Flexible operation and risk control method and device for high-voltage power distribution network and medium

Abstract

The invention discloses a high-voltage power distribution network flexible operation and risk control method and device and a medium, and belongs to the technical field of high-voltage power distribution networks, and the method comprises the steps: carrying out the simplification of an urban power grid topological structure through employing a load transfer unit; based on the simplified urban power grid topological structure, establishing an output system model, a high-voltage power distribution network flexible reconstruction model and an electric vehicle cluster flexible scheduling model; based on the output system model, the high-voltage power distribution network flexible reconstruction model and the electric vehicle cluster flexible scheduling model, establishing an objective function of a cooperative scheduling strategy; and solving the objective function to obtain an optimal collaborative scheduling strategy. Through a dual-dimension flexible operation mechanism cooperating with electric vehicle charging and discharging flexibility and HVDN flexible reconstruction, the equipment overload risk is solved, and the operation safety of a power distribution network is improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of high-voltage power distribution, and particularly relates to a high-voltage power distribution network flexible operation and risk control method, device and medium. BACKGROUND

[0002] With the acceleration of urbanization and the vigorous development of new infrastructure construction, the demand for urban electricity continues to rise. In particular, during the peak summer, the large use of air conditioning and other refrigeration loads significantly increases the peak demand for electricity. At the same time, the surge in industrial investment, the widespread popularity of electric vehicles, and the rapid development of emerging load infrastructure such as data centers and 5G base stations further lead to an increase in electricity consumption. However, due to multiple factors, the development of urban power grids lags behind the rapid expansion of power loads. During the peak period of urban electricity, the local power grid structure is at serious risk of overload operation, threatening the safe operation of the power system. Therefore, how to fully tap the potential of flexible operation of urban power grids to meet the growing demand for loads has become a key challenge that needs to be addressed.

[0003] In view of the frequent device overload problems in urban power grids, many researchers have proposed various scheduling and optimization methods, which have been studied from multiple angles such as optimal transmission switching (OTS) and power generation rescheduling. However, existing research relies heavily on generator scheduling and multi-region 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 the urban power transmission system.

[0004] In order to overcome these problems, many researchers have resorted to high-voltage distribution network (HVDN) control decisions to optimize load curves for device overload risk management. However, frequent HVDN load transfer can result in high maintenance costs, and load transfer is also a discrete adjustment method that can lead to inaccurate and unnecessary load reduction.

[0005] With the rapid growth in the number of electric vehicles (EVs), their impact on urban power grids is increasingly significant. As a load resource with spatial and temporal adjustability, flexible scheduling of electric vehicle charging and discharging behavior shows great potential in the overload risk management of urban power grids. However, existing research has not fully explored the coordination of electric vehicle scheduling and HVDN load transfer. SUMMARY

[0006] In order to solve the technical problems existing in the prior art, the application provides a high-voltage power distribution network flexible operation and risk control method, equipment and medium, which solves the device overload risk and improves the operation safety of the power distribution network through a dual-dimension flexible operation mechanism of cooperative electric vehicle charging and discharging flexibility and HVDN flexibility reconstruction.

[0007] The application is implemented through the following technical solutions: A high-voltage power distribution network flexible operation and risk control method, comprising: Simplifying a city power grid topology structure by using a transfer unit; Based on the simplified city power grid topology structure, an output system model, a high-voltage power distribution network flexible reconstruction model and an electric vehicle cluster flexible scheduling model are established; Based on the output system model, the high-voltage power distribution network flexible reconstruction model and the electric vehicle cluster flexible scheduling model, a target function of a cooperative scheduling strategy is established; Solving the target function to obtain an optimal cooperative scheduling strategy.

[0008] In some embodiments, the simplifying of the city power grid topology structure by using the transfer unit comprises: Defining two switch states of a bus coupler switch in a substation of a high-voltage power distribution network in the city power grid topology structure as two modes, and defining a transformer and a switch series structure as a transfer unit; Using a connection relationship of the transfer unit in the two modes to replace the high-voltage power distribution network in the city power grid topology structure, so as to obtain a simplified city power grid topology structure.

[0009] In some embodiments, the establishment process of the output system model comprises: According to active power and reactive power injected by a node in a power transmission system, active power and reactive power consumed by the node, an electric conductance and an electric inductance of a power 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 active power and 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 network; Constraint conditions of the power flow equation are established, including: voltage constraint, relaxed second-order cone programming constraint, injected power constraint, power transmission line power constraint and phase angle constraint.

[0010] In some embodiments, the power flow equation of the power transmission system is expressed as:

[0011]

[0012] wherein, and are the active and reactive power injections at the nodes i ; and are the active and reactive power consumptions at the nodes i ; is the set of transmission nodes; is the set of time intervals; is the set of nodes connected to the node i ; and are the conductance and inductance of the transmission line i,j ; and are the , and are the auxiliary variables for convexifying the original AC power flow.

[0013] In some embodiments, the process of establishing the flexible restoration model of the high-voltage power distribution network comprises: establishing linearized power flow equations of the high-voltage power distribution network according to the active and reactive power through the lines connecting the transmission system nodes and the high-voltage power distribution network nodes, the active and reactive power consumed by the loads at the high-voltage power distribution network nodes, the load shedding amount at the high-voltage power distribution network nodes, the active and reactive power through the high-voltage power distribution network lines, the square of the current amplitude through the high-voltage power distribution network lines, and the resistance and reactance of the high-voltage power distribution network lines; establishing the constraint conditions of the linearized power flow equations, including: power constraints, high-voltage power distribution line voltage constraints, voltage limit constraints, relaxed second-order cone programming constraints, high-voltage power distribution network structure constraints, high-voltage power distribution line power constraints, and maximum load shedding amount constraints.

[0014] In some embodiments, the linearized power flow equations of the high-voltage power distribution network are expressed as:

[0015]

[0016] wherein, and are the active and reactive power through the lines i connecting the transmission system nodes k and the high-voltage power distribution network nodes i,k ; and are the active and reactive power consumed by the loads at the high-voltage power distribution network nodes k ; and respectively are active load curtailment and reactive power curtailment at a high-voltage distribution grid node; k respectively are active power and reactive power through a high-voltage distribution grid line ( l,k ); is the square of the current amplitude through a high-voltage distribution grid line ( l,k ); respectively are resistance and reactance of a high-voltage distribution grid line ( l,k ); is a set of high-voltage distribution grid nodes; is a set of transmission grid nodes; respectively are active power and reactive power through a high-voltage distribution grid line ( m,k ); is a set of sub-nodes connected to node k .

[0017] In some embodiments, the process of establishing the electric vehicle cluster flexible scheduling model comprises: establishing, for each electric vehicle cluster, a corresponding charging time interval matrix; establishing a total load calculation equation at a high-voltage distribution grid node according to a base load at the high-voltage distribution grid node, charging and discharging power of electric vehicles, and the charging time interval matrix; establishing state of charge constraints and charging and discharging power constraints of electric vehicles.

[0018] In some embodiments, the total load calculation equation at the high-voltage distribution grid node is expressed as:

[0019] wherein, is active power consumed by a load at a high-voltage distribution grid node k ; is a base load at a high-voltage distribution grid node k ; is charging and discharging power of an electric vehicle e at a time interval t ; is an element in the e th row and the t th column of the charging time interval matrix; is a set of electric vehicles; is a set of time intervals.

[0020] ​​​​In a second aspect, the present application proposes an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements any one of the above-mentioned methods for flexible operation and risk control of high-voltage distribution networks.

[0021] In a third aspect, the present application proposes a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, any implementation method of the above-mentioned high-voltage distribution network flexible operation and risk control method is implemented.

[0022] This application proposes a flexible operation and risk control method for a high-voltage distribution network. This method utilizes a dual-dimensional flexible operation mechanism, which includes the flexible reconfiguration capability of the high-voltage distribution network topology and 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 small number of high-voltage distribution network line switching operations, thereby ensuring system operation stability and safety. Correspondingly, the electronic device and computer-readable storage medium proposed in this application also embody the above technical effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The drawings described herein are used to provide a further understanding of the embodiments of the present application, constitute a part of the present application, and do not constitute a limitation of the embodiments of the present application. In the drawings: Figure 1 A flow chart of the flexible operation and risk control method for a high-voltage distribution network proposed in an embodiment of the present application; Figure 2 This is a typical topological diagram of the urban power grid; Figure 3 Typical operating mode of 110kV substation represented by transformer unit; Figure 4 for Figure 2 The simplified topology diagram of the urban power grid is shown; Figure 5 Simplify the topology diagram of the urban power grid in a certain area; Figure 6 is the initial transmission line load rate; Figure 7 Initial charging of electric vehicle clusters; Figure 8 Transmission line load factor optimized for load shedding; Figure 9 is the load reduction amount at each transmission system node; Figure 10 The status changes of high-voltage distribution network lines in a day; Figure 11 Load reduction after optimization for flexible reconstruction of high-voltage distribution network; Figure 12 The charging and discharging situation of the electric vehicle after flexible scheduling optimization Figure 13 The load reduction of the power transmission system node after flexible scheduling optimization of the electric vehicle Figure 14 The line state change of the high-voltage distribution network after optimization considering the combined flexible scheduling strategy Figure 15 The electric vehicle charging situation after optimization considering the combined flexible scheduling strategy Figure 16 The principle block diagram of the high-voltage distribution network flexible operation and risk control device proposed by the embodiment of the application Figure 17 The high-voltage distribution network flexible operation and risk control system architecture schematic diagram proposed by the embodiment of the application Figure 18 The electronic device schematic diagram proposed by the embodiment of the application Figure 19 The computer readable storage medium schematic diagram proposed by the embodiment of the application The figure mark and the corresponding part name: 200-flexible operation and risk control device, 201-simplification unit, 202-modeling unit, 203-target construction unit, 204-optimization solving 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 DESCRIPTION

[0024] Hereinafter, the term "include" or "may include" used in various embodiments of the present application indicates the existence of the invented function, operation or element, and does not limit the addition of one or more functions, operations or elements. In addition, as used in various embodiments of the present application, the terms "include", "have" and their synonyms only mean to indicate the presence of a specific feature, number, step, operation, element, component or combination of the foregoing, and should not be understood as first excluding the presence or addition of one or more other features, numbers, steps, operations, elements, components or combinations of the foregoing or the possibility of one or more features, numbers, steps, operations, elements, components or combinations of the foregoing.

[0025] In various embodiments of the present application, the expression "or" or "at least one of A or / and B" includes any and all combinations of the listed terms. For example, the expression "A or B" or "at least one of A or / and B" can include A, can include B, or can include both A and B.

[0026] The expressions used in various embodiments of the present application, such as "first", "second", and the like, can modify various constituent elements in various embodiments, but can not limit the corresponding constituent elements. For example, the above expressions do not limit the order and / or importance of the elements. The above expressions are used only for the purpose of distinguishing one element from another. For example, the first user device and the second user device indicate different user devices, although both are user devices. For example, a first element can be called a second element, and likewise, a second element can be called a first element, without departing from the scope of various embodiments of the present application.

[0027] It should be noted that if a description connects one constituent element to another constituent element, the first constituent element can be directly connected to the second constituent element, and a third constituent element can be "connected" between the first constituent element and the second constituent element. Conversely, when one constituent element is "directly connected" to another constituent element, it can be understood that there is no third constituent element between the first constituent element and the second constituent element.

[0028] The terms used in various embodiments of the present application are used only for the purpose of describing particular embodiments and are not intended to limit various embodiments of the present 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 terms and scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which various embodiments of the present application belong. The terms such as those defined in a generally used dictionary will be interpreted to have the same meaning as the contextual meaning in the relevant technical field and will not be interpreted to have idealized or overly formal meanings, unless clearly defined in various embodiments of the present application.

[0029] In order to make the purposes, technical solutions, and advantages of the present application more clear, further detailed description will be made to the present application by combining with embodiments and drawings. The illustrative embodiments of the present application and the description thereof are only used to explain the present application and do not limit the present application.

[0030] Embodiment 1: The embodiment of the present application proposes a high-voltage distribution network flexible operation and risk control method, which adopts a double-dimensional flexible operation mechanism: one is the flexible reconstruction capability of the high-voltage distribution network topology, that is, the dynamic adjustment of power flow is realized through the standby path switching; the other is the space-time flexible scheduling capability of the electric vehicle charging and discharging behavior; the two cooperates to constitute the core of the elastic operation of the urban power grid.

[0031] As shown in the specific embodiment of the present application Figure 1 The flexible operation and risk control method proposed by the embodiment of the present application comprises: Step 110, using the transfer unit to simplify the topology structure of the urban power grid; Step 120, based on the simplified topology structure of the urban power grid, establishing a power transmission system model, a high-voltage distribution network flexible reconstruction model and an electric vehicle cluster flexible scheduling model; Step 130, based on the power transmission system model, the high-voltage distribution network flexible reconstruction model and the electric vehicle cluster flexible scheduling model, establishing a target function of the collaborative scheduling strategy; Step 140, solving the target function to obtain the optimal collaborative scheduling strategy. The optimal collaborative scheduling strategy is used for the collaborative scheduling of the high-voltage distribution network of the urban power grid.

[0032] Further, in the embodiment of the present application, the simplification process is as follows: The urban power grid mainly refers to the backbone network composed of 220kV power transmission network, 110kV high-voltage distribution network and 110kV and below distribution network. The typical topology structure is as shown in Figure 2 .

[0033] As shown in the dashed box in Figure 2 , the 110kV high-voltage distribution network maintains the radial operation structure. The electric vehicles charge and discharge in the 10kV medium-voltage distribution network, thereby changing the load rate of the 110kV substation. In order to ensure the reliability of power supply, each 110kV substation is equipped with one or more standby power supply paths connected to different power sources (220kV bus), and the main and standby ratio is close to 1:1. In addition, the transformers in the 110kV substation can operate in split or parallel mode, which complements the standby power supply path, greatly enriching the flexible operation topology of the high-voltage distribution network. The change of main and standby configuration significantly improves the power flow distribution of the upper network, thereby becoming an effective tool to alleviate the high load rate of the 220kV power transmission line. However, due to the large operation space of the high-voltage distribution network, it is difficult for the operator to quickly determine the optimal load transfer scheme in a short time. Simply relying on experience to try and error manually may lead to operation risk and affect the stable operation of the urban power grid. In view of this, the present application proposes a simplified topology representation method of high-voltage distribution network to realize variable dimension reduction, thereby improving the calculation efficiency.

[0034] High voltage distribution network substations usually adopt a single busbar segment configuration and are equipped with dual parallel transformers. The capacity of each transformer is 30 to 60MWA. Figure 3 As shown in the figure, there are two common operating modes in actual operation. The high-voltage side is connected to the 220kV transmission network, and the state of its bus tie breaker determines the load power of the 220kV substation. The low-voltage side is mainly connected to the medium-voltage distribution network, and the state of its bus tie breaker determines the power of each transformer. Therefore, to highlight the main source-load relationship during load transfer, the two switching states of the bus tie breaker within the substation are designated as Mode 1 and Mode 2. The series connection of the transformer and switch shown by the dashed line is defined as the transformer unit (TU), denoted by the letter U.

[0035] After using the transfer unit, Figure 2 The urban power grid topology shown can be represented by Figure 4 Since the high voltage distribution network needs to maintain a radial structure during operation, Figure 4 The disconnection of the high-voltage distribution network, indicated by the dashed line, is used to separate the 220kV-110kV-220kV loop. By changing the location of the disconnection, the loads of the various 220kV substations are shifted, mitigating overload risks on the transmission lines. Furthermore, load can be adjusted by rerouting the charging and discharging power of the electric vehicle clusters connected to the TU.

[0036] Furthermore, in step 120 of the embodiment of the present application, the transmission system model established includes: a power flow equation, namely, as shown in equations (1) to (4); a voltage constraint, namely, as shown in equation (5); a relaxed second-order cone programming (SOCP) constraint; an injection power constraint, namely, as shown in equations (7) and (8), which are the injected active power constraint and reactive power constraint, respectively; a transmission line power constraint, namely, as shown in equations (9) to (11); and a phase angle constraint, namely, as shown in equations (12) to (15), which defines four planes to approximate the phase angle constraint.

[0037] (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) where, and are the active and reactive power injections at bus i , respectively; and are the active and reactive power consumptions at bus i , respectively, computed by equations (3) and (4), respectively; is the set of transmission buses; is the set of time intervals; is the set of buses connected to bus i ; and are the conductance and inductance of transmission line i,j , respectively; and are the conductance and susceptance of bus i, respectively; , and are the auxiliary variables used to convexify the original AC power flow; is the set of high voltage distribution network buses connected to transmission system bus i ; and are the active and reactive power injections from transmission system bus i and high voltage distribution network bus k , respectively, through line i,k ; is the square of bus i voltage magnitude; and are the minimum and maximum of voltage magnitude, respectively; and are the minimum and maximum of active power injection, respectively; and are the minimum and maximum of reactive power injection, respectively; and are the active and reactive power through transmission line i,j , computed by equations (10) and (11), respectively; is the maximum apparent power of transmission line i,j ; is a node i The phase angle; is a node j The phase angle; 、 、 and They are h Parameters of a plane.

[0038] Furthermore, in step 120 of the embodiment of the present application, the flexible reconstruction model of the high-voltage distribution network established includes: the linearized power flow equation of the high-voltage distribution network, that is, as shown in Equation (16) and Equation (17); the power constraint, that is, as shown in Equation (18); the voltage constraint of the high-voltage distribution line, that is, as shown in Equation (19); the voltage limit constraint, that is, as shown in Equation (20); the relaxed SOCP constraint, that is, as shown in Equation (21); the high-voltage distribution network structure constraint, that is, as shown in Equation (22) and Equation (23); the high-voltage distribution line power constraint, that is, as shown in Equation (24); and the maximum load reduction constraint, that is, as shown in Equation (25).

[0039] (16) (17) (18) (19) (20) (twenty one) (twenty two) (twenty three) (twenty four) (25) in, and By connecting the transmission system nodes i and high-voltage distribution network nodes k Line ( i,k )’s active power and reactive power; and They are high-voltage distribution network nodes k Active power and reactive power consumed by the load; and They are high-voltage distribution network nodes k Active load reduction and reactive power reduction at the location; and They are respectively through the high voltage distribution network lines ( l,k )’s active power and reactive power; is the square of the current amplitude through the high-voltage distribution network line ( l,k ) ; and are the resistance and reactance of the high-voltage distribution network line ( l,k ), respectively; and are the active power and reactive power through the high-voltage distribution network line ( m,k ), respectively; is the set of parent nodes connected to node k ; is the set of child nodes connected to node k ; is the set of high-voltage distribution nodes (i.e., transformer units) ; is a binary variable representing the on-off state of line ( i , k ) ; is the maximum apparent power through line ( i , k ) ; and are the squares of the voltage amplitudes at node k and node l , respectively; is a binary variable representing the on-off state of line ( l,k ) ; is a very large constant; and are the minimum and maximum values of the square of the voltage amplitude at node k , respectively; is a binary variable representing the on-off state of line ( k,l ) ; is a binary variable representing the on-off state of line ( m,k ) ; is the maximum active power through the high-voltage distribution network line ( l,k ) ; is the maximum load shedding at high-voltage distribution network node k .

[0040] Further, in step 120 of the embodiments of the present application, the process of establishing the flexible scheduling model of the electric vehicle cluster is as follows: For each electric vehicle cluster, its corresponding charging time interval matrix is established, where the elements are defined as: (26) where, is the element in the th row and e th column of matrix t ; and are the arrival and departure times of the electric vehicles at the charging station, respectively; matrix C is a dimensional matrix; is the set of electric vehicles; is the set of time intervals.

[0041] Based on the charging time interval matrix of the electric vehicle cluster, the active power calculation equation of the load consumption at the high-voltage power distribution network node k is obtained as follows: (27) wherein, is the basic load at the high-voltage power distribution network node k ; is the charging and discharging power of the electric vehicle at the time interval e ; the state of charge (SOC) of the electric vehicle t is constrained by equation (28). e

[0042] (28) (29) (30) wherein, is the state of charge of the electric vehicle; is the energy of the electric vehicle e when arriving at the charging station; is the energy of the electric vehicle e when leaving the charging station; is the battery capacity of the electric vehicle e ; is the expected energy of the electric vehicle when leaving the charging station. The charging and discharging power constraint of the electric vehicle e is shown in equation (31): e (31) wherein, is the maximum charging and discharging power of the electric vehicle . e

[0043] Further, the target of the established high-voltage power distribution network load transfer and electric vehicle rescheduling collaborative scheduling strategy in step 130 proposed by the embodiments of the present application is to minimize the total operation cost, and the objective function is shown in equation (32).

[0044] (32) wherein, , and ​​The generation cost, load shedding cost, and electric vehicle flexible scheduling cost, respectively.

[0045] Further, in step 140 of the embodiment of the present application, a commercial solver is called to solve, and the constraint conditions, the objective function, and the control variables are input to the solver. The solver performs optimization calculation according to the input constraint conditions and objective function, combines branch and bound, heuristic search, and other methods, and outputs an optimal scheduling scheme, to ensure system operation efficiency and cost control.

[0046] To verify the effectiveness of the above flexible operation and risk control method proposed in the embodiment of the present application, the above flexible operation and risk control method proposed in the embodiment of the present application is applied to a simulation verification of a certain urban power grid. As shown in Figure 5 , the urban power grid contains 8 220kV substations, 8 220kV transmission lines, 57 110kV nodes, and 80 controllable 110kV lines. Five electric vehicle clusters are distributed in different 110kV high-voltage distribution network nodes. The initial load rate of the 220kV transmission line and the electric vehicle charging situation are shown in Figure 6 and Figure 7 , respectively.

[0047] As shown in Figure 6 , transmission lines L2, L3, L5, and L6 are overloaded at different times of the typical day. The peak charging demand occurs at 05:00, 14:00, and 21:00. To evaluate the effectiveness of the proposed method, four scenarios are considered: (1) load shedding; (2) high-voltage distribution network load transfer; (3) electric vehicle rescheduling; (4) electric vehicle rescheduling combined with load transfer. All simulations are performed using the Gurobi solver on a personal computer equipped with a 1.6GHz processor and 16GB of RAM.

[0048] (1) Load shedding scenario.

[0049] In this scenario, load shedding is the only method to alleviate transmission congestion. The optimization results are shown in Figure 8 and Figure 9 .

[0050] Figure 8 shows the change of the load rate of the transmission line after optimization under the load shedding scenario, reflecting the reduction effect of the load rate of each line (L1 to L8) in a day under the load shedding scenario. The load rate of all lines remains below 1.0 p.u. at all times, indicating that there is no risk of overload.

[0051] Figure 9 shows the load shedding of each transmission system node in a day. As shown in Figure 9As shown, the load shedding significantly increases around noon (about 12:00-15:00). During this period, the transmission system line load rate exceeds the power supply capacity, and load shedding is needed to maintain grid safety. Different transmission system nodes (S1-S8) contribute differently to the total load shedding. Some substations have little or no load shedding throughout the day. It is worth noting that S2 and S6 have relatively high load shedding during peak hours compared to other transmission system nodes. The total load shedding throughout the day reaches 162.1720 MWh, accounting for 24.86% of the total load.

[0052] (2) High-voltage distribution network flexible reconstruction scenario.

[0053] In this scenario, high-voltage distribution network flexible reconstruction is the only method to alleviate transmission congestion. The optimization results are shown in Figure 10 and Figure 11 .

[0054] As shown in Figure 10 , 12 high-voltage distribution network lines change their on-off state throughout the day. Most high-voltage distribution network lines switch once during 6:00-8:00, thereby alleviating the risk of overload. The load shedding of each transmission system node is shown in Figure 11 , where only S1 and S6 reduce the load demand at noon. The total load shedding reaches 96.3486 MWh, accounting for 14.21% of the total load. Compared with Figure 9 , the total load shedding is reduced by 40.59%.

[0055] (3) Electric vehicle flexible scheduling scenario.

[0056] In this scenario, electric vehicle flexible scheduling is the only method to alleviate transmission congestion. The optimization results are shown in Figure 12 and Figure 13 .

[0057] As shown in Figure 12 , each electric vehicle cluster has a charging (positive power) and discharging (negative power) period. Electric vehicle clusters 1 and 2 are more active in the early morning and evening. Compared with the initial curve, the optimized charging behavior shows more regular power demand, highlighting the potential for more effective load balancing and reducing the risk of transmission line overload across time.

[0058] Figure 13The load shedding of transmission system nodes S1, S2 and S6 between 00:00 and 11:00 is shown. The peak shedding occurs between 09:00 and 12:00, with a total shedding of 35-40 MW. The sub-peak appears between 03:00 and 06:00, with 15-20 MW. Node S6 (light gray) contributes about 40-50% of the total shedding throughout the day, highlighting its key role in reducing the risk of device overload. Nodes S1 and S2 also have significant involvement, especially during the early morning peak from 08:00 to 12:00. The total load shedding for the day is 141.0138 MWh, accounting for 20.79% of the total load demand. Compared with Figure 9 , the total load shedding is reduced by 13.05%.

[0059] (4) Combined scenario of electric vehicle flexible scheduling and high-voltage distribution network flexible reconstruction (i.e., the flexible operation and risk control method proposed in the embodiments of the present application).

[0060] In this scenario, electric vehicle flexible scheduling and high-voltage distribution network flexible reconstruction are jointly applied to reduce the risk of overload. The optimization results are shown in Figure 14 and Figure 15 .

[0061] As shown in Figure 14 , high-voltage distribution network lines 15 and 53 are connected at 20:00, while lines 27 and 40 are disconnected at the same time. Compared with Figure 10 , only four high-voltage distribution network lines change their on-off state, indicating that the network configuration is more stable.

[0062] The rescheduling strategy of different electric vehicle clusters is shown in Figure 15 . The peak charging of electric vehicles occurs between 18:00 and 24:00, while the charging between 06:00 and 12:00 is the smallest. Electric vehicle cluster 5 always provides the largest charging capacity. Clusters 1 to 4 show an interleaved charging pattern, with clusters 1 and 2 contributing significantly during the evening peak period (18:00-24:00). It is worth noting that under this combined strategy, no load shedding is required throughout the day.

[0063] The comparison of the four scenarios is summarized in Table 1.

[0064] Table 1 Comparison of the four scenarios

[0065] As shown in Table 1, the load shedding scenario results in the highest total shedding, reaching 162.1720 MWh. In the high-voltage distribution network load transfer scenario, through 12 high-voltage distribution network line switches, the shedding is reduced by 40.59% to 96.3486 MWh. Although this method is effective, frequent line switching may affect system operation stability.

[0066] The flexible scheduling scenario of electric vehicles generates an economic benefit of 1.0051x10 4 dollars through the rescheduling of electric vehicles, proving the effectiveness of demand-side flexibility in reducing load shedding and related operating costs.

[0067] In contrast, the flexible scheduling scenario of electric vehicles combined with flexible reconstruction of high-voltage distribution networks proposed in the embodiments of the present application completely eliminates load shedding, only requires four line switching, and achieves a maximum economic benefit of 2.9764x10 4 dollars. This highlights the synergistic advantages of combining electric vehicle flexibility with grid operation strategies (flexible reconstruction of high-voltage distribution networks addresses physical network constraints, while flexible scheduling of electric vehicles promotes load balancing in time and space). Overall, the method proposed in the embodiments of the present application provides the best solution by simultaneously eliminating unnecessary load shedding and maximizing economic performance.

[0068] Based on the same technical concept described above, the embodiments of the present application also propose a flexible operation and risk control device for high-voltage distribution networks, as shown in Figure 16 which includes: a simplification unit 201 that simplifies the topology of the urban power grid using a transfer unit. The specific simplification process is described in step 110 above, and will not be repeated here.

[0069] a modeling unit 202 that establishes a power transmission system model, a flexible reconstruction model of high-voltage distribution networks, and a flexible scheduling model of electric vehicle clusters based on the simplified topology of the urban power grid. The specific modeling process is described in step 120 above, and will not be repeated here.

[0070] a target construction unit 203 that establishes a target function of the collaborative scheduling strategy based on the power transmission system model, the flexible reconstruction model of high-voltage distribution networks, and the flexible scheduling model of electric vehicle clusters. The specific target function is described in step 130 above, and will not be repeated here.

[0071] and an optimization solving unit 204 that solves the target function to obtain an optimal collaborative scheduling strategy. The specific solving method is described in step 140 above, and will not be repeated here.

[0072] Based on the same technical concept described above, the embodiments of the present application also propose a flexible operation and risk control system for high-voltage distribution networks, as shown in Figure 17 which includes: Input device 301, output device 302, processor A303 and memory A304; wherein the number of processor A303 and memory A304 can be one or more, Figure 3 The input device 301, the output device 302, the processor A303 and the memory A304 can be connected by a bus or other means. Figure 3 The bus connection is taken as an example.

[0073] By calling the operation instructions stored in the memory A304, the processor A303 is configured to perform the following steps: The topology of the urban power grid is simplified by using the power transfer unit; Based on the simplified urban power grid topology, a transmission system model, a high-voltage distribution network flexible reconstruction model, and an electric vehicle cluster flexible scheduling model were established; Based on the transmission system model, the high-voltage distribution network flexible reconstruction model and the electric vehicle cluster flexible scheduling model, the objective function of the coordinated scheduling strategy is established; Solve the objective function and obtain the optimal collaborative scheduling strategy.

[0074] Optionally, by calling the operation instructions stored in the memory A304, the processor A303 is also used to execute any implementation method in the corresponding embodiments of the above-mentioned flexible operation and risk control method.

[0075] Based on the same technical concept as above, the embodiment of the present 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, the following steps are implemented: The topology of the urban power grid is simplified by using the power transfer unit; Based on the simplified urban power grid topology, a transmission system model, a high-voltage distribution network flexible reconstruction model, and an electric vehicle cluster flexible scheduling model were established; Based on the transmission system model, the high-voltage distribution network flexible reconstruction model and the electric vehicle cluster flexible scheduling model, the objective function of the coordinated scheduling strategy is established; Solve the objective function and obtain the optimal collaborative scheduling strategy.

[0076] Optionally, when the processor B420 executes the computer program A411, any implementation method corresponding to the above-mentioned flexible operation and risk control method can be implemented.

[0077] It should be noted that the electronic device proposed in the embodiment of the present application 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 the embodiment of the present application, technical personnel in this field can understand the specific implementation methods of the electronic device in the embodiment of the present application and its various variations. Therefore, how the electronic device specifically implements the above-mentioned flexible operation and risk control method will not be introduced in detail here. As long as the electronic device used by technical personnel in this field to implement the above-mentioned flexible operation and risk control method falls within the scope of protection to be protected by this application.

[0078] Based on the same technical concept as above, the embodiment of the present application also proposes a computer-readable storage medium, such as Figure 19 As shown, the computer readable storage medium 500 stores a computer program B511. When the computer program B511 is executed by the processor, the following steps are implemented: The topology of the urban power grid is simplified by using the power transfer unit; Based on the simplified urban power grid topology, a transmission system model, a high-voltage distribution network flexible reconstruction model, and an electric vehicle cluster flexible scheduling model were established; Based on the transmission system model, the high-voltage distribution network flexible reconstruction model and the electric vehicle cluster flexible scheduling model, the objective function of the coordinated scheduling strategy is established; Solve the objective function and obtain the optimal collaborative scheduling strategy.

[0079] Optionally, when the computer program B511 is executed by a processor, it can implement any implementation method in the embodiments corresponding to the above-mentioned flexible operation and risk control method.

[0080] It should be noted that, in the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0081] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0082] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart or flowsheet block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 one or more flowcharts and / or blocks

[0083] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart or flowsheet block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 one or more flowcharts and / or blocks

[0084] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart or flowsheet block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 one or more flowcharts and / or blocks

[0085] The above detailed description has been presented for the purposes of illustration and description. It is not intended to be exhaustive or to limit the application to the precise form described, and obviously many modifications and variations are possible in light of the above teaching. It is intended that the scope of the application be limited not with this detailed description, but rather by the claims appended hereto.

Claims

1. A flexible operation and risk control method for high-voltage power distribution networks, characterized in that, The application relates to a high-voltage power distribution network flexible operation and risk control method. The method comprises the following steps: S1, simplifying the topology structure of a city power grid by using a transfer unit; S2, establishing an output system model, a high-voltage power distribution network flexible reconstruction model and an electric vehicle cluster flexible scheduling model based on the simplified topology structure of the city power grid; S3, establishing a target function of a collaborative scheduling strategy based on the output system model, the high-voltage power distribution network flexible reconstruction model and the electric vehicle cluster flexible scheduling model; 2. The flexible operation and risk control method for high-voltage distribution network according to claim 1, characterized in that, S4, solving the target function to obtain an optimal collaborative scheduling strategy. The step S1 of simplifying the topology structure of the city power grid by using the transfer unit comprises the following steps: S11, defining two switch states of a bus coupler switch in a transformer substation in the high-voltage power distribution network in the topology structure of the city power grid as two modes, and defining a series structure of a transformer and a switch as a transfer unit; 3. The flexible operation and risk control method for high-voltage distribution network according to claim 1, characterized in that, S12, replacing the high-voltage power distribution network in the topology structure of the city power grid by using the connection relationship of the transfer unit in the two modes to obtain the simplified topology structure of the city power grid. The step S2 of establishing the output system model comprises the following steps: S21, establishing a power flow equation of a power transmission system according to active power and reactive power injected by nodes in the power transmission system, active power and reactive power consumed by the nodes, conductance and inductance of transmission lines in the power transmission system and auxiliary variables for convexifying original alternating current power flow; wherein the active power and the reactive power consumed by the nodes in the power transmission system are calculated according to active power and reactive power drawn from the nodes in the power transmission system through lines connecting the nodes in the power transmission system and nodes in a high-voltage power distribution network; 4. The flexible operation and risk control method for high-voltage distribution network according to claim 3, characterized in that, S22, establishing constraint conditions of the power flow equation, including voltage constraints, relaxed second-order cone programming constraints, injected power constraints, transmission line power constraints and phase angle constraints. in, and Node i Injected active and reactive power; and Node i Active and reactive power consumed; is the set of transmission nodes; is a set of time intervals; Is with the node i The set of connected nodes; and They are transmission lines ( The power flow equation of the power transmission system is expressed as: )'s conductance and inductance; and They are; 、 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, i,j The step S3 of establishing the high-voltage power distribution network flexible reconstruction model comprises the following steps: S31, establishing a linearized power flow equation of the high-voltage power distribution network according to active power and reactive power through lines connecting nodes in a power transmission system and nodes in a high-voltage power distribution network, active power and reactive power consumed by loads at the nodes in the high-voltage power distribution network, load reduction amount at the nodes in the high-voltage power distribution network, active power and reactive power through high-voltage power distribution network lines, square of current amplitude through the high-voltage power distribution network lines and resistance and reactance of the high-voltage power distribution network lines; 6. The flexible operation and risk control method for a high-voltage distribution network according to claim 5, characterized in that, S32, establishing constraint conditions of the linearized power flow equation, including power constraints, high-voltage power distribution line voltage constraints, voltage limit constraints, relaxed second-order cone programming constraints, high-voltage power distribution network structure constraints, high-voltage power distribution line power constraints and maximum load reduction amount constraints. in, and By connecting the transmission system nodes i and high-voltage distribution network nodes k Line ( The linearized power flow equation of the high-voltage power distribution network is expressed as: )’s active power and reactive power; and They are high-voltage distribution network nodes k Active power and reactive power consumed by the load; and They are high-voltage distribution network nodes k Active load reduction and reactive power reduction at the location; and They are respectively through the high voltage distribution network lines ( l,k )’s active power and reactive power; It is through the high-voltage distribution network line ( l,k ) of the square of the current amplitude; and They are high voltage distribution network lines ( l,k )’s resistance and reactance; It is a collection of high-voltage distribution nodes; is the set of transmission nodes; and They are respectively through the high voltage distribution network lines ( i,k )’s active power and reactive power; Is with the node k The collection of connected child nodes.

7. The flexible operation and risk control method for high voltage distribution network according to any one of claims 1-6, characterized in that, m,k The step S4 of establishing the electric vehicle cluster flexible scheduling model comprises the following steps: S41, establishing a corresponding charging time interval matrix for each electric vehicle cluster; S42, establishing a total load calculation equation at a node in the high-voltage power distribution network according to basic load at the node in the high-voltage power distribution network, charging and discharging power of the electric vehicle and the charging time interval matrix; 8. The flexible operation and risk control method for a high-voltage distribution network according to claim 7, characterized in that, S43, establishing state of charge constraints and charging and discharging power constraints of the electric vehicle. wherein is a high-voltage distribution grid node k consumes active power; is a high-voltage distribution grid node k consumes base load; is an electric vehicle e charges and discharges power in the time interval t ; is the element in the e th row and t th column of the charging time interval matrix; is a set of electric vehicles; is a set of time intervals. 9.An electronic device comprising a memory and a processor, the memory storing a computer program, wherein, The total load calculation equation at the node in the high-voltage power distribution network is expressed as: The processor executes the computer program to realize the high-voltage power distribution network flexible operation and risk control method in any one of claims 1-8.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by a processor to implement the flexible operation and risk control method of the high-voltage power distribution network in any one of claims 1-8.

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