Double-layer emergency load shedding control method considering large-scale distributed photovoltaic grid connection

Abstract

The invention provides a double-layer emergency load shedding control method considering large-scale distributed photovoltaic grid connection, and belongs to the technical field of power system control. The method comprises the steps that a main network layer constructs a main network optimization model with the minimum load shedding cost as a target function; solving the main network optimization model by adopting an improved Hemma optimization algorithm to obtain a load shedding scheme; the distribution network layer takes the total load shedding amount issued by the main network layer as a boundary condition, and constructs a multi-target optimization model taking the minimization of feed line shedding loss and minimization of main network bus voltage drop as targets; solving the multi-objective optimization model by adopting an NSGA-II algorithm to obtain a feeder cutting scheme of each power distribution area; and executing a corresponding emergency load shedding control strategy when the power grid breaks down. According to the invention, collaborative optimization of safety, stability and power supply economy under the power grid fault is realized.

Description

Technical Field

[0001] This invention relates to a two-layer emergency load shedding control method considering large-scale distributed photovoltaic grid connection, belonging to the field of power system control technology. Background Technology

[0002] With the high proportion of distributed photovoltaic (PV) power and the integration of multiple critical loads into the new power system, the characteristics of power grid operation are becoming increasingly complex and interactive. The emergency load shedding strategy, traditionally used as a second line of defense, is no longer adequate for the operational needs of highly coupled interconnected power grids. When a severe fault causes a voltage drop, traditional methods often ignore the real-time output status of distributed power sources, and their control commands may mistakenly disconnect these sources. This not only amplifies load shedding losses but also leads to large-scale disconnection of distributed PV power due to voltage exceeding limits. This lack of selectivity in disconnection can trigger drastic power flow shifts, induce cascading faults, and ultimately even escalate into regional blackout risks.

[0003] To address the shortcomings of traditional emergency load shedding control strategies in scenarios involving large-scale distributed photovoltaic systems, the main technical issues include the following two aspects: (1) Ensure the reliability of distributed photovoltaic grid connection. When formulating the scheme at the main grid level, the objective function is to minimize economic losses, while the low voltage ride-through requirement of distributed photovoltaic is taken as a key constraint. The improved Hippo optimization algorithm is used for solution. This method can not only ensure that the load shedding loss required for system voltage recovery is minimized, but also effectively reduce the grid disconnection loss of distributed photovoltaic caused by voltage instability, thereby improving the utilization efficiency and grid connection reliability of distributed energy.

[0004] (2) Reduce losses from critical load shedding. At the distribution network level, a multi-objective optimization model is constructed with the total load shedding amount issued by the main grid as a constraint. The objective functions are to minimize the cost of load feeder shedding and to minimize voltage drop. The model is solved using the NSGA-II algorithm. The aim is to achieve the dual objectives of minimizing critical load losses and improving local voltage recovery capability, while also improving computational efficiency.

[0005] Therefore, there is an urgent need for an emergency load shedding control strategy that can adapt to large-scale distributed photovoltaic grid-connected scenarios and take into account both system security and power supply reliability. Summary of the Invention

[0006] The purpose of this invention is to provide a two-layer emergency load shedding control method that considers large-scale distributed photovoltaic grid connection, so as to solve the problems of voltage instability, photovoltaic grid disconnection and large loss of important loads in the traditional method under high photovoltaic penetration scenario, and achieve synergistic optimization of safety and stability and power supply economy under grid fault.

[0007] To achieve the above objectives, the present invention employs the following technical solution: A two-layer emergency load shedding control method considering large-scale distributed photovoltaic grid connection includes the following steps: Collect grid status information for main grid faults; The main grid layer constructs a main grid optimization model with the objective function of minimizing load shedding costs. The objective function includes load shedding costs and distributed photovoltaic shedding costs. The model includes voltage security constraints, load shedding upper limit constraints, frequency security constraints, power angle constraints, and voltage recovery constraints. The improved Hippo optimization algorithm is used to solve the main grid optimization model to obtain the load shedding scheme, including the load shedding amount and photovoltaic power cut-off amount of each load node; The distribution network layer uses the load shedding amount of each load node issued by the main network layer as the boundary condition to construct a multi-objective optimization model with the objectives of minimizing feeder shedding loss and minimizing the voltage drop of the main network bus. The NSGA-II algorithm is used to solve the multi-objective optimization model to obtain the feeder cut-off scheme for each power distribution area.

[0008] Preferably, the objective function is: , in, For nodes that need to be shelved, The number of load shedding types for each node. It is the first Value coefficient of similar load, It is the value coefficient of photovoltaics. It is the first Nodes The magnitude of the load shedding for a given type of load. This is the amount of photovoltaic losses.

[0009] Preferably, the constraints of the mainnet optimization model include: , , , , , , in, This is the sum of the load shedding amounts at each node. This represents the maximum load that a node can remove. This represents the minimum allowable voltage under stable node conditions. The voltage magnitude after load is removed from each node. This represents the maximum transmission current of each transmission line in the power grid. This refers to the current after the load on the transmission line is disconnected. This is the lower limit of the allowed frequency of the system. The frequency after load removal at each node. This is a limit for the stability of the system's power angle. The power angle after the generator load is removed. To meet the load low-voltage ride-through requirements of distributed photovoltaic grid-connected point voltage that varies over time, The voltage at the grid connection point of the distributed photovoltaic system varies over time. For fault clearing time, This is the time it takes for the voltage to eventually recover.

[0010] Preferably, the step of solving the mainnet optimization model using the improved hippo optimization algorithm includes the following steps: Based on mainnet nodes The ability of load shedding to boost grid voltage The initial load shedding amount is allocated in stages. Nodes with larger values ​​are allocated more load-switching capacity; The initial population was generated using Latin hypercube sampling, and the main network optimization model was optimized using an improved hippo optimization algorithm; the improved hippo optimization algorithm introduced adaptive constraint processing.

[0011] Preferably, the main network node The ability of load shedding to boost grid voltage The calculation formula is: , , , in, Represents a node Load to Node The effect of voltage, The number of load nodes, For nodes With nodes mutual impedance between For nodes voltage, For nodes The load power factor, The sum of the four phase angles is calculated. For nodes With nodes The phase angle between them For nodes voltage phase angle, For nodes The load power factor angle, For nodes The voltage phase angle.

[0012] Preferably, the specific formula for the graded allocation of the initial load shedding amount is as follows: , in, This is the upper limit of the configurable load shelving capacity of a load node. It is the lower limit of the configurable load shelving capacity of a load node. This represents the average maximum value of load nodes that require emergency load shedding. The minimum average value for load nodes that require emergency load shedding. The load shelvable size that needs to be configured for the load node.

[0013] Preferably, the adaptive constraint processing dynamically adjusts the constraint violation threshold during the evolution of the hippo optimization algorithm; including: Criterion 1: If all load shedding schemes satisfy the constraints of the main grid optimization model, then the load shedding scheme with the lower objective function value is selected. Criterion 2: If there are load shedding schemes that satisfy and do not satisfy the main network optimization model constraints, calculate the constraint violation degree of the load shedding schemes that do not satisfy the main network optimization model constraints. If the constraint violation degree is less than or equal to the constraint violation degree threshold If the objective function value is lower, then the load shedding scheme with the lower objective function value is selected. If the constraint violation degree is greater than the constraint violation degree threshold, then... If so, then select the load shedding scheme that satisfies the constraints of the main network optimization model; Criterion 3: If none of the load shedding schemes satisfy the constraints of the main network optimization model, and if the degree of constraint violation is less than [a certain value]... Choose the load shedding scheme with the lowest objective function value, if the constraint violation degree is greater than or equal to If the constraint violation is small, then choose the load shedding scheme.

[0014] Preferably, the constraint violation degree is calculated as follows: , in, To calculate the constraint violators of load shedding in the time domain, For constraint limits; The method for dynamically adjusting the constraint violation threshold is as follows: , In the formula, The decreasing coefficient, To truncate algebra, For the number of iterations, To constrain the initial value of the violation threshold, It is a constant.

[0015] Preferably, the objective function of the multi-objective optimization model includes: , , , in, This represents the losses in the load shedding feeder scheme of the distribution network. This indicates the feeder status; a value of 0 means the feeder is retained, and a value of 1 means the feeder is disconnected. This indicates the value of the corresponding load feeder. Represents a node The number of feeders, objective function To minimize the voltage drop on the main grid bus. This represents the main grid node voltage after the load feeder is disconnected. The load shedding amount issued by the main network. The set load variation range is used to configure the feeder combination. This corresponds to the load size of the feeder.

[0016] The advantages of this invention are: This invention ensures the reliability of distributed photovoltaic (PV) grid connection. When formulating the scheme at the main grid level, minimizing economic losses is the objective function, while the low-voltage ride-through requirement of distributed PV is considered a key constraint. An improved Hippo optimization algorithm is used for solution. This method not only ensures that the load shedding loss required for system voltage recovery is minimized, but also effectively reduces the grid disconnection loss of distributed PV caused by voltage instability, thereby improving the utilization efficiency and grid connection reliability of distributed energy.

[0017] To reduce losses from critical load shedding, at the distribution network level, a multi-objective optimization model is constructed with the total load shedding amount from the main grid as a constraint. The model aims to minimize both the cost of load feeder shedding and voltage drop, and is solved using the NSGA-II algorithm. This approach aims to achieve the dual objectives of minimizing critical load losses and improving local voltage recovery capabilities, while simultaneously improving computational efficiency. An initial population generation mechanism based on the average analytical sensitivity of nodes enables the optimization algorithm to quickly identify load nodes that significantly support voltage, achieving rapid system voltage recovery even with relatively small load shedding amounts.

[0018] The improved hippo optimization algorithm used in the main network layer accelerates the convergence process while enhancing population diversity through sensitivity-guided initial solution generation and adaptive constraint processing mechanism, thus improving the convergence speed and effectively avoiding getting trapped in local optima. Attached Figure Description

[0019] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0020] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0021] Figure 2 A schematic diagram of the HO optimization algorithm process.

[0022] Figure 3 This is a flowchart illustrating the implementation and application of the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] Example 1 like Figures 1-3 As shown, a two-layer emergency load shedding control method considering large-scale distributed photovoltaic grid connection is proposed. By optimizing the load shedding schemes of the main grid layer and the distribution grid layer respectively, it can ensure that the total load shedding is minimized, while also ensuring that the system voltage can be quickly restored to the level that meets the low voltage ride-through requirements of distributed photovoltaic, and minimizing the shedding of important loads, thereby improving the safety and power supply reliability under grid fault conditions.

[0025] Specifically, it includes the following steps: S1: Collect grid status information for main grid faults; S2: The main grid layer constructs a main grid optimization model with the objective function of minimizing the load shedding cost. The objective function includes the load shedding cost and the distributed photovoltaic shedding cost. The model includes voltage security constraints, load shedding upper limit constraints, frequency security constraints, power angle constraints, and voltage recovery constraints. S3: The improved Hippo optimization algorithm is used to solve the main grid optimization model to obtain the load shedding scheme, including the load shedding amount and photovoltaic power cut-off amount of each load node; S4: The distribution network layer uses the sum of the load shedding amounts of each load node issued by the main network layer as the boundary condition to construct a multi-objective optimization model with the objectives of minimizing feeder shedding losses and minimizing the voltage drop of the main network bus. S5: The NSGA-II algorithm is used to solve the multi-objective optimization model to obtain the feeder cut-off scheme for each power distribution area.

[0026] As a refinement of the above embodiments, the objective function in step S1 is: , in, For nodes that need to be shelved, The number of load shedding types for each node. It is the first Class load, and It is the value coefficient of photovoltaics. It is the first Nodes The magnitude of the load shedding for a given type of load. This is the amount of photovoltaic losses.

[0027] The feeder system is divided into critical load power, non-critical load power, and distributed photovoltaic (PV) output. The cost factor for load shedding can be set based on the unit outage loss for different types of loads. The shedding costs for critical and non-critical loads are set at RMB 10.07 / kW and RMB 0.62 / kW, respectively. The cost factor for disabling distributed PV can be calculated based on the difference in unit cost between coal-fired power generation and PV power generation. When considering environmental costs, the long-term return on investment for both power generation methods is analyzed. The cost per kilowatt-hour of coal-fired power generation is approximately RMB 0.34 / (kW·h) higher than that of PV power generation. In addition, considering that the actual load power cut when a feeder containing distributed PV is disconnected is greater than the measured value, this load loss should be added when calculating the cost of disabling distributed PV, resulting in a cost of RMB 0.96 / kW.

[0028] As a refinement of the above embodiments, for complex large power grids, emergency load shedding measures need to protect the safe operation of the grid and prevent the second line of defense from being triggered when a serious power system fault occurs. Therefore, the inequality constraints for emergency load shedding optimization should include voltage safety constraints, load shedding upper limit constraints, and frequency safety constraints. During the emergency load shedding process after a fault, in addition to directly disconnecting some loads and photovoltaic units, if control measures cause the voltage at the distributed photovoltaic grid connection point to deviate from the allowable range, it will trigger a larger-scale passive grid disconnection of photovoltaic power, thereby exacerbating the system power deficit. To prevent such cascading faults caused by voltage instability, it is essential to ensure that the grid connection point voltage always meets the requirements during the control process; therefore, voltage recovery constraints are designed.

[0029] Specifically, the constraints of the mainnet optimization model include: , , , , , , in, This is the sum of the load shedding amounts at each node. This represents the maximum load that a node can remove. This represents the minimum allowable voltage under stable node conditions. The voltage magnitude after load is removed from each node. This represents the maximum transmission current of each transmission line in the power grid. This refers to the current after the load on the transmission line is disconnected. This is the lower limit of the allowed frequency of the system. The frequency after load removal at each node. This is a limit for the stability of the system's power angle. The power angle of the generator after load shedding. To meet the load low-voltage ride-through requirements of distributed photovoltaic grid-connected point voltage that varies over time, The voltage at the grid connection point of the distributed photovoltaic system varies over time. For fault clearing time, This is the time it takes for the voltage to eventually recover.

[0030] As a refinement of the above embodiments, step S2 employs an improved hippo optimization algorithm to solve the mainnet optimization model, including the following steps: S201: Based on mainnet nodes The ability of load shedding to boost grid voltage The initial load shedding amount is allocated in stages. Nodes with larger values ​​are allocated more load-switching capacity. This includes: S2011: Initial Population Optimization: This method prioritizes allocating more shelvable load capacity to high-sensitivity node regions. By shedding a small amount of load at high-sensitivity nodes, effective voltage regulation of critical nodes can be achieved, thus significantly reducing the total load shedding while maintaining the same stability, avoiding resource waste. This method accelerates algorithm convergence by improving the quality of the initial population. Its control principle is as follows: , In the formula: and These are the voltage and current phasors of the load node, respectively; Let be the voltage phasor of the generator node; m and n are the number of generator nodes and the number of load nodes, respectively; Z is the system network impedance matrix oriented towards the load nodes; K is the relationship matrix between the load node voltage vector and the generator node voltage vector when all load branches are open.

[0031] Simplifying the above equation and replacing the current with complex power, we get: , in, For nodes voltage, This represents the number of generator nodes. This represents the relationship between the load node voltage phasors and the generator node voltage phasors when all load branches are open. For the first Generator node voltage phasors The number of load nodes, For nodes With nodes mutual impedance between For nodes The conjugate value of the power, For nodes The conjugate value of the voltage phasor.

[0032] Furthermore, expressing the above formula in terms of amplitude and phase angle, and simplifying it further, we can obtain the following formula: , , in, Represents a node Load to Node The effect of voltage, The number of load nodes, For nodes With nodes mutual impedance between For nodes voltage, For nodes The load power factor, The sum of the four phase angles is calculated. For nodes With nodes The phase angle between them For nodes voltage phase angle, For nodes The load power factor angle, For nodes The voltage phase angle.

[0033] use Characterizing mainnet nodes The ability of load shedding to boost grid voltage is used as the average analytical sensitivity of each load node. Represents a node Load to Node The effect of voltage can be determined by calculating its average value to identify suitable nodes for load shedding. , To improve population coverage and simulation efficiency, based on the calculated... The initial load shedding amount of each load node is classified according to its size. Larger nodes can be allocated more slicable negative values.

[0034] Based on the average analytical sensitivity of each load node, the initial shearable load capacity is allocated: , In the formula, and These are the upper and lower limits of the configurable load shelving capacity of the load node, respectively. and These are the average maximum and minimum values ​​of the load nodes that require emergency load shedding, respectively. The load shelvable size that needs to be configured for the load node.

[0035] S202: The initial population is generated using Latin hypercube sampling, and the main network optimization model is optimized using an improved hippo optimization algorithm; the improved hippo optimization algorithm introduces adaptive constraint processing.

[0036] (1) After optimizing the initial shearable load size of each node, LHS sampling (Latin hypercube sampling) is used to generate an initial population to improve population coverage: , In the formula, and These represent the number of samples and the dimension, respectively. A random number between 0 and 1 The total number of partitions for each dimension. This is a randomly generated permutation.

[0037] The algorithm used to solve this optimization problem must possess both excellent global exploration and local exploitation capabilities. The former ensures the algorithm escapes the trap of local optima, extensively explores the solution space, and locates the globally optimal region; the latter guarantees that it can perform a fine-grained search within this region, ultimately converging precisely to the global optimum. The HO algorithm selected in this paper uses a three-stage model, combining the hippopotamus's position update in a river or pond with its predator defense strategies and escape methods. This algorithm adaptively adjusts the resolution and speed of the search space to quickly and accurately find the optimal solution, exhibiting fast convergence speed and high solution accuracy. The specific steps are as follows: Figure 2 As shown.

[0038] (2) Constraint handling methods An adaptive constraint handling technique is introduced, which improves upon traditional feasibility rules by incorporating potentially infeasible solutions into the optimization process, thereby more effectively exploring the global optimum located near the feasible region boundary. The specific comparison criteria are as follows: Criterion 1: If all load shedding schemes satisfy the constraints of the main network optimization model, then the load shedding scheme with the lower objective function value is selected.

[0039] Criterion 2: If there are load shedding schemes that satisfy and do not satisfy the main network optimization model constraints, calculate the constraint violation degree of the load shedding schemes that do not satisfy the main network optimization model constraints. If the constraint violation degree is less than or equal to the constraint violation degree threshold If the objective function value is lower, then the load shedding scheme with the lower objective function value is selected. If the constraint violation degree is greater than the constraint violation degree threshold, then... Then, select the load shedding scheme that satisfies the constraints of the main network optimization model.

[0040] The constraint violation degree is calculated as follows: , In the formula, To calculate the constraint violators of load shedding in the time domain, These are constraint limits.

[0041] Criterion 3: If none of the load shedding schemes satisfy the constraints of the main network optimization model, and if the degree of constraint violation is less than [a certain value]... Choose the load shedding scheme with the lowest objective function value, if the constraint violation degree is greater than or equal to If the constraint violation is small, then choose the load shedding scheme.

[0042] The population adapts to the degree of constraint violation in the current stage, incorporating infeasible individuals in the early stages and later integrating them into the later stages of evolution. Set to 0, as shown in the formula: , In the formula, It is a decreasing coefficient (with a value greater than 3). To truncate the algebra, when the value is 0, the constraint handling method becomes a feasibility rule. For the number of iterations, To constrain the initial value of the violation threshold, It is a constant.

[0043] As a refinement of the above embodiments, in step S3, the distribution network model uses the shelvable load amount issued by the transmission network master station as the boundary condition, and aims to minimize feeder shedding losses and the voltage drop level of the main grid bus as the objectives, to construct an optimization model for the distribution network load feeder shedding scheme. The specific mathematical model construction is as follows: , , , In the formula, This represents the losses in the load shedding feeder scheme of the distribution network. This indicates the feeder status; a value of 0 means the feeder is retained, and a value of 1 means the feeder is disconnected. This indicates the value of the corresponding load feeder. Represents a node The number of feeders, objective function To minimize the voltage drop on the main grid bus. This represents the main grid node voltage after the load feeder is disconnected. The load shedding amount issued by the main network. The set load variation range is used to configure the feeder combination. This corresponds to the load size of the feeder.

[0044] This indicates that the total load shedding amount in the final load feeder shedding scheme of the distribution network should be as close as possible to the load shedding amount issued by the main grid. It is a relatively small value, ranging from 5% to 10% of the maximum shearable load.

[0045] The NSGA-II algorithm is used to solve this mathematical model. Due to its robustness and high convergence accuracy, the NSGA-II algorithm is widely used in multi-objective optimization. Its main ideas are threefold: fast non-dominated sorting accelerates convergence, an elitist strategy ensures convergence robustness, and the use of a crowding operator to replace shared parameters maintains population diversity. Therefore, this algorithm is used to solve the computation of multi-objective optimization models.

[0046] Example 2 This disclosure also provides a two-layer emergency load shedding control device for large-scale distributed photovoltaic grid connection, including a processor and a memory. Optionally, the device may further include a communication interface and a bus. The processor, communication interface, and memory can communicate with each other via the bus. The communication interface can be used for information transmission. The processor can call logical instructions in the memory to execute the two-layer emergency load shedding control method for large-scale distributed photovoltaic grid connection described in the above embodiments.

[0047] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium.

[0048] Memory, as a computer-readable storage medium, can be used to store software programs and computer-executable programs, such as program instructions / modules corresponding to the methods in the embodiments of this disclosure. The processor executes functional applications and data processing by running the program instructions / modules stored in the memory, thereby implementing the two-layer emergency load shedding control method for large-scale distributed photovoltaic grid connection considered in the above embodiments.

[0049] The memory may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the terminal device. Furthermore, the memory may include high-speed random access memory and may also include non-volatile memory.

[0050] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A two-layer emergency load shedding control method considering large-scale distributed photovoltaic grid connection, characterized in that, Includes the following steps: Collect grid status information for main grid faults; The main grid layer constructs a main grid optimization model with the objective function of minimizing load shedding costs. The objective function includes load shedding costs and distributed photovoltaic shedding costs. The model includes voltage security constraints, load shedding upper limit constraints, frequency security constraints, power angle constraints, and voltage recovery constraints. The improved Hippo optimization algorithm is used to solve the main grid optimization model to obtain the load shedding scheme, including the load shedding amount and photovoltaic power cut-off amount of each load node; The distribution network layer uses the load shedding amount of each load node issued by the main network layer as the boundary condition to construct a multi-objective optimization model with the objectives of minimizing feeder shedding loss and minimizing the voltage drop of the main network bus. The NSGA-II algorithm is used to solve the multi-objective optimization model to obtain the feeder cut-off scheme for each power distribution area.

2. The two-layer emergency load shedding control method considering large-scale distributed photovoltaic grid connection as described in claim 1, characterized in that, The objective function is: ， in, For nodes that need to be shelved, The number of load shedding types for each node. It is the first Value coefficient of similar load, It is the value coefficient of photovoltaics. It is the first Nodes The magnitude of the load shedding for a given type of load. This is the amount of photovoltaic losses.

3. The two-layer emergency load shedding control method considering large-scale distributed photovoltaic grid connection as described in claim 2, characterized in that, The constraints of the mainnet optimization model include: ， ， ， ， ， ， in, This is the sum of the load shedding amounts at each node. This represents the maximum load that a node can remove. This represents the minimum allowable voltage under stable node conditions. The voltage magnitude after load is removed from each node. This represents the maximum transmission current of each transmission line in the power grid. This refers to the current after the load on the transmission line is disconnected. This is the lower limit of the allowed frequency of the system. The frequency after load removal at each node. This is a limit for the stability of the system's power angle. The power angle after the generator load is removed. To meet the load low-voltage ride-through requirements of distributed photovoltaic grid-connected point voltage that varies over time, The voltage at the grid connection point of the distributed photovoltaic system varies over time. For fault clearing time, This is the time it takes for the voltage to eventually recover.

4. The two-layer emergency load shedding control method considering large-scale distributed photovoltaic grid connection as described in claim 1, characterized in that, The method of solving the mainnet optimization model using the improved hippo optimization algorithm includes the following steps: The initial load shedding amount is allocated in stages based on the average analytical sensitivity of each load node. Nodes with larger values ​​are allocated more load-switching capacity; The initial population was generated using Latin hypercube sampling, and the main network optimization model was optimized using an improved hippo optimization algorithm; the improved hippo optimization algorithm introduced adaptive constraint processing.

5. The two-layer emergency load shedding control method considering large-scale distributed photovoltaic grid connection as described in claim 4, characterized in that, The formula for calculating the average analytical sensitivity of each load node is as follows: ， ， ， in, mainnet node The ability of load shedding to boost grid voltage, i.e., the load node Average resolution sensitivity Represents a node Load to Node The effect of voltage, The number of load nodes, For nodes With nodes mutual impedance between For nodes voltage, For nodes The load power factor, The sum of the four phase angles is calculated. For nodes With nodes The phase angle between them For nodes voltage phase angle, For nodes The load power factor angle, For nodes The voltage phase angle.

6. The two-layer emergency load shedding control method considering large-scale distributed photovoltaic grid connection as described in claim 5, characterized in that, The specific formula for the graded allocation of the initial load shedding is as follows: ， in, This is the upper limit of the configurable load shelving capacity of a load node. It is the lower limit of the configurable load shelving capacity of a load node. This represents the average maximum value of load nodes that require emergency load shedding. The minimum average value for load nodes that require emergency load shedding. The load shelvable size that needs to be configured for the load node.

7. The two-layer emergency load shedding control method considering large-scale distributed photovoltaic grid connection as described in claim 4, characterized in that, The adaptive constraint processing dynamically adjusts the constraint violation threshold during the evolution of the hippo optimization algorithm; including: Criterion 1: If all load shedding schemes satisfy the constraints of the main grid optimization model, then the load shedding scheme with the lower objective function value is selected. Criterion 2: If there are load shedding schemes that satisfy and do not satisfy the main network optimization model constraints, calculate the constraint violation degree of the load shedding schemes that do not satisfy the main network optimization model constraints. If the constraint violation degree is less than or equal to the constraint violation degree threshold If the objective function value is lower, then the load shedding scheme with the lower objective function value is selected. If the constraint violation degree is greater than the constraint violation degree threshold, then... If so, then select the load shedding scheme that satisfies the constraints of the main network optimization model; Criterion 3: If none of the load shedding schemes satisfy the constraints of the main network optimization model, and if the degree of constraint violation is less than [a certain value]... Choose the load shedding scheme with the lowest objective function value, if the constraint violation degree is greater than or equal to If the constraint violation is small, then choose the load shedding scheme.

8. The two-layer emergency load shedding control method considering large-scale distributed photovoltaic grid connection as described in claim 7, characterized in that, The constraint violation degree is calculated as follows: ， in, To calculate the constraint violators of load shedding in the time domain, For constraint limits; The method for dynamically adjusting the constraint violation threshold is as follows: ， In the formula, The decreasing coefficient, To truncate algebra, For the number of iterations, To constrain the initial value of the violation threshold, It is a constant.

9. The two-layer emergency load shedding control method considering large-scale distributed photovoltaic grid connection according to any one of claims 1-8, characterized in that, The objective function of the multi-objective optimization model includes: ， ， ， in, This represents the losses in the load shedding feeder scheme of the distribution network. This indicates the feeder status; a value of 0 means the feeder is retained, and a value of 1 means the feeder is disconnected. This indicates the value of the corresponding load feeder. Represents a node The number of feeders, objective function To minimize the voltage drop on the main grid bus. This represents the main grid node voltage after the load feeder is disconnected. The load shedding amount issued by the main network. The set load variation range is used to configure the feeder combination. This corresponds to the load size of the feeder.

10. A two-layer emergency load shedding control device considering large-scale distributed photovoltaic grid connection, comprising a processor and a memory storing program instructions, characterized in that, The processor is configured to execute, when running the program instructions, a two-layer emergency load shedding control method considering large-scale distributed photovoltaic grid connection as described in any one of claims 1-9.

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