Power distribution network charging load bearing capacity evaluation method, device, equipment and medium

Abstract

The method is mainly applied to the technical field of power distribution network systems. The invention discloses a power distribution network charging load bearing capacity evaluation method and device, equipment and a medium, and the method comprises the steps: dividing a feasible region of the power distribution network charging load bearing capacity through constructing an inner approximation set and an outer approximation set; and selecting specific charging station configuration containing at least one node capacity in a power grid region to be investigated as a candidate scheme, constructing an optimization model, and judging the robust feasibility of the candidate scheme by taking the power grid operation constraint out-of-limit degree in the worst uncertainty scene as output. If the scheme is feasible, adding the scheme into an inner approximate set and expanding the boundary; and if not, generating a constraint condition for excluding one type of infeasible scheme, and shrinking an outer approximate set boundary. And finally determining a power grid capacity feasible region based on the two updated sets. According to the method, the bearing capacity of the power grid to the electric vehicle can be quickly and accurately evaluated under the high-dimensional uncertainty condition, and the calculation complexity is remarkably reduced.

Description

Methods, devices, equipment and media for assessing the charging load carrying capacity of power distribution networks Technical Field

[0001] This invention relates to the field of power distribution network system technology, specifically to a method, apparatus, equipment, and medium for assessing the charging load carrying capacity of a power distribution network. Background Technology

[0002] With the accelerated electrification of transportation, the surge in electric vehicle (EV) charging loads has brought severe challenges to the power distribution network. Large-scale EV integration can lead to grid overload and voltage exceedance issues. Therefore, accurately assessing the grid's hosting capacity for EVs is a prerequisite for ensuring the safe operation of the grid and guiding infrastructure investment. During the planning phase, policymakers need to determine the charging station capacity at multiple candidate locations under uncertainties (such as baseload fluctuations and uncertainties in renewable energy output).

[0003] The current technological challenge lies in the significant high-dimensionality of EV charging uncertainty. This uncertainty is coupled not only with source-load side stochasticities such as base load fluctuations and the intermittency of renewable energy output, but also with social behavioral uncertainties such as user travel behavior, charging preferences, and spatiotemporal migration. Traditional dimensionality reduction methods, such as principal component analysis or typical scenario extraction, while improving computational efficiency, inevitably discard crucial tail information, leading to an underestimation of risk. Conversely, retaining the complete uncertainty set faces the "curse of dimensionality," causing the optimization model to lose computational feasibility due to the interplay of scale, non-convexity, and mixed-integer characteristics. This sharp contradiction between model fidelity and solution cost makes it difficult for existing methods to simultaneously meet the comprehensive requirements of assessment accuracy, computational efficiency, and risk controllability in the planning stage. A breakthrough theoretical framework is urgently needed to achieve a Pareto optimal balance between feasibility and accuracy under high-dimensional uncertainty. Summary of the Invention

[0004] This invention provides a method, apparatus, equipment, and medium for assessing the charging load carrying capacity of a power distribution network, which can quickly and accurately assess the power grid's carrying capacity for electric vehicles under high-dimensional uncertainty conditions, while significantly reducing computational complexity.

[0005] This invention provides a method for assessing the charging load carrying capacity of a distribution network. The method includes: generating an inner approximation set and an outer approximation set to describe the feasible region of the charging load carrying capacity of the power grid, wherein the inner approximation set is a set consisting of confirmed feasible multi-node capacity configuration schemes, and the outer approximation set is a set containing the feasible region of the charging load carrying capacity of the power grid, the initial state of the outer approximation set typically containing all possible configuration schemes within the area to be examined; selecting a specific charging station capacity configuration scheme containing at least one node capacity value as a candidate scheme within the power grid area to be examined; and constructing and solving a power grid operation scheme with the candidate scheme as input under the worst-case uncertainty scenario. The degree of constraint violation is used as the output optimization model, and the robust feasibility of the candidate scheme is judged based on the degree of constraint violation. If the robust feasibility judgment result is feasible, the candidate scheme is added to the inner approximation set, and the boundary of the inner approximation set is expanded to include the candidate scheme. If the robust feasibility judgment result is infeasible, constraints for excluding a class of infeasible schemes are generated based on the information of the candidate scheme, and the constraints are used to shrink the boundary of the region defined by the outer approximation set inward. Based on the region corresponding to the updated inner approximation set and the region corresponding to the updated outer approximation set, the capacity feasible region of the power grid is determined.

[0006] Optionally, the specific method for determining the robust feasibility of the candidate solution based on the degree of constraint violation includes: calculating the constraint violation amount of the candidate solution in the worst uncertainty scenario; determining whether the constraint violation amount is greater than a preset feasibility threshold; if so, determining that the node in the candidate solution is an infeasible point; if not, determining that the node in the candidate solution is a feasible point.

[0007] Optionally, the constraint exceedance quantity is calculated by solving a mixed-integer linear programming model, wherein the mixed-integer linear programming model is constructed through the following steps: constructing a two-layer optimization model based on a robust feasibility detection problem, wherein the outer layer model of the two-layer optimization model is used to search for maximizing the constraint exceedance risk under the worst uncertainty scenario, and the inner layer model of the two-layer optimization model is used to describe the operation process of minimizing the constraint exceedance quantity by scheduling resources within the power grid under a given uncertainty scenario; performing a dual transformation on the inner layer model to transform the two-layer optimization model into a single-layer optimization model that maximizes a dual objective function on the outer approximation set; linearizing the norm terms in the single-layer optimization model that involve absolute value operations due to the dual transformation; after the linearization process, the mixed-integer linear programming model is obtained, wherein the constraint exceedance quantity is obtained by solving the mixed-integer linear programming model using a preset commercial solver.

[0008] Optionally, the inner model further includes a total resource constraint, which is used to: limit the total amount of power regulation performed by all nodes in the power grid through electric vehicle charging load during the period of operation in which resources minimize the constraint limit, so that the total amount is less than or equal to a preset flexibility budget value, wherein adjusting the flexibility budget value is used to quantitatively evaluate the contribution of the electric vehicle charging load regulation capability to improving the power grid charging load carrying capacity.

[0009] Optionally, if the robust feasibility judgment result is infeasible, then based on the information of the candidate solutions, generating constraints to exclude a class of infeasible solutions, and using the constraints to shrink the region boundary defined by the outer approximation set inward, includes: if the robust feasibility judgment result is infeasible, generating a linear cutting plane based on the dual information obtained during the solution of the optimization model; using the linear cutting plane to cut the region corresponding to the outer approximation set, so as to exclude the infeasible region containing the candidate solutions from the region corresponding to the outer approximation set, thereby reducing the region boundary of the outer approximation set.

[0010] Optionally, before constructing and solving an optimization model that takes the candidate solution as input and the degree of exceedance of power grid operation constraints under the worst-case uncertainty scenario as output, the method further includes: determining whether the candidate solution is already included in the inner approximation set; if the candidate solution is already included in the inner approximation set, the robust feasibility judgment result is feasible, and the step of constructing and solving an optimization model that takes the candidate solution as input and the degree of exceedance of power grid operation constraints under the worst-case uncertainty scenario as output is skipped; if the candidate solution is not included in the inner approximation set, the method further determines whether the candidate solution is feasible. Whether the candidate solution is outside the scope defined by the outer approximation set; if the candidate solution is outside the scope defined by the outer approximation set, the robust feasibility judgment result is infeasible, and the step of constructing and solving the optimization model with the candidate solution as input and the degree of exceeding the limit of the power grid operation constraints under the worst uncertainty scenario as output is skipped; if the candidate solution is not included in the inner approximation set and is not outside the scope defined by the outer approximation set, the step of constructing and solving the optimization model with the candidate solution as input and the degree of exceeding the limit of the power grid operation constraints under the worst uncertainty scenario as output is executed.

[0011] Optionally, a method for assessing the charging load carrying capacity of a distribution network further includes: after updating the inner approximation set and the outer approximation set, returning to the step of selecting a specific charging station capacity configuration scheme containing at least one node capacity value as a candidate scheme within the power grid area to be examined, until the difference between the inner approximation set and the outer approximation set is less than a preset threshold or the number of times a candidate scheme is selected reaches the maximum number of times, and outputting the updated inner approximation set as the carrying capacity set.

[0012] This invention also provides a device for assessing the charging load carrying capacity of a distribution network. The device includes: a generation module for generating an inner approximation set and an outer approximation set describing the feasible region of the charging load carrying capacity of the power grid, wherein the inner approximation set is a set consisting of confirmed feasible multi-node capacity configuration schemes, and the outer approximation set is a set containing the feasible region of the charging load carrying capacity of the power grid; the initial state of the outer approximation set typically includes all possible configuration schemes within the area to be examined; a selection module for selecting a specific charging station capacity configuration scheme containing at least one node capacity value as a candidate scheme within the power grid area to be examined; and a calculation module for constructing and solving a calculation based on the candidate scheme as input and the power grid operation constraints under the worst-case uncertainty scenario. The optimization model is output based on the degree of exceeding the limit, and the robust feasibility of the candidate scheme is judged based on the degree of exceeding the limit; the first update module is used to add the candidate scheme to the inner approximation set and expand the boundary of the inner approximation set to include the candidate scheme if the robust feasibility judgment result is feasible; the second update module is used to generate constraints to exclude a class of infeasible schemes based on the information of the candidate scheme if the robust feasibility judgment result is infeasible, and use the constraints to shrink the boundary of the region defined by the outer approximation set inward; the third update module is used to determine the capacity feasible region of the power grid based on the region corresponding to the updated inner approximation set and the region corresponding to the updated outer approximation set.

[0013] The present invention also provides an electronic device, the electronic device including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method for assessing the charging load carrying capacity of a distribution network as described in any of the preceding claims.

[0014] The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for assessing the charging load carrying capacity of a distribution network as described in any of the preceding claims.

[0015] This invention offers at least the following advantages: The technical solution constructs two sets to delineate known feasible and unknown feasible charging load configuration schemes, forming the feasible domain boundary of the power grid's charging load carrying capacity. Candidate schemes are selected within the power grid area to be examined, and an optimization model is constructed to evaluate their robust feasibility under the worst-case uncertainty scenario. If feasible, an inner approximation set is added, and the feasible domain boundary is expanded; if infeasible, constraints are generated to shrink the outer approximation set boundary, narrowing the unknown region. This stepwise approximation approach avoids exhaustively calculating all possible schemes under high-dimensional uncertainty conditions. Instead, by dynamically adjusting the boundaries of the two sets, it quickly focuses on the feasible region, thereby achieving rapid and accurate evaluation of the power grid's carrying capacity for electric vehicles while significantly reducing computational complexity. Attached Figure Description

[0016] The accompanying drawings are provided to further understand the technical solutions of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the technical solutions of the present invention, and do not constitute a limitation on the technical solutions of the present invention.

[0017] Figure 1 is a flowchart of a method for assessing the charging load carrying capacity of a distribution network; Figure 2 is a simulation result diagram of a method for assessing the charging load carrying capacity of a distribution network with 20 candidate schemes; Figure 3 is a simulation result diagram of a method for assessing the charging load carrying capacity of a distribution network with 50 candidate schemes; Figure 4 is a simulation result diagram of a method for assessing the charging load carrying capacity of a distribution network with 100 candidate schemes; Figure 5 is a simulation result diagram of a method for assessing the charging load carrying capacity of a distribution network with 500 candidate schemes; Figure 6 is a simulation result diagram of a method for assessing the charging load carrying capacity of a distribution network with 1000 candidate schemes; Figure 7 is a simulation result diagram of a method for assessing the charging load carrying capacity of a distribution network with 5000 candidate schemes; Figure 8 is a flowchart of a method for assessing the charging load carrying capacity of a distribution network by constructing a mixed integer linear programming model; Figure 9 is a structural schematic diagram of a device for assessing the charging load carrying capacity of a distribution network; Figure 10 is a structural schematic diagram of an electronic device. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0019] It should be noted that the Hosting Capacity Set (HCS) refers to the feasible domain of electric vehicle charging station capacity that all candidate nodes in the power grid can simultaneously accommodate, provided that power grid security constraints (such as voltage and thermal stability limits) are met.

[0020] Robust Acceptability Check: A calculation process used to determine whether a given charging station capacity planning scheme can maintain the safe operation of the power grid under all possible load / generation uncertainty scenarios.

[0021] Flexibility Budget: A preset parameter used to limit the amount of electric vehicle charging load adjustment (such as the total energy for peak shaving and valley filling) that can be called upon during the operation of the entire network.

[0022] The researchers in this application found that the closest existing technology is traditional Hosting Capacity Analysis (HCA).

[0023] Existing technologies are mainly divided into two categories: (1) Simulation-based methods: Monte Carlo simulation is used to generate a large number of random scenarios, combined with power flow calculation, to evaluate the maximum EV penetration rate that the power grid can accommodate without violating power grid constraints.

[0024] (2) Optimization-based approach: The carrying capacity assessment is modeled as an optimization problem with the goal of maximizing the total available capacity of the entire network, and the constraints are the grid operation limitations.

[0025] The implementation logic of existing technologies is usually to calculate the maximum capacity of a single node while keeping other conditions fixed; or to calculate a scalar metric for the total capacity of the entire network. For example, it might calculate that "the grid can accommodate a total of 5MW of charging piles" without distinguishing how these 5MW are specifically allocated to different nodes.

[0026] The drawbacks of existing technologies include: they typically only provide a total capacity value, failing to reflect the constraints between different nodes. In reality, increasing capacity at node A may deplete the system's voltage margin, thereby reducing the available capacity at node B. A single value cannot provide planners with this trade-off information.

[0027] Existing methods often neglect the adjustment capability of EV charging load during actual operation (i.e., wait-and-see decision-making), or employ overly simplified uncertainty models for computational convenience, resulting in assessments that are either overly conservative (leading to wasted investment) or unsafe in extreme scenarios. Considering high-dimensional uncertainty and time coupling, traditional robust optimization methods involve enormous computational costs, making it difficult to solve within a reasonable timeframe. To address these technical problems, this technical solution provides a method, device, equipment, and medium for assessing the charging load carrying capacity of a distribution network. This method can quickly and accurately assess the grid's carrying capacity for electric vehicles under high-dimensional uncertainty conditions, while significantly reducing computational complexity. The following are various embodiments of this technical solution.

[0028] Please refer to Figure 1, which is a flowchart of a method for assessing the charging load carrying capacity of a power distribution network.

[0029] This embodiment provides a method for evaluating the charging load carrying capacity of a distribution network, including: S101, generating an inner approximate set and an outer approximate set to describe the feasible region of the charging load carrying capacity of the power grid, wherein the inner approximate set is a set consisting of confirmed feasible multi-node capacity configuration schemes, and the outer approximate set is a set containing the feasible region of the charging load carrying capacity of the power grid. The initial state of the outer approximate set usually contains all possible configuration schemes within the region to be examined.

[0030] S102. Within the power grid area to be investigated, select a specific charging station capacity configuration scheme that includes at least one node capacity value as a candidate scheme.

[0031] S103. Construct and solve an optimization model that takes candidate solutions as input and the degree of exceeding the limits of power grid operation constraints under the worst uncertainty scenario as output, and judge the robustness and feasibility of candidate solutions based on the degree of exceeding the limits.

[0032] S104. If the robust feasibility judgment result is feasible, the candidate scheme is added to the inner approximation set, and the boundary of the region defined by the inner approximation set is expanded outward.

[0033] S105. If the robust feasibility judgment result is infeasible, then based on the information of the candidate solutions, generate constraints to exclude a class of infeasible solutions, and use the constraints to shrink the boundary of the region defined by the outer approximation set inward.

[0034] S106. Based on the regions corresponding to the updated inner approximation set and the regions corresponding to the updated outer approximation set, determine the feasible capacity region of the power grid.

[0035] In some embodiments, two sets are established to approximate the actual carrying capacity set F.

[0036] Initialize the inner approximation set F in The inner approximate set (the region that has been confirmed as feasible) is an empty set.

[0037] Initialize the external approximation set F out The outer approximation set (the region that may contain feasible solutions) is the entire nonnegative space.

[0038] Specifically, a candidate charging station capacity configuration scheme p is selected within the planned space. CS For example: Node 1 has 2MW installed, and Node 2 has 3MW installed.

[0039] In some embodiments, the specific method for determining the robust feasibility of a candidate solution based on the degree of constraint violation includes: calculating the constraint violation amount of the candidate solution in the worst uncertainty scenario for power grid operation; determining whether the constraint violation amount is greater than a preset feasibility threshold; if so, determining that a node in the candidate solution is an infeasible point; if not, determining that a node in the candidate solution is a feasible point.

[0040] Specifically, a dedicated optimization solver is invoked to calculate the limit-breaking quantity v of the scheme under the worst-case uncertainty scenario. * If v * If v ≤ 0, the candidate solution is considered feasible; if v * If the value is greater than 0, the candidate solution is deemed infeasible.

[0041] In some embodiments, if the robust feasibility judgment result is infeasible, a linear cutting plane is generated based on the dual information obtained during the solution of the optimization model; the linear cutting plane is used to cut the region corresponding to the outer approximation set, so as to exclude the infeasible region containing candidate solutions from the region corresponding to the outer approximation set, thereby narrowing the region boundary of the outer approximation set.

[0042] Specifically, if a candidate solution is feasible, then the candidate solution is added to the inner approximation set F. in The convex hull is updated; if the candidate solution is infeasible, a linear "feasibility cut" is generated using the dual variables obtained during the solution process, and this plane is used to cut the outer approximation set F. out Eliminate infeasible areas.

[0043] Please refer to Figures 2 through 7, which respectively illustrate the simulation results for testing candidate schemes with numbers of 20, 50, 100, 500, 1000, and 5000. It should be noted that the green area in the figures represents the inner approximation set F. in The corresponding region, the red region is the external approximation set F. out The corresponding area, the black line in the figure is the feasibility cutting plane.

[0044] Understandably, this embodiment constructs two sets to divide known feasible and unknown feasible charging load configuration schemes, forming the feasible domain boundary of the power grid's charging load carrying capacity. Candidate schemes are selected within the power grid area to be examined, and an optimization model is constructed to evaluate their robust feasibility under the worst-case uncertainty scenario. If feasible, the inner approximation set is added and the feasible domain boundary is expanded; if infeasible, constraints are generated to shrink the outer approximation set boundary, narrowing the unknown region. This stepwise approximation approach avoids exhaustively calculating all possible schemes under high-dimensional uncertainty conditions. Instead, by dynamically adjusting the boundaries of the two sets, it quickly focuses on the feasible region, thereby achieving a rapid and accurate assessment of the power grid's carrying capacity for electric vehicles while significantly reducing computational complexity.

[0045] Please refer to Figure 8, which is a flowchart of the steps in constructing a mixed integer linear programming model in a method for assessing the charging load carrying capacity of a power distribution network.

[0046] In some embodiments, the constraint exceedance is calculated by solving a mixed-integer linear programming model. The mixed-integer linear programming model is constructed through the following steps: S201, constructing a two-layer optimization model based on the robust feasibility detection problem. The outer layer model in the two-layer optimization model is used to search for the maximum constraint exceedance risk under the worst uncertainty scenario, and the inner layer model in the two-layer optimization model is used to describe the operation process of minimizing constraint exceedance by scheduling resources in the power grid under a given uncertainty scenario.

[0047] S202. Perform a dual transformation on the inner model to transform the two-layer optimization model into a single-layer optimization model that maximizes a dual objective function on the outer approximation set.

[0048] S203. Linearize the norm terms in the single-layer optimization model that involve absolute value operations due to dual transformation.

[0049] S204. After linearization, a mixed-integer linear programming model is obtained, wherein the mixed-integer linear programming model is solved by a preset commercial solver to obtain the constraint limit.

[0050] In this embodiment, the robust feasibility detection problem is transformed into a bi-level optimization problem, and a bi-level optimization model is constructed, with the outer layer searching for the worst-case uncertainty scenario. (To maximize the limit), the inner layer seeks the optimal flexibility adjustment strategy y (to minimize the limit). The expression for this two-layer optimization model is:

[0051] In this embodiment, the dual transformation refers to the transformation of the inner minimization problem into a maximization problem using strong duality theory, since the inner minimization problem is convex. The original problem then becomes a "Max-Max" form, that is, maximizing the dual objective function on the uncertainty set U.

[0052] The objective function after dual transformation contains Norm term (absolute value summation form) ), resulting in non-convexity.

[0053] In this embodiment, an auxiliary binary variable is introduced. and continuous variables , Using the Big-M method to Norm linearization: Constraints:

[0054] Constraints:

[0055] After the above transformation, the originally complex robustness detection problem is transformed into a standard mixed-integer linear programming problem. Mixed-integer linear programming problems can be solved exactly using existing commercial solvers (such as Gurobi), yielding the worst-case threshold v. * .

[0056] Understandably, this embodiment constructs a two-layer optimization model. The outer layer searches for maximizing the risk of exceeding constraints under the worst-case uncertainty, while the inner layer describes the process of scheduling resources to minimize the risk of exceeding constraints under a given scenario. Through dual transformation, the two-layer model is converted into a single-layer optimization model, and the norm term is linearized, ultimately forming a MILP model that can be efficiently solved by commercial solvers. This improvement makes the power grid's assessment of the carrying capacity of electric vehicles more accurate and computationally less complex under high-dimensional uncertainty conditions, significantly improving the efficiency and reliability of the assessment.

[0057] In some embodiments, the inner model further includes a total resource constraint, which is used to: limit the total amount of power regulation performed by all nodes in the power grid through electric vehicle charging load during the period of operation in which resources minimize the constraint limit, so that the total amount is less than or equal to a preset flexibility budget value, wherein adjusting the flexibility budget value is used to quantitatively evaluate the contribution of the electric vehicle charging load regulation capability to improving the power grid charging load carrying capacity.

[0058] Specifically, this total resource constraint can be a flexible budget constraint: Constraint conditions:

[0059] This constraint limits the upper limit of the total amount of EV charging power that can be adjusted across all nodes throughout the entire operating cycle. By adjusting the value of the flexibility budget constraint, the specific contribution of EV orderly charging (V1G) or vehicle-to-grid interaction (V2G) to improving the grid carrying capacity can be quantitatively analyzed.

[0060] In some embodiments, before step S103, the method further includes: determining whether the candidate solution is already included in the inner approximation set; if the candidate solution is already included in the inner approximation set, the robust feasibility determination result is feasible, and step S103 is skipped.

[0061] If the candidate solution is not included in the inner approximation set, it is further determined whether the candidate solution is outside the scope defined by the outer approximation set; if the candidate solution is outside the scope defined by the outer approximation set, the robust feasibility judgment result is infeasible, and step S103 is skipped.

[0062] If the candidate solution is not included in the inner approximation set and is not outside the scope defined by the outer approximation set, then proceed to step S103.

[0063] In this embodiment, if the scheme is already included in the current F in In the process, it is directly determined as "feasible" and complex calculations are skipped.

[0064] If the solution is already in the current F out Otherwise, it is directly judged as "infeasible" and the complex calculations are skipped.

[0065] If none of the above conditions are met, then proceed to step S103.

[0066] Understandably, this embodiment avoids repeatedly performing complex calculations on solutions that have already been determined to be feasible or infeasible by pre-judging whether candidate solutions already exist in the inner approximation set or exceed the range of the outer approximation set before executing the solution of the complex optimization model. This improvement significantly reduces unnecessary consumption of computational resources, further reduces computational complexity, and improves evaluation efficiency, ensuring a rapid and accurate assessment of the grid charging load carrying capacity under high-dimensional uncertainty conditions.

[0067] In some embodiments, a method for assessing the charging load carrying capacity of a distribution network further includes: after updating the inner approximation set and the outer approximation set, returning to the step of selecting a specific charging station capacity configuration scheme containing at least one node capacity value as a candidate scheme within the power grid area to be examined, until the difference between the inner approximation set and the outer approximation set is less than a preset threshold or the number of times a candidate scheme is selected reaches the maximum number, and outputting the updated inner approximation set as the carrying capacity set. Preferably, the Hausdorff distance between the inner approximation set and the outer approximation set is used as the difference degree and compared with the preset threshold.

[0068] Understandably, this embodiment, after updating the inner and outer approximation sets, iteratively executes the step of selecting candidate solutions, continuously optimizing the set boundaries until the set difference is less than a preset threshold or the maximum number of iterations is reached, ultimately outputting the carrying capacity set. This iterative process can dynamically approximate the optimal feasible region of the grid charging load carrying capacity, ensuring more accurate evaluation results. Simultaneously, by combining the limitations of the preset threshold and the maximum number of iterations, infinite loops are effectively avoided, ensuring the convergence and efficiency of the calculation, further improving the accuracy and reliability of evaluating grid carrying capacity under high-dimensional uncertainty conditions.

[0069] The method for assessing the charging load carrying capacity of the distribution network in this technical solution can be applied to distribution network planning software, power grid auxiliary decision-making systems, or energy management systems (EMS). It can provide power grid planners with a visualized "decision map" to help evaluate the capacity allocation scheme for the construction of multi-site charging stations.

[0070] Please refer to Figure 9, which is a schematic diagram of a power distribution network charging load carrying capacity assessment device.

[0071] This embodiment also provides a distribution network charging load carrying capacity assessment device. The device includes: a generation module 301, used to generate an inner approximate set and an outer approximate set for describing the feasible region of the power grid charging load carrying capacity. The inner approximate set is a set consisting of confirmed feasible multi-node capacity configuration schemes, and the outer approximate set is a set containing the feasible region of the power grid charging load carrying capacity. The initial state of the outer approximate set usually contains all possible configuration schemes within the region to be examined.

[0072] The selection module 302 is used to select a specific charging station capacity configuration scheme containing at least one node capacity value as a candidate scheme within the power grid area to be investigated.

[0073] The computation module 303 is used to construct and solve an optimization model that takes candidate solutions as input and the degree of exceeding the limits of power grid operation constraints under the worst uncertainty scenario as output, and to judge the robustness and feasibility of candidate solutions based on the degree of exceeding the limits.

[0074] The first update module 304 is used to add the candidate scheme to the inner approximation set if the robust feasibility judgment result is feasible, and to expand the boundary of the region defined by the inner approximation set outward.

[0075] The second update module 305 is used to generate constraints to exclude a class of infeasible solutions based on the information of the candidate solutions if the robust feasibility judgment result is infeasible, and to use the constraints to shrink the boundary of the region defined by the outer approximation set inward.

[0076] The third update module 306 is used to determine the feasible capacity region of the power grid based on the regions corresponding to the updated inner approximation set and the regions corresponding to the updated outer approximation set.

[0077] It will be understood by those skilled in the art that all or some of the steps and apparatuses in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. As is known to those skilled in the art, communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0078] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0079] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement any of the above-mentioned methods for assessing the charging load carrying capacity of a power distribution network.

[0080] Referring to Figure 10, which illustrates the hardware structure of an electronic device according to another embodiment, the electronic device includes: a processor 401, which can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, for executing related programs to implement the technical solutions provided in the embodiments of this application; and a memory 402, which can be implemented using a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM), etc. The memory 402 can store operating devices and other applications. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program code is stored in the memory 402 and is called and executed by the processor 401 to execute the power distribution network charging load carrying capacity assessment method of the embodiments of this application. The input / output interface 403 is used to realize information input and output. The communication interface 404 is used to realize communication interaction between this device and other devices. Communication can be realized by wired means (such as USB, network cable, etc.) or by wireless means (such as mobile network, WIFI, Bluetooth, etc.). The bus 405 transmits information between various components of the device (such as processor 401, memory 402, input / output interface 403 and communication interface 404). The processor 401, memory 402, input / output interface 403 and communication interface 404 realize communication connection between each other within the device through the bus 405.

[0081] It is understood that the content of the above method embodiments is applicable to the embodiments of this electronic device. The specific functions implemented by the embodiments of this electronic device are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0082] This application also provides a computer-readable storage medium storing a processor-executable program, which, when executed by a processor, is used to implement the power distribution network charging load carrying capacity assessment method as described in any of the above specific embodiments.

[0083] This application also discloses a computer program product, including a computer program or computer instructions, which are stored in a computer-readable storage medium. The processor of the computer device reads the computer program or computer instructions from the computer-readable storage medium and executes the computer program or computer instructions, causing the computer device to perform the power distribution network charging load carrying capacity assessment method as described in any of the preceding embodiments.

[0084] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0085] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, apparatus, product, or system that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or systems. It should be understood that in this application, “at least one” means one or more, and “more than one” means two or more.

[0086] In the several embodiments provided in this application, it should be understood that the disclosed apparatus, system, and method can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0087] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0088] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0089] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0090] Although the description of this application has been quite detailed and particularly focused on several of the described embodiments, it is not intended to limit itself to any of these details or embodiments or any particular embodiment. Rather, it should be considered as effectively covering the intended scope of this application by referring to the appended claims and taking into account the prior art, which provides for a broad possible interpretation of these claims. Furthermore, the foregoing description of this application with respect to embodiments foreseeable by the inventors is intended to provide a useful description, and non-substantial modifications to this application that have not yet been foreseen may still represent equivalent modifications.

Claims

1. A method for assessing the charging load carrying capacity of a distribution network, characterized in that, The method includes: generating an inner approximation set and an outer approximation set to describe the feasible region of the grid charging load carrying capacity, wherein the inner approximation set is a set consisting of confirmed feasible multi-node capacity configuration schemes, and the outer approximation set is a set containing the feasible region of the grid charging load carrying capacity, the initial state of the outer approximation set typically containing all possible configuration schemes within the area to be examined; selecting a specific charging station capacity configuration scheme containing at least one node capacity value as a candidate scheme within the grid area to be examined; and constructing and solving a solution with the candidate scheme as input and the degree of exceedance of grid operation constraints under the worst-case uncertainty scenario as output. An optimization model is used, and the robust feasibility of the candidate scheme is judged based on the degree of limit violation. If the robust feasibility judgment result is feasible, the candidate scheme is added to the inner approximation set, and the boundary of the inner approximation set is expanded to include the candidate scheme. If the robust feasibility judgment result is infeasible, constraints for excluding a class of infeasible schemes are generated based on the information of the candidate scheme, and the constraints are used to shrink the boundary of the region defined by the outer approximation set inward. Based on the region corresponding to the updated inner approximation set and the region corresponding to the updated outer approximation set, the capacity feasible region of the power grid is determined.

2. The method for assessing the charging load carrying capacity of a distribution network according to claim 1, characterized in that, The specific method for determining the robust feasibility of the candidate solution based on the degree of constraint exceedance includes: calculating the constraint exceedance amount of the candidate solution in the worst uncertainty scenario for power grid operation; determining whether the constraint exceedance amount is greater than a preset feasibility threshold; if so, determining that the node in the candidate solution is an infeasible point; if not, determining that the node in the candidate solution is a feasible point.

3. The method for assessing the charging load carrying capacity of a distribution network according to claim 2, characterized in that, The constraint exceedance quantity is calculated by solving a mixed-integer linear programming model. The mixed-integer linear programming model is constructed through the following steps: A two-layer optimization model is constructed based on a robust feasibility detection problem. The outer layer model searches for maximizing the constraint exceedance risk under the worst-case uncertainty scenario, while the inner layer model describes the process of minimizing the constraint exceedance quantity by scheduling resources within the power grid under a given uncertainty scenario. A dual transformation is performed on the inner layer model to transform the two-layer optimization model into a single-layer optimization model that maximizes a dual objective function on the outer approximation set. The norm terms in the single-layer optimization model that involve absolute value operations due to the dual transformation are linearized. After the linearization process, the mixed-integer linear programming model is obtained, and the constraint exceedance quantity is obtained by solving the mixed-integer linear programming model using a pre-defined commercial solver.

4. The method for assessing the charging load carrying capacity of a distribution network according to claim 3, characterized in that, The inner model also includes a total resource constraint, which is used to limit the total amount of power regulation performed by all nodes in the power grid through electric vehicle charging load during the period of operation in which resources minimize the constraint limit, so that the total amount is less than or equal to a preset flexibility budget value. The flexibility budget value is adjusted to quantitatively evaluate the contribution of the electric vehicle charging load regulation capability to improving the power grid charging load carrying capacity.

5. The method for assessing the charging load carrying capacity of a distribution network according to claim 1, characterized in that, If the robust feasibility judgment result is infeasible, then based on the information of the candidate solutions, constraints are generated to exclude a class of infeasible solutions, and the constraints are used to shrink the boundary of the region defined by the outer approximation set inward, including: if the robust feasibility judgment result is infeasible, then based on the dual information obtained in the process of solving the optimization model, a linear cutting plane is generated; the linear cutting plane is used to cut the region corresponding to the outer approximation set, so as to exclude the infeasible region containing the candidate solutions from the region corresponding to the outer approximation set, thereby reducing the boundary of the region of the outer approximation set.

6. The method for assessing the charging load carrying capacity of a distribution network according to claim 1, characterized in that, Before constructing and solving an optimization model that takes the candidate solution as input and the degree of exceedance of power grid operation constraints under the worst-case uncertainty scenario as output, the method further includes: determining whether the candidate solution is already included in the inner approximation set; if the candidate solution is already included in the inner approximation set, the robust feasibility judgment result is feasible, and the step of constructing and solving an optimization model that takes the candidate solution as input and the degree of exceedance of power grid operation constraints under the worst-case uncertainty scenario as output is skipped; if the candidate solution is not included in the inner approximation set, then the method further determines whether the candidate solution is... If the candidate solution is outside the scope defined by the outer approximation set, the robust feasibility judgment result is infeasible, and the step of constructing and solving the optimization model with the candidate solution as input and the degree of exceeding the limit of the power grid operation constraints under the worst uncertainty scenario as output is skipped; if the candidate solution is not included in the inner approximation set and is not outside the scope defined by the outer approximation set, the step of constructing and solving the optimization model with the candidate solution as input and the degree of exceeding the limit of the power grid operation constraints under the worst uncertainty scenario as output is executed.

7. The method for assessing the charging load carrying capacity of a distribution network according to claim 1, characterized in that, The method further includes: after updating the inner approximation set and the outer approximation set, returning to the step of selecting a specific charging station capacity configuration scheme containing at least one node capacity value as a candidate scheme within the power grid area to be investigated, until the difference between the inner approximation set and the outer approximation set is less than a preset threshold or the number of times the candidate scheme is selected reaches the maximum number of times, and outputting the updated inner approximation set as the carrying capacity set.

8. A device for assessing the charging load carrying capacity of a power distribution network, characterized in that, The apparatus includes: a generation module for generating an inner approximation set and an outer approximation set for describing the feasible region of the grid charging load carrying capacity, wherein the inner approximation set is a set consisting of confirmed feasible multi-node capacity configuration schemes, and the outer approximation set is a set containing the feasible region of the grid charging load carrying capacity, and the initial state of the outer approximation set typically includes all possible configuration schemes within the area to be examined; a selection module for selecting a specific charging station capacity configuration scheme containing at least one node capacity value as a candidate scheme within the grid area to be examined; and a calculation module for constructing and solving an optimization algorithm that takes the candidate scheme as input and outputs the degree of exceedance of grid operation constraints under the worst-case uncertainty scenario. The model is used to determine the robust feasibility of the candidate schemes based on the degree of exceeding the limits; a first update module is used to add the candidate schemes to the inner approximation set and expand the boundary of the inner approximation set to include the candidate schemes if the robust feasibility determination result is feasible; a second update module is used to generate constraints to exclude a class of infeasible schemes based on the information of the candidate schemes if the robust feasibility determination result is infeasible, and use the constraints to shrink the boundary of the region defined by the outer approximation set inward; a third update module is used to determine the capacity feasible region of the power grid based on the region corresponding to the updated inner approximation set and the region corresponding to the updated outer approximation set.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the method for assessing the charging load carrying capacity of a distribution network as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for assessing the charging load carrying capacity of the distribution network as described in any one of claims 1 to 7.

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