Independent micro-grid virtual inertia optimal configuration method, device, equipment and medium

Abstract

The present application relates to a kind of independent microgrid virtual inertia optimal configuration method, device, equipment and medium, wherein method includes independent microgrid optimal inertia configuration part and fast frequency index evaluation part;The independent microgrid optimal inertia configuration part is with renewable energy load shedding, energy storage charge-discharge capacity and related parameters as optimization variable, constructs the virtual inertia optimization configuration model considering independent microgrid transient frequency nonlinear constraint, solves the virtual inertia optimization configuration model, obtains independent microgrid optimal inertia configuration scheme;Fast frequency index evaluation part is to the frequency dynamic response model of independent microgrid is Latin hypercube sampling, obtains the associated parameter of influence frequency dynamic response, then fitting associated parameter and the relationship function of independent microgrid maximum frequency change rate and frequency minimum point, to quickly evaluate frequency index.The present application can realize independent microgrid inertia configuration cost optimization under the constraint of system frequency stability.

Description

Technical Field

[0001] This invention relates to the field of microgrid optimization planning technology, and in particular to a method, apparatus, equipment and medium for optimal configuration of virtual inertia of an independent microgrid. Background Technology

[0002] In independent microgrids, distributed generation sources are mostly connected through grid-connected converters. These converters serve as the interface for distributed generation sources, and their control methods significantly impact the stability and power quality of the microgrid system. However, traditional grid-connected converters have low or no inertia and poor overload capacity. When the proportion of renewable energy sources in the microgrid is high, the system's equivalent inertia and equivalent primary frequency regulation reserve capacity continuously decrease. This leads to a continuous decline in the inertia and frequency support capabilities of the independent microgrid, and a sustained increase in the risk to the system's safe and stable operation.

[0003] Virtual synchronous machine (VSM) technology has attracted widespread attention to enhance the virtual inertia and damping of independent microgrids and improve system stability. VSM control draws upon the mechanical and electromagnetic equations of synchronous generators, adding virtual inertial and damping elements to converter control to effectively improve the output characteristics of distributed power sources. However, the converter output based on VSM control, the frequency characteristics of the independent microgrid after disturbance, and the energy storage capacity configured in the independent microgrid are all related to the parameters of the virtual synchronous control. In other words, the virtual inertia and primary frequency regulation capability of the independent microgrid will affect the system's operating characteristics. Therefore, it is necessary to optimize the virtual inertia of each converter in the independent microgrid to meet the frequency stability constraints after disturbance while minimizing system operating costs.

[0004] Current research focuses on the parameter optimization and control of energy storage converters in independent microgrids, simulating the inertia response and frequency damping effect of traditional units, which improves the frequency stability of the system. However, relying entirely on energy storage to bear the system's inertia and primary frequency regulation tasks will result in excessively large energy storage and converter capacities, leading to poor economy and reliability. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a method, device, equipment and medium for optimal configuration of virtual inertia of independent microgrids, which can achieve optimal cost configuration of inertia of independent microgrids under the constraint of system frequency stability.

[0006] The technical solution adopted by the present invention to solve its technical problem is: to provide an optimal configuration method for virtual inertia of independent microgrids, including an optimal inertia configuration part of independent microgrids with nonlinear constraints and a fast frequency index evaluation part based on Latin hypercube sampling;

[0007] The optimal inertia configuration part of the independent microgrid with nonlinear constraints uses renewable energy load shedding, energy storage charging and discharging, and related parameters as optimization variables to construct a virtual inertia optimization configuration model that takes into account the nonlinear constraints of the transient frequency of the independent microgrid. Solving the virtual inertia optimization configuration model yields the optimal inertia configuration scheme for the independent microgrid.

[0008] The fast frequency index evaluation part based on Latin hypercube sampling performs Latin hypercube sampling on the frequency dynamic response model of the independent microgrid to obtain the correlation parameters that affect the frequency dynamic response. Then, it fits the relationship function between the correlation parameters and the maximum frequency change rate and the lowest frequency point of the independent microgrid, thereby quickly evaluating the frequency index.

[0009] The optimal inertia configuration part of the independent microgrid with nonlinear constraints includes the following steps:

[0010] Based on the configuration capacity and load history data of renewable energy and energy storage systems within independent microgrids, evaluate renewable energy and energy storage systems with independent microgrid inertia support and primary frequency regulation capability;

[0011] Using renewable energy load shedding, energy storage charging and discharging, and related parameters as optimization variables, and considering the transient frequency nonlinearity constraint of independent microgrids, a virtual inertia optimization configuration model with microgrid transient frequency nonlinearity constraint is constructed.

[0012] The virtual inertia optimization configuration model is transformed into a master-slave two-level optimization problem, where the master problem is a mixed integer linear programming problem and the slave problem is a transient frequency index evaluation problem.

[0013] By iteratively calculating and evaluating the master-slave problem, the virtual inertia optimization configuration model is solved to obtain the optimal inertia configuration scheme for independent microgrids.

[0014] The evaluation of the problem based on the fast frequency index evaluation using Latin hypercube sampling includes the following steps:

[0015] Based on the configuration, grid structure, and converter control strategy of the independent microgrid, a full-order small-signal frequency response model of the independent microgrid is constructed: Δω=Ax+BΔP L The system state transition matrices A and B contain the relevant parameters of the microgrid, the state variable x contains the state variables within the independent microgrid, Δω is the frequency change, and ΔP L This refers to power disturbance.

[0016] The Latin hypercube sampling technique is used to sample the relevant parameters in the state transition matrices A and B, and the power disturbance ΔP is calculated based on the sampled values. L The maximum rate of change of frequency RoCoF max and the lowest frequency point ωnadir The RoCoF, which has the greatest impact on frequency change, was obtained through correlation analysis. max and the lowest frequency point ω nadir The associated parameters;

[0017] Based on the correlation parameters, the maximum frequency change rate and the minimum frequency point of an independent microgrid are respectively expressed as: ω nadir =f(C, ΔP) L ), RoCoF max =g(D, ΔP) L The form is ), where C and D are correlation parameter matrices containing correlation parameters, and the functions f and g are fitted based on the calculated values;

[0018] Based on the fitting function, the power disturbance is used as input, and the minimum point of the frequency of the independent microgrid system and the maximum rate of change of the system frequency are calculated by looking up the table under the current inertia allocation scheme. This allows for a rapid assessment of whether the transient frequency index constraints in the virtual inertia optimization configuration model are met.

[0019] The correlation parameters in the correlation parameter matrix include the inertial time constant elements and primary frequency regulation coefficient elements of the distributed power source. The calculation formulas for the inertial time constant elements and primary frequency regulation coefficient elements of the distributed power source are as follows: Among them, H Ri and H Bi S represents the inertial time constant of the renewable energy and energy storage systems, respectively. Bi Q represents the capacity of the converter in a distributed power source. Bi RoCoF is the output reactive power of the converter in the energy storage system. max_sys ω is the maximum rate of frequency change that enables an independent microgrid to maintain stable operation. min ω is the minimum frequency at which an independent microgrid can maintain stable operation. n The rated frequency of the independent microgrid; P Ri and P Bi These represent the maximum output of renewable energy and the active power output of the energy storage system, respectively; δ i The proportion of power reduction for renewable energy sources; R Ri and R Bi These are the virtual primary frequency regulation coefficients for renewable energy and energy storage systems, respectively.

[0020] The virtual inertia optimization configuration model is as follows: Among them, P i,t P Ri,t P Bi,t These represent the power output of the internal combustion engine, the maximum output of renewable energy, and the active power output of the energy storage system during time period t; a i b i c iδ represents the operating cost coefficient for the power supply of internal combustion engine models. i,t Let μ1 be the output reduction ratio of the i-th renewable energy source in time period t, μ1 be the unit output reduction cost coefficient of renewable energy, k1 be the maintenance cost coefficient of renewable energy per unit output, k2 be the maintenance cost coefficient of energy storage system per unit output, N, M, and G be the quantities of internal combustion engine power source, renewable energy source, and energy storage system, respectively, and T be the time interval.

[0021] The constraints of the virtual inertia optimization configuration model include system frequency constraints: Where, ω nadir At the lowest frequency point, RoCoF max For the maximum rate of change of frequency, ω min and RoCoF max_sys These are the minimum frequency and maximum frequency variation rate at which an independent microgrid can maintain stable operation, respectively.

[0022] The technical solution adopted by this invention to solve its technical problem is: to provide an independent microgrid virtual inertia optimal configuration device, comprising:

[0023] The independent microgrid optimal inertia configuration module is used to construct a virtual inertia optimization configuration model that takes into account the nonlinear constraints of the transient frequency of the independent microgrid, using renewable energy load reduction, energy storage charging and discharging and related parameters as optimization variables, and solve the virtual inertia optimization configuration model to obtain the optimal inertia configuration scheme of the independent microgrid.

[0024] The frequency index evaluation module is used to perform Latin hypercube sampling on the frequency dynamic response model of the independent microgrid to obtain the correlation parameters that affect the frequency dynamic response. Then, it fits the relationship function between the correlation parameters and the maximum frequency change rate and the minimum frequency point of the independent microgrid, thereby quickly evaluating the frequency index.

[0025] The technical solution adopted by the present invention to solve its technical problem is: to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-mentioned independent microgrid virtual inertia optimal configuration method.

[0026] The technical solution adopted by the present invention to solve its technical problem is: to provide a computer-readable storage medium on which a computer program is stored, wherein the computer program, when executed by a processor, implements the steps of the above-mentioned method for optimal configuration of virtual inertia of independent microgrids.

[0027] Beneficial effects

[0028] By adopting the above-mentioned technical solution, this invention has the following advantages and positive effects compared with the prior art: This invention can provide independent microgrid inertia support from distributed power sources based on virtual synchronous control technology, thereby compensating for the inertia loss caused by the access of new energy sources. This inertia allocation method avoids the need to expand energy storage capacity to increase system reserve capacity or rotational inertia, thus improving the economy and reliability of operation. Attached Figure Description

[0029] Figure 1 This is a flowchart of the method for optimal configuration of virtual inertia of independent microgrids according to an embodiment of the present invention. Detailed Implementation

[0030] The present invention will be further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Furthermore, it should be understood that after reading the teachings of this invention, those skilled in the art can make various alterations or modifications to the invention, and these equivalent forms also fall within the scope defined by the appended claims.

[0031] The first embodiment of the present invention relates to an optimal virtual inertia configuration method for independent microgrids. This method achieves optimal allocation of equivalent inertia of independent microgrids through two parts: optimal inertia configuration of independent microgrids with nonlinear constraints and fast frequency index evaluation based on Latin hypercube sampling. This enhances the frequency stability of the system while improving the economy and reliability of operation.

[0032] In this embodiment, the optimal inertia configuration of the independent microgrid is to construct a virtual inertia optimization configuration model that takes into account the nonlinear constraints of the transient frequency of the independent microgrid, using renewable energy load reduction, energy storage charging and discharging and related parameters as optimization variables. The optimal inertia configuration scheme of the independent microgrid is obtained by solving the virtual inertia optimization configuration model.

[0033] In this embodiment, the rapid frequency index assessment involves performing Latin hypercube sampling on the frequency dynamic response model of the independent microgrid to obtain the correlation parameters affecting the frequency dynamic response. Then, the relationship function between the correlation parameters and the maximum frequency change rate and the lowest frequency point of the independent microgrid is fitted to quickly assess the frequency index.

[0034] like Figure 1 As shown, the optimal inertia configuration for an independent microgrid with nonlinear constraints includes the following steps:

[0035] Step 1: Based on the configuration capacity and load history data of renewable energy and energy storage systems within the independent microgrid, evaluate renewable energy and energy storage systems that have independent microgrid inertia support and primary frequency regulation capabilities;

[0036] Step 2: Using renewable energy load shedding, energy storage charging and discharging, and related parameters as optimization variables, and considering the nonlinear constraints of the transient frequency of the independent microgrid, a virtual inertia optimization configuration model with nonlinear constraints is constructed; this virtual inertia optimization configuration model is as follows:

[0037]

[0038] Among them, P i,t P Ri,t P Bi,t These represent the power output of the internal combustion engine, the maximum output of renewable energy, and the active power output of the energy storage system during time period t; a i b i c i δ represents the operating cost coefficient for the power supply of internal combustion engine models. i,t Let μ1 be the output reduction ratio of the i-th renewable energy source in time period t, μ1 be the unit output reduction cost coefficient of renewable energy, k1 be the maintenance cost coefficient of renewable energy per unit output, k2 be the maintenance cost coefficient of energy storage system per unit output, N, M, and G be the quantities of internal combustion engine power sources, renewable energy sources, and energy storage systems, respectively, and T be the time interval. Its constraints include not only traditional linear power constraints but also system frequency constraints.

[0039]

[0040] Where, ω nadir At the lowest frequency point, RoCoF max For the maximum rate of change of frequency, ω min and RoCoF max_sys These are the minimum frequency and maximum frequency variation rate at which an independent microgrid can maintain stable operation, respectively.

[0041] Step 3: Transform the virtual inertia optimization configuration model with nonlinear constraints established in Step 2 into a master-slave bi-level optimization problem. The master problem is a mixed-integer linear programming problem, and the slave problem is a transient frequency index evaluation problem. The slave problem is evaluated based on the fast frequency index evaluation part using Latin hypercube sampling, specifically including the following steps:

[0042] Step (1): Based on the configuration, grid structure, and converter control strategy of the independent microgrid, construct the full-order small-signal frequency response model of the independent microgrid: Δω=Ax+BΔP L The system state transition matrices A and B contain various relevant parameters of the microgrid, the state variable x contains various state variables (P, Q, ω, etc.) within the independent microgrid, Δω is the frequency change, and ΔP... L This refers to power disturbance.

[0043] Step (2) uses Latin hypercube sampling technique to sample the relevant parameters in state transition matrices A and B, and calculates the power disturbance ΔP based on the sampled values. L The maximum rate of change of frequency RoCoF max and the lowest frequency point ω nadir The RoCoF, which has the greatest impact on frequency change, was obtained through correlation analysis. max and the lowest frequency point ω nadir The associated parameters;

[0044] Step (3), based on the correlation parameters, the maximum frequency change rate and the minimum frequency point of the independent microgrid are respectively written as: ω nadir =f(C, ΔP) L ), RoCoF max =g(D, ΔP) L The form is ), where C and D are correlation parameter matrices containing correlation parameters, and the fitting functions f and g are based on the calculated values; wherein the correlation parameters include the inertial time constant elements and primary frequency regulation coefficient elements of the distributed power source, and the calculation formulas for the inertial time constant elements and primary frequency regulation coefficient elements of the distributed power source are:

[0045]

[0046] Among them, H Ri and H Bi S represents the inertial time constant of the renewable energy and energy storage systems, respectively. Bi Q represents the capacity of the converter in a distributed power source. Bi ω represents the output reactive power of the converter in the energy storage system. n R is the rated frequency of the independent microgrid; Ri and R Bi These are the virtual primary frequency regulation coefficients for renewable energy and energy storage systems, respectively.

[0047] Step (4): Based on the fitting function, the power disturbance is used as input. The table is consulted to calculate the minimum point of the frequency of the independent microgrid system and the maximum value of the system frequency change rate under the current inertia allocation scheme. The transient frequency index constraint in the virtual inertia optimization configuration model is quickly evaluated to determine whether the condition is met.

[0048] Step 4: Through iterative calculation and evaluation of the master-slave problem, solve the mixed integer nonlinear model to obtain the optimal inertia configuration scheme for the independent microgrid.

[0049] It is easy to see that this invention can provide independent microgrid inertia support through distributed power sources based on virtual synchronous control technology, thereby compensating for the inertia loss caused by the integration of new energy sources. This inertia allocation method avoids the need to expand energy storage capacity to increase system reserve capacity or rotational inertia, thus improving the economy and reliability of operation.

[0050] The second embodiment of the present invention relates to an independent microgrid virtual inertia optimal configuration device, comprising:

[0051] The independent microgrid optimal inertia configuration module is used to construct a virtual inertia optimization configuration model that takes into account the nonlinear constraints of the transient frequency of the independent microgrid, using renewable energy load reduction, energy storage charging and discharging and related parameters as optimization variables, and solve the virtual inertia optimization configuration model to obtain the optimal inertia configuration scheme of the independent microgrid.

[0052] The frequency index evaluation module is used to perform Latin hypercube sampling on the frequency dynamic response model of the independent microgrid to obtain the correlation parameters that affect the frequency dynamic response. Then, it fits the relationship function between the correlation parameters and the maximum frequency change rate and the minimum frequency point of the independent microgrid, thereby quickly evaluating the frequency index.

[0053] The independent microgrid optimal inertia configuration module includes:

[0054] The evaluation unit is used to evaluate renewable energy and energy storage systems with independent microgrid inertia support and primary frequency regulation capability based on the configuration capacity and load history data of renewable energy and energy storage systems in independent microgrids.

[0055] The building unit is used to construct a virtual inertia optimization configuration model with the transient frequency nonlinearity constraint of the microgrid, taking the renewable energy load reduction, energy storage charging and discharging and related parameters as optimization variables and considering the transient frequency nonlinearity constraint of the independent microgrid.

[0056] The transformation unit is used to transform the virtual inertia optimization configuration model into a master-slave two-level optimization problem, where the master problem is a mixed integer linear programming problem and the slave problem is a transient frequency index evaluation problem.

[0057] The computing unit solves the virtual inertia optimization configuration model through iterative calculation and evaluation of the master-slave problem, and obtains the optimal inertia configuration scheme for the independent microgrid.

[0058] The problem is evaluated through a frequency index evaluation module, which includes:

[0059] The frequency response model building unit is used to construct the full-order small-signal frequency response model of an independent microgrid based on its configuration, grid structure, and converter control strategy: Δω=Ax+BΔP L The system state transition matrices A and B contain the relevant parameters of the microgrid, the state variable x contains the state variables within the independent microgrid, Δω is the frequency change, and ΔP L This refers to power disturbance.

[0060] The sampling calculation unit is used to sample the relevant parameters in the state transition matrices A and B using the Latin hypercube sampling technique, and calculate the power disturbance ΔP based on the sampled values. L The maximum rate of change of frequency RoCoF max and the lowest frequency point ω nadir The RoCoF, which has the greatest impact on frequency change, was obtained through correlation analysis. max and the lowest frequency point ω nadir The associated parameters;

[0061] The function fitting unit is used to express the maximum frequency change rate and the minimum frequency point of an independent microgrid as ω based on the correlation parameters. nadir =f(C, ΔP) L ), RoCoF max =g(D, ΔP) L The form is ), where C and D are correlation parameter matrices containing correlation parameters, and the functions f and g are fitted based on the calculated values;

[0062] The table lookup evaluation unit is used to calculate the minimum point of the frequency of the independent microgrid system and the maximum rate of change of the system frequency under the current inertia allocation scheme based on the fitting function and the power disturbance as input, so as to quickly evaluate whether the transient frequency index constraint in the virtual inertia optimization configuration model meets the conditions.

[0063] The correlation parameters in the correlation parameter matrix include the inertial time constant elements and primary frequency regulation coefficient elements of the distributed power source. The calculation formulas for the inertial time constant elements and primary frequency regulation coefficient elements of the distributed power source are as follows: Among them, H Ri and H Bi S represents the inertial time constant of the renewable energy and energy storage systems, respectively. Bi Q represents the capacity of the converter in a distributed power source. Bi RoCoF is the output reactive power of the converter in the energy storage system. max_sys ω is the maximum frequency change rate that enables an independent microgrid to maintain stable operation. min ω is the minimum frequency at which an independent microgrid can maintain stable operation. n The rated frequency of the independent microgrid; P Ri and P Bi These represent the maximum output of renewable energy and the active power output of the energy storage system, respectively; δ i The proportion of power reduction for renewable energy sources; R Ri and R Bi These are the virtual primary frequency regulation coefficients for renewable energy and energy storage systems, respectively.

[0064] The virtual inertia optimization configuration model is as follows: Among them, Pi,t P Ri,t P Bi,t These represent the power output of the internal combustion engine, the maximum output of renewable energy, and the active power output of the energy storage system during time period t; a i b i c i δ represents the operating cost coefficient for the power supply of internal combustion engine models. i,t Let μ1 be the output reduction ratio of the i-th renewable energy source in time period t, μ1 be the unit output reduction cost coefficient of renewable energy, k1 be the maintenance cost coefficient of renewable energy per unit output, k2 be the maintenance cost coefficient of energy storage system per unit output, N, M, and G be the quantities of internal combustion engine power source, renewable energy source, and energy storage system, respectively, and T be the time interval.

[0065] The constraints of the virtual inertia optimization configuration model include system frequency constraints: Where, ω nadir At the lowest frequency point, RoCoF max For the maximum rate of change of frequency, ω min and RoCoF max_sys These are the minimum frequency and maximum frequency variation rate at which an independent microgrid can maintain stable operation, respectively.

[0066] A third embodiment of the present invention relates to an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method for optimal virtual inertia configuration of independent microgrids.

[0067] The fourth embodiment of the present invention relates to a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method for optimal configuration of virtual inertia of independent microgrids.

[0068] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0069] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0070] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0071] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0072] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0073] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for optimal configuration of virtual inertia in an independent microgrid, characterized in that, It includes a section on optimal inertia configuration for independent microgrids with nonlinear constraints and a section on fast frequency index evaluation based on Latin hypercube sampling; The optimal inertia configuration part of the independent microgrid with nonlinear constraints uses renewable energy load shedding, energy storage charging and discharging, and related parameters as optimization variables to construct a virtual inertia optimization configuration model that takes into account the nonlinear constraints of the transient frequency of the independent microgrid. Solving the virtual inertia optimization configuration model yields the optimal inertia configuration scheme for the independent microgrid. The fast frequency index evaluation part based on Latin hypercube sampling performs Latin hypercube sampling on the frequency dynamic response model of the independent microgrid to obtain the correlation parameters that affect the frequency dynamic response. Then, it fits the relationship function between the correlation parameters and the maximum frequency change rate and the lowest frequency point of the independent microgrid, thereby quickly evaluating the frequency index. The optimal inertia configuration part of the independent microgrid with nonlinear constraints includes the following steps: Based on the configuration capacity and load history data of renewable energy and energy storage systems within independent microgrids, evaluate renewable energy and energy storage systems with independent microgrid inertia support and primary frequency regulation capability; Using renewable energy load shedding, energy storage charging and discharging, and related parameters as optimization variables, and considering the transient frequency nonlinearity constraint of independent microgrids, a virtual inertia optimization configuration model with microgrid transient frequency nonlinearity constraint is constructed. The virtual inertia optimization configuration model is transformed into a master-slave two-level optimization problem, where the master problem is a mixed integer linear programming problem and the slave problem is a transient frequency index evaluation problem. By iteratively calculating and evaluating the master-slave problem, the virtual inertia optimization configuration model is solved to obtain the optimal inertia configuration scheme for the independent microgrid; The virtual inertia optimization configuration model is as follows: , where P i,t P Ri,t P Bi,t These represent the power output of the internal combustion engine, the maximum output of renewable energy, and the active power output of the energy storage system during time period t; a i b i c i δ represents the operating cost coefficient for the power supply of internal combustion engine models. i,t Let μ1 be the output reduction ratio of the i-th renewable energy source in time period t, μ1 be the unit output reduction cost coefficient of renewable energy, k1 be the maintenance cost coefficient of renewable energy per unit output, k2 be the maintenance cost coefficient of energy storage system per unit output, N, M, and G be the number of internal combustion engine power sources, renewable energy sources, and energy storage systems, respectively, and T be the time interval. The constraints of the virtual inertia optimization configuration model include system frequency constraints: , where ω nadir At the lowest frequency point, RoCoF max For the maximum rate of change of frequency, ω min and RoCoF max_sys These are the minimum frequency and maximum frequency variation rate at which an independent microgrid can maintain stable operation, respectively.

2. The method for optimal virtual inertia configuration of independent microgrids according to claim 1, characterized in that, The evaluation of the problem based on the fast frequency index evaluation using Latin hypercube sampling includes the following steps: Based on the configuration, grid structure, and converter control strategy of the independent microgrid, a full-order small-signal frequency response model of the independent microgrid is constructed: ∆ω=Ax+B∆P L The system state transition matrices A and B contain the relevant parameters of the microgrid, and the state variable x contains the state variables within the independent microgrid. The change in frequency This refers to the power disturbance. The Latin hypercube sampling technique is used to sample the relevant parameters in the state transition matrices A and B, and the power disturbance is calculated based on the sampled values. The maximum rate of change of frequency RoCoF max and the lowest frequency point ω nadir The RoCoF, which has the greatest impact on frequency change, was obtained through correlation analysis. max and the lowest frequency point ω nadir The associated parameters; Based on the correlation parameters, the maximum frequency change rate and the minimum frequency point of an independent microgrid are respectively expressed as: ω nadir =f(C, ∆P) L ), RoCoF max =g(D,∆P L The form is ), where C and D are correlation parameter matrices containing correlation parameters, and the functions f and g are fitted based on the calculated values; Based on the fitting function, the power disturbance is used as input, and the minimum point of the frequency of the independent microgrid system and the maximum rate of change of the system frequency are calculated by looking up the table under the current inertia allocation scheme. This allows for a rapid assessment of whether the transient frequency index constraints in the virtual inertia optimization configuration model are met.

3. The method for optimal virtual inertia configuration of independent microgrids according to claim 2, characterized in that, The correlation parameters in the correlation parameter matrix include the inertial time constant elements and primary frequency regulation coefficient elements of the distributed power source. The calculation formulas for the inertial time constant elements and primary frequency regulation coefficient elements of the distributed power source are as follows: ,in, and S represents the inertial time constant of the renewable energy and energy storage systems, respectively. Bi Q represents the capacity of the converter in a distributed power source. Bi RoCoF is the output reactive power of the converter in the energy storage system. max_sys ω is the maximum rate of frequency change that enables an independent microgrid to maintain stable operation. min ω is the minimum frequency at which an independent microgrid can maintain stable operation. n The rated frequency of the independent microgrid; P Ri and P Bi These represent the maximum output of renewable energy and the active power output of the energy storage system, respectively; δ i The proportion of power reduction for renewable energy sources; R Ri and R Bi These are the virtual primary frequency regulation coefficients for renewable energy and energy storage systems, respectively.

4. A device for optimal virtual inertia configuration of an independent microgrid, characterized in that, include: The independent microgrid optimal inertia configuration module is used to construct a virtual inertia optimization configuration model that takes into account the nonlinear constraints of the transient frequency of the independent microgrid, using renewable energy load reduction, energy storage charging and discharging and related parameters as optimization variables, and solve the virtual inertia optimization configuration model to obtain the optimal inertia configuration scheme of the independent microgrid. The frequency index evaluation module is used to perform Latin hypercube sampling on the frequency dynamic response model of the independent microgrid to obtain the correlation parameters that affect the frequency dynamic response. Then, it fits the relationship function between the correlation parameters and the maximum frequency change rate and the minimum frequency point of the independent microgrid, thereby quickly evaluating the frequency index. The independent microgrid optimal inertia configuration module includes: The evaluation unit is used to evaluate renewable energy and energy storage systems with independent microgrid inertia support and primary frequency regulation capability based on the configuration capacity and load history data of renewable energy and energy storage systems in independent microgrids. The building unit is used to construct a virtual inertia optimization configuration model with the transient frequency nonlinearity constraint of the microgrid, taking the renewable energy load reduction, energy storage charging and discharging and related parameters as optimization variables and considering the transient frequency nonlinearity constraint of the independent microgrid. The transformation unit is used to transform the virtual inertia optimization configuration model into a master-slave two-level optimization problem, where the master problem is a mixed integer linear programming problem and the slave problem is a transient frequency index evaluation problem. The computing unit solves the virtual inertia optimization configuration model through iterative calculation and evaluation of the master-slave problem to obtain the optimal inertia configuration scheme for the independent microgrid. The virtual inertia optimization configuration model is as follows: , where P i,t P Ri,t P Bi,t These represent the power output of the internal combustion engine, the maximum output of renewable energy, and the active power output of the energy storage system during time period t; a i b i c i δ represents the operating cost coefficient for the power supply of internal combustion engine models. i,t Let μ1 be the output reduction ratio of the i-th renewable energy source in time period t, μ1 be the unit output reduction cost coefficient of renewable energy, k1 be the maintenance cost coefficient of renewable energy per unit output, k2 be the maintenance cost coefficient of energy storage system per unit output, N, M, and G be the number of internal combustion engine power sources, renewable energy sources, and energy storage systems, respectively, and T be the time interval. The constraints of the virtual inertia optimization configuration model include system frequency constraints: , where ω nadir At the lowest frequency point, RoCoF max For the maximum rate of change of frequency, ω min and RoCoF max_sys These are the minimum frequency and maximum frequency variation rate at which an independent microgrid can maintain stable operation, respectively.

5. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method for optimal configuration of virtual inertia of an independent microgrid as described in any one of claims 1-3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for optimal configuration of virtual inertia of an independent microgrid as described in any one of claims 1-3.

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