Micro-grid virtual inertia optimal distribution method and device

Abstract

The invention discloses a micro-grid virtual inertia optimal distribution method, and relates to the technical field of micro-grid inertia optimal distribution. Establishing a micro-grid transient model containing photovoltaic, energy storage and controllable load to quantitatively describe virtual inertia support requirements of the maximum frequency deviation and the maximum frequency change rate of the micro-grid, and obtaining the critical inertia of the micro-grid; setting constraint conditions based on the critical inertia of the micro-grid, and establishing a virtual inertia optimal distribution model of the micro-grid by taking the minimum maximum frequency deviation as an optimization target; and solving the micro-grid virtual inertia optimal distribution model to obtain a village-level micro-grid internal virtual inertia optimal distribution scheme. The invention further discloses micro-grid virtual inertia optimization sub-configuration, and a photovoltaic, energy storage and controllable load virtual inertia distribution scheme is obtained on the premise of guaranteeing that the micro-grid inertia is at a safety level and photovoltaic, energy storage and controllable load stable operation.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrid inertia optimization distribution, and in particular to a microgrid virtual inertia optimization distribution method and device. Background Art

[0002] With the promotion of the national dual-carbon policy, the access of distributed renewable energy and distributed energy storage to the distribution network has become an inevitable trend. The construction of microgrids in areas with heavy electricity load and long transmission radius is an important measure to promote the development of photovoltaics and new energy. Distributed photovoltaic and energy storage systems have significant advantages in improving energy self-sufficiency and reducing dependence on remote power grids, but due to the volatility and intermittency of these systems, existing microgrid operation and management methods often cannot effectively coordinate photovoltaic power generation, energy storage scheduling and charging facility operations. The lack of such comprehensive coordination will lead to inefficient energy utilization and the inability to give full play to the self-balancing and virtual inertia active support capabilities of the microgrid, which in turn affects the robustness of the microgrid. Summary of the invention

[0003] The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art and provide a method and device for optimizing the allocation of virtual inertia of a microgrid. The present invention can achieve the optimal allocation of virtual inertia of a microgrid.

[0004] The present invention adopts the following technical solutions to solve the above technical problems:

[0005] In a first aspect, a microgrid virtual inertia optimization allocation method proposed by the present invention includes:

[0006] Taking the three-phase grounding fault of the upper power grid of the microgrid as the extreme operating condition, a microgrid transient model including photovoltaic, energy storage and controllable load is established. The microgrid transient model is used to quantitatively characterize the virtual inertia support demand of the maximum frequency deviation and maximum frequency change rate of the microgrid, and obtain the critical inertia of the microgrid.

[0007] Based on the critical inertia of the microgrid, with the inertia level of the microgrid, the virtual inertia support capacity of photovoltaic power generation, the virtual inertia support capacity of energy storage, the virtual inertia support capacity of controllable loads and frequency stability as constraints, and the minimum maximum frequency deviation as the optimization goal, a microgrid virtual inertia optimization allocation model is established.

[0008] A genetic algorithm is used to solve the microgrid virtual inertia optimization allocation model, and the optimal allocation scheme of the virtual inertia of photovoltaic, energy storage and controllable load in the microgrid virtual inertia is obtained.

[0009] As a further optimization scheme of a microgrid virtual inertia optimization allocation method described in the present invention, the microgrid transient model includes photovoltaic, energy storage and controllable load, all of which use VSG grid-connected inverters; taking the occurrence of a three-phase grounding fault in the upper power grid of the microgrid as an extreme operating condition, a microgrid transient model including photovoltaic, energy storage and controllable load is established, and the microgrid transient model is used to quantitatively characterize the virtual inertia support requirements of the maximum frequency deviation and the maximum frequency change rate of the microgrid, and obtain the critical inertia of the microgrid; specifically as follows:

[0010] Assume that the microgrid includes I controllable loads, J photovoltaic systems and K energy storage systems, and the inertia size of the microgrid is H MIC The calculation formula is:

[0011]

[0012] in, H Gi are the rated power and inertia time constant of the ith controllable load respectively; H Pj are the rated power and inertia time constant of the jth PV system respectively; H Sk are the rated power and inertia time constant of the kth energy storage system respectively;

[0013] After taking into account the virtual inertia of the new energy unit and the primary frequency regulation service, according to formula (1), the rotor motion equation in the process of frequency change affected by the active power regulation of the microgrid is expressed as:

[0014]

[0015] Among them, f0 and Δf(t) are the rated frequency and frequency deviation of the microgrid respectively; ΔP ∑V is the auxiliary frequency regulation power of the new energy auxiliary unit; D is the system damping; t is the microgrid operation time;

[0016] When a three-phase grounding fault occurs in the upper power grid of the microgrid, assuming that the maximum frequency deviation and the maximum frequency change rate of the microgrid are t max and t' max ;The maximum frequency deviation of the microgrid is expressed as:

[0017]

[0018] in, is the equivalent unit damping ratio; ω n is the system angular frequency; α is the coefficient generated when deriving the maximum frequency deviation expression; K is the generator speed regulator gain; ΔP' ∑V is the auxiliary frequency regulation power of the new energy auxiliary unit when the microgrid has the maximum frequency deviation; s is the Laplace operator;

[0019] Through equations (2) and (3), the virtual inertia support demand H corresponding to the maximum frequency deviation of the microgrid is obtained: Δfmax :

[0020]

[0021] Correspondingly, according to formula (2), the corresponding support demand H of the microgrid with the maximum frequency deviation change rate is obtained: RoCoF for:

[0022]

[0023] Among them, RoCoF max is the maximum frequency deviation change rate of the microgrid; Δf(t' max ) is the frequency deviation at the maximum frequency deviation change rate of the microgrid;

[0024] Take the virtual inertia support demand H corresponding to the maximum frequency change rate of the microgrid RoCoF The virtual inertia support requirement H corresponding to the maximum frequency deviation Δfmax The maximum value of the two can be used to obtain the critical inertia H of the microgrid min :

[0025] H min =max{H RoCoF ,H Δfmax}(6).

[0026] As a further optimization scheme of the microgrid virtual inertia optimization allocation method described in the present invention, the microgrid inertia level in the constraint condition is determined in the following manner:

[0027] The critical inertia H of the microgrid is obtained based on the virtual inertia support demand of the microgrid's maximum frequency deviation and maximum frequency change rate. min , thus determining the inertia ΔH that needs to be compensated inside the microgrid:

[0028] ΔH=H min (7)

[0029] When the actual inertia of the microgrid is less than the critical inertia, the grid inertia compensation inertia is:

[0030] ΔH′=H MIC =H G +H PV +H SG (8)

[0031] Among them, ΔH' is the inertia compensation provided to the power grid after the implementation of virtual inertia control; H G , H PV , H SGThe inertia compensation provided to the grid after implementing virtual inertia control for all controllable loads, photovoltaic systems and energy storage systems;

[0032] According to the controllable load, the virtual inertia of the photovoltaic system and the energy storage system is not less than the critical inertia H min The condition of microgrid inertia level constraint is expressed as:

[0033] H min ≤H G +H PV +H SG (9).

[0034] As a further optimization scheme of the microgrid virtual inertia optimization allocation method described in the present invention, the photovoltaic virtual inertia support capacity in the constraint condition is determined in the following manner:

[0035] Photovoltaic system virtual inertia H PV for:

[0036]

[0037] Among them, S B is the capacity of the photovoltaic grid-connected system; C dc is the DC bus capacitor on the high voltage side of the photovoltaic grid-connected system; U dc0 K is the rated value of the DC bus voltage on the high voltage side; v (s) is the virtual inertia controller; K h ,ω h are the gain coefficient and cutoff frequency of the virtual inertia controller respectively; s is a complex variable; ω0 is the rated angular frequency of the photovoltaic grid-connected system; ω g is the measured angular frequency of the power grid;

[0038] According to the virtual inertia H of the photovoltaic system PV The photovoltaic virtual inertia support capacity in the constraint conditions is obtained as follows:

[0039] H PV ≤H PV,max (11)

[0040] Among them, H pv,max is the maximum value of the virtual inertia of the photovoltaic system.

[0041] As a further optimization scheme of the microgrid virtual inertia optimization allocation method described in the present invention, the energy storage virtual inertia support capacity in the constraint condition is determined in the following manner:

[0042] If the energy storage systems in the microgrid are all supercapacitor energy storage, the charging and discharging power P c It is expressed as:

[0043]

[0044] Among them, C, S c 、u c They are the supercapacitor energy storage capacitance, rated capacity, and output voltage;

[0045] The charging or discharging process of supercapacitor energy storage simulates the motion equation of the rotating synchronous motor rotor, which is:

[0046]

[0047] Where H is the inertia time constant of the rotating synchronous generator; f is the rotating synchronous machine frequency; P m is the mechanical power of the rotating synchronous machine, P e is the electromagnetic power;

[0048] Combining equations (11) and (12), we get the virtual inertia of the energy storage system:

[0049]

[0050] Among them, ω c0 ,ω c are the initial virtual angular frequency and current virtual angular frequency of supercapacitor energy storage respectively;

[0051] According to the virtual inertia H of the energy storage system SG , the energy storage virtual inertia support capacity constraint is expressed as:

[0052] H SG ≤H SG,max (15)

[0053] Among them, H SG,max is the maximum value of virtual inertia of the energy storage system.

[0054] As a further optimization scheme of the microgrid virtual inertia optimization allocation method described in the present invention, the virtual inertia support capacity of the controllable load in the constraint condition is determined in the following manner:

[0055] Assuming that all controllable loads within the microgrid are movable loads, the virtual inertia of the controllable loads H G It is expressed as:

[0056]

[0057] Where f is the frequency of the microgrid, △P G is the newly added frequency when the controllable load is adjusted once when the microgrid is disturbed, S G is the controllable load capacity;

[0058] According to the virtual inertia of the controllable load, the support capacity of the virtual inertia of the controllable load is obtained as follows:

[0059] H G ≤H G,max (17)

[0060] Among them, H G,max It is the maximum value of virtual inertia of controllable load.

[0061] As a further optimization scheme of the microgrid virtual inertia optimization allocation method described in the present invention, the frequency stability in the constraint condition is determined in the following manner:

[0062] Based on the principle of ensuring that the system maximum frequency deviation and frequency change rate meet the safety requirements of microgrid operation, the system frequency constraint is obtained as follows:

[0063] -Δf max-c ≤Δf max ≤Δf max-c (18)

[0064] -RoCoF max ≤RoCoF≤RoCoF max (19)

[0065] Where Δf max-c is the maximum frequency deviation safety value of the microgrid; RoCoF max is the maximum frequency change rate safety value of the microgrid; Δf max is the maximum frequency deviation of the microgrid, and RoCoF is the frequency change rate of the microgrid.

[0066] As a further optimization scheme of the microgrid virtual inertia optimization allocation method described in the present invention, a microgrid virtual inertia optimization allocation model is specifically as follows:

[0067] The maximum frequency deviation of the microgrid is Δf max Minimum is the optimization target, and the microgrid virtual inertia optimization allocation model is obtained:

[0068]

[0069] Where Δf max is the maximum frequency deviation of the microgrid; Δf max-c is the maximum frequency deviation safety value of the microgrid; RoCoF max is the maximum frequency change rate safety value of the microgrid; RoCoF is the frequency change rate of the microgrid; RoCoF max is the maximum frequency change rate safety value of the microgrid; H min The critical inertia of the microgrid is obtained based on the virtual inertia support demand of the maximum frequency deviation and the maximum frequency change rate of the village microgrid; H G , HPV and H SG are the virtual inertia of all controllable loads, photovoltaic system and energy storage system respectively; H G,max , H PV,max and H SG,max are the maximum virtual inertia of all controllable loads, photovoltaic system and energy storage system respectively.

[0070] As a further optimization scheme of the microgrid virtual inertia optimization allocation method described in the present invention, a genetic algorithm is used to solve the microgrid virtual inertia optimization allocation model to obtain a microgrid virtual inertia optimization allocation scheme for photovoltaic, energy storage and controllable load virtual inertia; the details are as follows:

[0071] According to the constraints of photovoltaic virtual inertia support capacity, energy storage virtual inertia support capacity, and controllable load virtual inertia support capacity, a genetic algorithm is used to automatically generate initial population information. The initial population information includes multiple groups of individuals, each group of individuals includes A random virtual inertia of controllable loads, B random virtual inertia of photovoltaic systems, and C random virtual inertia of energy storage systems, where A, B, and C are all integers greater than or equal to 1.

[0072] In the initial population information, the random virtual inertia of the controllable loads in each group of individuals is equal, the random virtual inertia of the photovoltaic system is equal, and the random virtual inertia of the energy storage system is equal to simplify the complexity of the initial population information, thereby obtaining the simplified initial population information;

[0073] The simplified initial population information is passed to the feedback layer to evaluate whether multiple groups of controllable loads, photovoltaic systems and energy storage systems meet the constraints of virtual inertia support capacity of controllable loads, photovoltaic systems and energy storage systems, frequency stability constraints and microgrid inertia level constraints through the feedback layer; according to the judgment results, different fitness values ​​F are assigned to each group of controllable loads, photovoltaic and energy storage random virtual inertia: if a group of microgrid frequency stability, photovoltaic and energy storage inertia support capacity and microgrid inertia level meet the requirements, then the corresponding F is the sum of frequency deviation elements in the microgrid; otherwise, F is assigned a maximum value; then, according to the fitness value F, it is judged whether there is an individual that meets the termination condition: if it exists, the virtual inertia of controllable loads, energy storage virtual inertia and photovoltaic virtual inertia in the microgrid when the maximum frequency deviation is minimized is obtained by decoding the best individual information, as the virtual inertia optimization allocation scheme of photovoltaic, energy storage and controllable load inertia of the microgrid; if it does not exist, continue to iterate.

[0074] In a second aspect, a microgrid virtual inertia optimization distribution device includes an inertia support demand module, an inertia optimization module and an inertia optimization distribution module, wherein the inertia support demand module is used to establish a village-level microgrid transient model including photovoltaic, energy storage and controllable loads with a three-phase grounding fault occurring in the upper power grid of the microgrid as an extreme operating condition, and the village-level microgrid model is used to describe the maximum frequency drop depth inside the village-level microgrid, quantitatively characterize the virtual inertia support demand of the maximum frequency deviation and the maximum frequency change rate of the village-level microgrid, and obtain the critical inertia of the village-level microgrid;

[0075] The inertia optimization module is used to establish a village-level microgrid virtual inertia optimization allocation model based on the critical inertia of the village-level microgrid, with the microgrid inertia level, photovoltaic virtual inertia support capacity, energy storage virtual inertia support capacity, controllable load virtual inertia support capacity and frequency stability as constraints, and with the minimum maximum frequency deviation as the optimization goal;

[0076] The inertia optimization distribution module is used to solve the village-level microgrid virtual inertia optimization distribution model using a genetic algorithm to obtain the virtual inertia optimization distribution plan for photovoltaic, energy storage and controllable loads in the microgrid virtual inertia.

[0077] As a further optimization scheme of a microgrid virtual inertia optimization distribution device described in the present invention, in the inertia support demand module, the microgrid transient model includes photovoltaic, energy storage and controllable load, all of which use VSG grid-connected inverters; taking the occurrence of a three-phase grounding fault in the upper power grid of the microgrid as an extreme operating condition, a microgrid transient model including photovoltaic, energy storage and controllable load is established, and the microgrid transient model is used to quantitatively characterize the virtual inertia support demand of the maximum frequency deviation and the maximum frequency change rate of the microgrid, and obtain the critical inertia of the microgrid; specifically as follows:

[0078] Assume that the microgrid includes I controllable loads, J photovoltaic systems and K energy storage systems, and the inertia size of the microgrid is H MIC The calculation formula is:

[0079]

[0080] in, H Gi are the rated power and inertia time constant of the ith controllable load respectively; H Pj are the rated power and inertia time constant of the jth PV system respectively; H Sk are the rated power and inertia time constant of the kth energy storage system respectively;

[0081] After taking into account the virtual inertia of the new energy unit and the primary frequency regulation service, according to formula (20), the rotor motion equation in the process of frequency change affected by the active power regulation of the microgrid is expressed as:

[0082]

[0083] Among them, f0 and Δf(t) are the rated frequency and frequency deviation of the microgrid respectively; ΔP ∑V is the auxiliary frequency regulation power of the new energy auxiliary unit; D is the system damping; t is the microgrid operation time;

[0084] When a three-phase grounding fault occurs in the upper power grid of the microgrid, assuming that the maximum frequency deviation and the maximum frequency change rate of the microgrid are t max and t' max ;The maximum frequency deviation of the microgrid is expressed as:

[0085]

[0086] in, is the equivalent unit damping ratio; ω n is the system angular frequency; α is the coefficient generated when deriving the maximum frequency deviation expression; K is the generator speed regulator gain; ΔP' ∑V is the auxiliary frequency regulation power of the new energy auxiliary unit when the microgrid has the maximum frequency deviation; s is the Laplace operator;

[0087] Through equations (22) and (23), the virtual inertia support demand H corresponding to the maximum frequency deviation of the microgrid is obtained: Δfmax :

[0088] Correspondingly, according to formula (22), the corresponding support demand H of the microgrid with the maximum frequency deviation change rate is obtained: RoCoF for:

[0089]

[0090] Among them, RoCoF max is the maximum frequency deviation change rate of the microgrid; Δf(t' max ) is the frequency deviation at the maximum frequency deviation change rate of the microgrid;

[0091] Take the virtual inertia support demand H corresponding to the maximum frequency change rate of the microgrid RoCoF The virtual inertia support requirement H corresponding to the maximum frequency deviation Δfmax The maximum value of the two can be used to obtain the critical inertia H of the microgrid min :

[0092] H min =max{H RoCoF ,H Δfmax} (26).

[0093] As a further optimization scheme of a microgrid virtual inertia optimization distribution device according to the present invention, in the inertia optimization module, the microgrid inertia level in the constraint condition is determined in the following manner:

[0094] The critical inertia H of the microgrid is obtained based on the virtual inertia support demand of the microgrid's maximum frequency deviation and maximum frequency change rate. min , thus determining the inertia ΔH that needs to be compensated inside the microgrid:

[0095] ΔH=H min (27)

[0096] When the actual inertia of the microgrid is less than the critical inertia, the grid inertia compensation inertia is:

[0097] ΔH′=H MIC =H G +H PV +H SG (28)

[0098] Among them, ΔH' is the inertia compensation provided to the power grid after the implementation of virtual inertia control; H G , H PV , H SG The inertia compensation provided to the grid after implementing virtual inertia control for all controllable loads, photovoltaic systems and energy storage systems;

[0099] According to the controllable load, the virtual inertia of the photovoltaic system and the energy storage system is not less than the critical inertia H min The condition of microgrid inertia level constraint is expressed as:

[0100] H min ≤H G +H PV +H SG (29).

[0101] As a further optimization scheme of a microgrid virtual inertia optimization distribution device according to the present invention, in the inertia optimization module, the photovoltaic virtual inertia support capacity in the constraint condition is determined in the following manner:

[0102] Photovoltaic system virtual inertia H PV for:

[0103]

[0104] Among them, S B is the capacity of the photovoltaic grid-connected system; C dc is the DC bus capacitor on the high voltage side of the photovoltaic grid-connected system; U dc0 K is the rated value of the DC bus voltage on the high voltage side; v (s) is the virtual inertia controller; Kh ,ω h are the gain coefficient and cutoff frequency of the virtual inertia controller respectively; s is a complex variable; ω0 is the rated angular frequency of the photovoltaic grid-connected system; ω g is the measured angular frequency of the power grid;

[0105] According to the virtual inertia H of the photovoltaic system PV The photovoltaic virtual inertia support capacity in the constraint conditions is obtained as follows:

[0106] H PV ≤H PV,max (31)

[0107] Among them, H pv,max is the maximum value of the virtual inertia of the photovoltaic system.

[0108] As a further optimization scheme of a microgrid virtual inertia optimization distribution device according to the present invention, in the inertia optimization module, the energy storage virtual inertia support capacity in the constraint condition is determined in the following manner:

[0109] If the energy storage systems in the microgrid are all supercapacitor energy storage, the charging and discharging power P c It is expressed as:

[0110]

[0111] Among them, C, S c 、u c They are the supercapacitor energy storage capacitance, rated capacity, and output voltage;

[0112] The charging or discharging process of supercapacitor energy storage simulates the motion equation of the rotating synchronous motor rotor, which is:

[0113]

[0114] Where H is the inertia time constant of the rotating synchronous generator; f is the rotating synchronous machine frequency; P m is the mechanical power of the rotating synchronous machine, P e is the electromagnetic power;

[0115] Combining equations (31) and (32), we get the virtual inertia of the energy storage system:

[0116]

[0117] Among them, ω c0 ,ω c are the initial virtual angular frequency and current virtual angular frequency of supercapacitor energy storage respectively;

[0118] According to the virtual inertia H of the energy storage system SG, the energy storage virtual inertia support capacity constraint is expressed as:

[0119] H SG ≤H SG,max (35)

[0120] Among them, H SG,max is the maximum value of virtual inertia of the energy storage system.

[0121] As a further optimization scheme of a microgrid virtual inertia optimization distribution device according to the present invention, in the inertia optimization module, the virtual inertia support capacity of the controllable load in the constraint condition is determined in the following manner:

[0122] Assuming that all controllable loads within the microgrid are movable loads, the virtual inertia of the controllable loads H G It is expressed as:

[0123]

[0124] Where f is the frequency of the microgrid, △P G is the newly added frequency when the controllable load is adjusted once when the microgrid is disturbed, S G is the controllable load capacity;

[0125] According to the virtual inertia of the controllable load, the support capacity of the virtual inertia of the controllable load is obtained as follows:

[0126] H G ≤H G,max (37)

[0127] Among them, H G,max It is the maximum value of virtual inertia of controllable load.

[0128] As a further optimization scheme of a microgrid virtual inertia optimization distribution device according to the present invention, in the inertia optimization module, the frequency stability in the constraint condition is determined in the following manner:

[0129] Based on the principle of ensuring that the system maximum frequency deviation and frequency change rate meet the safety requirements of microgrid operation, the system frequency constraint is obtained as follows:

[0130] -Δf max-c ≤Δf max ≤Δf max-c (38)

[0131] -RoCoF max ≤RoCoF≤RoCoF max (39)

[0132] Where Δf max-c is the maximum frequency deviation safety value of the microgrid; RoCoF maxis the maximum frequency change rate safety value of the microgrid; Δf max is the maximum frequency deviation of the microgrid, and RoCoF is the frequency change rate of the microgrid.

[0133] As a further optimization scheme of a microgrid virtual inertia optimization allocation device according to the present invention, a microgrid virtual inertia optimization allocation model is specifically as follows:

[0134] The maximum frequency deviation of the microgrid is Δf max Minimum is the optimization target, and the microgrid virtual inertia optimization allocation model is obtained:

[0135]

[0136] Where Δf max is the maximum frequency deviation of the microgrid; Δf max-c is the maximum frequency deviation safety value of the microgrid; RoCoF max is the maximum frequency change rate safety value of the microgrid; RoCoF is the frequency change rate of the microgrid; RoCoF max is the maximum frequency change rate safety value of the microgrid; H min The critical inertia of the microgrid is obtained based on the virtual inertia support demand of the maximum frequency deviation and the maximum frequency change rate of the village microgrid; H G , H PV and H SG are the virtual inertia of all controllable loads, photovoltaic system and energy storage system respectively; H G,max , H PV,max and H SG,max are the maximum virtual inertia of all controllable loads, photovoltaic system and energy storage system respectively.

[0137] As a further optimization scheme of a microgrid virtual inertia optimization distribution device described in the present invention, in the inertia optimization distribution module, a genetic algorithm is used to solve the microgrid virtual inertia optimization distribution model to obtain a microgrid virtual inertia optimization distribution scheme for photovoltaic, energy storage and controllable load virtual inertia; the details are as follows:

[0138] According to the constraints of photovoltaic virtual inertia support capacity, energy storage virtual inertia support capacity, and controllable load virtual inertia support capacity, a genetic algorithm is used to automatically generate initial population information. The initial population information includes multiple groups of individuals, each group of individuals includes A random virtual inertia of controllable loads, B random virtual inertia of photovoltaic systems, and C random virtual inertia of energy storage systems, where A, B, and C are all integers greater than or equal to 1.

[0139] In the initial population information, the random virtual inertia of the controllable loads in each group of individuals is equal, the random virtual inertia of the photovoltaic system is equal, and the random virtual inertia of the energy storage system is equal to simplify the complexity of the initial population information, thereby obtaining the simplified initial population information;

[0140] The simplified initial population information is passed to the feedback layer to evaluate whether multiple groups of controllable loads, photovoltaic systems and energy storage systems meet the constraints of virtual inertia support capacity of controllable loads, photovoltaic systems and energy storage systems, frequency stability constraints and microgrid inertia level constraints through the feedback layer; according to the judgment results, different fitness values ​​F are assigned to each group of controllable loads, photovoltaic and energy storage random virtual inertia: if a group of microgrid frequency stability, photovoltaic and energy storage inertia support capacity and microgrid inertia level meet the requirements, then the corresponding F is the sum of frequency deviation elements in the microgrid; otherwise, F is assigned a maximum value; then, according to the fitness value F, it is judged whether there is an individual that meets the termination condition: if it exists, the virtual inertia of controllable loads, energy storage virtual inertia and photovoltaic virtual inertia in the microgrid when the maximum frequency deviation is minimized is obtained by decoding the best individual information, as the virtual inertia optimization allocation scheme of photovoltaic, energy storage and controllable load inertia of the microgrid; if it does not exist, continue to iterate.

[0141] In a third aspect, an embodiment of the present invention further provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein when the processor executes the computer program, the steps of the microgrid virtual inertia optimization allocation method as described in the first aspect above or any corresponding embodiment thereof are implemented.

[0142] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the microgrid virtual inertia optimization allocation method as described in the first aspect above or any corresponding embodiment thereof are implemented.

[0143] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects:

[0144] The present invention is put into the entire microgrid system, which can enable the microgrid system to achieve system frequency regulation without exceeding the microgrid operation threshold after a disturbance occurs; the adjustment time is also significantly improved compared to before compensation, thereby achieving optimal distribution of virtual inertia of the village-level microgrid. BRIEF DESCRIPTION OF THE DRAWINGS

[0145] Figure 1 It is a flowchart of the steps of the method for optimizing the allocation of virtual inertia of village-level microgrid based on genetic algorithm;

[0146] Figure 2 It is a block diagram of virtual inertia control based on high-pass filter in photovoltaic virtual inertia support constraint;

[0147] Figure 3 It is a flowchart of genetic algorithm solving optimization model;

[0148] Figure 4 It is based on the IEEE13 node simulation model diagram;

[0149] Figure 5a This is the result diagram of the frequency deviation change of the village microgrid after the virtual inertia compensation of photovoltaic storage and controllable load;

[0150] Figure 5b It is a comparison chart of the frequency change results of the village-level microgrid after the virtual inertia compensation of photovoltaic storage and controllable load and the frequency change results of the village-level microgrid without compensation;

[0151] Figure 6 This is the working principle diagram of the village-level microgrid virtual inertia optimization distribution device based on genetic algorithm. DETAILED DESCRIPTION

[0152] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0153] In order to solve the problems of low inertia, weak anti-disturbance capability and large-scale fluctuations in voltage / frequency when massive distributed resources are connected to the distribution network, the present invention intends to study the adaptive allocation of virtual inertia. Considering the main inertia support of microgrids - photovoltaic, energy storage and controllable loads, the virtual inertia of microgrids is optimized based on genetic algorithms to meet the needs of new energy consumption and active support, and fill the technical shortcomings and gaps in the wide-area collaborative active support of massive distributed sources and loads.

[0154] In order to achieve the above goals, Figure 1 ,The village microgrid virtual inertia optimization allocation method includes the following steps:

[0155] Taking the three-phase grounding fault of the upper power grid of the microgrid as the extreme operating condition, a microgrid transient model including photovoltaic, energy storage and controllable load is established. The microgrid transient model is used to quantitatively describe the virtual inertia support demand of the maximum frequency deviation and maximum frequency change rate of the microgrid, and obtain the critical inertia of the microgrid;

[0156] Based on the critical inertia of the microgrid, with the inertia level of the microgrid, the virtual inertia support capacity of photovoltaic power generation, the virtual inertia support capacity of energy storage, the virtual inertia support capacity of controllable loads and frequency stability as constraints, and the minimum maximum frequency deviation as the optimization goal, a microgrid virtual inertia optimization allocation model is established.

[0157] Genetic algorithm is used to solve the optimal allocation model of virtual inertia of microgrid, and the virtual inertia of photovoltaic, energy storage and controllable load in the virtual inertia of microgrid is obtained;

[0158] Specifically, the microgrid transient model includes photovoltaic, energy storage and controllable load, all of which use VSG grid-connected inverters. Taking the three-phase grounding fault of the upper power grid of the microgrid as the extreme operating condition, a microgrid transient model including photovoltaic, energy storage and controllable load is established. The microgrid transient model is used to quantitatively characterize the virtual inertia support requirements of the maximum frequency deviation and maximum frequency change rate of the microgrid, and obtain the critical inertia of the microgrid; the details are as follows:

[0159] Assume that the village-level microgrid includes I controllable loads, J photovoltaic systems and K energy storage systems, and the inertia size of the microgrid is H MIC The calculation formula is:

[0160]

[0161] in, H Gi are the rated power and inertia time constant of the ith controllable load respectively; H Pj are the rated power and inertia time constant of the jth PV system respectively; H Sk are the rated power and inertia time constant of the j-th energy storage system respectively;

[0162] After taking into account the virtual inertia of the new energy unit and the primary frequency regulation service, according to formula (1), the rotor motion equation in the process of frequency change affected by the active power regulation of the microgrid is expressed as:

[0163]

[0164] Among them, f0 and Δf(t) are the system rated frequency and frequency deviation respectively; ΔP ∑V is the auxiliary frequency regulation power of the new energy auxiliary unit; D is the system damping; t is the operation time of the village-level microgrid model;

[0165] When a three-phase grounding fault occurs in the upper power grid of the microgrid, assuming that the maximum frequency deviation and the maximum frequency change rate of the microgrid are t max and t' max ;The maximum frequency deviation of the microgrid is expressed as:

[0166]

[0167] in, is the equivalent unit damping ratio; ω n is the system angular frequency; α is the coefficient generated when deriving the maximum frequency deviation expression; t max is the time when the maximum frequency deviation occurs; K is the generator speed regulator gain; ΔP' ΣV is the auxiliary frequency regulation power of the new energy auxiliary unit when the microgrid has the maximum frequency deviation; s is the Laplace operator;

[0168] Through equations (2) and (3), the virtual inertia support demand H corresponding to the maximum frequency deviation of the microgrid is obtained: Δfmax :

[0169]

[0170] Correspondingly, according to formula (2), the corresponding support demand H of the microgrid with the maximum frequency deviation change rate is obtained: RoCoF :

[0171]

[0172] Among them, RoCoF max is the maximum frequency deviation change rate of the microgrid; Δf(t' max ) is the frequency deviation of the microgrid when the microgrid has the maximum frequency deviation change rate;

[0173] Take the virtual inertia support demand H corresponding to the maximum frequency change rate of the microgrid RoCoF The virtual inertia support requirement H corresponding to the maximum frequency deviation Δfmax The maximum value of the two is obtained, and the critical inertia of the microgrid H min ,

[0174] H min =max{H RoCoF ,H Δfmax} (6)

[0175] Specifically: The constraints in the S2 optimization model are the grid inertia level, photovoltaic virtual inertia support capacity, energy storage virtual inertia support capacity, controllable load virtual inertia support capacity and frequency stability. The calculation process is as follows:

[0176] 1) Microgrid inertia level constraint

[0177] Based on the virtual inertia support demand of the maximum frequency deviation of the microgrid and the maximum frequency change rate, the critical inertia H of the microgrid is obtained. min , thus determining the inertia ΔH that needs to be compensated inside the microgrid:

[0178] ΔH=H min (7)

[0179] When the actual inertia of the microgrid is less than the critical inertia, the grid inertia compensation inertia is:

[0180] ΔH′=H MIC =H G +H PV +H SG (8)

[0181] Among them, ΔH' is the inertia compensation provided to the power grid after the implementation of virtual inertia control; H G , H PV , H SG The inertia compensation provided to the grid after implementing virtual inertia control for all controllable loads, photovoltaic systems and energy storage systems;

[0182] According to the controllable load, distributed photovoltaic and distributed energy storage virtual inertia is greater than or equal to the critical inertia H min The condition of microgrid inertia level constraint is expressed as:

[0183] H min ≤H G +H PV +H SG (9)

[0184] 2) Photovoltaic virtual inertia support capability

[0185] Photovoltaic virtual inertia control is divided into virtual inertia control, virtual inertia control based on low-pass filter and virtual inertia control based on high-pass filter. To simplify the inertia optimization model, it is assumed that all photovoltaic systems within the scope of the village microgrid adopt high-pass filter virtual inertia control.

[0186] Define the virtual inertia H of the photovoltaic system using high-pass filter virtual inertia control PV for:

[0187]

[0188] Among them, S B is the capacity of the photovoltaic grid-connected system; C dc is the DC bus capacitor on the high voltage side of the photovoltaic grid-connected system; U dc0 is the rated value of the DC bus voltage on the high voltage side; U dc K is the DC bus voltage detection value on the high voltage side; v (s) is the virtual inertia controller; ω0 is the rated angular frequency of the photovoltaic grid-connected system;

[0189] When virtual inertia control is adopted Figure 2 The high-pass filter virtual inertia control shown in the figure extracts the frequency fast-changing component, converts it into an inertia response power signal, and then feeds it back to the energy storage device to simulate the inertia support to stabilize the microgrid frequency. v The transfer function of (s) is expressed as:

[0190]

[0191] Among them, K h ,ω hare the gain coefficient and cutoff frequency of VICHF respectively, and s is a complex variable;

[0192] Considering that the dynamic speed of the DC voltage middle loop and the current inner loop is much faster than that of the virtual inertia control outer loop, the DC bus voltage on the high-voltage side can be simplified as:

[0193] U dc =K v (s)(ω g -ω0)+U dc0 (12)

[0194] Among them, ω g is the measured angular frequency of the power grid;

[0195] Substituting equations (11) and (12) into equation (10), we can get the inertia compensation provided to the grid by the photovoltaic system using high-pass filter virtual inertia control after implementing virtual inertia control:

[0196]

[0197] According to the virtual inertia H of the photovoltaic system controlled by high-pass filter virtual inertia PV , the photovoltaic virtual inertia support capacity constraint is expressed as:

[0198] H PV ≤H PV,max (14)

[0199] Among them, H pv,max is the maximum value of the virtual inertia of the photovoltaic system.

[0200] 3) Constraints on the virtual inertia support capacity of energy storage

[0201] If the energy storage systems within the microgrid are all supercapacitor energy storage, supercapacitors are configured in the microgrid to provide the kinetic energy required for simulating inertia, and its charging and discharging power P c It is expressed as:

[0202]

[0203] Among them, C, S c 、u c They are the supercapacitor energy storage capacitance, rated capacity, and output voltage;

[0204] The charging or discharging process of supercapacitor energy storage simulates the motion equation of the rotating synchronous motor rotor, which is:

[0205]

[0206] Where H is the inertia time constant of the rotating synchronous generator; f is the rotating synchronous machine frequency; Pm is the mechanical power of the rotating synchronous machine, P e is the electromagnetic power.

[0207] Combining equations (14) and (15), the virtual inertia of the energy storage system is obtained as:

[0208]

[0209] Among them, ω c0 ,ω c are the initial virtual angular frequency and current virtual angular frequency of supercapacitor energy storage respectively;

[0210] According to the virtual inertia H of the energy storage system SG , the energy storage virtual inertia support capacity constraint is expressed as:

[0211] H SG ≤H SG,max (18)

[0212] Among them, H SG,max is the maximum value of virtual inertia of the energy storage system.

[0213] 4) Controllable load support capacity constraints

[0214] Assuming that all controllable loads within the microgrid are shiftable loads, the virtual inertia compensation provided by the controllable loads to the grid after implementing virtual inertia control is expressed as:

[0215]

[0216] Where f is the system frequency; △P G It is the newly added frequency when the controllable load is adjusted once when the microgrid is disturbed; S G is the controllable load capacity;

[0217] According to the virtual inertia of the controllable load, the controllable load support capacity constraint is expressed as:

[0218] H G ≤H G,max (20)

[0219] Among them, H G,max It is the maximum value of virtual inertia of controllable load.

[0220] 5) Frequency stability constraints

[0221] Based on the principle that the system maximum frequency deviation and frequency change rate meet the safety requirements of village-level microgrid operation, the frequency stability constraint is obtained as follows:

[0222] -Δf max-c ≤Δf max ≤Δf max-c(twenty one)

[0223] -RoCoF max ≤RoCoF≤RoCoF max (twenty two)

[0224] Where Δf max-c is the maximum frequency deviation safety value of the microgrid; RoCoF max is the maximum frequency change rate safety value of the microgrid; Δf max is the maximum frequency deviation of the microgrid, RoCoF is the frequency change rate of the microgrid;

[0225] Specifically, the microgrid inertia level, photovoltaic virtual inertia support capacity, energy storage virtual inertia support capacity, controllable load virtual inertia support capacity and frequency stability are used as constraints, and the microgrid maximum frequency deviation Δf max Taking the minimum as the optimization target, an optimization allocation model of virtual inertia of village-level microgrid is established; the optimization allocation model is as follows:

[0226] The maximum frequency deviation of the microgrid is Δf max The minimum is the optimization target, and the optimal allocation model of virtual inertia of village-level microgrid is obtained:

[0227]

[0228] Where Δf max is the maximum frequency deviation of the microgrid; Δf max-c is the maximum frequency deviation safety value of the microgrid; RoCoF max is the maximum frequency change rate safety value of the microgrid; RoCoF is the frequency change rate of the microgrid; RoCoF max is the maximum frequency change rate safety value of the microgrid; H min The critical inertia of the microgrid is obtained based on the virtual inertia support demand of the maximum frequency deviation and the maximum frequency change rate of the village microgrid; H G , H PV and H SG are the virtual inertia of all controllable loads, photovoltaic system and energy storage system respectively; H G,max , H PV,max and H SG,max are the maximum virtual inertia of all controllable loads, photovoltaic system and energy storage system respectively.

[0229] Specifically, Figure 3 , the genetic algorithm is used to solve the microgrid virtual inertia optimization model, and the virtual inertia optimization allocation scheme of microgrid photovoltaic, energy storage and controllable load is obtained; the details are as follows:

[0230] According to the constraints of photovoltaic virtual inertia support capacity, energy storage virtual inertia support capacity, and controllable load virtual inertia support capacity, a genetic algorithm is used to automatically generate initial population information. The initial population information includes multiple groups of individuals, each group of individuals includes A random virtual inertia of controllable loads, B random virtual inertia of photovoltaic systems, and C random virtual inertia of energy storage systems, where A, B, and C are all integers greater than or equal to 1.

[0231] In the initial population information, the random virtual inertia of the controllable loads in each group of individuals is equal, the random virtual inertia of the photovoltaic system is equal, and the random virtual inertia of the energy storage system is equal to simplify the complexity of the initial population information, thereby obtaining the simplified initial population information;

[0232] The simplified initial population information is transmitted from multiple groups of random inertia value combinations to the feedback layer to evaluate whether the random virtual inertia of multiple groups of controllable loads, photovoltaic systems and energy storage systems meets the controllable load, photovoltaic system and energy storage system virtual inertia support capacity constraints, frequency stability constraints and microgrid inertia level constraints through the feedback layer; according to the judgment results, different fitness values ​​F are assigned to each group of controllable loads, photovoltaic and energy storage random virtual inertia: if the microgrid frequency is stable, the controllable load, photovoltaic and energy storage inertia support capacity and the microgrid inertia level meet the requirements, then F is the sum of the frequency deviation elements in the microgrid; otherwise, F is assigned a maximum value; then, According to the fitness value F, it is judged whether there is an individual that meets the termination condition: if there is, a group of controllable load virtual inertia, energy storage virtual inertia and photovoltaic virtual inertia corresponding to the fitness value F is obtained through the fitness value F as the microgrid virtual inertia photovoltaic, energy storage and controllable load virtual inertia optimization allocation scheme; and when there are multiple groups of corresponding controllable load virtual inertia, energy storage virtual inertia and photovoltaic virtual inertia for the same fitness value, a group of controllable load virtual inertia, energy storage virtual inertia and photovoltaic virtual inertia with the largest energy storage virtual inertia is selected as the microgrid virtual inertia photovoltaic, energy storage and controllable load virtual inertia optimization allocation scheme; if it does not exist, continue to iterate.

[0233] In MATLAB, we create Figure 4 The IEEE 13-node microgrid simulation model shown in the figure simulates the operation scenario of a small microgrid, which includes 6 controllable loads, 2 photovoltaic systems and 4 capacitor energy storage systems, and sets up a simulation scenario of a three-phase grounding fault at the PCC point (three-phase grid connection point) to study the virtual inertia optimization allocation method of the microgrid under the condition of a three-phase grounding fault in the upper power grid. The photovoltaic and supercapacitor energy storage parameters involved are: photovoltaic capacity S B =20MW, DC bus capacitor C on the high voltage side of the photovoltaic grid-connected system dc =12mF, rated value of high voltage side DC bus voltage U dc0=380V, VICHF gain factor K h =500, cut-off frequency ω h =300rad / s; supercapacitor energy storage capacitance C = 1000F, supercapacitor rated capacity S c =10MW, over-capacity AC line voltage u c =400V, initial virtual angular frequency of supercapacitor energy storage ω c0 =0;

[0234] Simulation results from MATALB Figure 5a , Figure 5b It is observed that when a disturbance occurs, the virtual inertia H of the controllable load obtained by the genetic algorithm is G1 =H G2 =H G3 =H G3＝ H G4 =H G5 =H G6 =0.108MWs, energy storage system inertia H SG1 =H SG2 =H SG3 =0.384MWs, photovoltaic system inertia H PV1 =H PV1 =0.264MWs. Putting it into the entire village-level micro-power system can enable the micro-grid system to achieve system frequency regulation without exceeding the micro-grid operation threshold after a disturbance occurs; the adjustment time is also significantly improved compared to before compensation, thereby achieving the optimal distribution of micro-grid virtual inertia.

[0235] The present invention also discloses Figure 6 A microgrid virtual inertia optimization distribution device shown includes an inertia support demand module, an inertia optimization module and an inertia optimization distribution module, wherein:

[0236] The inertia support demand module is used to establish a village-level microgrid transient model including photovoltaic, energy storage and controllable loads, taking the occurrence of a three-phase grounding fault in the upper power grid of the microgrid as an extreme operating condition. The village-level microgrid model is used to describe the maximum frequency drop depth inside the village-level microgrid, quantitatively characterize the virtual inertia support demand of the maximum frequency deviation and maximum frequency change rate of the village-level microgrid, and obtain the critical inertia of the village-level microgrid;

[0237] The inertia optimization module is used to establish a village-level microgrid virtual inertia optimization allocation model based on the critical inertia of the village-level microgrid, with the microgrid inertia level, photovoltaic virtual inertia support capacity, energy storage virtual inertia support capacity, controllable load virtual inertia support capacity and frequency stability as constraints, and with the minimum maximum frequency deviation as the optimization goal;

[0238] The inertia optimization distribution module is used to solve the village-level microgrid virtual inertia optimization distribution model using a genetic algorithm to obtain the virtual inertia optimization distribution plan for photovoltaic, energy storage and controllable loads in the microgrid virtual inertia.

[0239] A microgrid virtual inertia optimization distribution device, in which the microgrid transient model includes photovoltaic, energy storage and controllable load in the inertia support demand module, and all three adopt VSG grid-connected inverter; taking the occurrence of three-phase grounding fault in the upper power grid of the microgrid as the extreme operating condition, a microgrid transient model including photovoltaic, energy storage and controllable load is established, and the microgrid transient model is used to quantitatively characterize the virtual inertia support demand of the maximum frequency deviation and the maximum frequency change rate of the microgrid, and obtain the critical inertia of the microgrid; the details are as follows:

[0240] Assume that the microgrid includes I controllable loads, J photovoltaic systems and K energy storage systems, and the inertia size of the microgrid is H MIC The calculation formula is:

[0241]

[0242] in, H Gi are the rated power and inertia time constant of the ith controllable load respectively; H Pj are the rated power and inertia time constant of the jth PV system respectively; H Sk are the rated power and inertia time constant of the kth energy storage system respectively;

[0243] After taking into account the virtual inertia of the new energy unit and the primary frequency regulation service, according to formula (24), the rotor motion equation in the process of frequency change affected by the active power regulation of the microgrid is expressed as:

[0244]

[0245] Among them, f0 and Δf(t) are the rated frequency and frequency deviation of the microgrid respectively; ΔP ∑V is the auxiliary frequency regulation power of the new energy auxiliary unit; D is the system damping; t is the microgrid operation time;

[0246] When a three-phase grounding fault occurs in the upper power grid of the microgrid, assuming that the maximum frequency deviation and the maximum frequency change rate of the microgrid are t max and t' max ;The maximum frequency deviation of the microgrid is expressed as:

[0247]

[0248] in, is the equivalent unit damping ratio; ω nis the system angular frequency; α is the coefficient generated when deriving the maximum frequency deviation expression; K is the generator speed regulator gain;

[0249] ΔP' ΣV is the auxiliary frequency regulation power of the new energy auxiliary unit when the microgrid has the maximum frequency deviation; s is the Laplace operator;

[0250] Through equations (25) and (26), the virtual inertia support demand H corresponding to the maximum frequency deviation of the microgrid is obtained: Δfmax :

[0251] Correspondingly, according to formula (25), the corresponding support demand H of the microgrid with the maximum frequency deviation change rate is obtained: RoCoF for:

[0252]

[0253] Among them, RoCoF max is the maximum frequency deviation change rate of the microgrid; Δf(t' max ) is the frequency deviation at the maximum frequency deviation change rate of the microgrid;

[0254] Take the virtual inertia support demand H corresponding to the maximum frequency change rate of the microgrid RoCoF The virtual inertia support requirement H corresponding to the maximum frequency deviation Δfmax The maximum value of the two can be used to obtain the critical inertia H of the microgrid min :

[0255] H min =max{H RoCoF ,H Δfmax} (29).

[0256] As a further optimization scheme of a microgrid virtual inertia optimization distribution device according to the present invention, in the inertia optimization module, the microgrid inertia level in the constraint condition is determined in the following manner:

[0257] The critical inertia H of the microgrid is obtained based on the virtual inertia support demand of the microgrid's maximum frequency deviation and maximum frequency change rate. min , thus determining the inertia ΔH that needs to be compensated inside the microgrid:

[0258] ΔH=H min (30)

[0259] When the actual inertia of the microgrid is less than the critical inertia, the grid inertia compensation inertia is:

[0260] ΔH′=H MIC =H G +H PV +HSG (31)

[0261] Among them, ΔH' is the inertia compensation provided to the power grid after the implementation of virtual inertia control; H G , H PV , H SG The inertia compensation provided to the grid after implementing virtual inertia control for all controllable loads, photovoltaic systems and energy storage systems;

[0262] According to the controllable load, the virtual inertia of the photovoltaic system and the energy storage system is not less than the critical inertia H min The condition of microgrid inertia level constraint is expressed as:

[0263] H min ≤H G +H PV +H SG (32).

[0264] As a further optimization scheme of a microgrid virtual inertia optimization distribution device according to the present invention, in the inertia optimization module, the photovoltaic virtual inertia support capacity in the constraint condition is determined in the following manner:

[0265] Photovoltaic system virtual inertia H PV for:

[0266]

[0267] Among them, S B is the capacity of the photovoltaic grid-connected system; C dc is the DC bus capacitor on the high voltage side of the photovoltaic grid-connected system; U dc0 K is the rated value of the DC bus voltage on the high voltage side; v (s) is the virtual inertia controller; K h ,ω h are the gain coefficient and cutoff frequency of the virtual inertia controller respectively; s is a complex variable; ω0 is the rated angular frequency of the photovoltaic grid-connected system; ω g is the measured angular frequency of the power grid;

[0268] According to the virtual inertia H of the photovoltaic system PV The photovoltaic virtual inertia support capacity in the constraint conditions is obtained as follows:

[0269] H PV ≤H PV,max (34)

[0270] Among them, H pv,max is the maximum value of the virtual inertia of the photovoltaic system.

[0271] As a further optimization scheme of a microgrid virtual inertia optimization distribution device according to the present invention, in the inertia optimization module, the energy storage virtual inertia support capacity in the constraint condition is determined in the following manner:

[0272] If the energy storage systems in the microgrid are all supercapacitor energy storage, the charging and discharging power P c It is expressed as:

[0273]

[0274] Among them, C, S c 、u c They are the supercapacitor energy storage capacitance, rated capacity, and output voltage;

[0275] The charging or discharging process of supercapacitor energy storage simulates the motion equation of the rotating synchronous motor rotor, which is:

[0276]

[0277] Where H is the inertia time constant of the rotating synchronous generator; f is the rotating synchronous machine frequency; P m is the mechanical power of the rotating synchronous machine, P e is the electromagnetic power;

[0278] Combining equations (34) and (35), we get the virtual inertia of the energy storage system:

[0279]

[0280] Among them, ω c0 ,ω c are the initial virtual angular frequency and current virtual angular frequency of supercapacitor energy storage respectively;

[0281] According to the virtual inertia H of the energy storage system SG , the energy storage virtual inertia support capacity constraint is expressed as:

[0282] H SG ≤H SG,max (38)

[0283] Among them, H SG,max is the maximum value of virtual inertia of the energy storage system.

[0284] As a further optimization scheme of a microgrid virtual inertia optimization distribution device according to the present invention, in the inertia optimization module, the virtual inertia support capacity of the controllable load in the constraint condition is determined in the following manner:

[0285] Assuming that all controllable loads within the microgrid are movable loads, the virtual inertia of the controllable loads H G It is expressed as:

[0286]

[0287] Where f is the frequency of the microgrid, △P G is the newly added frequency when the controllable load is adjusted once when the microgrid is disturbed, S G is the controllable load capacity;

[0288] According to the virtual inertia of the controllable load, the support capacity of the virtual inertia of the controllable load is obtained as follows:

[0289] H G ≤H G,max (40)

[0290] Among them, H G,max It is the maximum value of virtual inertia of controllable load.

[0291] As a further optimization scheme of a microgrid virtual inertia optimization distribution device according to the present invention, in the inertia optimization module, the frequency stability in the constraint condition is determined in the following manner:

[0292] Based on the principle of ensuring that the system maximum frequency deviation and frequency change rate meet the safety requirements of microgrid operation, the system frequency constraint is obtained as follows:

[0293] -Δf max-c ≤Δf max ≤Δf max-c (41)

[0294] -RoCoF max ≤RoCoF≤RoCoF max (42)

[0295] Where Δf max-c is the maximum frequency deviation safety value of the microgrid; RoCoF max is the maximum frequency change rate safety value of the microgrid; Δf max is the maximum frequency deviation of the microgrid, and RoCoF is the frequency change rate of the microgrid.

[0296] As a further optimization scheme of a microgrid virtual inertia optimization allocation device according to the present invention, a microgrid virtual inertia optimization allocation model is specifically as follows:

[0297] The maximum frequency deviation of the microgrid is Δf max Minimum is the optimization target, and the microgrid virtual inertia optimization allocation model is obtained:

[0298]

[0299] Where Δf max is the maximum frequency deviation of the microgrid; Δfmax-c is the maximum frequency deviation safety value of the microgrid; RoCoF max is the maximum frequency change rate safety value of the microgrid; RoCoF is the frequency change rate of the microgrid; RoCoF max is the maximum frequency change rate safety value of the microgrid; H min The critical inertia of the microgrid is obtained based on the virtual inertia support demand of the maximum frequency deviation and the maximum frequency change rate of the village microgrid; H G , H PV and H SG are the virtual inertia of all controllable loads, photovoltaic system and energy storage system respectively; H G,max , H PV,max and H SG,max are the maximum virtual inertia of all controllable loads, photovoltaic system and energy storage system respectively.

[0300] As a further optimization scheme of a microgrid virtual inertia optimization distribution device described in the present invention, in the inertia optimization distribution module, a genetic algorithm is used to solve the microgrid virtual inertia optimization distribution model to obtain a microgrid virtual inertia optimization distribution scheme for photovoltaic, energy storage and controllable load virtual inertia; the details are as follows:

[0301] According to the constraints of photovoltaic virtual inertia support capacity, energy storage virtual inertia support capacity, and controllable load virtual inertia support capacity, a genetic algorithm is used to automatically generate initial population information. The initial population information includes multiple groups of individuals, each group of individuals includes A random virtual inertia of controllable loads, B random virtual inertia of photovoltaic systems, and C random virtual inertia of energy storage systems, where A, B, and C are all integers greater than or equal to 1.

[0302] In the initial population information, the random virtual inertia of the controllable loads in each group of individuals is equal, the random virtual inertia of the photovoltaic system is equal, and the random virtual inertia of the energy storage system is equal to simplify the complexity of the initial population information, thereby obtaining the simplified initial population information;

[0303] The simplified initial population information is transmitted from multiple groups of random inertia value combinations to the feedback layer to evaluate whether the random virtual inertia of multiple groups of controllable loads, photovoltaic systems and energy storage systems meets the controllable load, photovoltaic system and energy storage system virtual inertia support capacity constraints, frequency stability constraints and microgrid inertia level constraints through the feedback layer; according to the judgment results, different fitness values ​​F are assigned to each group of controllable loads, photovoltaic and energy storage random virtual inertia: if the microgrid frequency is stable, the controllable load, photovoltaic and energy storage inertia support capacity and the microgrid inertia level meet the requirements, then F is the sum of the frequency deviation elements in the microgrid; otherwise, F is assigned a maximum value; then, According to the fitness value F, it is judged whether there is an individual that meets the termination condition: if there is, a group of controllable load virtual inertia, energy storage virtual inertia and photovoltaic virtual inertia corresponding to the fitness value F is obtained through the fitness value F as the microgrid virtual inertia photovoltaic, energy storage and controllable load virtual inertia optimization allocation scheme; and when there are multiple groups of corresponding controllable load virtual inertia, energy storage virtual inertia and photovoltaic virtual inertia for the same fitness value, a group of controllable load virtual inertia, energy storage virtual inertia and photovoltaic virtual inertia with the largest energy storage virtual inertia is selected as the microgrid virtual inertia photovoltaic, energy storage and controllable load virtual inertia optimization allocation scheme; if it does not exist, continue to iterate.

[0304] In a third aspect, an embodiment of the present invention further provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein when the processor executes the computer program, the steps of the microgrid virtual inertia optimization allocation method as described in the first aspect above or any corresponding embodiment thereof are implemented.

[0305] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the microgrid virtual inertia optimization allocation method as described in the first aspect above or any corresponding embodiment thereof are implemented.

[0306] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt 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.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0307] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0308] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0309] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0310] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0311] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for optimizing allocation of virtual inertia of a microgrid, characterized in that: include: Taking the three-phase grounding fault of the upper power grid of the microgrid as the extreme operating condition, a microgrid transient model including photovoltaic, energy storage and controllable load is established. The microgrid transient model is used to quantitatively characterize the virtual inertia support demand of the maximum frequency deviation and maximum frequency change rate of the microgrid, and obtain the critical inertia of the microgrid. Based on the critical inertia of the microgrid, with the inertia level of the microgrid, the virtual inertia support capacity of photovoltaic power generation, the virtual inertia support capacity of energy storage, the virtual inertia support capacity of controllable loads and frequency stability as constraints, and the minimum maximum frequency deviation as the optimization goal, a microgrid virtual inertia optimization allocation model is established. A genetic algorithm is used to solve the microgrid virtual inertia optimization allocation model, and the optimal allocation scheme of the virtual inertia of photovoltaic, energy storage and controllable load in the microgrid virtual inertia is obtained.

2. A microgrid virtual inertia optimization allocation method according to claim 1, characterized in that: The transient model of the microgrid includes photovoltaic, energy storage and controllable loads, all of which use VSG grid-connected inverters. Taking the three-phase grounding fault of the upper power grid of the microgrid as the extreme operating condition, a transient model of the microgrid including photovoltaic, energy storage and controllable loads is established. The microgrid transient model is used to quantitatively characterize the virtual inertia support requirements of the maximum frequency deviation and maximum frequency change rate of the microgrid, and obtain the critical inertia of the microgrid, as follows: Assume that the microgrid includes I controllable loads, J photovoltaic systems and K energy storage systems, and the inertia size of the microgrid is H MIC The calculation formula is: in, H Gi are the rated power and inertia time constant of the ith controllable load respectively; H Pj are the rated power and inertia time constant of the jth PV system respectively; H Sk are the rated power and inertia time constant of the kth energy storage system respectively; After taking into account the virtual inertia of the new energy unit and the primary frequency regulation service, according to formula (1), the rotor motion equation in the process of frequency change affected by the active power regulation of the microgrid is expressed as: Among them, f0 and Δf(t) are the rated frequency and frequency deviation of the microgrid respectively; ΔP ∑V is the auxiliary frequency regulation power of the new energy auxiliary unit; D is the system damping; t is the microgrid operation time; When a three-phase grounding fault occurs in the upper power grid of the microgrid, assuming that the maximum frequency deviation and the maximum frequency change rate of the microgrid are t max and t' max ;The maximum frequency deviation of the microgrid is expressed as: in, is the equivalent unit damping ratio; ω n is the system angular frequency; α is the coefficient generated when deriving the maximum frequency deviation expression; K is the generator speed regulator gain; ΔP' ∑V is the auxiliary frequency regulation power of the new energy auxiliary unit when the microgrid has the maximum frequency deviation; s is the Laplace operator; Through equations (2) and (3), the virtual inertia support demand H corresponding to the maximum frequency deviation of the microgrid is obtained: Δfmax : Correspondingly, according to formula (2), the corresponding support demand H of the microgrid with the maximum frequency deviation change rate is obtained: RoCoF for: Among them, RoCoF max is the maximum frequency deviation change rate of the microgrid; Δf(t' max ) is the frequency deviation at the maximum frequency deviation change rate of the microgrid; Take the virtual inertia support demand H corresponding to the maximum frequency change rate of the microgrid RoCoF The virtual inertia support requirement H corresponding to the maximum frequency deviation Δfmax The maximum value of the two can be used to obtain the critical inertia H of the microgrid min : H min =max{H RoCoF ,H Δfmax }(6)。 3. A microgrid virtual inertia optimization allocation method according to claim 2, characterized in that: The inertia level of the microgrid in the constraint condition is determined as follows: The critical inertia H of the microgrid is obtained based on the virtual inertia support demand of the microgrid's maximum frequency deviation and maximum frequency change rate. min , thus determining the inertia ΔH that needs to be compensated inside the microgrid: ΔH=H min (7) When the actual inertia of the microgrid is less than the critical inertia, the grid inertia compensation inertia is: ΔH′=H MIC =H G +H PV +H SG (8) Among them, ΔH' is the inertia compensation provided to the power grid after the implementation of virtual inertia control; H G , H PV , H SG The inertia compensation provided to the grid after implementing virtual inertia control for all controllable loads, photovoltaic systems and energy storage systems; According to the controllable load, the virtual inertia of the photovoltaic system and the energy storage system is not less than the critical inertia H min The condition of microgrid inertia level constraint is expressed as: H min ≤H G +H PV +H SG (9)。 4. A microgrid virtual inertia optimization allocation method according to claim 1, characterized in that: The photovoltaic virtual inertia support capacity in the constraint conditions is determined as follows: Photovoltaic system virtual inertia H PV for: Among them, S B is the capacity of the photovoltaic grid-connected system; C dc It is the DC bus capacitor on the high voltage side of the photovoltaic grid-connected system; U dc0 K is the rated value of the DC bus voltage on the high voltage side; v (s) is the virtual inertia controller; K h ,ω h are the gain coefficient and cutoff frequency of the virtual inertia controller respectively; s is a complex variable; ω0 is the rated angular frequency of the photovoltaic grid-connected system; ω g is the measured angular frequency of the power grid; According to the virtual inertia H of the photovoltaic system PV The photovoltaic virtual inertia support capacity in the constraint conditions is obtained as follows: H PV ≤H PV,max (11) Among them, H pv,max is the maximum value of the virtual inertia of the photovoltaic system.

5. A microgrid virtual inertia optimization allocation method according to claim 1, characterized in that: The energy storage virtual inertia support capacity in the constraint conditions is determined as follows: If the energy storage systems in the microgrid are all supercapacitor energy storage, the charging and discharging power P c It is expressed as: Among them, C, S c 、u c They are the supercapacitor energy storage capacitance, rated capacity, and output voltage; The charging or discharging process of supercapacitor energy storage simulates the motion equation of the rotating synchronous motor rotor, which is: Where H is the inertia time constant of the rotating synchronous generator; f is the rotating synchronous machine frequency; P m is the mechanical power of the rotating synchronous machine, P e is the electromagnetic power; Combining equations (11) and (12), we get the virtual inertia of the energy storage system: Among them, ω c0 ,ω c are the initial virtual angular frequency and current virtual angular frequency of supercapacitor energy storage respectively; According to the virtual inertia H of the energy storage system SG , the virtual inertia support capacity constraint of energy storage is expressed as: H SG ≤H SG,max (15) Among them, H SG,max is the maximum value of virtual inertia of the energy storage system.

6. A microgrid virtual inertia optimization allocation method according to claim 1, characterized in that: The virtual inertia support capacity of the controllable load in the constraint conditions is determined as follows: Assuming that all controllable loads within the microgrid are movable loads, the virtual inertia of the controllable loads H G It is expressed as: Where f is the frequency of the microgrid, △P G is the newly added frequency when the controllable load is adjusted once when the microgrid is disturbed, S G is the controllable load capacity; According to the virtual inertia of the controllable load, the support capacity of the virtual inertia of the controllable load is obtained as follows: H G ≤H G,max (17) Among them, H G,max It is the maximum value of virtual inertia of controllable load.

7. A microgrid virtual inertia optimization allocation method according to claim 1, characterized in that: The frequency in the constraint condition is stable, and the determination method is as follows: Based on the principle of ensuring that the system maximum frequency deviation and frequency change rate meet the safety requirements of microgrid operation, the system frequency constraint is obtained as follows: -Δf max-c ≤Δf max ≤Δf max-c (18) -RoCoF max ≤RoCoF≤RoCoF max (19) Where Δf max-c is the maximum frequency deviation safety value of the microgrid; RoCoF max is the maximum frequency change rate safety value of the microgrid; Δf max is the maximum frequency deviation of the microgrid, and RoCoF is the frequency change rate of the microgrid.

8. A microgrid virtual inertia optimization allocation method according to claim 1, characterized in that: The microgrid virtual inertia optimization distribution model is as follows: The maximum frequency deviation of the microgrid is Δf max Minimum is the optimization target, and the microgrid virtual inertia optimization allocation model is obtained: Where Δf max is the maximum frequency deviation of the microgrid; Δf max-c is the maximum frequency deviation safety value of the microgrid; RoCoF max is the maximum frequency change rate safety value of the microgrid; RoCoF is the frequency change rate of the microgrid; RoCoF max is the maximum frequency change rate safety value of the microgrid; H min The critical inertia of the microgrid is obtained based on the virtual inertia support demand of the maximum frequency deviation and the maximum frequency change rate of the village microgrid; H G , H PV and H SG are the virtual inertia of all controllable loads, photovoltaic system and energy storage system respectively; H G,max , H PV,max and H SG,max are the maximum virtual inertia of all controllable loads, photovoltaic system and energy storage system respectively.

9. A microgrid virtual inertia optimization allocation method according to claim 8, characterized in that: The genetic algorithm is used to solve the microgrid virtual inertia optimization distribution model, and the virtual inertia optimization distribution scheme of photovoltaic, energy storage and controllable load virtual inertia of the microgrid is obtained; the details are as follows: According to the constraints of photovoltaic virtual inertia support capacity, energy storage virtual inertia support capacity, and controllable load virtual inertia support capacity, a genetic algorithm is used to automatically generate initial population information. The initial population information includes multiple groups of individuals, each group of individuals includes A random virtual inertia of controllable loads, B random virtual inertia of photovoltaic systems, and C random virtual inertia of energy storage systems, where A, B, and C are all integers greater than or equal to 1. In the initial population information, the random virtual inertia of the controllable loads in each group of individuals is equal, the random virtual inertia of the photovoltaic system is equal, and the random virtual inertia of the energy storage system is equal to simplify the complexity of the initial population information, thereby obtaining the simplified initial population information; The simplified initial population information is passed to the feedback layer to evaluate whether multiple groups of controllable loads, photovoltaic systems and energy storage systems meet the constraints of virtual inertia support capacity of controllable loads, photovoltaic systems and energy storage systems, frequency stability constraints and microgrid inertia level constraints through the feedback layer; according to the judgment results, different fitness values ​​F are assigned to each group of controllable loads, photovoltaic and energy storage random virtual inertia: if a group of microgrid frequency stability, photovoltaic and energy storage inertia support capacity and microgrid inertia level meet the requirements, then the corresponding F is the sum of frequency deviation elements in the microgrid; otherwise, F is assigned a maximum value; then, according to the fitness value F, it is judged whether there is an individual that meets the termination condition: if it exists, the virtual inertia of controllable loads, energy storage virtual inertia and photovoltaic virtual inertia in the microgrid when the maximum frequency deviation is minimized is obtained by decoding the best individual information, as the virtual inertia optimization allocation scheme of photovoltaic, energy storage and controllable load inertia of the microgrid; if it does not exist, continue to iterate.

10. A microgrid virtual inertia optimization distribution device, characterized in that: It includes inertia support requirement module, inertia optimization module and inertia optimization allocation module, among which: The inertia support demand module is used to establish a village-level microgrid transient model including photovoltaic, energy storage and controllable loads, taking the occurrence of a three-phase grounding fault in the upper power grid of the microgrid as an extreme operating condition. The village-level microgrid model is used to describe the maximum frequency drop depth inside the village-level microgrid, quantitatively characterize the virtual inertia support demand of the maximum frequency deviation and maximum frequency change rate of the village-level microgrid, and obtain the critical inertia of the village-level microgrid; The inertia optimization module is used to establish a village-level microgrid virtual inertia optimization allocation model based on the critical inertia of the village-level microgrid, with the microgrid inertia level, photovoltaic virtual inertia support capacity, energy storage virtual inertia support capacity, controllable load virtual inertia support capacity and frequency stability as constraints, and with the minimum maximum frequency deviation as the optimization goal; The inertia optimization distribution module is used to solve the village-level microgrid virtual inertia optimization distribution model using a genetic algorithm to obtain the virtual inertia optimization distribution plan for photovoltaic, energy storage and controllable loads in the microgrid virtual inertia.

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