A high-voltage power distribution network flexible operation and risk control method, device and medium

Abstract

The application discloses a high-voltage power distribution network flexible operation and risk control method, equipment and medium, and belongs to the technical field of high-voltage power distribution networks, and the method comprises the following steps: simplifying the city power grid topology by using a transfer unit; based on the simplified city power grid topology, establishing a power transmission system model, a high-voltage power distribution network flexible reconstruction model and an electric vehicle cluster flexible scheduling model; based on the power transmission system model, the high-voltage power distribution network flexible reconstruction model and the electric vehicle cluster flexible scheduling model, establishing a target function of a collaborative scheduling strategy; and solving the target function to obtain an optimal collaborative scheduling strategy. Through the dual-dimension flexible operation mechanism of the collaborative electric vehicle charging and discharging flexibility and the HVDN flexible reconstruction, the application solves the equipment overload risk and improves the operation safety of the power distribution network.

Description

Technical Field

[0001] This application belongs to the field of high-voltage power distribution technology, specifically relating to a method, equipment, and medium for flexible operation and risk control of high-voltage power distribution networks. Background Technology

[0002] In recent years, China's urban electricity load has shown a trend of rapid growth and diversified demand. With the acceleration of urbanization and the vigorous development of new infrastructure construction, urban electricity demand continues to climb. Especially during the peak summer season, the large-scale use of cooling loads such as air conditioners significantly increases peak electricity demand. At the same time, the surge in industrial investment, the widespread adoption of electric vehicles, and the rapid development of emerging load infrastructure such as data centers and 5G base stations have further contributed to the growth in electricity consumption. However, constrained by multiple factors, the development of urban power grids has lagged behind the rapid expansion of electricity load. During peak urban electricity consumption periods, local power grid structures face serious overload risks, threatening the safe operation of the power system. Therefore, how to fully tap the flexible operation potential of urban power grids to meet the ever-increasing load demand has become a critical challenge that urgently needs to be addressed.

[0003] To address the frequent equipment overload problems in urban power grids, many researchers have proposed various scheduling and optimization methods, exploring solutions from multiple perspectives, including optimal transmission switching (OTS) and generator rescheduling. However, existing research heavily relies on generator scheduling and multi-regional coordination to achieve line switching and overload risk management. Many urban power grids lack adjustable generators, and frequent line switching can adversely affect the reliability of urban power grid transmission systems.

[0004] To overcome these problems, many studies have turned to regulation decisions in high-voltage distribution networks (HVDNs) to optimize load curves for equipment overload risk management. However, frequent HVDN load shifting leads to high maintenance costs, and load shifting is also a discrete adjustment method that may result in inaccurate and unnecessary load reduction.

[0005] With the rapid increase in the number of electric vehicles (EVs), their impact on urban power grids is becoming increasingly significant. As a load resource with spatiotemporal adjustability, EVs demonstrate great potential for overload risk management in urban power grids through flexible scheduling of their charging and discharging behavior. However, existing research has not fully explored the coordination between EV scheduling and HVDN load transfer. Summary of the Invention

[0006] To address the technical problems existing in the prior art, this application proposes a method, equipment, and medium for flexible operation and risk control of high-voltage distribution networks. Through a dual-dimensional flexible operation mechanism that coordinates the flexible charging and discharging of electric vehicles with the flexible reconfiguration of HVDN, it solves the risk of equipment overload and improves the operational safety of the distribution network.

[0007] This application is achieved through the following technical solution:

[0008] A method for flexible operation and risk control of high-voltage distribution networks includes:

[0009] The urban power grid topology is simplified by using power transfer units;

[0010] Based on the simplified urban power grid topology, a power transmission system model, a high-voltage distribution network flexible reconfiguration model, and an electric vehicle cluster flexible scheduling model are established.

[0011] Based on the aforementioned power transmission system model, high-voltage distribution network flexible reconfiguration model, and electric vehicle cluster flexible scheduling model, an objective function for the collaborative scheduling strategy is established.

[0012] The objective function is solved to obtain the optimal cooperative scheduling strategy.

[0013] In some implementations, the simplification of the urban power grid topology using a power transfer unit includes:

[0014] The two switching states of the bus tie switch in the substation of the high-voltage distribution network in the urban power grid topology are defined as two modes, and the series connection structure of transformer and switch is defined as a power transfer unit.

[0015] By replacing the high-voltage distribution network in the urban power grid topology with the connection relationships of the transfer units under the two modes, a simplified urban power grid topology is obtained.

[0016] In some implementations, the process of establishing the power transmission system model includes:

[0017] Based on the active and reactive power injected into the nodes of the transmission system, the active and reactive power consumed by the nodes, the conductance and inductance of the transmission lines in the transmission system, and the auxiliary variables used to convex the original AC power flow, the power flow equation of the transmission system is established; wherein the active and reactive power consumed by the nodes in the transmission system are calculated based on the active and reactive power drawn from the transmission system nodes through the lines connecting the transmission system nodes and the high-voltage distribution network nodes.

[0018] The constraints for establishing the power flow equations include: voltage constraints, relaxed second-order cone programming constraints, injected power constraints, transmission line power constraints, and phase angle constraints.

[0019] In some implementations, the power flow equations of the transmission system are expressed as:

[0020]

[0021]

[0022] in, and These are nodes i Injected active and reactive power; and These are nodes i The active and reactive power consumed; It is a collection of transmission nodes; It is a set of time intervals; It is with nodes i The set of connected nodes; and These are the nodes connecting the power transmission system. i and transmission system nodes j The conductance and inductance of the circuit; and These are nodes i Conductivity and susceptance; , and It is an auxiliary variable used to highlight the original communication current.

[0023] In some implementations, the process of establishing the high-voltage distribution network flexible reconfiguration model includes:

[0024] Based on the active and reactive power of the lines connecting the transmission system nodes and the high-voltage distribution network nodes, the active and reactive power consumed by the load at the high-voltage distribution network nodes, the load reduction at the high-voltage distribution network nodes, the active and reactive power of the lines passing through the high-voltage distribution network, the square of the current amplitude of the lines passing through the high-voltage distribution network, and the resistance and reactance of the high-voltage distribution network lines, a linearized power flow equation for the high-voltage distribution network is established.

[0025] The constraints for establishing the linearized power flow equations include: power constraints, high-voltage distribution line voltage constraints, voltage limit constraints, relaxed second-order cone programming constraints, high-voltage distribution network structure constraints, high-voltage distribution line power constraints, and maximum load reduction constraints.

[0026] In some implementations, the linearized power flow equations of the high-voltage distribution network are expressed as:

[0027]

[0028]

[0029] in, and These are achieved by connecting power transmission system nodes. i and high-voltage distribution network nodes k The active and reactive power of the line; and These are high-voltage distribution network nodes k The active and reactive power consumed by the load; and These are high-voltage distribution network nodes k The reduction in active load and reactive power at the location; and These are achieved by connecting high-voltage distribution network nodes. l and high-voltage distribution network nodes k The active and reactive power of the line; By connecting high-voltage distribution network nodes l and high-voltage distribution network nodes k The square of the current amplitude of the line; and These are the nodes connecting the high-voltage distribution network. l and high-voltage distribution network nodes k The resistance and reactance of the circuit; It is a collection of high-voltage power distribution nodes; It is a collection of transmission nodes; and These are achieved by connecting high-voltage distribution network nodes. k and high-voltage distribution network nodes m The active and reactive power of the line; It is with nodes k The set of connected child nodes.

[0030] In some implementations, the process of establishing the flexible scheduling model for the electric vehicle cluster includes:

[0031] For each electric vehicle cluster, establish a corresponding charging time interval matrix;

[0032] Based on the base load at the high-voltage distribution network node, the charging and discharging power of electric vehicles, and the charging time interval matrix, a total load calculation equation is established at the high-voltage distribution network node.

[0033] Establish state-of-charge constraints and charging / discharging power constraints for electric vehicles.

[0034] In some implementations, the total load calculation equation at the high-voltage distribution network node is expressed as:

[0035]

[0036] in, It is a high-voltage distribution network node k The active power consumed by the load; It is a high-voltage distribution network node k The base load at the location; It is an electric car e In time interval t The charging and discharging power; It is the first in the charging time interval matrix e Line number t Column elements; It is a collection of electric vehicles; It is a set of time intervals.

[0037] Secondly, this application proposes an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement any of the above-described methods for flexible operation and risk control of high-voltage power distribution networks.

[0038] Thirdly, this application proposes a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements any of the above-described methods for flexible operation and risk control of high-voltage power distribution networks.

[0039] This application proposes a flexible operation and risk control method for high-voltage distribution networks. It adopts a dual-dimensional flexible operation mechanism that combines the flexible reconfiguration capability of the high-voltage distribution network topology with the spatiotemporal flexible scheduling capability of electric vehicle charging and discharging behavior. This mechanism can eliminate unnecessary load reduction and provide maximum economic performance, while requiring only a few high-voltage distribution network line switching operations, thus ensuring the stability and safety of system operation.

[0040] Accordingly, the electronic device and computer-readable storage medium proposed in this application also embody the aforementioned technical effects. Attached Figure Description

[0041] The accompanying drawings, which are included to provide a further understanding of the embodiments of this application and form part of this application, do not constitute a limitation on the embodiments of this application. In the drawings:

[0042] Figure 1 This is a flowchart of the high-voltage distribution network flexible operation and risk control method proposed in the embodiments of this application;

[0043] Figure 2 This is a typical topology diagram of a city power grid;

[0044] Figure 3 The typical operating mode of a 110kV substation is represented by a transformer unit;

[0045] Figure 4 for Figure 2 The diagram shows a simplified topology of the urban power grid.

[0046] Figure 5 A simplified topology diagram of a city's power grid in a certain region;

[0047] Figure 6 This represents the initial transmission line load factor.

[0048] Figure 7 Initial charging status of the electric vehicle cluster;

[0049] Figure 8 The load factor of the transmission line after load reduction optimization;

[0050] Figure 9 The load reduction amount for each node in the power transmission system;

[0051] Figure 10 This describes the changes in the status of high-voltage distribution network lines throughout the day.

[0052] Figure 11 This refers to the load reduction amount after optimization for flexible reconfiguration of the high-voltage distribution network;

[0053] Figure 12 The charging and discharging status of electric vehicles after optimization for flexible scheduling;

[0054] Figure 13 The load reduction of transmission system nodes after optimization for flexible scheduling of electric vehicles;

[0055] Figure 14 To take into account the changes in the status of high-voltage distribution network lines after optimization of the combined flexible dispatch strategy;

[0056] Figure 15 To consider the electric vehicle charging situation after optimization using a combined flexible scheduling strategy;

[0057] Figure 16 This is a schematic diagram of the high-voltage distribution network flexible operation and risk control device proposed in the embodiments of this application;

[0058] Figure 17 This is a schematic diagram of the architecture of the flexible operation and risk control system for high-voltage distribution networks proposed in the embodiments of this application;

[0059] Figure 18 This is a schematic diagram of the electronic device proposed in the embodiments of this application;

[0060] Figure 19 This is a schematic diagram of a computer-readable storage medium proposed in an embodiment of this application;

[0061] Figure reference numerals and corresponding component names:

[0062] 200-Flexible operation and risk control device, 201-Simplified unit, 202-Modeling unit, 203-Target construction unit, 204-Optimization solution unit, 300-Flexible operation and risk control system, 301-Input device, 302-Output device, 303-Processor A, 304-Memory A, 400-Electronic device, 410-Memory B, 420-Processor B, 411-Computer program A, 500-Computer readable storage medium, 511-Computer program B. Detailed Implementation

[0063] In the following, the terms “comprising” or “may include” as used in the various embodiments of this application indicate the presence of a function, operation, or element of the invention and do not limit the addition of one or more functions, operations, or elements. Furthermore, as used in the various embodiments of this application, the terms “comprising,” “having,” and their cognates are intended only to indicate a specific feature, number, step, operation, element, component, or combination of the foregoing and should not be construed as primarily excluding the presence of one or more other features, numbers, steps, operations, elements, components, or combinations of the foregoing, or adding one or more combinations of the foregoing.

[0064] In various embodiments of this application, the expression "or" or "at least one of A and / or B" includes any combination or all combinations of the words listed simultaneously. For example, the expression "A or B" or "at least one of A and / or B" may include A, may include B, or may include both A and B.

[0065] The terms used in the various embodiments of this application (such as "first," "second," etc.) may modify various constituent elements in the various embodiments, but do not limit the corresponding constituent elements. For example, the above terms do not limit the order and / or importance of the elements. The above terms are only used for the purpose of distinguishing one element from other elements. For example, a first user device and a second user device refer to different user devices, although both are user devices. For example, without departing from the scope of the various embodiments of this application, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element.

[0066] It should be noted that if a description is made of "connecting" one component to another, then the first component can be directly connected to the second component, and a third component can be "connected" between the first and second components. Conversely, when a component is "directly connected" to another component, it can be understood that there is no third component between the first and second components.

[0067] The terminology used in the various embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the various embodiments of this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which the various embodiments of this application pertain. The terms (such as those defined in a generally used dictionary) are to be interpreted as having the same meaning as in the context of the relevant technical field and are not to be interpreted as having an idealized or overly formal meaning, unless clearly defined in the various embodiments of this application.

[0068] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the embodiments and accompanying drawings. The illustrative embodiments and descriptions of this application are only for explaining this application and are not intended to limit this application. Example

[0069] This application proposes a flexible operation and risk control method for high-voltage distribution networks, which adopts a dual-dimensional flexible operation mechanism: first, the flexible reconfiguration capability of the high-voltage distribution network topology, that is, dynamic adjustment of power flow through backup path switching; second, the spatiotemporal flexible scheduling capability of electric vehicle charging and discharging behavior; the two together constitute the core of the flexible operation of urban power grids.

[0070] Specifically, such as Figure 1 As shown in the embodiments of this application, the flexible operation and risk control method includes:

[0071] Step 110: Simplify the urban power grid topology using a power transfer unit;

[0072] Step 120: Based on the simplified urban power grid topology, establish a power transmission system model, a high-voltage distribution network flexible reconfiguration model, and an electric vehicle cluster flexible scheduling model.

[0073] Step 130: Based on the power transmission system model, the high-voltage distribution network flexible reconfiguration model, and the electric vehicle cluster flexible scheduling model, establish the objective function of the collaborative scheduling strategy;

[0074] Step 140: Solve the objective function to obtain the optimal cooperative scheduling strategy. Use this optimal cooperative scheduling strategy for the cooperative scheduling of the urban power grid's high-voltage distribution network.

[0075] Furthermore, in embodiment 110 of this application, the simplification process is as follows:

[0076] Urban power grids mainly refer to the backbone network composed of 220kV transmission networks, 110kV high-voltage distribution networks, and distribution networks below 110kV. Its typical topology is as follows: Figure 2 As shown.

[0077] like Figure 2 As shown in the dashed box, the 110kV high-voltage distribution network maintains a radial operating structure. Electric vehicles charge and discharge within the 10kV medium-voltage distribution network, thereby altering the load factor of the 110kV substations. To ensure power supply reliability, each 110kV substation is equipped with one or more backup power supply paths connecting different power sources (220kV busbars), with a primary-to-backup ratio approaching 1:1. Furthermore, transformers within the 110kV substations can operate in split or parallel modes, complementing the backup power supply paths and significantly enriching the flexible operating topology of the high-voltage distribution network. The change in primary and backup configurations significantly improves the power flow distribution of the upper-level network, thus becoming an effective tool for alleviating high load factors on 220kV transmission lines. However, due to the large operating space of the high-voltage distribution network, operators find it difficult to quickly determine the optimal load transfer scheme within a short period. Relying solely on experience for manual trial and error may lead to operational risks and affect the stable operation of the urban power grid. To address this, this application proposes a simplified topology representation method for high-voltage distribution networks to achieve variable dimensionality reduction, thereby improving computational efficiency.

[0078] High-voltage distribution network substations typically employ a single busbar segmented configuration, equipped with two parallel transformers. Each transformer has a capacity of 30 to 60 MVA. For example... Figure 3 As shown, there are two common operating modes in actual operation. The high-voltage side connects to the 220kV transmission network, and the state of its bus tie switch determines the load power of the 220kV substation; the low-voltage side mainly connects to the medium-voltage distribution network, and the state of its bus tie switch determines the power of each transformer. Therefore, to highlight the main source-load relationship during load transfer, the two switch states of the bus tie switch in the substation are defined as Mode 1 and Mode 2. The series connection structure of the transformer and switch shown by the dashed line is defined as a transformer unit (TU), denoted by the letter U.

[0079] After using the power supply unit Figure 2 The urban power grid topology shown can be derived from Figure 4 This indicates that, because high-voltage distribution networks need to maintain a radial structure during operation, Figure 4 The dashed line indicates the disconnection of high-voltage distribution lines, used to break up the 220kV-110kV-220kV loop. By changing the location of the disconnected high-voltage distribution lines, the loads of the various 220kV substations will be transferred to each other, thereby mitigating the risk of overload on the transmission lines. Furthermore, the load can be adjusted by rearranging the charging and discharging power of electric vehicle clusters connected to the TU.

[0080] Furthermore, in step 120 of this application embodiment, the established power transmission system model includes: power flow equations, i.e., equations (1) to (4); voltage constraints, i.e., equation (5); relaxed second-order cone programming (SOCP) constraints; injected power constraints, i.e., equations (7) and (8), which are injected active power constraints and reactive power constraints, respectively; transmission line power constraints, i.e., equations (9) to (11); and phase angle constraints, i.e., equations (12) to (15), which define four planes to approximate the phase angle constraints.

[0081] (1)

[0082] (2)

[0083] (3)

[0084] (4)

[0085] (5)

[0086] (6)

[0087] (7)

[0088] (8)

[0089] (9)

[0090] (10)

[0091] (11)

[0092] (12)

[0093] (13)

[0094] (14)

[0095] (15)

[0096] in, and These are nodes i Injected active and reactive power; and These are nodes i The active power and reactive power consumed are calculated using equations (3) and (4), respectively. It is a collection of transmission nodes; It is a set of time intervals; It is with nodes i The set of connected nodes; and These are the nodes connecting the power transmission system. i and transmission system nodes j The conductance and inductance of the circuit; and These are the conductance and susceptance of node i, respectively; , and It is an auxiliary variable used to highlight the original AC power flow; It is related to the power transmission system node i A collection of connected high-voltage distribution network nodes; and These are achieved by connecting power transmission system nodes. i and high-voltage distribution network nodes k The active and reactive power drawn from the transmission system nodes by the lines; It is a node i The square of the voltage amplitude; and These are the minimum and maximum values ​​of the voltage amplitude, respectively. and These are the minimum and maximum values ​​of the injected active power, respectively; and These are the minimum and maximum values ​​of the injected reactive power, respectively. and These are achieved by connecting power transmission system nodes. i and transmission system nodes j The active and reactive power of the line are calculated using equations (10) and (11), respectively. It is a node connecting the power transmission system. i and transmission system nodes j The maximum apparent power of the line; It is a node i The phase angle; It is a node j The phase angle; , , and They are the first h Parameters of a plane.

[0097] Further, in step 120 of this application embodiment, the established high-voltage distribution network flexible reconfiguration model includes: the linearized power flow equation of the high-voltage distribution network, i.e., equations (16) and (17); power constraints, i.e., equation (18); voltage constraints of high-voltage distribution lines, i.e., equation (19); voltage limit constraints, i.e., equation (20); relaxed SOCP constraints, i.e., equation (21); high-voltage distribution network structural constraints, i.e., equations (22) and (23); power constraints of high-voltage distribution lines, i.e., equation (24); and maximum load reduction constraints, i.e., equation (25).

[0098] (16)

[0099] (17)

[0100] (18)

[0101] (19)

[0102] (20)

[0103] (twenty one)

[0104] (twenty two)

[0105] (twenty three)

[0106] (twenty four)

[0107] (25)

[0108] in, and These are achieved by connecting power transmission system nodes. i and high-voltage distribution network nodes k The active and reactive power of the line; and These are high-voltage distribution network nodes k The active and reactive power consumed by the load; and These are high-voltage distribution network nodes k The reduction in active load and reactive power at the location; and These are achieved by connecting high-voltage distribution network nodes. l and high-voltage distribution network nodes k The active and reactive power of the line; By connecting high-voltage distribution network nodesl and high-voltage distribution network nodes k The square of the current amplitude of the line; and These are the nodes connecting the high-voltage distribution network. l and high-voltage distribution network nodes k The resistance and reactance of the circuit; and These are achieved by connecting high-voltage distribution network nodes. k and high-voltage distribution network nodes m The active and reactive power of the line; It is with nodes k The set of parent nodes to be connected; It is with nodes k The set of connected child nodes; It is a collection of high-voltage power distribution nodes (i.e., transformer units); By connecting power transmission system nodes i and high-voltage distribution network nodes k The maximum apparent power of the line; and These are nodes k and nodes l The square of the voltage amplitude at that point; It is used to represent nodes connected to the power transmission system. i and high-voltage distribution network nodes k A binary variable representing the on / off state of the line; It is a very large constant; and These are nodes k The minimum and maximum values ​​of the square of the voltage amplitude at that location; It is used to represent the nodes connected to the high-voltage distribution network. k and high-voltage distribution network nodes l A binary variable representing the on / off state of the line; It is used to represent the nodes connected to the high-voltage distribution network. m and high-voltage distribution network nodes k A binary variable representing the on / off state of the line; By connecting high-voltage distribution network nodes l and high-voltage distribution network nodes k The maximum active power of the line; It is a high-voltage distribution network node k The maximum load reduction at that location.

[0109] Furthermore, in step 120 of this application embodiment, the process of establishing the flexible scheduling model for electric vehicle clusters is as follows:

[0110] For each electric vehicle cluster, a corresponding charging time interval matrix was established. The elements therein are defined as:

[0111] (26)

[0112] in, It is a matrix The Middle e Line number t Column elements; and These represent the times when the electric vehicle arrives at or leaves the charging station; matrix C is a... 3D matrix; It is a collection of electric vehicles; It is a set of time intervals.

[0113] Based on the charging time interval matrix of the electric vehicle cluster, the high-voltage distribution network nodes are obtained. k The equation for calculating the active power consumed by the load at a given location is:

[0114] (27)

[0115] in, It is a high-voltage distribution network node k The base load at the location; It is an electric car e In time interval t Charging and discharging power; electric vehicles e The state of charge (SOC) is constrained by equation (28).

[0116] (28)

[0117] (29)

[0118] (30)

[0119] in, This refers to the state of charge of the electric vehicle. It is an electric car e Energy upon arrival at the charging station; It is an electric car e Energy when leaving the charging station; It is an electric car e Battery capacity; It is an electric car e Expected energy level upon leaving the charging station. Electric vehicle. e The charging and discharging power constraints are shown in (31):

[0120] (31)

[0121] in, It is an electric car e Maximum charging and discharging power.

[0122] Furthermore, in step 130 of the present application embodiment, the goal of the established coordinated scheduling strategy for high-voltage distribution network load transfer and electric vehicle rescheduling is to minimize the total operating cost, and its objective function is shown in equation (32).

[0123] (32)

[0124] in, , and These are generation costs, load reduction costs, and electric vehicle flexible dispatch costs.

[0125] Furthermore, in step 140 of this embodiment, a commercial solver is invoked to solve the problem. The constraints, objective function, and control variables are input to the solver. The solver performs optimization calculations based on the input constraints and objective function, combined with methods such as branch and bound and heuristic search, and outputs the optimal scheduling scheme to ensure system operating efficiency and cost control.

[0126] To verify the effectiveness of the flexible operation and risk control method proposed in the embodiments of this application, the method was applied to a city power grid for simulation verification. Figure 5 As shown, the city's power grid comprises eight 220kV substations, eight 220kV transmission lines, 57 110kV nodes, and 80 controllable 110kV lines. Five electric vehicle clusters are distributed across different 110kV high-voltage distribution network nodes. The initial load factor of the 220kV transmission lines and the charging status of the electric vehicles are shown in the figures below. Figure 6 and Figure 7 As shown.

[0127] like Figure 6 In the simulation, transmission lines L2, L3, L5, and L6 experienced overload at different times on a typical day. Peak charging demand occurred at 05:00, 14:00, and 21:00. To evaluate the effectiveness of the proposed method, four scenarios were considered: (1) load shedding; (2) load transfer from the high-voltage distribution network; (3) electric vehicle rescheduling; and (4) a combination of electric vehicle rescheduling and load transfer. All simulations were performed using the Gurobi solver on a PC equipped with a 1.6 GHz processor and 16 GB of RAM.

[0128] (1) Load reduction scenario.

[0129] In this scenario, load shedding is the only way to alleviate transmission congestion. The optimization results are as follows: Figure 8 and Figure 9 As shown.

[0130] Figure 8 This demonstrates the change in the optimized transmission line load factor over time under a load reduction scenario, reflecting the load reduction effect of each line (L1 to L8) throughout the day under this scenario. The load factor of all lines remained below 1.0 pu at all times, indicating that there is no risk of overload.

[0131] Figure 9 This shows the load reduction at each node of the power transmission system throughout the day. For example... Figure 9 As shown, load shedding increases significantly around noon (approximately 12:00-15:00). During this period, the transmission system line load rate exceeds the supply capacity, necessitating load shedding to maintain grid security. Different transmission system nodes (S1-S8) contribute differently to the total load shedding. Some substations experience little or no load shedding throughout the day. Notably, S2 and S6 show relatively high load shedding during peak hours compared to other transmission system nodes. The total load shedding for the day reaches 162.1720 MWh, accounting for 24.86% of the total load.

[0132] (2) Flexible reconfiguration scenario of high voltage distribution network.

[0133] In this scenario, flexible reconfiguration of the high-voltage distribution network is the only way to alleviate transmission congestion. The optimization results are as follows: Figure 10 and Figure 11 As shown.

[0134] like Figure 10 As shown, 12 high-voltage distribution network lines changed their on / off status during the day. Most high-voltage distribution network lines switched once between 6:00 and 8:00 to mitigate overload risks. The load reduction at each transmission system node is as follows: Figure 11 As shown, only S1 and S6 reduced their load demand at midday. The total load reduction reached 96.3486 MWh, accounting for 14.21% of the total load. Figure 9 In comparison, the total load reduction was 40.59%.

[0135] (3) Flexible scheduling scenario for electric vehicles.

[0136] In this scenario, flexible scheduling of electric vehicles is the only way to alleviate power transmission congestion. The optimization results are as follows: Figure 12 and Figure 13 As shown.

[0137] like Figure 12As shown, each electric vehicle cluster has periods of charging (positive power) and discharging (negative power). Electric vehicle clusters 1 and 2 are more active in the early morning and evening. Compared with the initial curves, the optimized charging behavior shows a more regular power demand, highlighting the potential for more effective load balancing across time and reducing the risk of transmission line overload.

[0138] Figure 13 This diagram illustrates the load reduction at transmission system nodes S1, S2, and S6 between 00:00 and 11:00. Peak load reduction occurred between 09:00 and 12:00, totaling 35-40 MW. A secondary peak occurred between 03:00 and 06:00, amounting to 15-20 MW. Node S6 (light gray) contributed approximately 40-50% of the total load reduction throughout the day, highlighting its crucial role in mitigating equipment overload risk. Nodes S1 and S2 also participated significantly, particularly during the morning peak from 08:00 to 12:00. The total load reduction for the day was 141.0138 MWh, representing 20.79% of the total load demand. Figure 9 In comparison, the total load reduction was 13.05%.

[0139] (4) Combined scenario of flexible dispatching of electric vehicles and flexible reconfiguration of high voltage distribution network (i.e., the flexible operation and risk control method proposed in the embodiments of this application).

[0140] In this scenario, flexible dispatching of electric vehicles and flexible reconfiguration of the high-voltage distribution network are jointly applied to reduce overload risk. The optimization results are as follows: Figure 14 and Figure 15 As shown.

[0141] like Figure 14 As shown, high-voltage distribution network lines 15 and 53 were connected at 20:00, while lines 27 and 40 were simultaneously disconnected. Figure 10 In contrast, only four high-voltage distribution lines changed their on / off status, indicating that the network configuration was more stable.

[0142] Rescheduling strategies for different electric vehicle clusters, such as Figure 15 As shown, peak charging times for electric vehicles occur between 18:00 and 24:00, while the lowest charging volume occurs between 06:00 and 12:00. Electric vehicle cluster 5 consistently provides the largest charging capacity. Clusters 1 through 4 exhibit staggered charging patterns, with clusters 1 and 2 contributing significantly during the evening peak period (18:00-24:00). Notably, under this combined strategy, no load reduction is required throughout the day.

[0143] The comparison and summary of the four scenarios are shown in Table 1.

[0144] Table 1 Comparison of the four scenarios

[0145]

[0146] As shown in Table 1, the load shedding scenario resulted in the highest total load reduction, reaching 162.1720 MWh. In the high-voltage distribution network load transfer scenario, the load reduction was reduced by 40.59% to 96.3486 MWh through 12 high-voltage distribution network line switchings. Although this method is effective, frequent line switching may affect the stability of system operation.

[0147] The flexible scheduling scenario for electric vehicles generated 1.0051 × 10⁻⁶ units through electric vehicle rescheduling. 4 The economic benefits of the US dollar demonstrate the effectiveness of demand-side flexibility in reducing load cuts and related operating costs.

[0148] In contrast, the combined scenario of flexible electric vehicle dispatching and flexible high-voltage distribution network reconfiguration proposed in this application completely eliminates load reduction, requires only four line switching operations, and achieves 2.9764 × 10 4 The highest economic benefits of the US dollar. This highlights the synergistic advantages of combining electric vehicle flexibility with grid operation strategies (flexible reconfiguration of high-voltage distribution networks addresses physical network constraints, while flexible dispatch of electric vehicles facilitates load balancing in time and space). Overall, the method proposed in this application provides an optimal solution by simultaneously eliminating unnecessary load shedding and maximizing economic performance.

[0149] Based on the same technical concept described above, this application also proposes a flexible operation and risk control device for high-voltage distribution networks, such as... Figure 16 As shown, the flexible operation and risk control device 200 includes:

[0150] Simplification unit 201 simplifies the urban power grid topology using a power transfer unit. The specific simplification process is as described in step 110 above, and will not be repeated here.

[0151] Modeling unit 202 is used to establish a transmission system model, a high-voltage distribution network flexible reconfiguration model, and an electric vehicle cluster flexible scheduling model based on the simplified urban power grid topology. The specific modeling process is as described in step 120 above, and will not be repeated here.

[0152] The objective construction unit 203 is used to establish the objective function of the collaborative scheduling strategy based on the power transmission system model, the high-voltage distribution network flexible reconfiguration model, and the electric vehicle cluster flexible scheduling model. The specific objective function is as described in step 130 above, and will not be repeated here.

[0153] Furthermore, the optimization and solution unit 204 solves the objective function to obtain the optimal cooperative scheduling strategy. The specific solution method is as described in step 140 above, and will not be repeated here.

[0154] Based on the same technical concept described above, this application also proposes a flexible operation and risk control system for high-voltage distribution networks, such as... Figure 17 As shown, the flexible operation and risk control system 300 proposed in this application includes:

[0155] The system comprises an input device 301, an output device 302, a processor A303, and a memory A304; wherein the number of processors A303 and memory A304 can be one or more. Figure 17 The following description uses a processor A303 and a memory A304 as an example. The input device 301, output device 302, processor A303, and memory A304 can be connected via a bus or other means. Figure 17 Taking the example of a connection between China and Israel via a bus.

[0156] Specifically, by calling the operation instructions stored in memory A304, processor A303 executes the following steps:

[0157] The urban power grid topology is simplified by using power transfer units;

[0158] Based on the simplified urban power grid topology, a power transmission system model, a high-voltage distribution network flexible reconfiguration model, and an electric vehicle cluster flexible scheduling model are established.

[0159] Based on the power transmission system model, the high-voltage distribution network flexible reconfiguration model, and the electric vehicle cluster flexible scheduling model, an objective function for the collaborative scheduling strategy is established.

[0160] Solving the objective function yields the optimal cooperative scheduling strategy.

[0161] Optionally, by calling the operation instructions stored in memory A304, processor A303 is also used to execute any of the embodiments in the corresponding examples of the above-described flexible operation and risk control method.

[0162] Based on the same technical concept described above, this application also proposes an electronic device, such as... Figure 18 As shown, the electronic device 400 includes: a memory B410, a processor B420, and a computer program A411 stored in the memory B410 and executable on the processor B420. When the processor B420 executes the computer program A411, it performs the following steps:

[0163] The urban power grid topology is simplified by using power transfer units;

[0164] Based on the simplified urban power grid topology, a power transmission system model, a high-voltage distribution network flexible reconfiguration model, and an electric vehicle cluster flexible scheduling model are established.

[0165] Based on the power transmission system model, the high-voltage distribution network flexible reconfiguration model, and the electric vehicle cluster flexible scheduling model, an objective function for the collaborative scheduling strategy is established.

[0166] Solving the objective function yields the optimal cooperative scheduling strategy.

[0167] Optionally, when processor B420 executes computer program A411, it can implement any of the embodiments in the corresponding examples of the above-described flexible operation and risk control method.

[0168] It should be noted that the electronic device proposed in this application embodiment is a device used to implement the above-mentioned flexible operation and risk control method. Therefore, based on the above-mentioned flexible operation and risk control method proposed in this application embodiment, those skilled in the art can understand the specific implementation method and various variations of the electronic device in this application embodiment. Therefore, the specific implementation method of the above-mentioned flexible operation and risk control method will not be described in detail here. Any electronic device used by those skilled in the art to implement the above-mentioned flexible operation and risk control method falls within the scope of protection of this application.

[0169] Based on the same technical concept described above, embodiments of this application also propose a computer-readable storage medium, such as... Figure 19 As shown, the computer-readable storage medium 500 stores a computer program B511, which, when executed by a processor, performs the following steps:

[0170] The urban power grid topology is simplified by using power transfer units;

[0171] Based on the simplified urban power grid topology, a power transmission system model, a high-voltage distribution network flexible reconfiguration model, and an electric vehicle cluster flexible scheduling model are established.

[0172] Based on the power transmission system model, the high-voltage distribution network flexible reconfiguration model, and the electric vehicle cluster flexible scheduling model, an objective function for the collaborative scheduling strategy is established.

[0173] Solving the objective function yields the optimal cooperative scheduling strategy.

[0174] Optionally, when the computer program B511 is executed by the processor, it can implement any of the embodiments corresponding to the above-described flexible operation and risk control method.

[0175] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0176] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0177] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0178] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0179] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0180] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of this application. It should be understood that the above description is only a specific embodiment of this application and is not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A flexible operation and risk control method for high-voltage power distribution networks, characterized in that, The application relates to a method for establishing a high-voltage power distribution flexible reconstruction model and a high-voltage power distribution flexible reconstruction method. The method comprises the following steps: Simplifying the topology structure of a city power grid by using a transfer unit; Based on the simplified topology structure of the city power grid, a power transmission system model, a high-voltage power distribution flexible reconstruction model and an electric vehicle cluster flexible scheduling model are established; Based on the power transmission system model, the high-voltage power distribution flexible reconstruction model and the electric vehicle cluster flexible scheduling model, a target function of a high-voltage power distribution load transfer and electric vehicle cluster flexible collaborative scheduling strategy is established, and the target function is a total operation cost minimum composed of a power generation cost, a load reduction cost and an electric vehicle flexible scheduling cost; The target function is solved to obtain an optimal collaborative scheduling strategy; ； ； wherein and are the active power and the reactive power through the line connecting the transmission system node i and the high voltage distribution grid node k ; and are the active power and the reactive power consumed by the load at the high voltage distribution grid node k ; and are the active power and the reactive power curtailment at the high voltage distribution grid node k ; and are the active power and the reactive power through the line connecting the high voltage distribution grid node l and the high voltage distribution grid node k ; is the square of the current amplitude through the line connecting the high voltage distribution grid node l and the high voltage distribution grid node k ; and are the resistance and the reactance of the line connecting the high voltage distribution grid node l and the high voltage distribution grid node k ; and are the active power and the reactive power through the line connecting the high voltage distribution grid node k and the high voltage distribution grid node m ; is the set of parent nodes connected to the node k ; is the set of child nodes connected to the node k ; is the set of high voltage distribution nodes; is the set of transmission nodes; The established high-voltage power distribution flexible reconstruction model is as follows: The establishment process of the electric vehicle cluster flexible scheduling model comprises the following steps: For each electric vehicle cluster, a corresponding charging time interval matrix is established; According to the basic load at a high-voltage power distribution grid node, the charging and discharging power of an electric vehicle and the charging time interval matrix, a total load calculation equation at the high-voltage power distribution grid node is established; The state of charge constraint and the charging and discharging power constraint of the electric vehicle are established; ； in, It is a high-voltage distribution network node k The active power consumed by the load; It is a high-voltage distribution network node k The base load at the location; It is an electric car e In time interval t The charging and discharging power; It is the first in the charging time interval matrix e Line number t Column elements; It is a collection of electric vehicles; It is a set of time intervals.

2. The flexible operation and risk control method for high-voltage distribution network according to claim 1, characterized in that, The total load calculation equation at the high-voltage power distribution grid node is expressed as: The simplification of the topology structure of the city power grid by using the transfer unit comprises the following steps: Two switch states of a bus coupler switch in a transformer substation of the high-voltage power distribution grid in the topology structure of the city power grid are defined as two modes, and a series structure of a transformer and a switch is defined as a transfer unit; 3. The flexible operation and risk control method for high-voltage distribution network according to claim 1, characterized in that, The connection relationship of the transfer unit in the two modes is used to replace the high-voltage power distribution grid in the topology structure of the city power grid, so that the simplified topology structure of the city power grid is obtained. The establishment process of the power transmission system model comprises the following steps: According to the active power and the reactive power injected by a node in a power transmission system, the active power and the reactive power consumed by the node, the conductance and the inductance of a transmission line in the power transmission system and an auxiliary variable for convexifying original alternating current power flow, a power flow equation of the power transmission system is established; wherein the active power and the reactive power consumed by the node in the power transmission system are calculated according to the active power and the reactive power drawn from the node in the power transmission system through a line connecting the node in the power transmission system and a node in a high-voltage power distribution grid; 4. The flexible operation and risk control method for high-voltage distribution network according to claim 3, characterized in that, Constraint conditions of the power flow equation are established, including a voltage constraint, a relaxed second-order cone programming constraint, an injected power constraint, a transmission line power constraint and a phase angle constraint. ； ； wherein and are the active and reactive power injected by the power system node i ; and are the active and reactive power consumed by the power system node i ; is a set of time intervals; is a set of nodes connected to the power system node i ; and are the conductance and inductance of the line connecting the power system node i and the power system node j ; and are the conductance and susceptance of the power system node i ; , and are auxiliary variables used to convexify the original AC power flow.

5. The flexible operation and risk control method for high-voltage distribution network according to claim 1, characterized in that, The power flow equation of the power transmission system is expressed as: The establishment process of the high-voltage power distribution flexible reconstruction model comprises the following steps: According to the active power and the reactive power through a line connecting a node in a power transmission system and a node in a high-voltage power distribution grid, the active power and the reactive power consumed by a load at the node in the high-voltage power distribution grid, a load reduction amount at the node in the high-voltage power distribution grid, the active power and the reactive power through a high-voltage power distribution line, the square of a current amplitude through the high-voltage power distribution line and the resistance and the reactance of the high-voltage power distribution line, a linearized power flow equation of the high-voltage power distribution grid is established; Constraint conditions of the linearized power flow equation are established, including a power constraint, a high-voltage power distribution line voltage constraint, a voltage limit constraint, a relaxed second-order cone programming constraint, a high-voltage power distribution grid structure constraint, a high-voltage power distribution line power constraint and a maximum load reduction amount constraint. 6.An electronic device comprising a memory and a processor, the memory storing a computer program, wherein, The computer program is executed by the processor to implement the flexible operation and risk control method of the high-voltage power distribution network in any one of claims 1-5.

7. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the flexible operation and risk control method of the high-voltage power distribution network in any one of claims 1-5.

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