worst output scenario as the objective, establish a robust assessment model of the distribution network electric vehicle charging load carrying capacity with a min-max dual-layer structure.

[0073] Step 4: For the robust evaluation model of electric vehicle charging load carrying capacity of the distribution network with the min-max dual-layer structure established in Step 3, the inner layer problem is equivalently transformed and merged with the outer layer using duality theory, and the robust evaluation result of electric vehicle charging load carrying capacity of the distribution network considering the uncertainty of new energy output is obtained.

[0074] In this embodiment, in step two, the objective function of the distribution network charging load carrying capacity assessment model, which aims to maximize the charging load that can be connected to the distribution network, is shown in formula (1):

[0075]

[0076] Where t is the time identifier, with values ​​of 1, 2, ..., T; T is the total number of time periods; i is the distribution network node identifier, with values ​​of 1, 2, ..., N; N is the total number of nodes; This represents the charging load (in MW) that can be connected to distribution network node i at time t.

[0077] In this embodiment, in step two, the charging load carrying capacity of the distribution network is subject to operational constraints such as power distribution equipment and power balance, as well as safety restrictions such as node voltage and equipment capacity; Formula (2) indicates that the active and reactive power output of the distribution network transformer should not exceed its capacity limit; Formula (3) indicates that the actual output of new energy power sources is determined by the available power and the abandoned power; The linearized Dist-flow model is used to calculate the power flow of the distribution network with radial characteristics, as shown in Formula (4); Formula (5) indicates that the voltage of the distribution network node and the transmission power of the line should not exceed the limit; Formula (6) is the active and reactive power balance constraint of the distribution network; Formula (7) indicates that the charging load connected to the distribution network is non-negative.

[0078]

[0079] In the formula, p gi,t p represents the active power output (MW) of the distribution transformer gi at time t. gi,max and p gi,min These represent the upper and lower limits of the active power of the distribution transformer gi (in MW); q gi,t q represents the zero power output (in MW) of the distribution transformer gi at time t; gi,max and q gi,min These represent the upper and lower limits of reactive power of the distribution transformer (gi) in MW, respectively. and These represent the upper and lower limits of the power factor gi of the distribution transformer, respectively; p drg,t and q drg,t These represent the active and reactive power output (in MW) of the new energy power source drg at time t; pa drg,t and PD drg,t These represent the available power and power curtailment (in MW) of the new energy power source drg at time t, respectively. The drg power factor of a new energy power source; v i,t This represents the voltage amplitude (kV) of distribution network node i at time t; v i,max and v i,min Represents the upper and lower limits of the voltage amplitude at node i in kV, respectively; R ij and X ij These represent the resistance and reactance values ​​of the line ij in kΩ, respectively; p ij,t and q ij,t These represent the active power and reactive power flowing through line ij in MW, respectively; p ij,max p represents the maximum active power of line ij in MW. i,t and q i,t Let represent the active and reactive loads (in MW) of node i at time t, respectively.

[0080] In this embodiment, in step three, considering that the available power of new energy sources is significantly uncertain due to weather conditions, in order to ensure the robustness of the power distribution network charging load carrying capacity assessment results, it is necessary to consider the worst-case scenario of new energy output. In this case, the uncertain parameter values ​​are at the boundary points, so the uncertain set can be described by the formula: It can be represented by the uncertain set shown in formula (8):

[0081]

[0082] in, The predicted available power of new energy sources; δ drg,t Uncertainty, used to characterize robustness, represents the range of fluctuation of the uncertain parameter. The larger the value, the more offset predictions there are, and the stronger the robustness of the result. and It is a 0-1 variable that characterizes whether drg takes the maximum output upward or the minimum output downward at time t.

[0083] Since an uncertain parameter cannot simultaneously take both maximum and minimum values, it needs to satisfy formula (9). In addition, in actual operation, it is rare for all new energy power sources to be at their maximum / minimum output at all times. Therefore, the "budget constraint" shown in formula (10) is also needed to limit the conservatism of the uncertain parameter values.

[0084]

[0085]

[0086] A robust evaluation model for the charging load carrying capacity of the distribution network considering the uncertainty of new energy output is established by adopting a min-max dual-layer structure. The aim is to obtain the charging load carrying capacity result of the distribution network under the worst new energy output scenario. Its objective function is shown in formula (11), and the constraints are distribution network operation constraints and uncertainty set constraints (2)-(10).

[0087]

[0088] In this embodiment, in step four, for the two-layer structure that cannot be directly solved by the robust evaluation model of the distribution network charging load carrying capacity considering the uncertainty of new energy output, its compact form is shown in formula (12):

[0089]

[0090] In the formula, u is the uncertain parameter of the new energy output; C T h, W are the coefficient matrices of each variable; X is the optimization decision variable, specifically expressed as:

[0091] X = [p] c ,p g ,q g ,p drg ,pd drg ,q drg ,v,p pl ,q pl (13)

[0092] Since the inner "max" problem (12) is a linear programming problem, its dual problem (14) can be obtained by introducing the dual variable λ:

[0093]

[0094] The two-level min-max problem is transformed into a min-min problem, which is equivalent to the single-level min problem (15). The nonlinear characteristics of the multiplication of variables u and λ in the equivalent single-level min problem can be linearized by the Big M method.

[0095]

[0096] Example 2

[0097] like Figure 1 As shown, the present invention provides a robust assessment method for the charging load carrying capacity of electric vehicles in a power distribution network, comprising the following steps:

[0098] Step 1: Obtain the parameters of the power distribution network structure, power distribution transformers, power distribution lines, new energy power sources, and other equipment, and obtain the predicted curve of new energy output.

[0099] This example is based on an improved IEEE-33 node distribution system, which comprises 33 nodes, 32 distribution lines, a power base of 10MW, a voltage base of 12.66kV, and a total load of 3.715MW + j2.3MVar. The system includes one distribution transformer with an active power capacity of 10MW and a reactive power capacity of 5MVar connected at node 1, and two distributed photovoltaic (PV) power sources with a capacity of 800kW each connected at nodes 3 and 12 respectively. The 24-hour distributed PV output and load power curves are shown below. Figure 2 As shown.

[0100] Step 2: Based on the parameters of the distribution network structure and equipment in Step 1, with the goal of maximizing the charging load that can be connected to the 33 nodes of the distribution network during 24 hours, and considering the safety operation limitations such as distribution transformers, line capacity, and node voltage, as well as constraints such as power flow equations and power balance, establish a charging load carrying capacity assessment model for the distribution network.

[0101] The allowable voltage range for each node is 0.95-1.05 pu, and the power flow equation of the distribution network adopts the linearized Dist-flow model.

[0102] Step 3: Taking into account the significant uncertainty of the available output of new energy power sources, and considering maximizing the charging load that the distribution network can access under the worst-case scenario of new energy power output, a robust evaluation model for the charging load carrying capacity of the distribution network with a min-max dual-layer structure is established.

[0103] Here, the uncertainty of distributed photovoltaic power output is taken as 0.4. Considering that in actual operation it is rare for all distributed photovoltaic power to be at maximum / minimum output at all times, the "budget constraint" is taken as 10.

[0104] Step 4: To address the dual-layer characteristic of the distribution network charging load carrying capacity assessment model that cannot be directly solved due to the uncertainty of new energy power output, a dual transformation is used to convert the inner max problem into an equivalent min problem, and then merge it with the outer min problem to obtain the robust assessment result of the distribution network charging load carrying capacity considering the uncertainty of new energy power output.

[0105] Firstly, to verify the adaptability advantage of this invention in considering the uncertainty of distributed power generation output, the 24-hour charging load carrying capacity assessment results of the distribution network obtained by this invention are compared with the charging load carrying capacity assessment results obtained by deterministic optimization, i.e., without considering the uncertainty of distributed photovoltaic output. The results are as follows: Figure 3 As shown. Note that, except for whether or not the uncertainty of distributed photovoltaic output is considered, all other parameters of the example are completely consistent.

[0106] As shown in the figure, during the period from 10:00 to 14:00, the distribution network charging load carrying capacity obtained by deterministic optimization is significantly higher than that obtained by robust optimization, reaching a maximum of 4.90% at 12:00. However, because the deterministic optimization method ignores the significant uncertainties that actually exist in distributed photovoltaic power generation, when the actual output of distributed photovoltaic power generation is as follows... Figure 4 As shown, if cloudy or other severe weather occurs during this period, the actual power output deviates significantly from the predicted output. In the worst-case scenario for distributed power generation output, the distribution network cannot actually support the charging load level obtained from the deterministic optimization assessment. Therefore, as the penetration rate of distributed power generation in the distribution network continues to increase, considering the uncertainty of renewable energy output when assessing the network's charging load carrying capacity can yield more adaptive results.

[0107] The uncertainty δ of distributed photovoltaic (PV) output is an important parameter in constructing the uncertainty set in the robust evaluation model. Uncertainty δ characterizes the degree to which the actual value of the uncertain variable deviates from the predicted value; the larger the value, the more drastic the fluctuation of the uncertain variable. To further demonstrate the effectiveness of this invention, the impact of uncertainty on the results in the robust evaluation model is analyzed, as shown in Table 1. If δ = 0, it means that the actual output of distributed PV matches the predicted output, which is equivalent to a deterministic optimization method.

[0108] Table 1. Comparison of the impact of uncertainty on the evaluation results (unit / MW)

[0109]

[0110] As can be seen from the table, since the uncertainty of actual distributed photovoltaic power output is not considered, the deterministic evaluation method obtains the highest distribution network charging load carrying capacity. However, as the uncertainty increases, the robust evaluation method of this invention will generate more severe distributed photovoltaic power output scenarios, and the charging load carrying capacity level will continue to decline. Correspondingly, the evaluation results also have a stronger adaptability to the uncertainty of actual distributed power output.

[0111] Example 3

[0112] like Figure 5 As shown, the present invention provides a robust evaluation system for the charging load carrying capacity of electric vehicles in a power distribution network, comprising:

[0113] The parameter acquisition unit acquires power distribution network parameters and new energy output parameters.

[0114] The first model building unit obtains the distribution network parameters in the unit based on the parameters and establishes a distribution network electric vehicle charging load carrying capacity assessment model. This model aims to maximize the electric vehicle charging load that the distribution network can access, and takes into account the capacity of power distribution equipment, node voltage, power flow equations and power balance safe operation constraints.

[0115] The second model building unit, based on the distribution network electric vehicle charging load carrying capacity assessment model in the first model building unit, establishes an uncertainty set representing the uncertainty of new energy output based on the new energy output parameters in the parameter acquisition unit. With the goal of maximizing the electric vehicle charging load that the distribution network can access under the worst output scenario, a robust assessment model of the distribution network electric vehicle charging load carrying capacity with a min-max dual-layer structure is established.

[0116] The solution unit, for the robust evaluation model of the electric vehicle charging load carrying capacity of the distribution network with a min-max dual-layer structure established in the second model establishment unit, adopts duality theory to equivalently transform the inner layer problem and merge it with the outer layer, and solves to obtain the robust evaluation result of the electric vehicle charging load carrying capacity of the distribution network taking into account the uncertainty of new energy output.

[0117] In this embodiment, the first model building unit establishes a power distribution network electric vehicle charging load carrying capacity assessment model. The model aims to maximize the electric vehicle charging load that the power distribution network can access. The objective function of the model is shown in formula (1):

[0118]

[0119] Where t is the time identifier, with values ​​of 1, 2, ..., T; T is the total number of time periods; i is the distribution network node identifier, with values ​​of 1, 2, ..., N; N is the total number of nodes; This represents the charging load (in MW) that can be connected to distribution network node i at time t.

[0120] In this embodiment, in the first model building unit, an evaluation model for the electric vehicle charging load carrying capacity of the distribution network is established. It is also necessary to consider the capacity of the distribution equipment, node voltage, power flow equation and power balance safe operation constraints. Formula (2) indicates that the active and reactive power output of the distribution network transformer should not exceed its capacity limit. Formula (3) indicates that the actual output of the new energy power source is determined by the available power and the abandoned power. The linearized Dist-flow model is used to calculate the power flow of the distribution network with radial characteristics, as shown in Formula (4). Formula (5) indicates that the node voltage and line transmission power of the distribution network should not exceed the limit. Formula (6) is the active and reactive power balance constraint of the distribution network. Formula (7) indicates that the charging load connected to the distribution network is non-negative.

[0121]

[0122] In the formula, p gi,t p represents the active power output (MW) of the distribution transformer gi at time t. gi,max and p gi,min These represent the upper and lower limits of the active power of the distribution transformer gi (in MW); q gi,t q represents the zero power output (in MW) of the distribution transformer gi at time t; gi,max and q gi,min These represent the upper and lower limits of reactive power of the distribution transformer (gi) in MW, respectively. and These represent the upper and lower limits of the power factor gi of the distribution transformer, respectively; p drg,t and q drg,t These represent the active and reactive power output (in MW) of the new energy power source drg at time t; pa drg,t and PD drg,t These represent the available power and power curtailment (in MW) of the new energy power source drg at time t, respectively. The drg power factor of a new energy power source; v i,t This represents the voltage amplitude (kV) of distribution network node i at time t; v i,max and v i,min Represents the upper and lower limits of the voltage amplitude at node i in kV, respectively; R ij and X ij These represent the resistance and reactance values ​​of the line ij in kΩ, respectively; p ij,t and q ij,t These represent the active power and reactive power flowing through line ij in MW, respectively; p ij,max p represents the maximum active power of line ij in MW. i,t and q i,t Let represent the active and reactive loads (in MW) of node i at time t, respectively.

[0123] In this embodiment, in the second model building unit, an uncertainty set representing the uncertainty of new energy power output is established. Considering that the available power of new energy sources is affected by weather conditions and has significant uncertainty, in order to ensure the robustness of the evaluation results of the electric vehicle charging load carrying capacity of the distribution network, it is necessary to consider the worst-case scenario of new energy power output. In this case, the uncertain parameter values ​​are at the boundary, so it can be represented by the uncertainty set as shown in formula (8):

[0124]

[0125] in, The predicted available power of new energy sources; δ drg,t Uncertainty, used to characterize robustness, represents the range of fluctuation of the uncertain parameter. The larger the value, the more offset predictions there are, and the stronger the robustness of the result. and It is a 0-1 variable that characterizes whether drg takes the maximum output upward or the minimum output downward at time t;

[0126] Since an uncertain parameter cannot simultaneously take both maximum and minimum values, it needs to satisfy formula (9). In addition, in actual operation, it is rare for all new energy power sources to have maximum / minimum output at all times. Therefore, the "budget constraint" shown in formula (10) is also needed to limit the conservatism of the uncertain parameter values.

[0127]

[0128]

[0129] A robust evaluation model for the charging load carrying capacity of the distribution network considering the uncertainty of new energy output is established by adopting a min-max dual-layer structure. The aim is to obtain the charging load carrying capacity of the distribution network under the worst new energy output scenario. Its objective function is shown in formula (11), and the constraints are distribution network operation constraints and uncertainty set constraints (2)-(10).

[0130]

[0131] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the scope of the invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0132] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can be appropriately combined to form other embodiments that can be understood by those skilled in the art. The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A robust evaluation method for the charging load carrying capacity of electric vehicles in a power distribution network, characterized in that, Includes the following steps: Step 1: Obtain power distribution network parameters and new energy output parameters; Step 2: Based on the distribution network parameters in Step 1, establish a load capacity assessment model for electric vehicle charging in the distribution network. This model aims to maximize the electric vehicle charging load that the distribution network can access, and takes into account the capacity of distribution equipment, node voltage, power flow equations, and power balance safety operation constraints. Step 3: Based on the distribution network electric vehicle charging load carrying capacity assessment model in Step 2, establish an uncertainty set characterizing the uncertainty of new energy output based on the new energy output parameters in Step 1. With the maximum electric vehicle charging load that the distribution network can access under the worst output scenario as the objective, establish a robust assessment model of the distribution network electric vehicle charging load carrying capacity with a min-max dual-layer structure. Step 4: For the robust evaluation model of electric vehicle charging load carrying capacity of the distribution network with the min-max dual-layer structure established in Step 3, the inner layer problem is equivalently transformed and merged with the outer layer using duality theory, and the robust evaluation result of electric vehicle charging load carrying capacity of the distribution network considering the uncertainty of new energy output is obtained.

2. The robust evaluation method for the charging load carrying capacity of electric vehicles in a power distribution network according to claim 1, characterized in that, In step two, an evaluation model for the electric vehicle charging load carrying capacity of the distribution network is established. The objective of this model is to maximize the electric vehicle charging load that the distribution network can connect to. The objective function of the model is shown in formula (1): Where t is the time identifier, with values ​​of 1, 2, ..., T; T is the total number of time periods; i is the distribution network node identifier, with values ​​of 1, 2, ..., N; N is the total number of nodes; This represents the charging load (in MW) that can be connected to distribution network node i at time t.

3. The robust evaluation method for the charging load carrying capacity of electric vehicles in a power distribution network according to claim 2, characterized in that, In step two, the evaluation model for the load carrying capacity of electric vehicle charging in the distribution network needs to be established, and the capacity of distribution equipment, node voltage, power flow equations and power balance safety operation constraints also need to be considered; Formula (2) shows that the active and reactive power output of the distribution network transformer should not exceed its capacity limit; Formula (3) shows that the actual output of new energy power sources is determined by the available power and the abandoned power; The linearized Dist-flow model is used to calculate the power flow of the distribution network with radial characteristics, as shown in Formula (4); Formula (5) shows that the node voltage and line transmission power of the distribution network should not exceed the limit; Formula (6) is the active and reactive power balance constraint of the distribution network; Formula (7) indicates that the charging load connected to the distribution network is non-negative; In the formula, p gi,t p represents the active power output (MW) of the distribution transformer gi at time t. gi,max and p gi,min These represent the upper and lower limits of the active power of the distribution transformer gi (in MW); q gi,t q represents the zero power output (in MW) of the distribution transformer gi at time t; gi,max and q gi,min These represent the upper and lower limits of reactive power of the distribution transformer (gi) in MW, respectively. and These represent the upper and lower limits of the power factor gi of the distribution transformer, respectively; p drg,t and q drg,t These represent the active and reactive power output (in MW) of the new energy power source drg at time t; pa drg,t and PD drg,t These represent the available power and power curtailment (in MW) of the new energy power source drg at time t, respectively. The drg power factor of a new energy power source; v i,t This represents the voltage amplitude (kV) of distribution network node i at time t; v i,max and v i,min Represents the upper and lower limits of the voltage amplitude at node i in kV, respectively; R ij and X ij These represent the resistance and reactance values ​​of the line ij in kΩ, respectively; p ij,t and q ij,t These represent the active power and reactive power flowing through line ij in MW, respectively; p ij,max p represents the maximum active power of line ij in MW. i,t and q i,t Let represent the active and reactive loads (in MW) of node i at time t, respectively.

4. The robust evaluation method for the charging load carrying capacity of electric vehicles in a power distribution network according to claim 3, characterized in that, In step three, an uncertainty set representing the uncertainty of new energy power output is established. Considering that the available power of new energy sources is affected by weather conditions and has significant uncertainty, in order to ensure the robustness of the assessment results of the electric vehicle charging load carrying capacity of the distribution network, it is necessary to consider the worst-case scenario of new energy power output. In this case, the uncertain parameters take values ​​at the boundary, so the uncertainty set can be represented by the uncertainty set shown in formula (8): in, The predicted available power of new energy sources; δ drg,t Uncertainty, used to characterize robustness, represents the range of fluctuation of the uncertain parameter. The larger the value, the more offset predictions there are, and the stronger the robustness of the result. and It is a 0-1 variable that characterizes whether drg takes the maximum output upward or the minimum output downward at time t; Since an uncertain parameter cannot simultaneously take both maximum and minimum values, it needs to satisfy formula (9). In addition, in actual operation, it is rare for all new energy power sources to have maximum / minimum output at all times. Therefore, the "budget constraint" shown in formula (10) is also needed to limit the conservatism of the uncertain parameter values. A robust evaluation model for the charging load carrying capacity of the distribution network considering the uncertainty of new energy output is established by adopting a min-max dual-layer structure. The aim is to obtain the charging load carrying capacity of the distribution network under the worst new energy output scenario. Its objective function is shown in formula (11), and the constraints are distribution network operation constraints and uncertainty set constraints (2)-(10).

5. The robust evaluation method for the charging load carrying capacity of electric vehicles in a power distribution network according to claim 4, characterized in that, In step three, with the goal of maximizing the electric vehicle charging load that the distribution network can access under the worst-case power output scenario, a robust evaluation model for the electric vehicle charging load carrying capacity of the distribution network with a min-max two-layer structure is established, as shown in equation (11):

6. The robust evaluation method for the charging load carrying capacity of electric vehicles in a power distribution network according to claim 5, characterized in that, In step four, for the established robust evaluation model of electric vehicle charging load carrying capacity of the distribution network with a min-max dual-layer structure, the inner layer problem is equivalently transformed and merged with the outer layer using dual theory. The result of the robust evaluation of electric vehicle charging load carrying capacity of the distribution network considering the uncertainty of new energy output is obtained. For ease of expression, the established robust evaluation model of electric vehicle charging load carrying capacity of the distribution network with a dual-layer min-max structure (11) can be written in a compact form as shown in formula (12): In the formula, u is the uncertain parameter of the new energy output; C T h, W are the coefficient matrices of each variable; X is the optimization decision variable, specifically expressed as: X=[p c ,p g ,q g ,p drg ,pd drg ,q drg ,v,p pl ,q pl ](13) The model's two-layer structure makes it impossible to solve directly. Since the inner "max" problem is a linear programming problem, its dual problem (14) can be obtained by introducing the dual variable λ: The two-layer min-max problem is transformed into a min-min problem, which is equivalent to the single-layer min problem (15). For the nonlinear characteristics of the multiplication of variables u and λ in the equivalent single-layer min problem, it can be linearized by the big M method to obtain the robust evaluation results of the electric vehicle charging load carrying capacity of the distribution network considering the uncertainty of new energy output.

7. A robust evaluation system for the charging load carrying capacity of electric vehicles in a power distribution network, characterized in that, include: The parameter acquisition unit acquires power distribution network parameters and new energy output parameters. The first model building unit obtains the distribution network parameters in the unit based on the parameters and establishes a distribution network electric vehicle charging load carrying capacity assessment model. This model aims to maximize the electric vehicle charging load that the distribution network can access, and takes into account the capacity of power distribution equipment, node voltage, power flow equations and power balance safe operation constraints. The second model building unit, based on the distribution network electric vehicle charging load carrying capacity assessment model in the first model building unit, establishes an uncertainty set representing the uncertainty of new energy output based on the new energy output parameters in the parameter acquisition unit. With the goal of maximizing the electric vehicle charging load that the distribution network can access under the worst output scenario, a robust assessment model of the distribution network electric vehicle charging load carrying capacity with a min-max dual-layer structure is established. The solution unit, for the robust evaluation model of the electric vehicle charging load carrying capacity of the distribution network with a min-max dual-layer structure established in the second model establishment unit, adopts duality theory to equivalently transform the inner layer problem and merge it with the outer layer, and solves to obtain the robust evaluation result of the electric vehicle charging load carrying capacity of the distribution network taking into account the uncertainty of new energy output.

8. A robust evaluation system for the charging load carrying capacity of electric vehicles in a power distribution network according to claim 7, characterized in that, In the first model establishment unit, an evaluation model for the electric vehicle charging load carrying capacity of the distribution network is established. The objective of this model is to maximize the electric vehicle charging load that the distribution network can access. The objective function of the model is shown in formula (1): Where t is the time identifier, with values ​​of 1, 2, ..., T; T is the total number of time periods; i is the distribution network node identifier, with values ​​of 1, 2, ..., N; N is the total number of nodes; This represents the charging load (in MW) that can be connected to distribution network node i at time t.

9. A robust evaluation system for the charging load carrying capacity of electric vehicles in a power distribution network according to claim 8, characterized in that, In the first model building unit, an evaluation model for the electric vehicle charging load carrying capacity of the distribution network is established. It is also necessary to consider the capacity of the distribution equipment, node voltage, power flow equation and power balance safe operation constraints. Formula (2) shows that the active and reactive power output of the distribution network transformer should not exceed its capacity limit. Formula (3) shows that the actual output of the new energy power source is determined by the available power and the abandoned power. The linearized Dist-flow model is used to calculate the power flow of the distribution network with radial characteristics, as shown in Formula (4). Formula (5) shows that the node voltage and line transmission power of the distribution network should not exceed the limit. Formula (6) is the active and reactive power balance constraint of the distribution network. Formula (7) indicates that the charging load connected to the distribution network is non-negative. In the formula, p gi,t p represents the active power output (MW) of the distribution transformer gi at time t. gi,max and p gi,min These represent the upper and lower limits of the active power of the distribution transformer gi (in MW); q gi,t q represents the zero power output (in MW) of the distribution transformer gi at time t; gi,max and q gi,min These represent the upper and lower limits of reactive power of the distribution transformer (gi) in MW, respectively. and These represent the upper and lower limits of the power factor gi of the distribution transformer, respectively; p drg,t and q drg,t These represent the active and reactive power output (in MW) of the new energy power source drg at time t; pa drg,t and PD drg,t These represent the available power and power curtailment (in MW) of the new energy power source drg at time t, respectively. The drg power factor of a new energy power source; v i,t This represents the voltage amplitude (kV) of distribution network node i at time t; v i,max and v i,min Represents the upper and lower limits of the voltage amplitude at node i in kV, respectively; R ij and X ij These represent the resistance and reactance values ​​of the line ij in kΩ, respectively; p ij,t and q ij,t These represent the active power and reactive power flowing through line ij in MW, respectively; p ij,max p represents the maximum active power of line ij in MW. i,t and q i,t Let represent the active and reactive loads (in MW) of node i at time t, respectively.

10. A robust evaluation system for the charging load carrying capacity of electric vehicles in a power distribution network according to claim 9, characterized in that, In the second model building unit, an uncertainty set representing the uncertainty of new energy power output is established. Considering that the available power of new energy sources is affected by weather conditions and has significant uncertainty, in order to ensure the robustness of the evaluation results of the electric vehicle charging load carrying capacity of the distribution network, it is necessary to consider the worst-case scenario of new energy power output. In this case, the uncertain parameters take values ​​at the boundary, so they can be represented by the uncertainty set as shown in formula (8): in, The predicted available power of new energy sources; δ drg,t Uncertainty, used to characterize robustness, represents the range of fluctuation of the uncertain parameter. The larger the value, the more offset predictions there are, and the stronger the robustness of the result. and It is a 0-1 variable that characterizes whether drg takes the maximum output upward or the minimum output downward at time t; Since an uncertain parameter cannot simultaneously take both maximum and minimum values, it needs to satisfy formula (9). In addition, in actual operation, it is rare for all new energy power sources to have maximum / minimum output at all times. Therefore, the "budget constraint" shown in formula (10) is also needed to limit the conservatism of the uncertain parameter values. A robust evaluation model for the charging load carrying capacity of the distribution network considering the uncertainty of new energy output is established by adopting a min-max dual-layer structure. The aim is to obtain the charging load carrying capacity of the distribution network under the worst new energy output scenario. Its objective function is shown in formula (11), and the constraints are distribution network operation constraints and uncertainty set constraints (2)-(10).

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